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'''
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
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