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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 torchvision
import torch.nn as nn
from ._base import EncoderMixin
class MobileNetV2Encoder(torchvision.models.MobileNetV2, EncoderMixin):
def __init__(self, out_channels, depth=5, **kwargs):
super().__init__(**kwargs)
self._depth = depth
self._out_channels = out_channels
self._in_channels = 3
del self.classifier
def get_stages(self):
return [
nn.Identity(),
self.features[:2],
self.features[2:4],
self.features[4:7],
self.features[7:14],
self.features[14:],
]
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("classifier.1.bias", None)
state_dict.pop("classifier.1.weight", None)
super().load_state_dict(state_dict, **kwargs)
mobilenet_encoders = {
"mobilenet_v2": {
"encoder": MobileNetV2Encoder,
"pretrained_settings": {
"imagenet": {
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225],
"url": "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
"input_space": "RGB",
"input_range": [0, 1],
},
},
"params": {"out_channels": (3, 16, 24, 32, 96, 1280),},
},
}
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