"""
Two ways for checking model architectures:
1. Use torchsummary.
2. Define your model classes as a BaseModel (instead of a nn.Module),
    which specifies a __str__() function.
"""

# from __future__ import print_function
# import torch
# import torchsummary


# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model_test = ResNet_UNet()
# model_test.to(device)

# #torchsummary.summary(model_test, (3, 256, 256))
# print(model_test)
"""
Two ways for checking model architectures:
1. Use torchsummary.
2. Define your model classes as a BaseModel (instead of a nn.Module),
    which specifies a __str__() function.
"""

# from __future__ import print_function
# import torch
# import torchsummary


# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model_test = ResNet_UNet()
# model_test.to(device)

# #torchsummary.summary(model_test, (3, 256, 256))
# print(model_test)
Two ways for checking model architectures: 1. Use torchsummary. 2. Define your model classes as a BaseModel (instead of a nn.Module),     which specifies a __str__() function. # from __future__ import print_function # import torch # import torchsummary # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # model_test = ResNet_UNet() # model_test.to(device) # #torchsummary.summary(model_test, (3, 256, 256)) # print(model_test)