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models_noshare.py
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import torch.nn as nn
import torch.nn.functional as F
import torch
import time
class ConvBlock(nn.Sequential):
def __init__(self, in_channel, out_channel, ker_size, padd, stride):
super(ConvBlock, self).__init__()
self.add_module('conv', nn.Conv2d(in_channel, out_channel, kernel_size=ker_size, stride=stride, padding=padd)),
self.add_module('norm', nn.BatchNorm2d(out_channel,track_running_stats=False)),
self.add_module('LeakyRelu', nn.LeakyReLU(0.2, inplace=True))
class ConvINReLU(nn.Sequential):
def __init__(self, in_channel, out_channel, kernel_size=3, stride=1, groups=1, dilation=1):
padding = (kernel_size - 1) // 2 + dilation - 1
super(ConvINReLU, self).__init__(
nn.Conv2d(in_channel, out_channel, kernel_size, stride, padding, groups=groups, dilation=dilation,
bias=False),
nn.BatchNorm2d(out_channel,track_running_stats=False),
nn.LeakyReLU(inplace=True)
)
class InvertedResidual(nn.Module):
def __init__(self, in_channel, dilation):
super(InvertedResidual, self).__init__()
expand_ratio = 2.
hidden_channel = int(in_channel * expand_ratio)
layers = []
# 1x1 pointwise conv
layers.append(ConvINReLU(in_channel, hidden_channel, kernel_size=1))
layers.extend([
# 3x3 depthwise conv
ConvINReLU(hidden_channel, hidden_channel, groups=hidden_channel, dilation=dilation),
# 1x1 pointwise conv(linear)
nn.Conv2d(hidden_channel, in_channel, kernel_size=1, bias=False),
nn.BatchNorm2d(in_channel,track_running_stats=False),
])
self.conv = nn.Sequential(*layers)
def forward(self, x):
return x + self.conv(x)
class Guider_noshare(nn.Module):
def __init__(self, in_channels=3, out_channels=3):
super(Guider_noshare, self).__init__()
self.is_cuda = torch.cuda.is_available()
self.nfc = 32
self.min_nfc = 32
self.num_layer = 3
self.head = ConvBlock(in_channels, 64, 3, 1,
1) # GenConvTransBlock(opt.nc_z,N,opt.ker_size,opt.padd_size,opt.stride)
self.body = nn.Sequential()
for i in range(self.num_layer - 2):
block = InvertedResidual(64, 1)
self.body.add_module('block%d' % (i + 1), block)
self.tail_state1 = nn.Sequential(
InvertedResidual(64, 1),
#InvertedResidual(64, 1),
#InvertedResidual(64, 1),
InvertedResidual(64, 2),
#InvertedResidual(64, 2),
#InvertedResidual(64, 4),
InvertedResidual(64, 4),
#InvertedResidual(64, 8),
InvertedResidual(64, 8),
#InvertedResidual(64, 1),
)
self.tail_mask_s2d1 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.tail_mask_d2s1 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.channel_expand1 = nn.Conv2d(1, 64, 1)
self.tail_state2 = nn.Sequential(
InvertedResidual(64, 1),
InvertedResidual(64, 1),
#InvertedResidual(64, 1),
InvertedResidual(64, 2),
#InvertedResidual(64, 2),
#InvertedResidual(64, 4),
InvertedResidual(64, 4),
#InvertedResidual(64, 8),
InvertedResidual(64, 8),
InvertedResidual(64, 1),
)
self.tail_mask_s2d2 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.tail_mask_d2s2 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.channel_expand2 = nn.Conv2d(1, 64, 1)
self.tail_state3 = nn.Sequential(
InvertedResidual(64, 1),
InvertedResidual(64, 1),
InvertedResidual(64, 1),
InvertedResidual(64, 2),
InvertedResidual(64, 2),
InvertedResidual(64, 4),
InvertedResidual(64, 4),
#InvertedResidual(64, 8),
InvertedResidual(64, 8),
InvertedResidual(64, 1),
)
self.tail_mask_s2d3 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.tail_mask_d2s3 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.channel_expand3 = nn.Conv2d(1, 64, 1)
