commit 91baeb0f588c99756ecd281c4ff19e2b5f005973 Author: zhurui <274461951@qq.com> Date: Thu Jul 4 17:09:13 2024 +0800 first commit diff --git a/Normalize.py b/Normalize.py new file mode 100644 index 0000000..a326186 --- /dev/null +++ b/Normalize.py @@ -0,0 +1,43 @@ +import torch +import torch.nn as nn + +class Normalize(nn.Module): + def __init__(self, mean, std): + super(Normalize, self).__init__() + self.mean = mean + self.std = std + + def forward(self, input): + size = input.size() + x = input.clone() + for i in range(size[1]): + x[:, i] = (x[:, i] - self.mean[i]) / self.std[i] + return x + +class TfNormalize(nn.Module): + + def __init__(self, mean=0, std=1, mode='tensorflow'): + super(TfNormalize, self).__init__() + self.mean = mean + self.std = std + self.mode = mode + + def forward(self, input): + size = input.size() + x = input.clone() + + if self.mode == 'tensorflow': + x = x * 2.0 - 1.0 + elif self.mode == 'torch': + for i in range(size[1]): + x[:, i] = (x[:, i] - self.mean[i]) / self.std[i] + return x + + +class Permute(nn.Module): + def __init__(self, permutation=[2, 1, 0]): + super().__init__() + self.permutation = permutation + + def forward(self, input): + return input[:, self.permutation] diff --git a/README.md b/README.md new file mode 100644 index 0000000..c8fa435 --- /dev/null +++ b/README.md @@ -0,0 +1,15 @@ +# Requirements +- Python 3.6.13 +- Pytorch 1.7.1 +- Torchvision 0.8.2 +- Numpy 1.19.2 +- Pillow 8.3.1 +- Timm 0.4.12 +- Scipy 1.5.4 + +# 生成对抗样本 +python attack.py --attack ADG --batch_size 1 --model_name vit_base_patch16_224 + +# 评估成功率 +bash run_evaluate.sh model_vit_base_patch16_224-method +python evaluate_cnn.py diff --git a/attack.py b/attack.py new file mode 100644 index 0000000..4ee38e4 --- /dev/null +++ b/attack.py @@ -0,0 +1,53 @@ +import warnings +warnings.filterwarnings("ignore") +import torch +import argparse +from torch.utils.data import DataLoader +import os +import pandas as pd +import time +import pickle as pkl + +from dataset import AdvDataset +from utils import BASE_ADV_PATH, ROOT_PATH +import methods + +def arg_parse(): + parser = argparse.ArgumentParser(description='') + parser.add_argument('--attack', type=str, default='', help='the name of specific attack method') + parser.add_argument('--gpu', type=str, default='0', help='gpu device.') + parser.add_argument('--batch_size', type=int, default=20, metavar='N', + help='input batch size for reference (default: 16)') + parser.add_argument('--model_name', type=str, default='', help='') + parser.add_argument('--filename_prefix', type=str, default='', help='') + args = parser.parse_args() + args.opt_path = os.path.join(BASE_ADV_PATH, 'model_{}-method_{}'.format(args.model_name, args.attack)) + if not os.path.exists(args.opt_path): + os.makedirs(args.opt_path) + return args + +if __name__ == '__main__': + args = arg_parse() + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu + + dataset = AdvDataset(args.model_name, os.path.join(ROOT_PATH, 'clean_resized_images')) + data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, num_workers=0) + print (args.attack, args.model_name) + + attack_method = getattr(methods, args.attack)(args.model_name) + + all_loss_info = {} + for batch_idx, batch_data in enumerate(data_loader): + if batch_idx%100 == 0: + print ('Runing batch_idx', batch_idx) + batch_x = batch_data[0] + batch_y = batch_data[1] + batch_name = batch_data[3] + + adv_inps, loss_info = attack_method(batch_x, batch_y) + attack_method._save_images(adv_inps, batch_name, args.opt_path) + if loss_info is not None: + all_loss_info[batch_name] = loss_info + if loss_info is not None: + with open(os.path.join(args.opt_path, 'loss_info.json'), 'wb') as opt: + pkl.dump(all_loss_info, opt) diff --git a/clean_resized_images/ILSVRC2012_val_00000031.png b/clean_resized_images/ILSVRC2012_val_00000031.png new file mode 100644 index 0000000..4db21d9 Binary files /dev/null and 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import Image +from torchvision import transforms +import math +import glob + +from timm.data.transforms import RandomResizedCropAndInterpolation, ToNumpy, ToTensor +from utils import ROOT_PATH + +name_to_class_ids_file = os.path.join(ROOT_PATH, 'image_name_to_class_id_and_name.json') +INPUT_SIZE = (3, 224, 224) +INTERPOLATION = 'bicubic' +DEFAULT_CROP_PCT = 0.875 +IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) +IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225) +IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5) +IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5) + +def params(model_name): + if model_name in ['vit_deit_base_distilled_patch16_224','levit_256','pit_b_224','cait_s24_224','convit_base', 'visformer_small', 'deit_base_distilled_patch16_224']: + params = {'mean': IMAGENET_DEFAULT_MEAN, + 'std': IMAGENET_DEFAULT_STD, + 'interpolation': INTERPOLATION, + 'crop_pct':0.9} + else: + params = {'mean': IMAGENET_INCEPTION_MEAN, + 'std': IMAGENET_INCEPTION_STD, + 'interpolation': INTERPOLATION, + 'crop_pct': 0.9} + return params + +def transforms_imagenet_wo_resize(params): + tfl = [ + transforms.ToTensor(), + transforms.Normalize( + mean=torch.tensor(params['mean']), + std=torch.tensor(params['std'])) + ] + return transforms.Compose(tfl) + +class AdvDataset(data.Dataset): + def __init__(self, model_name, adv_path): + self.transform = transforms_imagenet_wo_resize(params(model_name)) + paths = glob.glob(os.path.join(adv_path, '*.png')) + paths = [i.split('/')[-1] for i in paths] + print ('Using ', len(paths)) + paths = [i.strip() for i in paths] + self.query_paths = [i.split('.')[0]+'.JPEG' for i in paths] + self.paths = [os.path.join(adv_path, i) for i in paths] + self.model_name = model_name + + with open(name_to_class_ids_file, 'r') as ipt: + self.json_info = json.load(ipt) + + def __len__(self): + return len(self.paths) + + def __getitem__(self, index): + path = self.paths[index] + query_path = self.query_paths[index] + class_id = self.json_info[query_path]['class_id'] + class_name = self.json_info[query_path]['class_name'] + image_name = path.split('/')[-1] + img = Image.open(path).convert('RGB') + if self.model_name == "tf2torch_resnet_v2_101": + img = transforms.Resize((299,299))(img) + img = transforms.Compose([transforms.ToTensor()])(img) + else: + if self.transform is not None: + img = self.transform(img) + return img, class_id, class_name, image_name + +class CNNDataset(data.Dataset): + def __init__(self, model_name, adv_path): + self.transform = transforms_imagenet_wo_resize(params(model_name)) + paths = glob.glob(os.path.join(adv_path, '*.png')) + paths = [i.split('/')[-1] for i in paths] + print ('Using ', len(paths)) + paths = [i.strip() for i in paths] + self.query_paths = [i.split('.')[0]+'.JPEG' for i in paths] + self.paths = [os.path.join(adv_path, i) for i in paths] + + with open(name_to_class_ids_file, 'r') as ipt: + self.json_info = json.load(ipt) + + def __len__(self): + return len(self.paths) + + def __getitem__(self, index): + path = self.paths[index] + query_path = self.query_paths[index] + class_id = self.json_info[query_path]['class_id'] + class_name = self.json_info[query_path]['class_name'] + image_name = path.split('/')[-1] + img = Image.open(path).convert('RGB') + img = transforms.Resize((299,299))(img) + img = transforms.Compose([transforms.ToTensor()])(img) + return img, class_id, class_name, image_name diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000..a1482ef --- /dev/null +++ b/evaluate.py @@ -0,0 +1,72 @@ +import warnings +warnings.filterwarnings("ignore") +import torch +import argparse +from torch.utils.data import DataLoader +import os +import pandas as pd +import time +from dataset import AdvDataset +from model import get_model +from utils import BASE_ADV_PATH, accuracy, AverageMeter + +def arg_parse(): + parser = argparse.ArgumentParser(description='') + parser.add_argument('--adv_path', type=str, default='', help='the path of adversarial examples.') + parser.add_argument('--gpu', type=str, default='0', help='gpu device.') + parser.add_argument('--batch_size', type=int, default=20, metavar='N', + help='input batch size for reference (default: 16)') + parser.add_argument('--model_name', type=str, default='', help='') + args = parser.parse_args() + args.adv_path = os.path.join(BASE_ADV_PATH, args.adv_path) + return args + +if __name__ == '__main__': + args = arg_parse() + + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu + + dataset = AdvDataset(args.model_name, args.adv_path) + data_loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=False, num_workers=0) + print (len(dataset)) + + model = get_model(args.model_name) + model.cuda() + model.eval() + + top1 = AverageMeter() + top5 = AverageMeter() + batch_time = AverageMeter() + + prediction = [] + gts = [] + with torch.no_grad(): + end = time.time() + for batch_idx, batch_data in enumerate(data_loader): + batch_x = batch_data[0].cuda() + batch_y = batch_data[1].cuda() + batch_name = batch_data[2] + + output = model(batch_x) + acc1, acc5 = accuracy(output.detach(), batch_y, topk=(1, 5)) + top1.update(acc1.item(), batch_x.size(0)) + top5.update(acc5.item(), batch_x.size(0)) + + batch_time.update(time.time() - end) + end = time.time() + + _, pred = output.detach().topk(1, 1, True, True) + pred = pred.t() + prediction += list(torch.squeeze(pred.cpu()).numpy()) + gts += list(batch_y.cpu().numpy()) + success_count = 0 + df = pd.DataFrame(columns = ['path', 'pre', 'gt']) + df['path'] = dataset.paths[:len(prediction)] + df['pre'] = prediction + df['gt'] = gts + for i in range(len(df['pre'])): + if df['pre'][i] != df['gt'][i]: + success_count += 1 + print("Attack Success Rate for {0} : {1:.1f}%".format(args.model_name, success_count/ 1000. * 100)) + + \ No newline at end of file diff --git a/evaluate_cnn.py b/evaluate_cnn.py new file mode 100644 index 0000000..5f4680f --- /dev/null +++ b/evaluate_cnn.py @@ -0,0 +1,54 @@ +import os +import torch +from torch.autograd import Variable as V +from torch import nn +from torchvision import transforms as T +from Normalize import Normalize, TfNormalize +from torch.utils.data import DataLoader +from torch_nets import ( + tf_inception_v3, + tf_resnet_v2_50, + tf_resnet_v2_101, + ) +from dataset import CNNDataset +batch_size = 10 +adv_dir = './advimages/model_pit_b_224-method' +os.environ["CUDA_VISIBLE_DEVICES"] = '5' + +def get_model(net_name, model_dir): + model_path = os.path.join(model_dir, net_name + '.npy') + if net_name == 'tf2torch_inception_v3': + net = tf_inception_v3 + elif net_name == 'tf2torch_resnet_v2_50': + net = tf_resnet_v2_50 + elif net_name == 'tf2torch_resnet_v2_101': + net = tf_resnet_v2_101 + else: + print('Wrong model name!') + model = nn.Sequential( + TfNormalize('tensorflow'), + net.KitModel(model_path).eval().cuda(),) + return model + +def verify(model_name, path): + model = get_model(model_name, path) + dataset = CNNDataset("inc-v3", adv_dir) + data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0) + sum = 0 + for batch_idx, batch_data in enumerate(data_loader): + batch_x = batch_data[0].cuda() + batch_y = batch_data[1].cuda() + batch_name = batch_data[2] + with torch.no_grad(): + sum += (model(batch_x)[0].argmax(1) != batch_y+1).detach().sum().cpu() + print(model_name + ' acu = {:.2%}'.format(sum / 1000.0)) + +def main(): + model_names = ['tf2torch_inception_v3','tf2torch_resnet_v2_50','tf2torch_resnet_v2_101'] + models_path = './models/' + for model_name in model_names: + verify(model_name, models_path) + print("===================================================") + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/methods.py b/methods.py new file mode 100644 index 0000000..f61d64d --- /dev/null +++ b/methods.py @@ -0,0 +1,293 @@ +import torch +import torch.nn as nn +import numpy as np +from PIL import Image +import os +import random +import scipy.stats as st +import copy +from utils import ROOT_PATH +from functools import partial +import copy +import pickle as pkl +from torch.autograd import Variable +import torch.nn.functional as F +from dataset import params +from model import get_model + +class BaseAttack(object): + def __init__(self, attack_name, model_name, target): + self.attack_name = attack_name + self.model_name = model_name + self.target = target + if self.target: + self.loss_flag = -1 + else: + self.loss_flag = 1 + self.used_params = params(self.model_name) + self.model = get_model(self.model_name) + self.model.cuda() + self.model.eval() + + def forward(self, *input): + raise NotImplementedError + + def _mul_std_add_mean(self, inps): + dtype = inps.dtype + mean = torch.as_tensor(self.used_params['mean'], dtype=dtype).cuda() + std = torch.as_tensor(self.used_params['std'], dtype=dtype).cuda() + inps.mul_(std[:,None, None]).add_(mean[:,None,None]) + return inps + + def _sub_mean_div_std(self, inps): + dtype = inps.dtype + mean = torch.as_tensor(self.used_params['mean'], dtype=dtype).cuda() + std = torch.as_tensor(self.used_params['std'], dtype=dtype).cuda() + inps = (inps - mean[:,None,None])/std[:,None,None] + return inps + + def _save_images(self, inps, filenames, output_dir): + unnorm_inps = self._mul_std_add_mean(inps) + for i,filename in enumerate(filenames): + save_path = os.path.join(output_dir, filename) + image = unnorm_inps[i].permute([1,2,0]) + image[image<0] = 0 + image[image>1] = 1 + image = Image.fromarray((image.detach().cpu().numpy()*255).astype(np.uint8)) + image.save(save_path) + + def _update_inps(self, inps, grad, step_size): + unnorm_inps = self._mul_std_add_mean(inps.clone().detach()) + unnorm_inps = unnorm_inps + step_size * grad.sign() + unnorm_inps = torch.clamp(unnorm_inps, min=0, max=1).detach() + adv_inps = self._sub_mean_div_std(unnorm_inps) + return adv_inps + + def _update_perts(self, perts, grad, step_size): + perts = perts + step_size * grad.sign() + perts = torch.clamp(perts, -self.epsilon, self.epsilon) + return perts + + def _return_perts(self, clean_inps, inps): + clean_unnorm = self._mul_std_add_mean(clean_inps.clone().detach()) + adv_unnorm = self._mul_std_add_mean(inps.clone().detach()) + return adv_unnorm - clean_unnorm + + def __call__(self, *input, **kwargs): + images = self.forward(*input, **kwargs) + return images + +class ADG(BaseAttack): + def __init__(self, model_name, sample_num_batches=130, steps=10, epsilon=16/255, target=False, decay=1.0): + super(ADG, self).