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