from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module from model.encoder.encoders.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