__init__('ADG', model_name, target) + self.epsilon = epsilon + self.steps = steps + self.step_size = self.epsilon/self.steps + self.decay = decay + + self.image_size = 224 + self.crop_length = 16 + self.sample_num_batches = sample_num_batches + self.max_num_batches = int((224/16)**2) + assert self.sample_num_batches <= self.max_num_batches + self._register_model() + + def _register_model(self): + def attn_ADG(module, grad_in, grad_out, gamma): + + mask = torch.ones_like(grad_in[0]) * gamma + + out_grad = mask * grad_in[0][:] + + if self.model_name in ['vit_base_patch16_224', 'visformer_small', 'pit_b_224']: + B,C,H,W = grad_in[0].shape + out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B,C,H*W) + max_all = np.argmax(out_grad_cpu[0,:,:], axis = 1) + max_all_H = max_all//H + max_all_W = max_all%H + min_all = np.argmin(out_grad_cpu[0,:,:], axis = 1) + min_all_H = min_all//H + min_all_W = min_all%H + out_grad[:,range(C),max_all_H,:] = 0.0 + out_grad[:,range(C),:,max_all_W] = 0.0 + out_grad[:,range(C),min_all_H,:] = 0.0 + out_grad[:,range(C),:,min_all_W] = 0.0 + + if self.model_name in ['cait_s24_224']: + B,H,W,C = grad_in[0].shape + out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B, H*W, C) + max_all = np.argmax(out_grad_cpu[0,:,:], axis = 0) + max_all_H = max_all//H + max_all_W = max_all%H + min_all = np.argmin(out_grad_cpu[0,:,:], axis = 0) + min_all_H = min_all//H + min_all_W = min_all%H + + out_grad[:,max_all_H,:,range(C)] = 0.0 + out_grad[:,:,max_all_W,range(C)] = 0.0 + out_grad[:,min_all_H,:,range(C)] = 0.0 + out_grad[:,:,min_all_W,range(C)] = 0.0 + + return (out_grad, ) + + def attn_cait_ADG(module, grad_in, grad_out, gamma): + + mask = torch.ones_like(grad_in[0]) * gamma + + out_grad = mask * grad_in[0][:] + + B,H,W,C = grad_in[0].shape + out_grad_cpu = out_grad.data.clone().cpu().numpy() + max_all = np.argmax(out_grad_cpu[0,:,0,:], axis = 0) + min_all = np.argmin(out_grad_cpu[0,:,0,:], axis = 0) + + out_grad[:,max_all,:,range(C)] = 0.0 + out_grad[:,min_all,:,range(C)] = 0.0 + return (out_grad, ) + + def q_ADG(module, grad_in, grad_out, gamma): + mask = torch.ones_like(grad_in[0]) * gamma + out_grad = mask * grad_in[0][:] + out_grad[:] = 0.0 + return (out_grad, grad_in[1], grad_in[2]) + + def v_ADG(module, grad_in, grad_out, gamma): + + mask = torch.ones_like(grad_in[0]) * gamma + out_grad = mask * grad_in[0][:] + + if self.model_name in ['visformer_small']: + B,C,H,W = grad_in[0].shape + out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B,C,H*W) + max_all = np.argmax(out_grad_cpu[0,:,:], axis = 1) + max_all_H = max_all//H + max_all_W = max_all%H + min_all = np.argmin(out_grad_cpu[0,:,:], axis = 1) + min_all_H = min_all//H + min_all_W = min_all%H + out_grad[:,range(C),max_all_H,max_all_W] = 0.0 + out_grad[:,range(C),min_all_H,min_all_W] = 0.0 + + if self.model_name in ['vit_base_patch16_224', 'pit_b_224', 'cait_s24_224']: + c = grad_in[0].shape[2] + out_grad_cpu = out_grad.data.clone().cpu().numpy() + max_all = np.argmax(out_grad_cpu[0,:,:], axis = 0) + min_all = np.argmin(out_grad_cpu[0,:,:], axis = 0) + + out_grad[:,max_all,range(c)] = 0.0 + out_grad[:,min_all,range(c)] = 0.0 + return (out_grad, grad_in[1]) + + def mlp_ADG(module, grad_in, grad_out, gamma): + mask = torch.ones_like(grad_in[0]) * gamma + out_grad = mask * grad_in[0][:] + if self.model_name in ['visformer_small']: + B,C,H,W = grad_in[0].shape + out_grad_cpu = out_grad.data.clone().cpu().numpy().reshape(B,C,H*W) + max_all = np.argmax(out_grad_cpu[0,:,:], axis = 1) + max_all_H = max_all//H + max_all_W = max_all%H + min_all = np.argmin(out_grad_cpu[0,:,:], axis = 1) + min_all_H = min_all//H + min_all_W = min_all%H + out_grad[:,range(C),max_all_H,max_all_W] = 0.0 + out_grad[:,range(C),min_all_H,min_all_W] = 0.0 + if self.model_name in ['vit_base_patch16_224', 'pit_b_224', 'cait_s24_224', 'resnetv2_101']: + c = grad_in[0].shape[2] + out_grad_cpu = out_grad.data.clone().cpu().numpy() + + max_all = np.argmax(out_grad_cpu[0,:,:], axis = 0) + min_all = np.argmin(out_grad_cpu[0,:,:], axis = 0) + out_grad[:,max_all,range(c)] = 0.0 + out_grad[:,min_all,range(c)] = 0.0 + for i in range(len(grad_in)): + if i == 0: + return_dics = (out_grad,) + else: + return_dics = return_dics + (grad_in[i],) + return return_dics + attn_ADG_hook = partial(attn_ADG, gamma=0.25) + attn_cait_ADG_hook = partial(attn_cait_ADG, gamma=0.25) + v_ADG_hook = partial(v_ADG, gamma=0.75) + q_ADG_hook = partial(q_ADG, gamma=0.75) + mlp_ADG_hook = partial(mlp_ADG, gamma=0.5) + + if self.model_name in ['vit_base_patch16_224' ,'deit_base_distilled_patch16_224']: + for i in range(12): + self.model.blocks[i].attn.attn_drop.register_backward_hook(attn_ADG_hook) + self.model.blocks[i].attn.qkv.register_backward_hook(v_ADG_hook) + self.model.blocks[i].mlp.register_backward_hook(mlp_ADG_hook) + elif self.model_name == 'pit_b_224': + for block_ind in range(13): + if block_ind < 3: + transformer_ind = 0 + used_block_ind = block_ind + elif block_ind < 9 and block_ind >= 3: + transformer_ind = 1 + used_block_ind = block_ind - 3 + elif block_ind < 13 and block_ind >= 9: + transformer_ind = 2 + used_block_ind = block_ind - 9 + self.model.transformers[transformer_ind].blocks[used_block_ind].attn.attn_drop.register_backward_hook(attn_ADG_hook) + self.model.transformers[transformer_ind].blocks[used_block_ind].attn.qkv.register_backward_hook(v_ADG_hook) + self.model.transformers[transformer_ind].blocks[used_block_ind].mlp.register_backward_hook(mlp_ADG_hook) + elif self.model_name == 'cait_s24_224': + for block_ind in range(26): + if block_ind < 24: + self.model.blocks[block_ind].attn.attn_drop.register_backward_hook(attn_ADG_hook) + self.model.blocks[block_ind].attn.qkv.register_backward_hook(v_ADG_hook) + self.model.blocks[block_ind].mlp.register_backward_hook(mlp_ADG_hook) + elif block_ind > 24: + self.model.blocks_token_only[block_ind-24].attn.attn_drop.register_backward_hook(attn_cait_ADG_hook) + self.model.blocks_token_only[block_ind-24].attn.q.register_backward_hook(q_ADG_hook) + self.model.blocks_token_only[block_ind-24].attn.k.register_backward_hook(v_ADG_hook) + self.model.blocks_token_only[block_ind-24].attn.v.register_backward_hook(v_ADG_hook) + self.model.blocks_token_only[block_ind-24].mlp.register_backward_hook(mlp_ADG_hook) + elif self.model_name == 'visformer_small': + for block_ind in range(8): + if block_ind < 4: + self.model.stage2[block_ind].attn.attn_drop.register_backward_hook(attn_ADG_hook) + self.model.stage2[block_ind].attn.qkv.register_backward_hook(v_ADG_hook) + self.model.stage2[block_ind].mlp.register_backward_hook(mlp_ADG_hook) + elif block_ind >=4: + self.model.stage3[block_ind-4].attn.attn_drop.register_backward_hook(attn_ADG_hook) + self.model.stage3[block_ind-4].attn.qkv.register_backward_hook(v_ADG_hook) + self.model.stage3[block_ind-4].mlp.register_backward_hook(mlp_ADG_hook) + + def _generate_samples_for_interactions(self, perts, seed): + add_noise_mask = torch.zeros_like(perts) + grid_num_axis = int(self.image_size/self.crop_length) + ids = [i for i in range(self.max_num_batches)] + random.seed(seed) + random.shuffle(ids) + ids = np.array(ids[:self.sample_num_batches]) + rows, cols = ids // grid_num_axis, ids % grid_num_axis + flag = 0 + for r, c in zip(rows, cols): + add_noise_mask[:,:,r*self.crop_length:(r+1)*self.crop_length,c*self.crop_length:(c+1)*self.crop_length] = 1 + add_perturbation = perts * add_noise_mask + return add_perturbation + + def forward(self, inps, labels): + inps = inps.cuda() + labels = labels.cuda() + loss = nn.CrossEntropyLoss() + + momentum = torch.zeros_like(inps).cuda() + unnorm_inps = self._mul_std_add_mean(inps) + perts = torch.zeros_like(unnorm_inps).cuda() + perts.requires_grad_() + + for i in range(self.steps): + outputs = self.model((self._sub_mean_div_std(unnorm_inps + perts))) + cost = self.loss_flag * loss(outputs, labels).cuda() + cost.backward() + grad = perts.grad.data + grad = grad / torch.mean(torch.abs(grad), dim=[1,2,3], keepdim=True) + grad += momentum*self.decay + momentum = grad + perts.data = self._update_perts(perts.data, grad, self.step_size) + perts.data = torch.clamp(unnorm_inps.data + perts.data, 0.0, 1.0) - unnorm_inps.data + perts.grad.data.zero_() + return (self._sub_mean_div_std(unnorm_inps+perts.data)).detach(), None + \ No newline at end of file diff --git a/model.py b/model.py new file mode 100644 index 0000000..3414ebc --- /dev/null +++ b/model.py @@ -0,0 +1,42 @@ +import argparse +import os +from timm.models import create_model +from utils import ROOT_PATH +import torch +from torch import nn +from Normalize import Normalize, TfNormalize +from torch_nets import ( + tf_inception_v3, + tf_resnet_v2_50, + tf_resnet_v2_101, + ) + +MODEL_NAMES = ['vit_base_patch16_224', + 'deit_base_distilled_patch16_224', + 'levit_256', + 'pit_b_224', + 'cait_s24_224', + 'convit_base', + 'tnt_s_patch16_224', + 'visformer_small'] + +CORR_CKPTS = ['jx_vit_base_p16_224-4ee7a4dc.pth', + 'deit_base_distilled_patch16_224-df68dfff.pth', + 'LeViT-256-13b5763e.pth', + 'pit_b_820.pth', + 'S24_224.pth', + 'convit_base.pth', + 'tnt_s_patch16_224.pth.tar', + 'visformer_small-839e1f5b.pth'] + +def get_model(model_name): + if model_name in MODEL_NAMES: + model = create_model( + model_name, + pretrained=True, + num_classes=1000, + in_chans=3, + global_pool=None, + scriptable=False) + print ('Loading Model.') + return model \ No newline at end of file diff --git a/run_evaluate.sh b/run_evaluate.sh new file mode 100644 index 0000000..7aa0c17 --- /dev/null +++ b/run_evaluate.sh @@ -0,0 +1,10 @@ +echo "The adv_path: $1" +python evaluate.py --adv_path $1 --model_name vit_base_patch16_224 --batch_size 10 +python evaluate.py --adv_path $1 --model_name deit_base_distilled_patch16_224 --batch_size 10 +python evaluate.py --adv_path $1 --model_name levit_256 --batch_size 10 +python evaluate.py --adv_path $1 --model_name pit_b_224 --batch_size 10 +python evaluate.py --adv_path $1 --model_name cait_s24_224 --batch_size 10 +python evaluate.py --adv_path $1 --model_name convit_base --batch_size 10 +python evaluate.py --adv_path $1 --model_name tnt_s_patch16_224 --batch_size 10 +python evaluate.py --adv_path $1 --model_name visformer_small --batch_size 10 +wait \ No newline at end of file diff --git a/torch_nets/tf_inception_v3.py b/torch_nets/tf_inception_v3.py new file mode 100644 index 0000000..795de91 --- /dev/null +++ b/torch_nets/tf_inception_v3.py @@ -0,0 +1,637 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +_weights_dict = dict() + +def load_weights(weight_file): + if weight_file == None: + return + + try: + weights_dict = np.load(weight_file, allow_pickle=True).item() + except: + weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item() + + return weights_dict + +class KitModel(nn.Module): + + def __init__(self, weight_file): + super(KitModel, self).