forked from mrq/DL-Art-School
442 lines
17 KiB
Python
442 lines
17 KiB
Python
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import torch
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import torchvision
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from torch import Tensor
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import torch.nn as nn
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from torchvision.models.utils import load_state_dict_from_url
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from typing import Type, Any, Callable, Union, List, Optional, OrderedDict, Iterator
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
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'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
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'wide_resnet50_2', 'wide_resnet101_2']
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from models.vqvae.scaled_weight_conv import ScaledWeightConv
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from trainer.networks import register_model
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from utils.util import checkpoint
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model_urls = {
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'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
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'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
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'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
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'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
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'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
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}
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def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1, breadth: int = 8) -> ScaledWeightConv:
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"""3x3 convolution with padding"""
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return ScaledWeightConv(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=dilation, groups=groups, bias=False, dilation=dilation, breadth=breadth)
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def conv1x1(in_planes: int, out_planes: int, stride: int = 1, breadth: int = 8) -> ScaledWeightConv:
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"""1x1 convolution"""
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return ScaledWeightConv(in_planes, out_planes, kernel_size=1, stride=stride, bias=False, breadth=breadth)
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# Provides similar API to nn.Sequential, but handles feed-forward networks that need to feed masks into their convolutions.
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class MaskedSequential(nn.Module):
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def __init__(self, *args):
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super().__init__()
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if len(args) == 1 and isinstance(args[0], OrderedDict):
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for key, module in args[0].items():
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self.add_module(key, module)
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else:
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for idx, module in enumerate(args):
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self.add_module(str(idx), module)
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def __iter__(self) -> Iterator[nn.Module]:
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return iter(self._modules.values())
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def forward(self, x):
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mask = self.masks
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for m in self:
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if isinstance(m, ScaledWeightConv) or isinstance(m, BasicBlock) or isinstance(m, Bottleneck):
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x = m(x, mask)
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else:
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x = m(x)
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return x
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class BasicBlock(nn.Module):
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expansion: int = 1
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def __init__(
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self,
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inplanes: int,
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planes: int,
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stride: int = 1,
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downsample: Optional[nn.Module] = None,
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groups: int = 1,
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base_width: int = 64,
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dilation: int = 1,
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norm_layer: Optional[Callable[..., nn.Module]] = None,
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breadth: int = 8
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) -> None:
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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if groups != 1 or base_width != 64:
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raise ValueError('BasicBlock only supports groups=1 and base_width=64')
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if dilation > 1:
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv3x3(inplanes, planes, stride, breadth=breadth)
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self.bn1 = norm_layer(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes, breadth=breadth)
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self.bn2 = norm_layer(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x: Tensor, mask: Tensor) -> Tensor:
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identity = x
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out = self.conv1(x, mask)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out, mask)
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out = self.bn2(out)
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if self.downsample is not None:
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identity = self.downsample(x, mask)
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out += identity
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
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# while original implementation places the stride at the first 1x1 convolution(self.conv1)
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# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
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# This variant is also known as ResNet V1.5 and improves accuracy according to
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# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
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expansion: int = 4
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def __init__(
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self,
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inplanes: int,
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planes: int,
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stride: int = 1,
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downsample: Optional[nn.Module] = None,
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groups: int = 1,
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base_width: int = 64,
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dilation: int = 1,
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norm_layer: Optional[Callable[..., nn.Module]] = None,
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breadth: int = 8
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) -> None:
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super(Bottleneck, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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width = int(planes * (base_width / 64.)) * groups
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# Both self.conv2 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv1x1(inplanes, width, breadth=breadth)
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self.bn1 = norm_layer(width)
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self.conv2 = conv3x3(width, width, stride, groups, dilation, breadth=breadth)
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self.bn2 = norm_layer(width)
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self.conv3 = conv1x1(width, planes * self.expansion, breadth=breadth)
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self.bn3 = norm_layer(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x: Tensor, mask: Tensor) -> Tensor:
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identity = x
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out = self.conv1(x, mask)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out, mask)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out, mask)
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out = self.bn3(out)
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if self.downsample is not None:
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self.downsample.masks = mask
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(
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self,
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block: Type[Union[BasicBlock, Bottleneck]],
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layers: List[int],
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num_classes: int = 1000,
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zero_init_residual: bool = False,
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groups: int = 1,
