File size: 6,312 Bytes
2a13495 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
"""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",
},
},
}
|