__init__() + global _weights_dict + _weights_dict = load_weights(weight_file) + + self.InceptionV3_InceptionV3_Conv2d_1a_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Conv2d_1a_3x3/Conv2D', in_channels=3, out_channels=32, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.InceptionV3_InceptionV3_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Conv2d_1a_3x3/BatchNorm/FusedBatchNorm', num_features=32, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Conv2d_2a_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Conv2d_2a_3x3/Conv2D', in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Conv2d_2a_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Conv2d_2a_3x3/BatchNorm/FusedBatchNorm', num_features=32, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Conv2d_2b_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Conv2d_2b_3x3/Conv2D', in_channels=32, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Conv2d_2b_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Conv2d_2b_3x3/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Conv2d_3b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Conv2d_3b_1x1/Conv2D', in_channels=64, out_channels=80, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Conv2d_3b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Conv2d_3b_1x1/BatchNorm/FusedBatchNorm', num_features=80, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Conv2d_4a_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Conv2d_4a_3x3/Conv2D', in_channels=80, out_channels=192, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Conv2d_4a_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Conv2d_4a_3x3/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5b/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=192, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5b/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=192, out_channels=48, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5b/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=192, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5b/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=48, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5b/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=192, out_channels=32, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=32, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5b/Branch_1/Conv2d_0b_5x5/Conv2D', in_channels=48, out_channels=64, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5b/Branch_2/Conv2d_0b_3x3/Conv2D', in_channels=64, out_channels=96, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5b/Branch_2/Conv2d_0c_3x3/Conv2D', in_channels=96, out_channels=96, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5c/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5c/Branch_1/Conv2d_0b_1x1/Conv2D', in_channels=256, out_channels=48, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5c/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5c/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5c/Branch_1/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=48, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5c/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5c/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5c/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5c/Branch_1/Conv_1_0c_5x5/Conv2D', in_channels=48, out_channels=64, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5c/Branch_2/Conv2d_0b_3x3/Conv2D', in_channels=64, out_channels=96, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5c/Branch_1/Conv_1_0c_5x5/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5c/Branch_2/Conv2d_0b_3x3/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5c/Branch_2/Conv2d_0c_3x3/Conv2D', in_channels=96, out_channels=96, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5c/Branch_2/Conv2d_0c_3x3/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5d/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=288, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5d/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=288, out_channels=48, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5d/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=288, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5d/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5d/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=48, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5d/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5d/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=288, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5d/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5d/Branch_1/Conv2d_0b_5x5/Conv2D', in_channels=48, out_channels=64, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5d/Branch_2/Conv2d_0b_3x3/Conv2D', in_channels=64, out_channels=96, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5d/Branch_1/Conv2d_0b_5x5/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5d/Branch_2/Conv2d_0b_3x3/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_5d/Branch_2/Conv2d_0c_3x3/Conv2D', in_channels=96, out_channels=96, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_5d/Branch_2/Conv2d_0c_3x3/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6a/Branch_0/Conv2d_1a_1x1/Conv2D', in_channels=288, out_channels=384, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6a/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=288, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6a/Branch_0/Conv2d_1a_1x1/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=64, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6a/Branch_1/Conv2d_0b_3x3/Conv2D', in_channels=64, out_channels=96, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6a/Branch_1/Conv2d_1a_1x1/Conv2D', in_channels=96, out_channels=96, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6a/Branch_1/Conv2d_1a_1x1/BatchNorm/FusedBatchNorm', num_features=96, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=128, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=128, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_1/Conv2d_0b_1x7/Conv2D', in_channels=128, out_channels=128, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0b_7x1/Conv2D', in_channels=128, out_channels=128, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_1/Conv2d_0b_1x7/BatchNorm/FusedBatchNorm', num_features=128, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0b_7x1/BatchNorm/FusedBatchNorm', num_features=128, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_1/Conv2d_0c_7x1/Conv2D', in_channels=128, out_channels=192, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0c_1x7/Conv2D', in_channels=128, out_channels=128, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_1/Conv2d_0c_7x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0c_1x7/BatchNorm/FusedBatchNorm', num_features=128, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0d_7x1/Conv2D', in_channels=128, out_channels=128, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0d_7x1/BatchNorm/FusedBatchNorm', num_features=128, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0e_1x7/Conv2D', in_channels=128, out_channels=192, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6b/Branch_2/Conv2d_0e_1x7/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=160, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=160, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_1/Conv2d_0b_1x7/Conv2D', in_channels=160, out_channels=160, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0b_7x1/Conv2D', in_channels=160, out_channels=160, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_1/Conv2d_0b_1x7/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0b_7x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_1/Conv2d_0c_7x1/Conv2D', in_channels=160, out_channels=192, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0c_1x7/Conv2D', in_channels=160, out_channels=160, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_1/Conv2d_0c_7x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0c_1x7/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0d_7x1/Conv2D', in_channels=160, out_channels=160, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0d_7x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0e_1x7/Conv2D', in_channels=160, out_channels=192, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0e_1x7/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=160, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=160, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_1/Conv2d_0b_1x7/Conv2D', in_channels=160, out_channels=160, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0b_7x1/Conv2D', in_channels=160, out_channels=160, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_1/Conv2d_0b_1x7/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0b_7x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_1/Conv2d_0c_7x1/Conv2D', in_channels=160, out_channels=192, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0c_1x7/Conv2D', in_channels=160, out_channels=160, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_1/Conv2d_0c_7x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0c_1x7/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0d_7x1/Conv2D', in_channels=160, out_channels=160, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0d_7x1/BatchNorm/FusedBatchNorm', num_features=160, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0e_1x7/Conv2D', in_channels=160, out_channels=192, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6d/Branch_2/Conv2d_0e_1x7/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_1/Conv2d_0b_1x7/Conv2D', in_channels=192, out_channels=192, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0b_7x1/Conv2D', in_channels=192, out_channels=192, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_1/Conv2d_0b_1x7/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0b_7x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_1/Conv2d_0c_7x1/Conv2D', in_channels=192, out_channels=192, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0c_1x7/Conv2D', in_channels=192, out_channels=192, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_1/Conv2d_0c_7x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0c_1x7/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0d_7x1/Conv2D', in_channels=192, out_channels=192, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0d_7x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0e_1x7/Conv2D', in_channels=192, out_channels=192, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_6e/Branch_2/Conv2d_0e_1x7/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7a/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=768, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_AuxLogits_Conv2d_1b_1x1_Conv2D = self.__conv(2, name='InceptionV3/AuxLogits/Conv2d_1b_1x1/Conv2D', in_channels=768, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_AuxLogits_Conv2d_1b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/AuxLogits/Conv2d_1b_1x1/BatchNorm/FusedBatchNorm', num_features=128, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7a/Branch_0/Conv2d_1a_3x3/Conv2D', in_channels=192, out_channels=320, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_0b_1x7/Conv2D', in_channels=192, out_channels=192, kernel_size=(1, 7), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm/FusedBatchNorm', num_features=320, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_0b_1x7/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_AuxLogits_Conv2d_2a_5x5_Conv2D = self.__conv(2, name='InceptionV3/AuxLogits/Conv2d_2a_5x5/Conv2D', in_channels=128, out_channels=768, kernel_size=(5, 5), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_AuxLogits_Conv2d_2a_5x5_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/AuxLogits/Conv2d_2a_5x5/BatchNorm/FusedBatchNorm', num_features=768, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_0c_7x1/Conv2D', in_channels=192, out_channels=192, kernel_size=(7, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_0c_7x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_AuxLogits_Conv2d_2b_1x1_Conv2D = self.