| ''' |
| def upsample_and_sum(x1, x2,output_channels,in_channels): |
| pool_size = 2 |
| deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) |
| deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) |
| |
| deconv_output = tf.add(deconv,x2) |
| return deconv_output |
| |
| |
| def sc_net_1f(input): |
| # scratch capture single frame denoise network |
| # unet_2down_res_relu_64c5 |
| |
| with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(), |
| weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None): |
| |
| conv1 = slim.conv2d(input, 64, [3, 3], rate=1, activation_fn=relu, scope='conv1_1') |
| res_conv1 = slim.conv2d(conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_1') |
| res_conv1 = slim.conv2d(res_conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_2') |
| res_block1 = conv1 + res_conv1 |
| |
| |
| pool2 = slim.avg_pool2d(res_block1,[2,2],padding='SAME') |
| res_conv2 = slim.conv2d(pool2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_1') |
| res_conv2 = slim.conv2d(res_conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_2') |
| res_block2 = pool2 + res_conv2 |
| |
| pool3 = slim.avg_pool2d(res_block2,[2,2],padding='SAME') |
| res_conv3 = slim.conv2d(pool3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_1') |
| res_conv3 = slim.conv2d(res_conv3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_2') |
| res_block3 = pool3 + res_conv3 |
| |
| deconv1 = upsample_and_sum(res_block3, res_block2, 64, 64) |
| |
| conv4 = slim.conv2d(deconv1, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv4_1') |
| res_conv4 = slim.conv2d(conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_1') |
| res_conv4 = slim.conv2d(res_conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_2') |
| res_block4 = conv4 + res_conv4 |
| |
| deconv2 = upsample_and_sum(res_block4, res_block1, 64, 64) |
| |
| conv5 = slim.conv2d(deconv2, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv5_1') |
| res_conv5 = slim.conv2d(conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_1') |
| res_conv5 = slim.conv2d(res_conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_2') |
| res_block5 = conv5 + res_conv5 |
| |
| conv6 = slim.conv2d(res_block5, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv6_1') |
| conv7 = slim.conv2d(conv6, 4, [3, 3], rate=1, stride=1, activation_fn=None, scope='conv7_1') |
| |
| out = conv7 |
| |
| return out |
| ''' |
|
|
| import numpy as np |
|
|
| import torch |
| import torch.nn as nn |
|
|
|
|
| class sc_net_1f(nn.Module): |
| def __init__(self): |
| super().__init__() |
| |
| self.conv1_1 = nn.Conv2d(in_channels=4, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv1_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv1_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| |
| self.pool2 = nn.AvgPool2d(2) |
| self.res_conv2_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv2_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| |
| self.pool3 = nn.AvgPool2d(2) |
| self.res_conv3_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv3_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| |
| self.deconv1 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=2, padding=0, stride=2, bias=False) |
| |
| self.conv4_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv4_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv4_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| |
| self.deconv2 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=2, padding=0, stride=2, bias=False) |
| |
| self.conv5_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv5_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.res_conv5_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| |
| self.conv6_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) |
| self.conv7_1 = nn.Conv2d(in_channels=64, out_channels=4, kernel_size=3, padding=1, stride=1, bias=False) |
| |
| self.relu = nn.ReLU(inplace=True) |
| |
|
|
| def upsample_and_sum(x1, x2,output_channels,in_channels): |
| pool_size = 2 |
| deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) |
| deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) |
|
|
| deconv_output = tf.add(deconv,x2) |
| return deconv_output |
| |
| def forward(self, inp): |
| conv1 = self.relu(self.conv1_1(inp)) |
| res_conv1 = self.relu(self.res_conv1_1(conv1)) |
| res_conv1 = self.relu(self.res_conv1_2(res_conv1)) |
| res_block1 = conv1 + res_conv1 |
|
|
| pool2 = self.pool2(res_block1) |
| res_conv2 = self.relu(self.res_conv2_1(pool2)) |
| res_conv2 = self.relu(self.res_conv2_2(res_conv2)) |
| res_block2 = pool2 + res_conv2 |
|
|
| pool3 = self.pool3(res_block2) |
| res_conv3 = self.relu(self.res_conv3_1(pool3)) |
| res_conv3 = self.relu(self.res_conv3_2(res_conv3)) |
| res_block3 = pool3 + res_conv3 |
|
|
| deconv1 = self.deconv1(res_block3) + res_block2 |
|
|
| conv4 = self.relu(self.conv4_1(deconv1)) |
| res_conv4 = self.relu(self.res_conv4_1(conv4)) |
| res_conv4 = self.relu(self.res_conv4_2(res_conv4)) |
| res_block4 = conv4 + res_conv4 |
|
|
| deconv2 = self.deconv2(res_block4) + res_block1 |
|
|
| conv5 = self.relu(self.conv5_1(deconv2)) |
| res_conv5 = self.relu(self.res_conv5_1(conv5)) |
| res_conv5 = self.relu(self.res_conv5_2(res_conv5)) |
| res_block5 = conv5 + res_conv5 |
|
|
| conv6 = self.relu(self.conv6_1(res_block5)) |
| conv7 = self.conv7_1(conv6) |
|
|
| out = conv7 |
| |
| return out |
| |
|
|
|
|
