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Dynamic Network and minor directory fixes and numpy latest version adaptability #9

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Binary file added networks/__pycache__/generators.cpython-310.pyc
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353 changes: 221 additions & 132 deletions networks/generators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,140 +139,229 @@ def forward(self, x_thisBranch, x_otherBranch):

return z

class dualAtt_24(nn.Module):

def __init__(self):
super(dualAtt_24, self).__init__()

self.relu = nn.ReLU(inplace=True)
self.conv3d_7 = nn.Conv3d(in_channels=64, out_channels=16, kernel_size=1, stride=(1, 1, 1), padding=0)
self.pathC_bn1 = nn.BatchNorm3d(64)


self.conv3d_8 = nn.Conv3d(in_channels=16, out_channels=4, kernel_size=3, stride=(1, 1, 1), padding=1)

self.conv3d_9 = nn.Conv3d(in_channels=4, out_channels=1, kernel_size=3, stride=(1, 1, 1), padding=1)
self.pathC_bn2 = nn.BatchNorm3d(1)

self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 32)
self.fc3 = nn.Linear(32, 6)

"""layers for path global"""
self.path1_block1_conv = nn.Conv3d(
in_channels=1,
out_channels=32,
kernel_size=3,
stride=(1, 1, 1),
padding=1,
bias=False)
self.path1_block1_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal11 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
self.path1_block2_conv = nn.Conv3d(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=(1, 1, 1),
padding=1,
bias=False)
self.path1_block2_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal12 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(1,2,2), padding=1)
self.path1_block3_NLCross = NLBlockND_cross(32)

self.path2_block1_conv = nn.Conv3d(
in_channels=1,
out_channels=32,
kernel_size=3,
stride=(1, 1, 1),
padding=1,
bias=False)
self.path2_block1_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal21 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
self.path2_block2_conv = nn.Conv3d(
in_channels=32,
out_channels=32,
kernel_size=3,
stride=(1, 1, 1),
padding=1,
bias=False)
self.path2_block2_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal22 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(1,2,2), padding=1)
self.path2_block3_NLCross = NLBlockND_cross(32)


for m in self.modules():
if isinstance(m, nn.Conv3d):
m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()

def forward(self, x):
# total_start_time = time.time()
x_path1 = torch.unsqueeze(x[:, 0, :, :, :], 1)
x_path2 = torch.unsqueeze(x[:, 1, :, :, :], 1)

"""path global (attention)"""
x_path1 = self.path1_block1_conv(x_path1)
x_path1 = self.path1_block1_bn(x_path1)
x_path1 = self.relu(x_path1)
x_path1 = self.maxpool_downsample_pathGlobal11(x_path1)
# print(x_path1.shape)

x_path1 = self.path1_block2_conv(x_path1)
x_path1 = self.path1_block2_bn(x_path1)
x_path1 = self.relu(x_path1)
x_path1_0 = self.maxpool_downsample_pathGlobal12(x_path1)
# print(x_path1.shape)


x_path2 = self.path2_block1_conv(x_path2)
x_path2 = self.path2_block1_bn(x_path2)
x_path2 = self.relu(x_path2)
x_path2 = self.maxpool_downsample_pathGlobal21(x_path2)
# print(x_path2.shape)

x_path2 = self.path2_block2_conv(x_path2)
x_path2 = self.path2_block2_bn(x_path2)
x_path2 = self.relu(x_path2)
x_path2_0 = self.maxpool_downsample_pathGlobal22(x_path2)
# print(x_path2.shape)

x_path1 = self.path1_block3_NLCross(x_path1_0, x_path2_0)
x_path1 = self.relu(x_path1)

x_path2 = self.path2_block3_NLCross(x_path2_0, x_path1_0)
x_path2 = self.relu(x_path2)

x_pathC = torch.cat((x_path1, x_path2), 1)

"""path combined"""
x = x_pathC
x = self.pathC_bn1(x)

x = self.conv3d_7(x)
x = self.relu(x)
# class dualAtt_24(nn.Module):

# def __init__(self):
# super(dualAtt_24, self).__init__()

# self.relu = nn.ReLU(inplace=True)
# self.conv3d_7 = nn.Conv3d(in_channels=64, out_channels=16, kernel_size=1, stride=(1, 1, 1), padding=0)
# self.pathC_bn1 = nn.BatchNorm3d(64)