__conv(2, name='InceptionV3/AuxLogits/Conv2d_2b_1x1/Conv2D', in_channels=768, out_channels=1001, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_1a_3x3/Conv2D', in_channels=192, out_channels=192, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=1280, out_channels=320, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=1280, out_channels=384, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=1280, out_channels=448, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=320, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=448, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=1280, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_1/Conv2d_0b_1x3/Conv2D', in_channels=384, out_channels=384, kernel_size=(1, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_1/Conv2d_0b_3x1/Conv2D', in_channels=384, out_channels=384, kernel_size=(3, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0b_3x3/Conv2D', in_channels=448, out_channels=384, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_1/Conv2d_0b_1x3/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_1/Conv2d_0b_3x1/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0b_3x3/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0c_1x3/Conv2D', in_channels=384, out_channels=384, kernel_size=(1, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0d_3x1/Conv2D', in_channels=384, out_channels=384, kernel_size=(3, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0c_1x3/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7b/Branch_2/Conv2d_0d_3x1/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_0/Conv2d_0a_1x1/Conv2D', in_channels=2048, out_channels=320, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_1/Conv2d_0a_1x1/Conv2D', in_channels=2048, out_channels=384, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0a_1x1/Conv2D', in_channels=2048, out_channels=448, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_0/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=320, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_1/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0a_1x1/BatchNorm/FusedBatchNorm', num_features=448, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_3/Conv2d_0b_1x1/Conv2D', in_channels=2048, out_channels=192, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_3/Conv2d_0b_1x1/BatchNorm/FusedBatchNorm', num_features=192, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_1/Conv2d_0b_1x3/Conv2D', in_channels=384, out_channels=384, kernel_size=(1, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_1/Conv2d_0c_3x1/Conv2D', in_channels=384, out_channels=384, kernel_size=(3, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0b_3x3/Conv2D', in_channels=448, out_channels=384, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_1/Conv2d_0b_1x3/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_1/Conv2d_0c_3x1/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0b_3x3/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0c_1x3/Conv2D', in_channels=384, out_channels=384, kernel_size=(1, 3), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Conv2D = self.__conv(2, name='InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0d_3x1/Conv2D', in_channels=384, out_channels=384, kernel_size=(3, 1), stride=(1, 1), groups=1, bias=None) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0c_1x3/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0d_3x1/BatchNorm/FusedBatchNorm', num_features=384, eps=0.0010000000474974513, momentum=0.0) + self.InceptionV3_Logits_Conv2d_1c_1x1_Conv2D = self.__conv(2, name='InceptionV3/Logits/Conv2d_1c_1x1/Conv2D', in_channels=2048, out_channels=1001, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + + def forward(self, x): + InceptionV3_InceptionV3_Conv2d_1a_3x3_Conv2D = self.InceptionV3_InceptionV3_Conv2d_1a_3x3_Conv2D(x) + InceptionV3_InceptionV3_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Conv2d_1a_3x3_Conv2D) + InceptionV3_InceptionV3_Conv2d_1a_3x3_Relu = F.relu(InceptionV3_InceptionV3_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Conv2d_2a_3x3_Conv2D = self.InceptionV3_InceptionV3_Conv2d_2a_3x3_Conv2D(InceptionV3_InceptionV3_Conv2d_1a_3x3_Relu) + InceptionV3_InceptionV3_Conv2d_2a_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Conv2d_2a_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Conv2d_2a_3x3_Conv2D) + InceptionV3_InceptionV3_Conv2d_2a_3x3_Relu = F.relu(InceptionV3_InceptionV3_Conv2d_2a_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Conv2d_2b_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Conv2d_2a_3x3_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Conv2d_2b_3x3_Conv2D = self.InceptionV3_InceptionV3_Conv2d_2b_3x3_Conv2D(InceptionV3_InceptionV3_Conv2d_2b_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Conv2d_2b_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Conv2d_2b_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Conv2d_2b_3x3_Conv2D) + InceptionV3_InceptionV3_Conv2d_2b_3x3_Relu = F.relu(InceptionV3_InceptionV3_Conv2d_2b_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_MaxPool_3a_3x3_MaxPool, InceptionV3_InceptionV3_MaxPool_3a_3x3_MaxPool_idx = F.max_pool2d(InceptionV3_InceptionV3_Conv2d_2b_3x3_Relu, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + InceptionV3_InceptionV3_Conv2d_3b_1x1_Conv2D = self.InceptionV3_InceptionV3_Conv2d_3b_1x1_Conv2D(InceptionV3_InceptionV3_MaxPool_3a_3x3_MaxPool) + InceptionV3_InceptionV3_Conv2d_3b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Conv2d_3b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Conv2d_3b_1x1_Conv2D) + InceptionV3_InceptionV3_Conv2d_3b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Conv2d_3b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Conv2d_4a_3x3_Conv2D = self.InceptionV3_InceptionV3_Conv2d_4a_3x3_Conv2D(InceptionV3_InceptionV3_Conv2d_3b_1x1_Relu) + InceptionV3_InceptionV3_Conv2d_4a_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Conv2d_4a_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Conv2d_4a_3x3_Conv2D) + InceptionV3_InceptionV3_Conv2d_4a_3x3_Relu = F.relu(InceptionV3_InceptionV3_Conv2d_4a_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_MaxPool_5a_3x3_MaxPool, InceptionV3_InceptionV3_MaxPool_5a_3x3_MaxPool_idx = F.max_pool2d(InceptionV3_InceptionV3_Conv2d_4a_3x3_Relu, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_MaxPool_5a_3x3_MaxPool) + InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_MaxPool_5a_3x3_MaxPool) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_MaxPool_5a_3x3_MaxPool) + InceptionV3_InceptionV3_Mixed_5b_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_MaxPool_5a_3x3_MaxPool, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5b_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0a_1x1_Relu, (2, 2, 2, 2)) + InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Conv2D = self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Conv2D(InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0a_1x1_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Conv2D) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0b_3x3_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5b_concat = torch.cat((InceptionV3_InceptionV3_Mixed_5b_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_5b_Branch_1_Conv2d_0b_5x5_Relu, InceptionV3_InceptionV3_Mixed_5b_Branch_2_Conv2d_0c_3x3_Relu, InceptionV3_InceptionV3_Mixed_5b_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5b_concat) + InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5b_concat) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5b_concat) + InceptionV3_InceptionV3_Mixed_5c_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_5b_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5c_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv2d_0b_1x1_Relu, (2, 2, 2, 2)) + InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Conv2D = self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Conv2D(InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0a_1x1_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Conv2D) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0b_3x3_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5c_concat = torch.cat((InceptionV3_InceptionV3_Mixed_5c_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_5c_Branch_1_Conv_1_0c_5x5_Relu, InceptionV3_InceptionV3_Mixed_5c_Branch_2_Conv2d_0c_3x3_Relu, InceptionV3_InceptionV3_Mixed_5c_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5c_concat) + InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5c_concat) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5c_concat) + InceptionV3_InceptionV3_Mixed_5d_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_5c_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5d_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0a_1x1_Relu, (2, 2, 2, 2)) + InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Conv2D = self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Conv2D(InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0a_1x1_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Conv2D) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0b_3x3_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_5d_concat = torch.cat((InceptionV3_InceptionV3_Mixed_5d_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_5d_Branch_1_Conv2d_0b_5x5_Relu, InceptionV3_InceptionV3_Mixed_5d_Branch_2_Conv2d_0c_3x3_Relu, InceptionV3_InceptionV3_Mixed_5d_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5d_concat) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_5d_concat) + InceptionV3_InceptionV3_Mixed_6a_Branch_2_MaxPool_1a_3x3_MaxPool, InceptionV3_InceptionV3_Mixed_6a_Branch_2_MaxPool_1a_3x3_MaxPool_idx = F.max_pool2d(InceptionV3_InceptionV3_Mixed_5d_concat, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0a_1x1_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_0b_3x3_Relu) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6a_concat = torch.cat((InceptionV3_InceptionV3_Mixed_6a_Branch_0_Conv2d_1a_1x1_Relu, InceptionV3_InceptionV3_Mixed_6a_Branch_1_Conv2d_1a_1x1_Relu, InceptionV3_InceptionV3_Mixed_6a_Branch_2_MaxPool_1a_3x3_MaxPool,), 1) + InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6a_concat) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6a_concat) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6a_concat) + InceptionV3_InceptionV3_Mixed_6b_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_6a_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6b_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0a_1x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0a_1x1_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0b_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0b_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0c_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0d_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6b_concat = torch.cat((InceptionV3_InceptionV3_Mixed_6b_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_6b_Branch_1_Conv2d_0c_7x1_Relu, InceptionV3_InceptionV3_Mixed_6b_Branch_2_Conv2d_0e_1x7_Relu, InceptionV3_InceptionV3_Mixed_6b_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6b_concat) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6b_concat) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6b_concat) + InceptionV3_InceptionV3_Mixed_6c_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_6b_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6c_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0a_1x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0a_1x1_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0b_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0b_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0c_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0d_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6c_concat = torch.cat((InceptionV3_InceptionV3_Mixed_6c_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_6c_Branch_1_Conv2d_0c_7x1_Relu, InceptionV3_InceptionV3_Mixed_6c_Branch_2_Conv2d_0e_1x7_Relu, InceptionV3_InceptionV3_Mixed_6c_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6c_concat) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6c_concat) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6c_concat) + InceptionV3_InceptionV3_Mixed_6d_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_6c_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6d_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0a_1x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0a_1x1_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0b_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0b_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0c_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0d_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6d_concat = torch.cat((InceptionV3_InceptionV3_Mixed_6d_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_6d_Branch_1_Conv2d_0c_7x1_Relu, InceptionV3_InceptionV3_Mixed_6d_Branch_2_Conv2d_0e_1x7_Relu, InceptionV3_InceptionV3_Mixed_6d_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6d_concat) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6d_concat) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6d_concat) + InceptionV3_InceptionV3_Mixed_6e_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_6d_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6e_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0a_1x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0a_1x1_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0b_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0b_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0c_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0d_7x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Conv2D) + InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_6e_concat = torch.cat((InceptionV3_InceptionV3_Mixed_6e_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_6e_Branch_1_Conv2d_0c_7x1_Relu, InceptionV3_InceptionV3_Mixed_6e_Branch_2_Conv2d_0e_1x7_Relu, InceptionV3_InceptionV3_Mixed_6e_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6e_concat) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_6e_concat) + InceptionV3_InceptionV3_Mixed_7a_Branch_2_MaxPool_1a_3x3_MaxPool, InceptionV3_InceptionV3_Mixed_7a_Branch_2_MaxPool_1a_3x3_MaxPool_idx = F.max_pool2d(InceptionV3_InceptionV3_Mixed_6e_concat, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + InceptionV3_AuxLogits_AvgPool_1a_5x5_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_6e_concat, kernel_size=(5, 5), stride=(3, 3), padding=(0,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_AuxLogits_Conv2d_1b_1x1_Conv2D = self.InceptionV3_AuxLogits_Conv2d_1b_1x1_Conv2D(InceptionV3_AuxLogits_AvgPool_1a_5x5_AvgPool) + InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_AuxLogits_Conv2d_1b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_AuxLogits_Conv2d_1b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_AuxLogits_Conv2d_1b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_0a_1x1_Relu) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0a_1x1_Relu, (3, 3, 0, 0)) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Conv2D = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Conv2D(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Conv2D_pad) + InceptionV3_AuxLogits_Conv2d_1b_1x1_Relu = F.relu(InceptionV3_AuxLogits_Conv2d_1b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Conv2D) + InceptionV3_AuxLogits_Conv2d_2a_5x5_Conv2D = self.InceptionV3_AuxLogits_Conv2d_2a_5x5_Conv2D(InceptionV3_AuxLogits_Conv2d_1b_1x1_Relu) + InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_BatchNorm_FusedBatchNorm) + InceptionV3_AuxLogits_Conv2d_2a_5x5_BatchNorm_FusedBatchNorm = self.InceptionV3_AuxLogits_Conv2d_2a_5x5_BatchNorm_FusedBatchNorm(InceptionV3_AuxLogits_Conv2d_2a_5x5_Conv2D) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0b_1x7_Relu, (0, 0, 3, 3)) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Conv2D(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Conv2D_pad) + InceptionV3_AuxLogits_Conv2d_2a_5x5_Relu = F.relu(InceptionV3_AuxLogits_Conv2d_2a_5x5_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Conv2D) + InceptionV3_AuxLogits_Conv2d_2b_1x1_Conv2D = self.InceptionV3_AuxLogits_Conv2d_2b_1x1_Conv2D(InceptionV3_AuxLogits_Conv2d_2a_5x5_Relu) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_BatchNorm_FusedBatchNorm) + InceptionV3_AuxLogits_SpatialSqueeze = torch.squeeze(InceptionV3_AuxLogits_Conv2d_2b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_0c_7x1_Relu) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7a_concat = torch.cat((InceptionV3_InceptionV3_Mixed_7a_Branch_0_Conv2d_1a_3x3_Relu, InceptionV3_InceptionV3_Mixed_7a_Branch_1_Conv2d_1a_3x3_Relu, InceptionV3_InceptionV3_Mixed_7a_Branch_2_MaxPool_1a_3x3_MaxPool,), 1) + InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7a_concat) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7a_concat) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7a_concat) + InceptionV3_InceptionV3_Mixed_7b_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_7a_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7b_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_Relu, (1, 1, 0, 0)) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Conv2D(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0a_1x1_Relu, (0, 0, 1, 1)) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Conv2D(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0a_1x1_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_1_concat = torch.cat((InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_1x3_Relu, InceptionV3_InceptionV3_Mixed_7b_Branch_1_Conv2d_0b_3x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Relu, (1, 1, 0, 0)) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Conv2D(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0b_3x3_Relu, (0, 0, 1, 1)) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Conv2D(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7b_Branch_2_concat = torch.cat((InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0c_1x3_Relu, InceptionV3_InceptionV3_Mixed_7b_Branch_2_Conv2d_0d_3x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_7b_concat = torch.cat((InceptionV3_InceptionV3_Mixed_7b_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_7b_Branch_1_concat, InceptionV3_InceptionV3_Mixed_7b_Branch_2_concat, InceptionV3_InceptionV3_Mixed_7b_Branch_3_Conv2d_0b_1x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7b_concat) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7b_concat) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7b_concat) + InceptionV3_InceptionV3_Mixed_7c_Branch_3_AvgPool_0a_3x3_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_7b_concat, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False) + InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_Conv2D(InceptionV3_InceptionV3_Mixed_7c_Branch_3_AvgPool_0a_3x3_AvgPool) + InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_Relu, (1, 1, 0, 0)) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Conv2D(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0a_1x1_Relu, (0, 0, 1, 1)) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Conv2D(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0a_1x1_Relu, (1, 1, 1, 1)) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Conv2D(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_1_concat = torch.cat((InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0b_1x3_Relu, InceptionV3_InceptionV3_Mixed_7c_Branch_1_Conv2d_0c_3x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Relu, (1, 1, 0, 0)) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Conv2D(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Conv2D_pad = F.pad(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0b_3x3_Relu, (0, 0, 1, 1)) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Conv2D = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Conv2D(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Conv2D_pad) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm = self.InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Conv2D) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Relu = F.relu(InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_BatchNorm_FusedBatchNorm) + InceptionV3_InceptionV3_Mixed_7c_Branch_2_concat = torch.cat((InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0c_1x3_Relu, InceptionV3_InceptionV3_Mixed_7c_Branch_2_Conv2d_0d_3x1_Relu,), 1) + InceptionV3_InceptionV3_Mixed_7c_concat = torch.cat((InceptionV3_InceptionV3_Mixed_7c_Branch_0_Conv2d_0a_1x1_Relu, InceptionV3_InceptionV3_Mixed_7c_Branch_1_concat, InceptionV3_InceptionV3_Mixed_7c_Branch_2_concat, InceptionV3_InceptionV3_Mixed_7c_Branch_3_Conv2d_0b_1x1_Relu,), 1) + kernel_size = self._reduced_kernel_size_for_small_input(InceptionV3_InceptionV3_Mixed_7c_concat, [8,8]) + InceptionV3_Logits_AvgPool_1a_8x8_AvgPool = F.avg_pool2d(InceptionV3_InceptionV3_Mixed_7c_concat, kernel_size=(kernel_size[0], kernel_size[1]), stride=(2, 2), padding=(0,), ceil_mode=False, count_include_pad=False) + InceptionV3_Logits_Conv2d_1c_1x1_Conv2D = self.InceptionV3_Logits_Conv2d_1c_1x1_Conv2D(InceptionV3_Logits_AvgPool_1a_8x8_AvgPool) + InceptionV3_Logits_SpatialSqueeze = torch.squeeze(InceptionV3_Logits_Conv2d_1c_1x1_Conv2D) + MMdnn_Output_input = [InceptionV3_Logits_SpatialSqueeze,InceptionV3_AuxLogits_SpatialSqueeze] + return MMdnn_Output_input + + def _reduced_kernel_size_for_small_input(self, input_tensor, kernel_size): + shape = input_tensor.shape + if shape[2] is None or shape[3] is None: + kernel_size_out = kernel_size + else: + kernel_size_out = [min(shape[2], kernel_size[0]), + min(shape[3], kernel_size[1])] + return kernel_size_out + + @staticmethod + def __batch_normalization(dim, name, **kwargs): + if dim == 0 or dim == 1: layer = nn.BatchNorm1d(**kwargs) + elif dim == 2: layer = nn.BatchNorm2d(**kwargs) + elif dim == 3: layer = nn.BatchNorm3d(**kwargs) + else: raise NotImplementedError() + + if 'scale' in _weights_dict[name]: + layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['scale'])) + else: + layer.weight.data.fill_(1) + + if 'bias' in _weights_dict[name]: + layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) + else: + layer.bias.data.fill_(0) + + layer.state_dict()['running_mean'].copy_(torch.from_numpy(_weights_dict[name]['mean'])) + layer.state_dict()['running_var'].copy_(torch.from_numpy(_weights_dict[name]['var'])) + return layer + + @staticmethod + def __conv(dim, name, **kwargs): + if dim == 1: layer = nn.Conv1d(**kwargs) + elif dim == 2: layer = nn.Conv2d(**kwargs) + elif dim == 3: layer = nn.Conv3d(**kwargs) + else: raise NotImplementedError() + + layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['weights'])) + if 'bias' in _weights_dict[name]: + layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) + return layer + diff --git a/torch_nets/tf_resnet_v2_101.py b/torch_nets/tf_resnet_v2_101.py new file mode 100644 index 0000000..088fb06 --- /dev/null +++ b/torch_nets/tf_resnet_v2_101.py @@ -0,0 +1,651 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +_weights_dict = dict() + +def load_weights(weight_file): + if weight_file == None: + return + + try: + weights_dict = np.load(weight_file, allow_pickle=True).item() + except: + weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item() + + return weights_dict + +class KitModel(nn.Module): + + + def __init__(self, weight_file): + super(KitModel, self).__init__() + global _weights_dict + _weights_dict = load_weights(weight_file) + + self.resnet_v2_101_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/conv1/Conv2D', in_channels=3, out_channels=64, kernel_size=(7, 7), stride=(2, 2), groups=1, bias=True) + self.resnet_v2_101_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block1/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block1/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=256, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_4/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_4/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_4/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_4/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block2/unit_4/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block2/unit_4/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=512, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_4/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_4/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_4/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_4/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_4/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_4/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_5/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_5/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_5/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_5/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_5/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_5/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_6/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_6/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_6/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_6/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_6/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_6/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_7/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_7/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_7/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_7/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_7/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_7/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_8/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_8/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_8/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_8/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_8/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_8/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_9/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_9/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_9/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_9/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_9/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_9/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_10/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_10/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_10/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_10/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_10/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_10/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_11/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_11/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_11/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_11/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_11/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_11/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_12/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_12/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_12/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