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| """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` | |
| Attributes: | |
| _out_channels (list of int): specify number of channels for each encoder feature tensor | |
| _depth (int): specify number of stages in decoder (in other words number of downsampling operations) | |
| _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) | |
| Methods: | |
| forward(self, x: torch.Tensor) | |
| produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of | |
| shape NCHW (features should be sorted in descending order according to spatial resolution, starting | |
| with resolution same as input `x` tensor). | |
| Input: `x` with shape (1, 3, 64, 64) | |
| Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes | |
| [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), | |
| (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) | |
| also should support number of features according to specified depth, e.g. if depth = 5, | |
| number of feature tensors = 6 (one with same resolution as input and 5 downsampled), | |
| depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). | |
| """ | |
| import torch.nn as nn | |
| from pretrainedmodels.models.inceptionresnetv2 import InceptionResNetV2 | |
| from pretrainedmodels.models.inceptionresnetv2 import pretrained_settings | |
| from ._base import EncoderMixin | |
| class InceptionResNetV2Encoder(InceptionResNetV2, EncoderMixin): | |
| def __init__(self, out_channels, depth=5, **kwargs): | |
| super().__init__(**kwargs) | |
| self._out_channels = out_channels | |
| self._depth = depth | |
| self._in_channels = 3 | |
| # correct paddings | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| if m.kernel_size == (3, 3): | |
| m.padding = (1, 1) | |
| if isinstance(m, nn.MaxPool2d): | |
| m.padding = (1, 1) | |
| # remove linear layers | |
| del self.avgpool_1a | |
| del self.last_linear | |
| def make_dilated(self, *args, **kwargs): | |
| raise ValueError( | |
| "InceptionResnetV2 encoder does not support dilated mode " | |
| "due to pooling operation for downsampling!" | |
| ) | |
| def get_stages(self): | |
| return [ | |
| nn.Identity(), | |
| nn.Sequential(self.conv2d_1a, self.conv2d_2a, self.conv2d_2b), | |
| nn.Sequential(self.maxpool_3a, self.conv2d_3b, self.conv2d_4a), | |
| nn.Sequential(self.maxpool_5a, self.mixed_5b, self.repeat), | |
| nn.Sequential(self.mixed_6a, self.repeat_1), | |
| nn.Sequential(self.mixed_7a, self.repeat_2, self.block8, self.conv2d_7b), | |
| ] | |
| def forward(self, x): | |
| stages = self.get_stages() | |
| features = [] | |
| for i in range(self._depth + 1): | |
| x = stages[i](x) | |
| features.append(x) | |
| return features | |
| def load_state_dict(self, state_dict, **kwargs): | |
| state_dict.pop("last_linear.bias", None) | |
| state_dict.pop("last_linear.weight", None) | |
| super().load_state_dict(state_dict, **kwargs) | |
| inceptionresnetv2_encoders = { | |
| "inceptionresnetv2": { | |
| "encoder": InceptionResNetV2Encoder, | |
| "pretrained_settings": pretrained_settings["inceptionresnetv2"], | |
| "params": {"out_channels": (3, 64, 192, 320, 1088, 1536), "num_classes": 1000}, | |
| } | |
| } | |