# self.conv3d_8 = nn.Conv3d(in_channels=16, out_channels=4, kernel_size=3, stride=(1, 1, 1), padding=1)

# self.conv3d_9 = nn.Conv3d(in_channels=4, out_channels=1, kernel_size=3, stride=(1, 1, 1), padding=1)
# self.pathC_bn2 = nn.BatchNorm3d(1)

# self.fc1 = nn.Linear(9216, 128)
# self.fc2 = nn.Linear(128, 32)
# self.fc3 = nn.Linear(32, 6)

# """layers for path global"""
# self.path1_block1_conv = nn.Conv3d(
# in_channels=1,
# out_channels=32,
# kernel_size=3,
# stride=(1, 1, 1),
# padding=1,
# bias=False)
# self.path1_block1_bn = nn.BatchNorm3d(32)
# self.maxpool_downsample_pathGlobal11 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
# self.path1_block2_conv = nn.Conv3d(
# in_channels=32,
# out_channels=32,
# kernel_size=3,
# stride=(1, 1, 1),
# padding=1,
# bias=False)
# self.path1_block2_bn = nn.BatchNorm3d(32)
# self.maxpool_downsample_pathGlobal12 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(1,2,2), padding=1)
# self.path1_block3_NLCross = NLBlockND_cross(32)

# self.path2_block1_conv = nn.Conv3d(
# in_channels=1,
# out_channels=32,
# kernel_size=3,
# stride=(1, 1, 1),
# padding=1,
# bias=False)
# self.path2_block1_bn = nn.BatchNorm3d(32)
# self.maxpool_downsample_pathGlobal21 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
# self.path2_block2_conv = nn.Conv3d(
# in_channels=32,
# out_channels=32,
# kernel_size=3,
# stride=(1, 1, 1),
# padding=1,
# bias=False)
# self.path2_block2_bn = nn.BatchNorm3d(32)
# self.maxpool_downsample_pathGlobal22 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(1,2,2), padding=1)
# self.path2_block3_NLCross = NLBlockND_cross(32)


# for m in self.modules():
# if isinstance(m, nn.Conv3d):
# m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')
# elif isinstance(m, nn.BatchNorm3d):
# m.weight.data.fill_(1)
# m.bias.data.zero_()

# def forward(self, x):
# # total_start_time = time.time()
# x_path1 = torch.unsqueeze(x[:, 0, :, :, :], 1)
# x_path2 = torch.unsqueeze(x[:, 1, :, :, :], 1)

# """path global (attention)"""
# x_path1 = self.path1_block1_conv(x_path1)
# x_path1 = self.path1_block1_bn(x_path1)
# x_path1 = self.relu(x_path1)
# x_path1 = self.maxpool_downsample_pathGlobal11(x_path1)
# # print(x_path1.shape)

# x_path1 = self.path1_block2_conv(x_path1)
# x_path1 = self.path1_block2_bn(x_path1)
# x_path1 = self.relu(x_path1)
# x_path1_0 = self.maxpool_downsample_pathGlobal12(x_path1)
# # print(x_path1.shape)


# x_path2 = self.path2_block1_conv(x_path2)
# x_path2 = self.path2_block1_bn(x_path2)
# x_path2 = self.relu(x_path2)
# x_path2 = self.maxpool_downsample_pathGlobal21(x_path2)
# # print(x_path2.shape)

# x_path2 = self.path2_block2_conv(x_path2)
# x_path2 = self.path2_block2_bn(x_path2)
# x_path2 = self.relu(x_path2)
# x_path2_0 = self.maxpool_downsample_pathGlobal22(x_path2)
# # print(x_path2.shape)

# x_path1 = self.path1_block3_NLCross(x_path1_0, x_path2_0)
# x_path1 = self.relu(x_path1)

# x_path2 = self.path2_block3_NLCross(x_path2_0, x_path1_0)
# x_path2 = self.relu(x_path2)

# x_pathC = torch.cat((x_path1, x_path2), 1)

# """path combined"""
# x = x_pathC
# x = self.pathC_bn1(x)

# x = self.conv3d_7(x)
# x = self.relu(x)

# x = self.conv3d_8(x)
# x = self.relu(x)

# x = self.conv3d_9(x)
# x = self.pathC_bn2(x)

# x = x.view(x.size()[0], -1)
# x = self.relu(x)

# x = self.fc1(x)
# x = self.relu(x)

# x = self.fc2(x)
# x = self.relu(x)

# x = self.fc3(x)
# # time_cost = time.time() - total_start_time
# # print('1 whole cycle time cost {}s'.format(time_cost))
# # time.sleep(30)
# return x