self.tail_state4 = nn.Sequential(
InvertedResidual(64, 1),
InvertedResidual(64, 1),
InvertedResidual(64, 1),
InvertedResidual(64, 2),
InvertedResidual(64, 2),
InvertedResidual(64, 4),
InvertedResidual(64, 4),
InvertedResidual(64, 8),
InvertedResidual(64, 8),
InvertedResidual(64, 1),
)
self.tail_mask_s2d4 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.tail_mask_d2s4 = nn.Sequential(
nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16,track_running_stats=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1)
)
self.score_final = nn.Conv2d(8, 1, 1)
def forward(self, x):
input_curr = x
mask_features = []
single_features = []
state_curr = 0
x1 = self.head(x)
tail_features = self.body(x1)
prev_features = 0
for step in range(4):
if step == 0:
state_curr1 = self.tail_state1(tail_features)
s2d_features1 = self.tail_mask_s2d1(state_curr1)
d2s_features1 = self.tail_mask_d2s1(state_curr1)
mask_features.append(s2d_features1)
single_features.append(
F.interpolate(d2s_features1, size=(input_curr.shape[2], input_curr.shape[3]), mode='bilinear'))
tail_features1 = self.channel_expand1(s2d_features1)
elif step == 1:
state_curr2 = self.tail_state2(F.max_pool2d(tail_features1 + state_curr1, kernel_size=2, stride=2, padding=0))
s2d_features2 = self.tail_mask_s2d2(state_curr2)
d2s_features2 = self.tail_mask_d2s2(state_curr2)
features2 = F.max_pool2d(s2d_features1, kernel_size=2, stride=2, padding=0).detach() + s2d_features2
mask_features.append(features2)
single_features.append(
F.interpolate(d2s_features2, size=(input_curr.shape[2], input_curr.shape[3]), mode='bilinear'))
tail_features2 = self.channel_expand2(features2)
elif step == 2:
state_curr3 = self.tail_state3(F.max_pool2d(tail_features2 + state_curr2, kernel_size=2, stride=2, padding=0))
s2d_features3 = self.tail_mask_s2d3(state_curr3)
d2s_features3 = self.tail_mask_d2s3(state_curr3)
features3 = F.max_pool2d(features2, kernel_size=2, stride=2, padding=0).detach() + s2d_features3
mask_features.append(features3)
single_features.append(
F.interpolate(d2s_features3, size=(input_curr.shape[2], input_curr.shape[3]), mode='bilinear'))
tail_features3 = self.channel_expand3(features3)
elif step == 3:
state_curr4 = self.tail_state4(F.max_pool2d(tail_features3 + state_curr3, kernel_size=2, stride=2, padding=0))
s2d_features4 = self.tail_mask_s2d4(state_curr4)
d2s_features4 = self.tail_mask_d2s4(state_curr4)
features4 = F.max_pool2d(features3, kernel_size=2, stride=2, padding=0).detach() + s2d_features4
mask_features.append(features4)
single_features.append(
F.interpolate(d2s_features4, size=(input_curr.shape[2], input_curr.shape[3]), mode='bilinear'))
s2d_1 = mask_features[0]
s2d_2 = F.interpolate(mask_features[1], size=(input_curr.shape[2], input_curr.shape[3]), mode='bilinear')
s2d_3 = F.interpolate(mask_features[2], size=(input_curr.shape[2], input_curr.shape[3]), mode='bilinear')
s2d_4 = F.interpolate(mask_features[3], size=(input_curr.shape[2], input_curr.shape[3]), mode='bilinear')
d2s_1 = single_features[0] + single_features[1].detach() + single_features[2].detach() + single_features[
3].detach()
d2s_2 = single_features[1] + single_features[2].detach() + single_features[3].detach()
d2s_3 = single_features[2] + single_features[3].detach()
d2s_4 = single_features[3]
fuse = self.score_final(torch.cat([s2d_1, s2d_2, s2d_3, s2d_4, d2s_1, d2s_2, d2s_3, d2s_4], dim=1))
return [s2d_1, s2d_2, s2d_3, s2d_4, d2s_1, d2s_2, d2s_3, d2s_4, fuse]