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width_per_group: int = 64,
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replace_stride_with_dilation: Optional[List[bool]] = None,
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norm_layer: Optional[Callable[..., nn.Module]] = None,
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breadth: int = 8
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) -> None:
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super(ResNet, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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self._norm_layer = norm_layer
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self.inplanes = 64
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self.dilation = 1
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if replace_stride_with_dilation is None:
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# each element in the tuple indicates if we should replace
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# the 2x2 stride with a dilated convolution instead
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = ScaledWeightConv(3, self.inplanes, kernel_size=7, stride=2, padding=3,
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bias=False, breadth=breadth)
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self.bn1 = norm_layer(self.inplanes)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0], breadth)
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self.layer2 = self._make_layer(block, 128, layers[1], breadth, stride=2,
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dilate=replace_stride_with_dilation[0])
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self.layer3 = self._make_layer(block, 256, layers[2], breadth, stride=2,
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dilate=replace_stride_with_dilation[1])
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self.layer4 = self._make_layer(block, 512, layers[3], breadth, stride=2,
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dilate=replace_stride_with_dilation[2])
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, ScaledWeightConv):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
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elif isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
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def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, breadth: int,
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stride: int = 1, dilate: bool = False) -> MaskedSequential:
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = MaskedSequential(
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conv1x1(self.inplanes, planes * block.expansion, stride, breadth=breadth),
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norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer, breadth=breadth))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups=self.groups,
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base_width=self.base_width, dilation=self.dilation,
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norm_layer=norm_layer, breadth=breadth))
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return MaskedSequential(*layers)
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def _forward_impl(self, x: Tensor, mask: Tensor) -> Tensor:
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# See note [TorchScript super()]
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x = self.conv1(x, mask)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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for m in [self.layer1, self.layer2, self.layer3, self.layer4]:
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m.masks = mask
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x = checkpoint(self.layer1, x)
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x = checkpoint(self.layer2, x)
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x = checkpoint(self.layer3, x)
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x = checkpoint(self.layer4, x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.fc(x)
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return x
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def forward(self, x: Tensor, mask: Tensor) -> Tensor:
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return self._forward_impl(x, mask)
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def _resnet(
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arch: str,
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block: Type[Union[BasicBlock, Bottleneck]],
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layers: List[int],
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pretrained: bool,
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progress: bool,
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**kwargs: Any
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) -> ResNet:
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model = ResNet(block, layers, **kwargs)
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if pretrained:
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state_dict = load_state_dict_from_url(model_urls[arch],
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progress=progress)
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model.load_state_dict(state_dict, strict=False)
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return model
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def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-18 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
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**kwargs)
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def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-34 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-50 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-101 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
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**kwargs)
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def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-152 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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|
pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
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**kwargs)
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def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNeXt-50 32x4d model from
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`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
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|
Args:
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|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
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|
progress (bool): If True, displays a progress bar of the download to stderr
|
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|
"""
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kwargs['groups'] = 32
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kwargs['width_per_group'] = 4
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return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
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pretrained, progress, **kwargs)
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def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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|
r"""ResNeXt-101 32x8d model from
|
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|
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
||
|
|
"""
|
||
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|
kwargs['groups'] = 32
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kwargs['width_per_group'] = 8
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|
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
|
||
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|
pretrained, progress, **kwargs)
|
||
|
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|
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|
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def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
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|
|
r"""Wide ResNet-50-2 model from
|
||
|
|
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
||
|
|
|
||
|
|
The model is the same as ResNet except for the bottleneck number of channels
|
||
|
|
which is twice larger in every block. The number of channels in outer 1x1
|
||
|
|
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
||
|
|
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
||
|
|
"""
|
||
|
|
kwargs['width_per_group'] = 64 * 2
|
||
|
|
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
|
||
|
|
pretrained, progress, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||
|
|
r"""Wide ResNet-101-2 model from
|
||
|
|
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
||
|
|
|
||
|
|
The model is the same as ResNet except for the bottleneck number of channels
|
||
|
|
which is twice larger in every block. The number of channels in outer 1x1
|
||
|
|
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
||
|
|
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||
|
|
progress (bool): If True, displays a progress bar of the download to stderr
|
||
|
|
"""
|
||
|
|
kwargs['width_per_group'] = 64 * 2
|
||
|
|
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
|
||
|
|
pretrained, progress, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def register_resnet50_weighted_conv(opt_net, opt):
|
||
|
|
model = resnet50(pretrained=opt_net['pretrained'], **opt_net['kwargs'])
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
orig = torchvision.models.resnet.resnet50(pretrained=True)
|
||
|
|
mod = resnet50(pretrained=True, breadth=4)
|
||
|
|
idim = 224
|
||
|
|
masks = {}
|
||
|
|
for j in range(6):
|
||
|
|
cdim = idim // (2 ** j)
|
||
|
|
masks[cdim] = torch.zeros((1,1,cdim,cdim), dtype=torch.long)
|
||
|
|
i = torch.rand(1,3,idim,idim)
|
||
|
|
r1 = mod(i, masks)
|
||
|
|
r2 = orig(i)
|