91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
""" 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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# correct paddings
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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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# remove linear layers
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del self.avgpool_1a
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del self.last_linear
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def make_dilated(self, output_stride):
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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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