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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}') |