(self, block_cfg, num_classes=1000, in_chans=3, output_stride=32,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_rate=0., global_pool='avg')
| 118 | """ |
| 119 | |
| 120 | def __init__(self, block_cfg, num_classes=1000, in_chans=3, output_stride=32, |
| 121 | act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_rate=0., global_pool='avg'): |
| 122 | super(XceptionAligned, self).__init__() |
| 123 | self.num_classes = num_classes |
| 124 | self.drop_rate = drop_rate |
| 125 | assert output_stride in (8, 16, 32) |
| 126 | |
| 127 | layer_args = dict(act_layer=act_layer, norm_layer=norm_layer) |
| 128 | self.stem = nn.Sequential(*[ |
| 129 | ConvBnAct(in_chans, 32, kernel_size=3, stride=2, **layer_args), |
| 130 | ConvBnAct(32, 64, kernel_size=3, stride=1, **layer_args) |
| 131 | ]) |
| 132 | |
| 133 | curr_dilation = 1 |
| 134 | curr_stride = 2 |
| 135 | self.feature_info = [] |
| 136 | self.blocks = nn.Sequential() |
| 137 | for i, b in enumerate(block_cfg): |
| 138 | b['dilation'] = curr_dilation |
| 139 | if b['stride'] > 1: |
| 140 | self.feature_info += [dict( |
| 141 | num_chs=to_3tuple(b['out_chs'])[-2], reduction=curr_stride, module=f'blocks.{i}.stack.act3')] |
| 142 | next_stride = curr_stride * b['stride'] |
| 143 | if next_stride > output_stride: |
| 144 | curr_dilation *= b['stride'] |
| 145 | b['stride'] = 1 |
| 146 | else: |
| 147 | curr_stride = next_stride |
| 148 | self.blocks.add_module(str(i), XceptionModule(**b, **layer_args)) |
| 149 | self.num_features = self.blocks[-1].out_channels |
| 150 | |
| 151 | self.feature_info += [dict( |
| 152 | num_chs=self.num_features, reduction=curr_stride, module='blocks.' + str(len(self.blocks) - 1))] |
| 153 | |
| 154 | self.head = ClassifierHead( |
| 155 | in_chs=self.num_features, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate) |
| 156 | |
| 157 | def get_classifier(self): |
| 158 | return self.head.fc |
nothing calls this directly
no test coverage detected