_12/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_12/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_12/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_13/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_13/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_13/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_13/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_13/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_13/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_14/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_14/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_14/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_14/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_14/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_14/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_15/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_15/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_15/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_15/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_15/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_15/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_16/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_16/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_16/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_16/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_16/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_16/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_17/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_17/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_17/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_17/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_17/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_17/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_18/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_18/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_18/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_18/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_18/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_18/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_19/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_19/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_19/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_19/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_19/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_19/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_20/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_20/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_20/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_20/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_20/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_20/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_21/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_21/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_21/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_21/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_21/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_21/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_22/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_22/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_22/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_22/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_22/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_22/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_23/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_23/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_23/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_23/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block3/unit_23/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block3/unit_23/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=1024, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=2048, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=2048, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=2048, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=2048, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/block4/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_101/block4/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_101_postnorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_101/postnorm/FusedBatchNorm', num_features=2048, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_101_logits_Conv2D = self.__conv(2, name='resnet_v2_101/logits/Conv2D', in_channels=2048, out_channels=1001, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + + def forward(self, x): + resnet_v2_101_Pad = F.pad(x, (3, 3, 3, 3), mode = 'constant', value = 0) + resnet_v2_101_conv1_Conv2D = self.resnet_v2_101_conv1_Conv2D(resnet_v2_101_Pad) + resnet_v2_101_pool1_MaxPool_pad = F.pad(resnet_v2_101_conv1_Conv2D, (0, 1, 0, 1), value=float('-inf')) + resnet_v2_101_pool1_MaxPool, resnet_v2_101_pool1_MaxPool_idx = F.max_pool2d(resnet_v2_101_pool1_MaxPool_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_101_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_pool1_MaxPool) + resnet_v2_101_block1_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block1_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_101_block1_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_101_block1_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block1_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block1_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block1_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block1_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block1_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_101_block1_unit_1_bottleneck_v2_add = resnet_v2_101_block1_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_101_block1_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block1_unit_1_bottleneck_v2_add) + resnet_v2_101_block1_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block1_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block1_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block1_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block1_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block1_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_101_block1_unit_2_bottleneck_v2_add = resnet_v2_101_block1_unit_1_bottleneck_v2_add + resnet_v2_101_block1_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block1_unit_2_bottleneck_v2_add) + resnet_v2_101_block1_unit_3_bottleneck_v2_shortcut_MaxPool, resnet_v2_101_block1_unit_3_bottleneck_v2_shortcut_MaxPool_idx = F.max_pool2d(resnet_v2_101_block1_unit_2_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_101_block1_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block1_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block1_unit_3_bottleneck_v2_Pad = F.pad(resnet_v2_101_block1_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0) + resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block1_unit_3_bottleneck_v2_Pad) + resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block1_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block1_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block1_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_101_block1_unit_3_bottleneck_v2_add = resnet_v2_101_block1_unit_3_bottleneck_v2_shortcut_MaxPool + resnet_v2_101_block1_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block1_unit_3_bottleneck_v2_add) + resnet_v2_101_block2_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block2_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_101_block2_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_101_block2_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block2_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block2_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block2_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block2_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_101_block2_unit_1_bottleneck_v2_add = resnet_v2_101_block2_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_101_block2_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block2_unit_1_bottleneck_v2_add) + resnet_v2_101_block2_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block2_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block2_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block2_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block2_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_101_block2_unit_2_bottleneck_v2_add = resnet_v2_101_block2_unit_1_bottleneck_v2_add + resnet_v2_101_block2_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block2_unit_2_bottleneck_v2_add) + resnet_v2_101_block2_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block2_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block2_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block2_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block2_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_101_block2_unit_3_bottleneck_v2_add = resnet_v2_101_block2_unit_2_bottleneck_v2_add + resnet_v2_101_block2_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block2_unit_3_bottleneck_v2_add) + resnet_v2_101_block2_unit_4_bottleneck_v2_shortcut_MaxPool, resnet_v2_101_block2_unit_4_bottleneck_v2_shortcut_MaxPool_idx = F.max_pool2d(resnet_v2_101_block2_unit_3_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_101_block2_unit_4_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block2_unit_4_bottleneck_v2_preact_Relu) + resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_4_bottleneck_v2_Pad = F.pad(resnet_v2_101_block2_unit_4_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0) + resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block2_unit_4_bottleneck_v2_Pad) + resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block2_unit_4_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block2_unit_4_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block2_unit_4_bottleneck_v2_conv2_Relu) + resnet_v2_101_block2_unit_4_bottleneck_v2_add = resnet_v2_101_block2_unit_4_bottleneck_v2_shortcut_MaxPool + resnet_v2_101_block2_unit_4_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block2_unit_4_bottleneck_v2_add) + resnet_v2_101_block3_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_101_block3_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_101_block3_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_1_bottleneck_v2_add = resnet_v2_101_block3_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_101_block3_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_1_bottleneck_v2_add) + resnet_v2_101_block3_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_2_bottleneck_v2_add = resnet_v2_101_block3_unit_1_bottleneck_v2_add + resnet_v2_101_block3_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_2_bottleneck_v2_add) + resnet_v2_101_block3_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_3_bottleneck_v2_add = resnet_v2_101_block3_unit_2_bottleneck_v2_add + resnet_v2_101_block3_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_3_bottleneck_v2_add) + resnet_v2_101_block3_unit_4_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_4_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_4_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_4_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_4_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_4_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_4_bottleneck_v2_add = resnet_v2_101_block3_unit_3_bottleneck_v2_add + resnet_v2_101_block3_unit_4_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_4_bottleneck_v2_add) + resnet_v2_101_block3_unit_5_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_5_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_5_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_5_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_5_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_5_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_5_bottleneck_v2_add = resnet_v2_101_block3_unit_4_bottleneck_v2_add + resnet_v2_101_block3_unit_5_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_5_bottleneck_v2_add) + resnet_v2_101_block3_unit_6_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_6_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_6_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_6_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_6_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_6_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_6_bottleneck_v2_add = resnet_v2_101_block3_unit_5_bottleneck_v2_add + resnet_v2_101_block3_unit_6_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_6_bottleneck_v2_add) + resnet_v2_101_block3_unit_7_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_7_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_7_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_7_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_7_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_7_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_7_bottleneck_v2_add = resnet_v2_101_block3_unit_6_bottleneck_v2_add + resnet_v2_101_block3_unit_7_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_7_bottleneck_v2_add) + resnet_v2_101_block3_unit_8_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_8_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_8_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_8_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_8_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_8_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_8_bottleneck_v2_add = resnet_v2_101_block3_unit_7_bottleneck_v2_add + resnet_v2_101_block3_unit_8_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_8_bottleneck_v2_add) + resnet_v2_101_block3_unit_9_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_9_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_9_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_9_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_9_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_9_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_9_bottleneck_v2_add = resnet_v2_101_block3_unit_8_bottleneck_v2_add + resnet_v2_101_block3_unit_9_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_9_bottleneck_v2_add) + resnet_v2_101_block3_unit_10_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_10_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_10_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_10_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_10_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_10_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_10_bottleneck_v2_add = resnet_v2_101_block3_unit_9_bottleneck_v2_add + resnet_v2_101_block3_unit_10_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_10_bottleneck_v2_add) + resnet_v2_101_block3_unit_11_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_11_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_11_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_11_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_11_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_11_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_11_bottleneck_v2_add = resnet_v2_101_block3_unit_10_bottleneck_v2_add + resnet_v2_101_block3_unit_11_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_11_bottleneck_v2_add) + resnet_v2_101_block3_unit_12_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_12_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_12_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_12_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_12_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_12_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_12_bottleneck_v2_add = resnet_v2_101_block3_unit_11_bottleneck_v2_add + resnet_v2_101_block3_unit_12_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_12_bottleneck_v2_add) + resnet_v2_101_block3_unit_13_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_13_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_13_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_13_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_13_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_13_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_13_bottleneck_v2_add = resnet_v2_101_block3_unit_12_bottleneck_v2_add + resnet_v2_101_block3_unit_13_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_13_bottleneck_v2_add) + resnet_v2_101_block3_unit_14_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_14_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_14_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_14_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_14_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_14_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_14_bottleneck_v2_add = resnet_v2_101_block3_unit_13_bottleneck_v2_add + resnet_v2_101_block3_unit_14_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_14_bottleneck_v2_add) + resnet_v2_101_block3_unit_15_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_15_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_15_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_15_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_15_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_15_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_15_bottleneck_v2_add = resnet_v2_101_block3_unit_14_bottleneck_v2_add + resnet_v2_101_block3_unit_15_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_15_bottleneck_v2_add) + resnet_v2_101_block3_unit_16_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_16_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_16_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_16_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_16_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_16_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_16_bottleneck_v2_add = resnet_v2_101_block3_unit_15_bottleneck_v2_add + resnet_v2_101_block3_unit_16_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_16_bottleneck_v2_add) + resnet_v2_101_block3_unit_17_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_17_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_17_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_17_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_17_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_17_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_17_bottleneck_v2_add = resnet_v2_101_block3_unit_16_bottleneck_v2_add + resnet_v2_101_block3_unit_17_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_17_bottleneck_v2_add) + resnet_v2_101_block3_unit_18_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_18_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_18_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_18_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_18_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_18_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_18_bottleneck_v2_add = resnet_v2_101_block3_unit_17_bottleneck_v2_add + resnet_v2_101_block3_unit_18_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_18_bottleneck_v2_add) + resnet_v2_101_block3_unit_19_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_19_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_19_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_19_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_19_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_19_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_19_bottleneck_v2_add = resnet_v2_101_block3_unit_18_bottleneck_v2_add + resnet_v2_101_block3_unit_19_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_19_bottleneck_v2_add) + resnet_v2_101_block3_unit_20_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_20_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_20_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_20_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_20_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_20_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_20_bottleneck_v2_add = resnet_v2_101_block3_unit_19_bottleneck_v2_add + resnet_v2_101_block3_unit_20_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_20_bottleneck_v2_add) + resnet_v2_101_block3_unit_21_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_21_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_21_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_21_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_21_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_21_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_21_bottleneck_v2_add = resnet_v2_101_block3_unit_20_bottleneck_v2_add + resnet_v2_101_block3_unit_21_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_21_bottleneck_v2_add) + resnet_v2_101_block3_unit_22_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_22_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block3_unit_22_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_22_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_22_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_22_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_22_bottleneck_v2_add = resnet_v2_101_block3_unit_21_bottleneck_v2_add + resnet_v2_101_block3_unit_22_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_22_bottleneck_v2_add) + resnet_v2_101_block3_unit_23_bottleneck_v2_shortcut_MaxPool, resnet_v2_101_block3_unit_23_bottleneck_v2_shortcut_MaxPool_idx = F.max_pool2d(resnet_v2_101_block3_unit_22_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_101_block3_unit_23_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block3_unit_23_bottleneck_v2_preact_Relu) + resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_23_bottleneck_v2_Pad = F.pad(resnet_v2_101_block3_unit_23_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0) + resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block3_unit_23_bottleneck_v2_Pad) + resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block3_unit_23_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block3_unit_23_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block3_unit_23_bottleneck_v2_conv2_Relu) + resnet_v2_101_block3_unit_23_bottleneck_v2_add = resnet_v2_101_block3_unit_23_bottleneck_v2_shortcut_MaxPool + resnet_v2_101_block3_unit_23_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block3_unit_23_bottleneck_v2_add) + resnet_v2_101_block4_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block4_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_101_block4_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_101_block4_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block4_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block4_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block4_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block4_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block4_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_101_block4_unit_1_bottleneck_v2_add = resnet_v2_101_block4_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_101_block4_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block4_unit_1_bottleneck_v2_add) + resnet_v2_101_block4_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block4_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block4_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block4_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block4_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block4_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_101_block4_unit_2_bottleneck_v2_add = resnet_v2_101_block4_unit_1_bottleneck_v2_add + resnet_v2_101_block4_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_101_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_101_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_101_block4_unit_2_bottleneck_v2_add) + resnet_v2_101_block4_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_101_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_101_block4_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_101_block4_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_101_block4_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_101_block4_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_101_block4_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_101_block4_unit_3_bottleneck_v2_add = resnet_v2_101_block4_unit_2_bottleneck_v2_add + resnet_v2_101_block4_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_101_postnorm_FusedBatchNorm = self.resnet_v2_101_postnorm_FusedBatchNorm(resnet_v2_101_block4_unit_3_bottleneck_v2_add) + resnet_v2_101_postnorm_Relu = F.relu(resnet_v2_101_postnorm_FusedBatchNorm) + resnet_v2_101_pool5 = torch.mean(resnet_v2_101_postnorm_Relu, 3, True) + resnet_v2_101_pool5 = torch.mean(resnet_v2_101_pool5, 2, True) + resnet_v2_101_logits_Conv2D = self.resnet_v2_101_logits_Conv2D(resnet_v2_101_pool5) + resnet_v2_101_SpatialSqueeze = torch.squeeze(resnet_v2_101_logits_Conv2D) + MMdnn_Output_input = [resnet_v2_101_SpatialSqueeze] + return MMdnn_Output_input + + + @staticmethod + def __conv(dim, name, **kwargs): + if dim == 1: layer = nn.Conv1d(**kwargs) + elif dim == 2: layer = nn.Conv2d(**kwargs) + elif dim == 3: layer = nn.Conv3d(**kwargs) + else: raise NotImplementedError() + + layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['weights'])) + if 'bias' in _weights_dict[name]: + layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) + return layer + + @staticmethod + def __batch_normalization(dim, name, **kwargs): + if dim == 0 or dim == 1: layer = nn.BatchNorm1d(**kwargs) + elif dim == 2: layer = nn.BatchNorm2d(**kwargs) + elif dim == 3: layer = nn.BatchNorm3d(**kwargs) + else: raise NotImplementedError() + + if 'scale' in _weights_dict[name]: + layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['scale'])) + else: + layer.weight.data.fill_(1) + + if 'bias' in _weights_dict[name]: + layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) + else: + layer.bias.data.fill_(0) + + layer.state_dict()['running_mean'].copy_(torch.from_numpy(_weights_dict[name]['mean'])) + layer.state_dict()['running_var'].copy_(torch.from_numpy(_weights_dict[name]['var'])) + return layer + diff --git a/torch_nets/tf_resnet_v2_50.py b/torch_nets/tf_resnet_v2_50.py new file mode 100644 index 0000000..616d3df --- /dev/null +++ b/torch_nets/tf_resnet_v2_50.py @@ -0,0 +1,362 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + +_weights_dict = dict() + +def load_weights(weight_file): + if weight_file == None: + return + + try: + weights_dict = np.load(weight_file, allow_pickle=True).item() + except: + weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item() + + return weights_dict + +class KitModel(nn.Module): + + + def __init__(self, weight_file): + super(KitModel, self).