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| import torch | |
| import torch.nn as nn | |
| import torchvision | |
| import sys | |
| import math | |
| # from config import get_args | |
| # global_args = get_args(sys.argv[1:]) | |
| def conv3x3(in_planes, out_planes, stride=1): | |
| """3x3 convolution with padding""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, | |
| padding=1, bias=False) | |
| def conv1x1(in_planes, out_planes, stride=1): | |
| """1x1 convolution""" | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | |
| def get_sinusoid_encoding(n_position, feat_dim, wave_length=10000): | |
| # [n_position] | |
| positions = torch.arange(0, n_position)#.cuda() | |
| # [feat_dim] | |
| dim_range = torch.arange(0, feat_dim)#.cuda() | |
| dim_range = torch.pow(wave_length, 2 * (dim_range // 2) / feat_dim) | |
| # [n_position, feat_dim] | |
| angles = positions.unsqueeze(1) / dim_range.unsqueeze(0) | |
| angles = angles.float() | |
| angles[:, 0::2] = torch.sin(angles[:, 0::2]) | |
| angles[:, 1::2] = torch.cos(angles[:, 1::2]) | |
| return angles | |
| class AsterBlock(nn.Module): | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(AsterBlock, self).__init__() | |
| self.conv1 = conv1x1(inplanes, planes, stride) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class ResNet_ASTER(nn.Module): | |
| """For aster or crnn | |
| borrowed from: https://github.com/ayumiymk/aster.pytorch | |
| """ | |
| def __init__(self, in_channels=1, out_channel=512, n_group=1): | |
| super(ResNet_ASTER, self).__init__() | |
| self.n_group = n_group | |
| in_channels = in_channels | |
| self.layer0 = nn.Sequential( | |
| nn.Conv2d(in_channels, 32, kernel_size=(3, 3), stride=1, padding=1, bias=False), | |
| nn.BatchNorm2d(32), | |
| nn.ReLU(inplace=True)) | |
| self.inplanes = 32 | |
| self.layer1 = self._make_layer(32, 3, [2, 2]) # [16, 50] | |
| self.layer2 = self._make_layer(64, 4, [2, 2]) # [8, 25] | |
| self.layer3 = self._make_layer(128, 6, [2, 2]) # [4, 25] | |
| self.layer4 = self._make_layer(256, 6, [1, ]) # [2, 25] | |
| self.layer5 = self._make_layer(out_channel, 3, [1, 1]) # [1, 25] | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") | |
| elif isinstance(m, nn.BatchNorm2d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def _make_layer(self, planes, blocks, stride): | |
| downsample = None | |
| if stride != [1, 1] or self.inplanes != planes: | |
| downsample = nn.Sequential( | |
| conv1x1(self.inplanes, planes, stride), | |
| nn.BatchNorm2d(planes)) | |
| layers = [] | |
| layers.append(AsterBlock(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes | |
| for _ in range(1, blocks): | |
| layers.append(AsterBlock(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| x0 = self.layer0(x) | |
| x1 = self.layer1(x0) | |
| x2 = self.layer2(x1) | |
| x3 = self.layer3(x2) | |
| x4 = self.layer4(x3) | |
| x5 = self.layer5(x4) | |
| return x5 | |
| def numel(model): | |
| return sum(p.numel() for p in model.parameters()) | |
| if __name__ == "__main__": | |
| x = torch.randn(3, 1, 64, 256) | |
| net = ResNet_ASTER() | |
| encoder_feat = net(x) | |
| print(encoder_feat.size()) # 3*512*h/4*w/4 | |
| num_params = numel(net) | |
| print(f'Number of parameters: {num_params}') |