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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 torch.nn as nn | |
| from efficientnet_pytorch import EfficientNet | |
| from efficientnet_pytorch.utils import url_map, url_map_advprop, get_model_params | |
| from ._base import EncoderMixin | |
| class EfficientNetEncoder(EfficientNet, EncoderMixin): | |
| def __init__(self, stage_idxs, out_channels, model_name, depth=5): | |
| blocks_args, global_params = get_model_params(model_name, override_params=None) | |
| super().__init__(blocks_args, global_params) | |
| self._stage_idxs = stage_idxs | |
| self._out_channels = out_channels | |
| self._depth = depth | |
| self._in_channels = 3 | |
| del self._fc | |
| def get_stages(self): | |
| return [ | |
| nn.Identity(), | |
| nn.Sequential(self._conv_stem, self._bn0, self._swish), | |
| self._blocks[: self._stage_idxs[0]], | |
| self._blocks[self._stage_idxs[0] : self._stage_idxs[1]], | |
| self._blocks[self._stage_idxs[1] : self._stage_idxs[2]], | |
| self._blocks[self._stage_idxs[2] :], | |
| ] | |
| def forward(self, x): | |
| stages = self.get_stages() | |
| block_number = 0.0 | |
| drop_connect_rate = self._global_params.drop_connect_rate | |
| features = [] | |
| for i in range(self._depth + 1): | |
| # Identity and Sequential stages | |
| if i < 2: | |
| x = stages[i](x) | |
| # Block stages need drop_connect rate | |
| else: | |
| for module in stages[i]: | |
| drop_connect = drop_connect_rate * block_number / len(self._blocks) | |
| block_number += 1.0 | |
| x = module(x, drop_connect) | |
| features.append(x) | |
| return features | |
| def load_state_dict(self, state_dict, **kwargs): | |
| state_dict.pop("_fc.bias", None) | |
| state_dict.pop("_fc.weight", None) | |
| super().load_state_dict(state_dict, **kwargs) | |
| def _get_pretrained_settings(encoder): | |
| pretrained_settings = { | |
| "imagenet": { | |
| "mean": [0.485, 0.456, 0.406], | |
| "std": [0.229, 0.224, 0.225], | |
| "url": url_map[encoder], | |
| "input_space": "RGB", | |
| "input_range": [0, 1], | |
| }, | |
| "advprop": { | |
| "mean": [0.5, 0.5, 0.5], | |
| "std": [0.5, 0.5, 0.5], | |
| "url": url_map_advprop[encoder], | |
| "input_space": "RGB", | |
| "input_range": [0, 1], | |
| }, | |
| } | |
| return pretrained_settings | |
| efficient_net_encoders = { | |
| "efficientnet-b0": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b0"), | |
| "params": { | |
| "out_channels": (3, 32, 24, 40, 112, 320), | |
| "stage_idxs": (3, 5, 9, 16), | |
| "model_name": "efficientnet-b0", | |
| }, | |
| }, | |
| "efficientnet-b1": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b1"), | |
| "params": { | |
| "out_channels": (3, 32, 24, 40, 112, 320), | |
| "stage_idxs": (5, 8, 16, 23), | |
| "model_name": "efficientnet-b1", | |
| }, | |
| }, | |
| "efficientnet-b2": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b2"), | |
| "params": { | |
| "out_channels": (3, 32, 24, 48, 120, 352), | |
| "stage_idxs": (5, 8, 16, 23), | |
| "model_name": "efficientnet-b2", | |
| }, | |
| }, | |
| "efficientnet-b3": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b3"), | |
| "params": { | |
| "out_channels": (3, 40, 32, 48, 136, 384), | |
| "stage_idxs": (5, 8, 18, 26), | |
| "model_name": "efficientnet-b3", | |
| }, | |
| }, | |
| "efficientnet-b4": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b4"), | |
| "params": { | |
| "out_channels": (3, 48, 32, 56, 160, 448), | |
| "stage_idxs": (6, 10, 22, 32), | |
| "model_name": "efficientnet-b4", | |
| }, | |
| }, | |
| "efficientnet-b5": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b5"), | |
| "params": { | |
| "out_channels": (3, 48, 40, 64, 176, 512), | |
| "stage_idxs": (8, 13, 27, 39), | |
| "model_name": "efficientnet-b5", | |
| }, | |
| }, | |
| "efficientnet-b6": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b6"), | |
| "params": { | |
| "out_channels": (3, 56, 40, 72, 200, 576), | |
| "stage_idxs": (9, 15, 31, 45), | |
| "model_name": "efficientnet-b6", | |
| }, | |
| }, | |
| "efficientnet-b7": { | |
| "encoder": EfficientNetEncoder, | |
| "pretrained_settings": _get_pretrained_settings("efficientnet-b7"), | |
| "params": { | |
| "out_channels": (3, 64, 48, 80, 224, 640), | |
| "stage_idxs": (11, 18, 38, 55), | |
| "model_name": "efficientnet-b7", | |
| }, | |
| }, | |
| } | |