x = self.conv3d_8(x)
x = self.relu(x)

x = self.conv3d_9(x)
x = self.pathC_bn2(x)

x = x.view(x.size()[0], -1)
x = self.relu(x)

x = self.fc1(x)
x = self.relu(x)

x = self.fc2(x)
x = self.relu(x)
class dualAtt_24(nn.Module):
def __init__(self):
super(dualAtt_24, self).__init__()

self.relu = nn.ReLU(inplace=True)
self.conv3d_7 = nn.Conv3d(in_channels=64, out_channels=16, kernel_size=1, stride=(1, 1, 1), padding=0)
self.pathC_bn1 = nn.BatchNorm3d(64)

self.conv3d_8 = nn.Conv3d(in_channels=16, out_channels=4, kernel_size=3, stride=(1, 1, 1), padding=1)

self.conv3d_9 = nn.Conv3d(in_channels=4, out_channels=1, kernel_size=3, stride=(1, 1, 1), padding=1)
self.pathC_bn2 = nn.BatchNorm3d(1)

self.fc1 = None # Defined dynamically below
self.fc2 = nn.Linear(128, 32)
self.fc3 = nn.Linear(32, 6)

"""layers for path global"""
self.path1_block1_conv = nn.Conv3d(in_channels=1, out_channels=32, kernel_size=3, stride=(1, 1, 1), padding=1, bias=False)
self.path1_block1_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal11 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
self.path1_block2_conv = nn.Conv3d(in_channels=32, out_channels=32, kernel_size=3, stride=(1, 1, 1), padding=1, bias=False)
self.path1_block2_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal12 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=1)
self.path1_block3_NLCross = NLBlockND_cross(32)

self.path2_block1_conv = nn.Conv3d(in_channels=1, out_channels=32, kernel_size=3, stride=(1, 1, 1), padding=1, bias=False)
self.path2_block1_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal21 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
self.path2_block2_conv = nn.Conv3d(in_channels=32, out_channels=32, kernel_size=3, stride=(1, 1, 1), padding=1, bias=False)
self.path2_block2_bn = nn.BatchNorm3d(32)
self.maxpool_downsample_pathGlobal22 = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(1, 2, 2), padding=1)
self.path2_block3_NLCross = NLBlockND_cross(32)

def forward(self, x):
x_path1 = torch.unsqueeze(x[:, 0, :, :, :], 1)
x_path2 = torch.unsqueeze(x[:, 1, :, :, :], 1)

x_path1 = self.path1_block1_conv(x_path1)
x_path1 = self.path1_block1_bn(x_path1)
x_path1 = self.relu(x_path1)
x_path1 = self.maxpool_downsample_pathGlobal11(x_path1)

x_path1 = self.path1_block2_conv(x_path1)
x_path1 = self.path1_block2_bn(x_path1)
x_path1 = self.relu(x_path1)
x_path1_0 = self.maxpool_downsample_pathGlobal12(x_path1)

x_path2 = self.path2_block1_conv(x_path2)
x_path2 = self.path2_block1_bn(x_path2)
x_path2 = self.relu(x_path2)
x_path2 = self.maxpool_downsample_pathGlobal21(x_path2)

x_path2 = self.path2_block2_conv(x_path2)
x_path2 = self.path2_block2_bn(x_path2)
x_path2 = self.relu(x_path2)
x_path2_0 = self.maxpool_downsample_pathGlobal22(x_path2)

x_path1 = self.path1_block3_NLCross(x_path1_0, x_path2_0)
x_path1 = self.relu(x_path1)

x_path2 = self.path2_block3_NLCross(x_path2_0, x_path1_0)
x_path2 = self.relu(x_path2)

x_pathC = torch.cat((x_path1, x_path2), 1)

x = x_pathC
x = self.pathC_bn1(x)
x = self.conv3d_7(x)
x = self.relu(x)
x = self.conv3d_8(x)
x = self.relu(x)
x = self.conv3d_9(x)
x = self.pathC_bn2(x)
x = x.view(x.size()[0], -1)

# Dynamically set the input size of fc1
if self.fc1 is None:
self.fc1 = nn.Linear(x.size(1), 128).to(x.device)

x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)

return x

x = self.fc3(x)
# time_cost = time.time() - total_start_time
# print('1 whole cycle time cost {}s'.format(time_cost))
# time.sleep(30)
return x
class dualAtt_25(nn.Module):

def __init__(self):
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