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Running
on
Zero
| # -*- coding: utf-8 -*- | |
| import torch | |
| import torch.nn as nn | |
| from .vit import (_make_pretrained_vitb16_384, _make_pretrained_vitb_rn50_384, | |
| _make_pretrained_vitl16_384) | |
| def _make_encoder( | |
| backbone, | |
| features, | |
| use_pretrained, | |
| groups=1, | |
| expand=False, | |
| exportable=True, | |
| hooks=None, | |
| use_vit_only=False, | |
| use_readout='ignore', | |
| ): | |
| if backbone == 'vitl16_384': | |
| pretrained = _make_pretrained_vitl16_384(use_pretrained, | |
| hooks=hooks, | |
| use_readout=use_readout) | |
| scratch = _make_scratch( | |
| [256, 512, 1024, 1024], features, groups=groups, | |
| expand=expand) # ViT-L/16 - 85.0% Top1 (backbone) | |
| elif backbone == 'vitb_rn50_384': | |
| pretrained = _make_pretrained_vitb_rn50_384( | |
| use_pretrained, | |
| hooks=hooks, | |
| use_vit_only=use_vit_only, | |
| use_readout=use_readout, | |
| ) | |
| scratch = _make_scratch( | |
| [256, 512, 768, 768], features, groups=groups, | |
| expand=expand) # ViT-H/16 - 85.0% Top1 (backbone) | |
| elif backbone == 'vitb16_384': | |
| pretrained = _make_pretrained_vitb16_384(use_pretrained, | |
| hooks=hooks, | |
| use_readout=use_readout) | |
| scratch = _make_scratch( | |
| [96, 192, 384, 768], features, groups=groups, | |
| expand=expand) # ViT-B/16 - 84.6% Top1 (backbone) | |
| elif backbone == 'resnext101_wsl': | |
| pretrained = _make_pretrained_resnext101_wsl(use_pretrained) | |
| scratch = _make_scratch([256, 512, 1024, 2048], | |
| features, | |
| groups=groups, | |
| expand=expand) # efficientnet_lite3 | |
| elif backbone == 'efficientnet_lite3': | |
| pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, | |
| exportable=exportable) | |
| scratch = _make_scratch([32, 48, 136, 384], | |
| features, | |
| groups=groups, | |
| expand=expand) # efficientnet_lite3 | |
| else: | |
| print(f"Backbone '{backbone}' not implemented") | |
| assert False | |
| return pretrained, scratch | |
| def _make_scratch(in_shape, out_shape, groups=1, expand=False): | |
| scratch = nn.Module() | |
| out_shape1 = out_shape | |
| out_shape2 = out_shape | |
| out_shape3 = out_shape | |
| out_shape4 = out_shape | |
| if expand is True: | |
| out_shape1 = out_shape | |
| out_shape2 = out_shape * 2 | |
| out_shape3 = out_shape * 4 | |
| out_shape4 = out_shape * 8 | |
| scratch.layer1_rn = nn.Conv2d(in_shape[0], | |
| out_shape1, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=False, | |
| groups=groups) | |
| scratch.layer2_rn = nn.Conv2d(in_shape[1], | |
| out_shape2, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=False, | |
| groups=groups) | |
| scratch.layer3_rn = nn.Conv2d(in_shape[2], | |
| out_shape3, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=False, | |
| groups=groups) | |
| scratch.layer4_rn = nn.Conv2d(in_shape[3], | |
| out_shape4, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=False, | |
| groups=groups) | |
| return scratch | |
| def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): | |
| efficientnet = torch.hub.load('rwightman/gen-efficientnet-pytorch', | |
| 'tf_efficientnet_lite3', | |
| pretrained=use_pretrained, | |
| exportable=exportable) | |
| return _make_efficientnet_backbone(efficientnet) | |
| def _make_efficientnet_backbone(effnet): | |
| pretrained = nn.Module() | |
| pretrained.layer1 = nn.Sequential(effnet.conv_stem, effnet.bn1, | |
| effnet.act1, *effnet.blocks[0:2]) | |
| pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) | |
| pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) | |
| pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) | |
| return pretrained | |
| def _make_resnet_backbone(resnet): | |
| pretrained = nn.Module() | |
| pretrained.layer1 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, | |
| resnet.maxpool, resnet.layer1) | |
| pretrained.layer2 = resnet.layer2 | |
| pretrained.layer3 = resnet.layer3 | |
| pretrained.layer4 = resnet.layer4 | |
| return pretrained | |
| def _make_pretrained_resnext101_wsl(use_pretrained): | |
| resnet = torch.hub.load('facebookresearch/WSL-Images', | |
| 'resnext101_32x8d_wsl') | |
| return _make_resnet_backbone(resnet) | |
| class Interpolate(nn.Module): | |
| """Interpolation module. | |
| """ | |
| def __init__(self, scale_factor, mode, align_corners=False): | |
| """Init. | |
| Args: | |
| scale_factor (float): scaling | |
| mode (str): interpolation mode | |
| """ | |
| super(Interpolate, self).__init__() | |
