import argparse
import os
import random
import shutil
import time
import warnings
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from torch.utils.data import DataLoader
from torch.optim import Adam
train_transformations = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32,padding=4),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))
])
train_set = r'DATADAT\training_1'
train_loader = DataLoader(train_set,batch_size=32,shuffle=True,num_workers=4)
test_transformations = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Load the test set, note that train is set to False
test_set = r'C:DATADATA\testing_1'
# Create a loder for the test set, note that both shuffle is set to false for the test loader
test_loader = DataLoader(test_set, batch_size=32, shuffle=False, num_workers=4)
cuda_avail = torch.cuda.is_available()
if cuda_avail:
model.cuda()
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
# Training from the training dataset sample
for i, data in enumerate(train_loader, 0):
inputs, labels = data
inputs, labels = Variable(inputs), Variable(labels)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
in the code above i am trying to do some prepr on my DICOM images and i want to display one or two to see the result … how can i do it ? if the " training path has more the one folder and each folder has it is own images "