__init__() + global _weights_dict + _weights_dict = load_weights(weight_file) + + self.resnet_v2_50_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/conv1/Conv2D', in_channels=3, out_channels=64, kernel_size=(7, 7), stride=(2, 2), groups=1, bias=True) + self.resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=256, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_4/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_4/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_4/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_4/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_4/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_4/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=512, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_4/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_4/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_4/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_4/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_4/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_4/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_5/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_5/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_5/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_5/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_5/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_5/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_6/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_6/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_6/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_6/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None) + self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_6/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_6/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=1024, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=2048, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=2048, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=2048, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=2048, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None) + self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + self.resnet_v2_50_postnorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/postnorm/FusedBatchNorm', num_features=2048, eps=1.0009999641624745e-05, momentum=0.0) + self.resnet_v2_50_logits_Conv2D = self.__conv(2, name='resnet_v2_50/logits/Conv2D', in_channels=2048, out_channels=1001, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True) + + def forward(self, x): + resnet_v2_50_Pad = F.pad(x, (3, 3, 3, 3), mode = 'constant', value = 0) + resnet_v2_50_conv1_Conv2D = self.resnet_v2_50_conv1_Conv2D(resnet_v2_50_Pad) + resnet_v2_50_pool1_MaxPool_pad = F.pad(resnet_v2_50_conv1_Conv2D, (0, 1, 0, 1), value=float('-inf')) + resnet_v2_50_pool1_MaxPool, resnet_v2_50_pool1_MaxPool_idx = F.max_pool2d(resnet_v2_50_pool1_MaxPool_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_pool1_MaxPool) + resnet_v2_50_block1_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_50_block1_unit_1_bottleneck_v2_add = resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block1_unit_1_bottleneck_v2_add) + resnet_v2_50_block1_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block1_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_50_block1_unit_2_bottleneck_v2_add = resnet_v2_50_block1_unit_1_bottleneck_v2_add + resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block1_unit_2_bottleneck_v2_add) + resnet_v2_50_block1_unit_3_bottleneck_v2_shortcut_MaxPool, resnet_v2_50_block1_unit_3_bottleneck_v2_shortcut_MaxPool_idx = F.max_pool2d(resnet_v2_50_block1_unit_2_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_50_block1_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block1_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block1_unit_3_bottleneck_v2_Pad = F.pad(resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0) + resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block1_unit_3_bottleneck_v2_Pad) + resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_50_block1_unit_3_bottleneck_v2_add = resnet_v2_50_block1_unit_3_bottleneck_v2_shortcut_MaxPool + resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block1_unit_3_bottleneck_v2_add) + resnet_v2_50_block2_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_50_block2_unit_1_bottleneck_v2_add = resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_1_bottleneck_v2_add) + resnet_v2_50_block2_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_50_block2_unit_2_bottleneck_v2_add = resnet_v2_50_block2_unit_1_bottleneck_v2_add + resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_2_bottleneck_v2_add) + resnet_v2_50_block2_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_50_block2_unit_3_bottleneck_v2_add = resnet_v2_50_block2_unit_2_bottleneck_v2_add + resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_3_bottleneck_v2_add) + resnet_v2_50_block2_unit_4_bottleneck_v2_shortcut_MaxPool, resnet_v2_50_block2_unit_4_bottleneck_v2_shortcut_MaxPool_idx = F.max_pool2d(resnet_v2_50_block2_unit_3_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_50_block2_unit_4_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_4_bottleneck_v2_preact_Relu) + resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_4_bottleneck_v2_Pad = F.pad(resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0) + resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_4_bottleneck_v2_Pad) + resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Relu) + resnet_v2_50_block2_unit_4_bottleneck_v2_add = resnet_v2_50_block2_unit_4_bottleneck_v2_shortcut_MaxPool + resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_4_bottleneck_v2_add) + resnet_v2_50_block3_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_50_block3_unit_1_bottleneck_v2_add = resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_1_bottleneck_v2_add) + resnet_v2_50_block3_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_50_block3_unit_2_bottleneck_v2_add = resnet_v2_50_block3_unit_1_bottleneck_v2_add + resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_2_bottleneck_v2_add) + resnet_v2_50_block3_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_50_block3_unit_3_bottleneck_v2_add = resnet_v2_50_block3_unit_2_bottleneck_v2_add + resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_3_bottleneck_v2_add) + resnet_v2_50_block3_unit_4_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_4_bottleneck_v2_preact_Relu) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Relu) + resnet_v2_50_block3_unit_4_bottleneck_v2_add = resnet_v2_50_block3_unit_3_bottleneck_v2_add + resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_4_bottleneck_v2_add) + resnet_v2_50_block3_unit_5_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_5_bottleneck_v2_preact_Relu) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Relu) + resnet_v2_50_block3_unit_5_bottleneck_v2_add = resnet_v2_50_block3_unit_4_bottleneck_v2_add + resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_5_bottleneck_v2_add) + resnet_v2_50_block3_unit_6_bottleneck_v2_shortcut_MaxPool, resnet_v2_50_block3_unit_6_bottleneck_v2_shortcut_MaxPool_idx = F.max_pool2d(resnet_v2_50_block3_unit_5_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False, return_indices=True) + resnet_v2_50_block3_unit_6_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_6_bottleneck_v2_preact_Relu) + resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_6_bottleneck_v2_Pad = F.pad(resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0) + resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_6_bottleneck_v2_Pad) + resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Relu) + resnet_v2_50_block3_unit_6_bottleneck_v2_add = resnet_v2_50_block3_unit_6_bottleneck_v2_shortcut_MaxPool + resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_6_bottleneck_v2_add) + resnet_v2_50_block4_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_preact_Relu) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Relu) + resnet_v2_50_block4_unit_1_bottleneck_v2_add = resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block4_unit_1_bottleneck_v2_add) + resnet_v2_50_block4_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block4_unit_2_bottleneck_v2_preact_Relu) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Relu) + resnet_v2_50_block4_unit_2_bottleneck_v2_add = resnet_v2_50_block4_unit_1_bottleneck_v2_add + resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D + resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block4_unit_2_bottleneck_v2_add) + resnet_v2_50_block4_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block4_unit_3_bottleneck_v2_preact_Relu) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1)) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D_pad) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm) + resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Relu) + resnet_v2_50_block4_unit_3_bottleneck_v2_add = resnet_v2_50_block4_unit_2_bottleneck_v2_add + resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D + resnet_v2_50_postnorm_FusedBatchNorm = self.resnet_v2_50_postnorm_FusedBatchNorm(resnet_v2_50_block4_unit_3_bottleneck_v2_add) + resnet_v2_50_postnorm_Relu = F.relu(resnet_v2_50_postnorm_FusedBatchNorm) + resnet_v2_50_pool5 = torch.mean(resnet_v2_50_postnorm_Relu, 3, True) + resnet_v2_50_pool5 = torch.mean(resnet_v2_50_pool5, 2, True) + resnet_v2_50_logits_Conv2D = self.resnet_v2_50_logits_Conv2D(resnet_v2_50_pool5) + resnet_v2_50_SpatialSqueeze = torch.squeeze(resnet_v2_50_logits_Conv2D) + MMdnn_Output_input = [resnet_v2_50_SpatialSqueeze] + return MMdnn_Output_input + + + @staticmethod + def __batch_normalization(dim, name, **kwargs): + if dim == 0 or dim == 1: layer = nn.BatchNorm1d(**kwargs) + elif dim == 2: layer = nn.BatchNorm2d(**kwargs) + elif dim == 3: layer = nn.BatchNorm3d(**kwargs) + else: raise NotImplementedError() + + if 'scale' in _weights_dict[name]: + layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['scale'])) + else: + layer.weight.data.fill_(1) + + if 'bias' in _weights_dict[name]: + layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) + else: + layer.bias.data.fill_(0) + + layer.state_dict()['running_mean'].copy_(torch.from_numpy(_weights_dict[name]['mean'])) + layer.state_dict()['running_var'].copy_(torch.from_numpy(_weights_dict[name]['var'])) + return layer + + @staticmethod + def __conv(dim, name, **kwargs): + if dim == 1: layer = nn.Conv1d(**kwargs) + elif dim == 2: layer = nn.Conv2d(**kwargs) + elif dim == 3: layer = nn.Conv3d(**kwargs) + else: raise NotImplementedError() + + layer.state_dict()['weight'].copy_(torch.from_numpy(_weights_dict[name]['weights'])) + if 'bias' in _weights_dict[name]: + layer.state_dict()['bias'].copy_(torch.from_numpy(_weights_dict[name]['bias'])) + return layer + diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..f5ab647 --- /dev/null +++ b/utils.py @@ -0,0 +1,51 @@ +import torch +import os +import time + +ROOT_PATH = '' +BASE_ADV_PATH = os.path.join(ROOT_PATH, 'advimages') + +class AverageMeter: + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.reshape(1, -1).expand_as(pred)) + return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] + +class Logger(object): + + def __init__(self, path, header): + self.log_file = open(path, 'w') + self.logger = csv.writer(self.log_file, delimiter='\t') + + self.logger.writerow(header) + self.header = header + + def __del(self): + self.log_file.close() + + def log(self, values): + write_values = [] + for col in self.header: + assert col in values + write_values.append(values[col]) + + self.logger.writerow(write_values) + self.log_file.flush() \ No newline at end of file