| self.interp = nn.functional.interpolate | |
| self.scale_factor = scale_factor | |
| self.mode = mode | |
| self.align_corners = align_corners | |
| def forward(self, x): | |
| """Forward pass. | |
| Args: | |
| x (tensor): input | |
| Returns: | |
| tensor: interpolated data | |
| """ | |
| x = self.interp(x, | |
| scale_factor=self.scale_factor, | |
| mode=self.mode, | |
| align_corners=self.align_corners) | |
| return x | |
| class ResidualConvUnit(nn.Module): | |
| """Residual convolution module. | |
| """ | |
| def __init__(self, features): | |
| """Init. | |
| Args: | |
| features (int): number of features | |
| """ | |
| super().__init__() | |
| self.conv1 = nn.Conv2d(features, | |
| features, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=True) | |
| self.conv2 = nn.Conv2d(features, | |
| features, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=True) | |
| self.relu = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| """Forward pass. | |
| Args: | |
| x (tensor): input | |
| Returns: | |
| tensor: output | |
| """ | |
| out = self.relu(x) | |
| out = self.conv1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| return out + x | |
| class FeatureFusionBlock(nn.Module): | |
| """Feature fusion block. | |
| """ | |
| def __init__(self, features): | |
| """Init. | |
| Args: | |
| features (int): number of features | |
| """ | |
| super(FeatureFusionBlock, self).__init__() | |
| self.resConfUnit1 = ResidualConvUnit(features) | |
| self.resConfUnit2 = ResidualConvUnit(features) | |
| def forward(self, *xs): | |
| """Forward pass. | |
| Returns: | |
| tensor: output | |
| """ | |
| output = xs[0] | |
| if len(xs) == 2: | |
| output += self.resConfUnit1(xs[1]) | |
| output = self.resConfUnit2(output) | |
| output = nn.functional.interpolate(output, | |
| scale_factor=2, | |
| mode='bilinear', | |
| align_corners=True) | |
| return output | |
| class ResidualConvUnit_custom(nn.Module): | |
| """Residual convolution module. | |
| """ | |
| def __init__(self, features, activation, bn): | |
| """Init. | |
| Args: | |
| features (int): number of features | |
| """ | |
| super().__init__() | |
| self.bn = bn | |
| self.groups = 1 | |
| self.conv1 = nn.Conv2d(features, | |
| features, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=True, | |
| groups=self.groups) | |
| self.conv2 = nn.Conv2d(features, | |
| features, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| bias=True, | |
| groups=self.groups) | |
| if self.bn is True: | |
| self.bn1 = nn.BatchNorm2d(features) | |
| self.bn2 = nn.BatchNorm2d(features) | |
| self.activation = activation | |
| self.skip_add = nn.quantized.FloatFunctional() | |
| def forward(self, x): | |
| """Forward pass. | |
| Args: | |
| x (tensor): input | |
| Returns: | |
| tensor: output | |
| """ | |
| out = self.activation(x) | |
| out = self.conv1(out) | |
| if self.bn is True: | |
| out = self.bn1(out) | |
| out = self.activation(out) | |
| out = self.conv2(out) | |
| if self.bn is True: | |
| out = self.bn2(out) | |
| if self.groups > 1: | |
| out = self.conv_merge(out) | |
| return self.skip_add.add(out, x) | |
| # return out + x | |
| class FeatureFusionBlock_custom(nn.Module): | |
| """Feature fusion block. | |
| """ | |
| def __init__(self, | |
| features, | |
| activation, | |
| deconv=False, | |
| bn=False, | |
| expand=False, | |
| align_corners=True): | |
| """Init. | |
| Args: | |
| features (int): number of features | |
| """ | |
| super(FeatureFusionBlock_custom, self).__init__() | |
| self.deconv = deconv | |
| self.align_corners = align_corners | |
| self.groups = 1 | |
| self.expand = expand | |
| out_features = features | |
| if self.expand is True: | |
| out_features = features // 2 | |
| self.out_conv = nn.Conv2d(features, | |
| out_features, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| bias=True, | |
| groups=1) | |
| self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) | |
| self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) | |
| self.skip_add = nn.quantized.FloatFunctional() | |
| def forward(self, *xs): | |
| """Forward pass. | |
| Returns: | |
| tensor: output | |
| """ | |
| output = xs[0] | |
| if len(xs) == 2: | |
| res = self.resConfUnit1(xs[1]) | |
| output = self.skip_add.add(output, res) | |
| # output += res | |
| output = self.resConfUnit2(output) | |
| output = nn.functional.interpolate(output, | |
| scale_factor=2, | |
| mode='bilinear', | |
| align_corners=self.align_corners) | |
| output = self.out_conv(output) | |
| return output | |