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cb9235c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | import torch
from torchvision import datasets, transforms
import numpy as np
from matplotlib import pyplot as plt
from utils import plot_tsne
import numpy as np
import random
import argparse
NUM_CLASSES = 10
def freeze_seeds(seed=0):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--seed', default=0, type=int, help='Seed for random number generators')
parser.add_argument('--data-path', default="/datasets/cv_datasets/data", type=str, help='Path to dataset')
parser.add_argument('--batch-size', default=8, type=int, help='Size of each batch')
parser.add_argument('--latent-dim', default=128, type=int, help='encoding dimension')
parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help='Default device to use')
parser.add_argument('--mnist', action='store_true', default=False,
help='Whether to use MNIST (True) or CIFAR10 (False) data')
parser.add_argument('--self-supervised', action='store_true', default=False,
help='Whether train self-supervised with reconstruction objective, or jointly with classifier for classification objective.')
return parser.parse_args()
if __name__ == "__main__":
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) #one possible convenient normalization. You don't have to use it.
])
args = get_args()
freeze_seeds(args.seed)
if args.mnist:
train_dataset = datasets.MNIST(root=args.data_path, train=True, download=False, transform=transform)
test_dataset = datasets.MNIST(root=args.data_path, train=False, download=False, transform=transform)
else:
train_dataset = datasets.CIFAR10(root=args.data_path, train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root=args.data_path, train=False, download=True, transform=transform)
# When you create your dataloader you should split train_dataset or test_dataset to leave some aside for validation
#this is just for the example. Simple flattening of the image is probably not the best idea
encoder_model = torch.nn.Linear(32*32*3,args.latent_dim).to(args.device)
decoder_model = torch.nn.Linear(args.latent_dim,32*32*3 if args.self_supervised else NUM_CLASSES).to(args.device)
sample = train_dataset[0][0][None].to(args.device) #This is just for the example - you should use a dataloader
output = decoder_model(encoder_model(sample.flatten()))
print(output.shape)
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