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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).
"""
from copy import deepcopy
import torch.nn as nn
from torchvision.models.resnet import ResNet
from torchvision.models.resnet import BasicBlock
from torchvision.models.resnet import Bottleneck
from pretrainedmodels.models.torchvision_models import pretrained_settings
from ._base import EncoderMixin
class ResNetEncoder(ResNet, 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.fc
del self.avgpool
def get_stages(self):
return [
nn.Identity(),
nn.Sequential(self.conv1, self.bn1, self.relu),
nn.Sequential(self.maxpool, self.layer1),
self.layer2,
self.layer3,
self.layer4,
]
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("fc.bias", None)
state_dict.pop("fc.weight", None)
super().load_state_dict(state_dict, **kwargs)
new_settings = {
"resnet18": {
"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth", # noqa
"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth", # noqa
},
"resnet50": {
"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth", # noqa
"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth", # noqa
},
"resnext50_32x4d": {
"imagenet": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth", # noqa
"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth", # noqa
},
"resnext101_32x4d": {
"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth", # noqa
"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth", # noqa
},
"resnext101_32x8d": {
"imagenet": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
"instagram": "https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth",
"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth", # noqa
"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth", # noqa
},
"resnext101_32x16d": {
"instagram": "https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth",
"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth", # noqa
"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth", # noqa
},
"resnext101_32x32d": {
"instagram": "https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth",
},
"resnext101_32x48d": {
"instagram": "https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth",
},
}
pretrained_settings = deepcopy(pretrained_settings)
for model_name, sources in new_settings.items():
if model_name not in pretrained_settings:
pretrained_settings[model_name] = {}
for source_name, source_url in sources.items():
pretrained_settings[model_name][source_name] = {
"url": source_url,
"input_size": [3, 224, 224],
"input_range": [0, 1],
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225],
"num_classes": 1000,
}
resnet_encoders = {
"resnet18": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnet18"],
"params": {
"out_channels": (3, 64, 64, 128, 256, 512),
"block": BasicBlock,
"layers": [2, 2, 2, 2],
},
},
"resnet34": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnet34"],
"params": {
"out_channels": (3, 64, 64, 128, 256, 512),
"block": BasicBlock,
"layers": [3, 4, 6, 3],
},
},
"resnet50": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnet50"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 6, 3],
},
},
"resnet101": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnet101"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 23, 3],
},
},
"resnet152": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnet152"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 8, 36, 3],
},
},
"resnext50_32x4d": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnext50_32x4d"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 6, 3],
"groups": 32,
"width_per_group": 4,
},
},
"resnext101_32x4d": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnext101_32x4d"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 23, 3],
"groups": 32,
"width_per_group": 4,
},
},
"resnext101_32x8d": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnext101_32x8d"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 23, 3],
"groups": 32,
"width_per_group": 8,
},
},
"resnext101_32x16d": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnext101_32x16d"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 23, 3],
"groups": 32,
"width_per_group": 16,
},
},
"resnext101_32x32d": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnext101_32x32d"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 23, 3],
"groups": 32,
"width_per_group": 32,
},
},
"resnext101_32x48d": {
"encoder": ResNetEncoder,
"pretrained_settings": pretrained_settings["resnext101_32x48d"],
"params": {
"out_channels": (3, 64, 256, 512, 1024, 2048),
"block": Bottleneck,
"layers": [3, 4, 23, 3],
"groups": 32,
"width_per_group": 48,
},
},
}
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