171 lines
5.7 KiB
Python
171 lines
5.7 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
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import torch.nn as nn
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import torch.nn.functional as F
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from pretrainedmodels.models.dpn import DPN
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from pretrainedmodels.models.dpn import pretrained_settings
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from ._base import EncoderMixin
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class DPNEncoder(DPN, EncoderMixin):
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def __init__(self, stage_idxs, out_channels, depth=5, **kwargs):
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super().__init__(**kwargs)
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self._stage_idxs = stage_idxs
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self._depth = depth
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self._out_channels = out_channels
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self._in_channels = 3
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del self.last_linear
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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.features[0].conv, self.features[0].bn, self.features[0].act),
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nn.Sequential(self.features[0].pool, self.features[1 : self._stage_idxs[0]]),
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self.features[self._stage_idxs[0] : self._stage_idxs[1]],
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self.features[self._stage_idxs[1] : self._stage_idxs[2]],
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self.features[self._stage_idxs[2] : self._stage_idxs[3]],
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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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if isinstance(x, (list, tuple)):
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features.append(F.relu(torch.cat(x, dim=1), inplace=True))
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else:
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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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dpn_encoders = {
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"dpn68": {
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"encoder": DPNEncoder,
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"pretrained_settings": pretrained_settings["dpn68"],
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"params": {
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"stage_idxs": (4, 8, 20, 24),
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"out_channels": (3, 10, 144, 320, 704, 832),
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"groups": 32,
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"inc_sec": (16, 32, 32, 64),
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"k_r": 128,
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"k_sec": (3, 4, 12, 3),
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"num_classes": 1000,
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"num_init_features": 10,
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"small": True,
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"test_time_pool": True,
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},
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},
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"dpn68b": {
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"encoder": DPNEncoder,
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"pretrained_settings": pretrained_settings["dpn68b"],
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"params": {
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"stage_idxs": (4, 8, 20, 24),
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"out_channels": (3, 10, 144, 320, 704, 832),
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"b": True,
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"groups": 32,
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"inc_sec": (16, 32, 32, 64),
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"k_r": 128,
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"k_sec": (3, 4, 12, 3),
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"num_classes": 1000,
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"num_init_features": 10,
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"small": True,
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"test_time_pool": True,
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},
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},
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"dpn92": {
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"encoder": DPNEncoder,
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"pretrained_settings": pretrained_settings["dpn92"],
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"params": {
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"stage_idxs": (4, 8, 28, 32),
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"out_channels": (3, 64, 336, 704, 1552, 2688),
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"groups": 32,
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"inc_sec": (16, 32, 24, 128),
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"k_r": 96,
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"k_sec": (3, 4, 20, 3),
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"num_classes": 1000,
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"num_init_features": 64,
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"test_time_pool": True,
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},
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},
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"dpn98": {
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"encoder": DPNEncoder,
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"pretrained_settings": pretrained_settings["dpn98"],
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"params": {
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"stage_idxs": (4, 10, 30, 34),
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"out_channels": (3, 96, 336, 768, 1728, 2688),
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"groups": 40,
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"inc_sec": (16, 32, 32, 128),
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"k_r": 160,
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"k_sec": (3, 6, 20, 3),
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"num_classes": 1000,
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"num_init_features": 96,
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"test_time_pool": True,
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},
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},
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"dpn107": {
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"encoder": DPNEncoder,
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"pretrained_settings": pretrained_settings["dpn107"],
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"params": {
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"stage_idxs": (5, 13, 33, 37),
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"out_channels": (3, 128, 376, 1152, 2432, 2688),
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"groups": 50,
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"inc_sec": (20, 64, 64, 128),
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"k_r": 200,
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"k_sec": (4, 8, 20, 3),
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"num_classes": 1000,
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"num_init_features": 128,
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"test_time_pool": True,
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},
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},
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"dpn131": {
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"encoder": DPNEncoder,
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"pretrained_settings": pretrained_settings["dpn131"],
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"params": {
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"stage_idxs": (5, 13, 41, 45),
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"out_channels": (3, 128, 352, 832, 1984, 2688),
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"groups": 40,
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"inc_sec": (16, 32, 32, 128),
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"k_r": 160,
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"k_sec": (4, 8, 28, 3),
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"num_classes": 1000,
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"num_init_features": 128,
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"test_time_pool": True,
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},
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},
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}
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