data_preprocess.py

# from tkinter import Variable
# import torch
# import torch.nn as nn

# from data_laoder import train_loader, val_loader
# from models import my_resnet, my_inception, my_densenet

# class LayerActivations():
#     features=[]

#     def __init__(self, model) -> None:
#         self.features = []
#         self.hook = model.register_forwward_hook(self.hook_fn)

#     def hook_fn(self, module, input, output):
#         self.features.extend(output.view(output.size(0), -1).cpu().data)

#     def remove(self):
#         self.hook.remove()

# ### For ResNet


# trn_labels = []
# trn_resnet_features = []
# for d, la in train_loader:
#     o = my_resnet(Variable(d.cuda()))
#     o = o.view(o.size(0), -1)
#     trn_labels.extend(la)
#     trn_resnet_features.extend(o.cpu().data)
# val_labels = []
# val_resnet_features = []
# for d, la in val_loader:
#     o = my_resnet(Variable(d.cuda()))
#     o = o.view(o.size(0), -1)
#     val_labels.extend(la)
#     val_resnet_features.extend(o.cpu().data)

# ### For Inception

# trn_inception_features = LayerActivations(my_inception.Mixed_7c)
# for da, la in train_loader:
#     _ = my_inception(Variable(da.cuda()))

# trn_inception_features.remove()

# val_inception_features = LayerActivations(my_inception.Mixed_7c)
# for da, la in val_loader:
#     _ = my_inception(Variable(da.cuda()))

# val_inception_features.remove()


# ### For DenseNet

# trn_densenet_features = []
# for d, la in train_loader:
#     o = my_densenet(Variable(d.cuda()))
#     o = o.view(o.size(0), -1)

#     trn_densenet_features.extend(o.cpu().data)

# val_densenet_features = []
# for d, la in val_loader:
#     o = my_densenet(Variable(d.cuda()))
#     o = o.view(o.size(0), -1)

#     val_densenet_features.extend(o.cpu().data)