| """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),}, | |
| }, | |
| } | |