PyTorch - Tiny-ImageNet

score.py

import torch
import torchvision.datasets as datasets
import torch.utils.data as data

import torchvision.transforms as transforms

from logger import logger

from torch.autograd import Variable

from train import ResNet, Bottleneck


# --- MAIN ---

if __name__ == "__main__":
    # load model

    logger.info("Loading stored model ...")
    model = ResNet(Bottleneck, [3, 4, 6, 3])
    model.load_state_dict(torch.load('/models/baseline-resnet50.pt'))
    logger.info("Loaded model successfully.")

    # load data set
    logger.info("Reading data...")
    val_dir = 'data/tiny-imagenet-200/val'
    val_dataset = datasets.ImageFolder(val_dir, transform=transforms.ToTensor())
    val_loader = data.DataLoader(val_dataset, batch_size=32)
    logger.info("Loaded: %s", val_dir)
    
    correct = 0
    total = 0
    for data in val_loader:
        images, labels = data
        outputs = model(Variable(images))
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()
        logger.info('Progress --- total: %s, correct: %s', total, correct)
    logger.info('Accuracy of the network on the 10000 test images: %s %%', (100 * correct / total))