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