87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import sys
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sys.path.append('/home/ly/StyleCLIP-main/models/facial_recognition')
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from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module
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from helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm
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"""
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Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch)
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"""
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class Backbone(Module):
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def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True):
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super(Backbone, self).__init__()
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assert input_size in [112, 224], "input_size should be 112 or 224"
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assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152"
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assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se"
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blocks = get_blocks(num_layers)
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if mode == 'ir':
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unit_module = bottleneck_IR
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elif mode == 'ir_se':
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unit_module = bottleneck_IR_SE
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self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False),
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BatchNorm2d(64),
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PReLU(64))
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if input_size == 112:
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self.output_layer = Sequential(BatchNorm2d(512),
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Dropout(drop_ratio),
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Flatten(),
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Linear(512 * 7 * 7, 512),
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BatchNorm1d(512, affine=affine))
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else:
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self.output_layer = Sequential(BatchNorm2d(512),
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Dropout(drop_ratio),
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Flatten(),
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Linear(512 * 14 * 14, 512),
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BatchNorm1d(512, affine=affine))
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modules = []
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for block in blocks:
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for bottleneck in block:
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modules.append(unit_module(bottleneck.in_channel,
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bottleneck.depth,
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bottleneck.stride))
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self.body = Sequential(*modules)
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def forward(self, x):
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x = self.input_layer(x)
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x = self.body(x)
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x = self.output_layer(x)
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return l2_norm(x)
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def IR_50(input_size):
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"""Constructs a ir-50 model."""
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model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False)
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return model
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def IR_101(input_size):
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"""Constructs a ir-101 model."""
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model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False)
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return model
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def IR_152(input_size):
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"""Constructs a ir-152 model."""
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model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False)
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return model
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def IR_SE_50(input_size):
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"""Constructs a ir_se-50 model."""
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model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False)
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return model
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def IR_SE_101(input_size):
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"""Constructs a ir_se-101 model."""
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model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False)
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return model
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def IR_SE_152(input_size):
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"""Constructs a ir_se-152 model."""
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model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False)
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return model
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