Print(‘Class labels of 10 examples:’, labels) Print(‘Image label dimensions:’, labels.shape)

Print(‘Image batch dimensions:’, images.shape) Testloader = (test_data, batch_size=4, shuffle=False) Trainloader = (train_data, batch_size=4, shuffle=True) Transforms.Grayscale(num_output_channels=3), The posted screenshot also doesn’t represent your code as I see: model = models.alexnet() Could you describe what “snap” is why you are not expecting 10 output features even though you explicitly replace the last linear layer with out_features=10? Sorry, but I don’t fully understand this claim. This is the snap of my summary after frozen layers and after updated last layer I got 10 classes output in place of 10000. Print(’ ‘.join(’%5s’ % classes] for j in range(4))) Test_data = (root=‘./data’, train=False, download=True, transform=transform) Trainloader = (train_data, batch_size=4, shuffle=True, num_workers=2) Train_data = (root=‘./data’, train=True, download=True, transform=transform) How to convert it into rgb?įrom torchvision import transforms as transforms I have mnist dataset that is in pytorch API its grayscale and I want to implement transfer learning using Alexnet. Actually I discovered I also have images with four channels so I implemented this code in my custom dataset import osĭef _init_(self,csv_file,root_dir,transform=None): Hello ptrblck, Thanks for your quick response. But I don’t know how to do it or where exactly on my special code. Now I know I have to convert these grayscale images if I want to train…my question is where can I catch the grayscale images and convert them to rgb? In matlab would be something like rgbImage = cat(3, A,A, A) where A is the grayscale image. I didn’t know what ImageNet had grayscale images and I actually found some and read them on matlab and yes they are grayscale…that’s the reason Im getting the error of batch size mismatch at position 0. Test_loader = DataLoader(test_dataset, batch_size,num_workers=num_workers,

Train_loader = DataLoader(train_dataset, batch_size,num_workers=num_workers, Test_dataset = TransformedDataset(test_dataset, partial(map_targets_fn, target_mapping=labels_mapping))įor idx, (data,image) in enumerate (train_dataset):

Train_dataset = TransformedDataset(train_dataset, partial(map_targets_fn, target_mapping=labels_mapping)) Test_dataset=CustomDataset(csv_file='/home/tboonesifuentes/Databases/ImageNet/Test/test.csv',root_dir='/home/tboonesifuentes/Databases/ImageNet/Test/Crops',Ĭlass TransformedDataset():ĭef _init_(self, dataset, transform_fn): Hello, I am trying to classify ImageNet using vgg and I am using a custom dataset as follows train_dataset=CustomDataset(csv_file='/home/tboonesifuentes/Databases/ImageNet/Train/train.csv',root_dir='/home/tboonesifuentes/Databases/ImageNet/Train/Crops',

Pil image convert rhb