239 lines
8.8 KiB
Python
239 lines
8.8 KiB
Python
""" Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
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Attributes:
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_out_channels (list of int): specify number of channels for each encoder feature tensor
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_depth (int): specify number of stages in decoder (in other words number of downsampling operations)
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_in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
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Methods:
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forward(self, x: torch.Tensor)
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produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
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shape NCHW (features should be sorted in descending order according to spatial resolution, starting
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with resolution same as input `x` tensor).
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Input: `x` with shape (1, 3, 64, 64)
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Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
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[(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
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(1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
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also should support number of features according to specified depth, e.g. if depth = 5,
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number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
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depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
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"""
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from copy import deepcopy
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import torch.nn as nn
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from torchvision.models.resnet import ResNet
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from torchvision.models.resnet import BasicBlock
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from torchvision.models.resnet import Bottleneck
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from pretrainedmodels.models.torchvision_models import pretrained_settings
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from ._base import EncoderMixin
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class ResNetEncoder(ResNet, EncoderMixin):
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def __init__(self, out_channels, depth=5, **kwargs):
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super().__init__(**kwargs)
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self._depth = depth
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self._out_channels = out_channels
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self._in_channels = 3
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del self.fc
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del self.avgpool
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def get_stages(self):
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return [
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nn.Identity(),
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nn.Sequential(self.conv1, self.bn1, self.relu),
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nn.Sequential(self.maxpool, self.layer1),
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self.layer2,
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self.layer3,
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self.layer4,
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]
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def forward(self, x):
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stages = self.get_stages()
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features = []
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for i in range(self._depth + 1):
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x = stages[i](x)
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features.append(x)
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return features
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def load_state_dict(self, state_dict, **kwargs):
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state_dict.pop("fc.bias", None)
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state_dict.pop("fc.weight", None)
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super().load_state_dict(state_dict, **kwargs)
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new_settings = {
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"resnet18": {
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth"
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},
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"resnet50": {
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth"
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},
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"resnext50_32x4d": {
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"imagenet": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth",
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},
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"resnext101_32x4d": {
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth"
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},
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"resnext101_32x8d": {
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"imagenet": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth",
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth",
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},
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"resnext101_32x16d": {
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth",
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth",
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},
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"resnext101_32x32d": {
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth",
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},
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"resnext101_32x48d": {
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth",
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}
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}
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pretrained_settings = deepcopy(pretrained_settings)
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for model_name, sources in new_settings.items():
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if model_name not in pretrained_settings:
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pretrained_settings[model_name] = {}
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for source_name, source_url in sources.items():
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pretrained_settings[model_name][source_name] = {
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"url": source_url,
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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resnet_encoders = {
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"resnet18": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnet18"],
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"params": {
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"out_channels": (3, 64, 64, 128, 256, 512),
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"block": BasicBlock,
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"layers": [2, 2, 2, 2],
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},
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},
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"resnet34": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnet34"],
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"params": {
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"out_channels": (3, 64, 64, 128, 256, 512),
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"block": BasicBlock,
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"layers": [3, 4, 6, 3],
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},
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},
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"resnet50": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnet50"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 6, 3],
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},
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},
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"resnet101": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnet101"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 23, 3],
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},
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},
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"resnet152": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnet152"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 8, 36, 3],
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},
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},
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"resnext50_32x4d": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnext50_32x4d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 6, 3],
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"groups": 32,
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"width_per_group": 4,
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},
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},
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"resnext101_32x4d": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnext101_32x4d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 23, 3],
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"groups": 32,
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"width_per_group": 4,
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},
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},
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"resnext101_32x8d": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnext101_32x8d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 23, 3],
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"groups": 32,
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"width_per_group": 8,
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},
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},
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"resnext101_32x16d": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnext101_32x16d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 23, 3],
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"groups": 32,
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"width_per_group": 16,
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},
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},
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"resnext101_32x32d": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnext101_32x32d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 23, 3],
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"groups": 32,
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"width_per_group": 32,
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},
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},
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"resnext101_32x48d": {
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"encoder": ResNetEncoder,
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"pretrained_settings": pretrained_settings["resnext101_32x48d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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"layers": [3, 4, 23, 3],
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"groups": 32,
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"width_per_group": 48,
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},
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},
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}
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