import torch
import torch.nn as nn

# m1 = nn.ConvTranspose2d(256, 128, 3)
# input1 = torch.randn(32, 256, 5, 5)
# output1 = m1(input1)
# print(output1.shape)
#
# m2 = nn.ConvTranspose2d(128, 64, 2, stride=2)
# input2 = torch.randn(32, 128, 7, 7)
# output2 = m2(input2)
# print(output2.shape)
#
# m3 = nn.ConvTranspose2d(64, 1, 2, stride=2)
# input3 = torch.randn(32, 64, 14, 14)
# output3 = m3(input3)
# print(output3.shape)

# from model import Generator
# G = Generator(100)
# z = torch.ones([100])
# op = G(z)
# print(op.shape)

# from model import Discriminator
# D = Discriminator()
# z = torch.ones([32, 1, 28, 28])
# op = D(z)
# print(op.shape)