| |
|
|
| from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module |
| from ldm.thirdp.psp.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 |
