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submodel.py
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submodel.py
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import torch
import torch.nn as nn
import torchvision
class SubNet(nn.Module):
def __init__(self):
super(SubNet, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2)
self.max_pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(10000, 4096)
self.fc2 = nn.Linear(4096, 1024)
self.fc3 = nn.Linear(1024, 4096)
self.fc4 = nn.Linear(4096,10000)
self.bn1 = nn.BatchNorm1d(4096)
self.bn2 = nn.BatchNorm1d(1024)
self.bn3 = nn.BatchNorm1d(4096)
def init_parameters(self):
torch.nn.init.zeros_(self.fc1.weight)
torch.nn.init.zeros_(self.fc2.weight)
torch.nn.init.zeros_(self.fc3.weight)
torch.nn.init.zeros_(self.fc4.weight)
def _subnet_forward(self, x):
x = self.fc1(x)
# x = torch.squeeze(x)
x = self.bn1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.fc3(x)
x = self.bn3(x)
x = self.relu(x)
x = self.fc4(x)
x = x.view(-1, 100, 100)
x = torch.sigmoid(x)
return x
# def _embedding(self, input, time_stamp = None):
# task_md = torch.zeros([input.shape[0], 100, 100])
# for i in range(input.shape[0]):
# x1 = int(input[i][0])
# y1 = int(input[i][1])
# x2 = int(input[i][2])
# y2 = int(input[i][3])
# if x1 == 0 and y1 == 0 and x2 == 0 and y2 == 0:
# continue
# else:
# task_md[i][x1][y1] = 1.00
# task_md[i][x2][y2] = 1.00
# return task_md
def forward(self, task_embedded, time_stamp = None):
#task_embedded = self._embedding(x_image, time_stamp)
#task_embedded = task_embedded.view(-1,10000).float()
#print("input task_embedded shape", task_embedded.shape)
#shape = task_embedded.shape
# print("x shape", x_image.shape)
# x_image = x_image.permute(0,)
#x_image = torch.squeeze(x_image)
x = self._subnet_forward(task_embedded)
#print("xoutput", x.shape) #32 100 100
return x
if __name__ == '__main__':
net = SubNet()
subx = torch.rand(15, 10000)
output = net(subx)
print(output.shape)
#15*100*100