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import os
import sys
if __name__ == "__main__":
from train import *
else: # relative import
from .train import *
from torchvision.datasets import CIFAR10
from torchvision import transforms
try:
test_item = sys.argv[1]
except IndexError:
assert __name__ == "__main__"
test_item = "./generated"
test_items = []
if os.path.isdir(test_item):
for item in os.listdir(test_item):
item = os.path.join(test_item, item)
test_items.append(item)
elif os.path.isfile(test_item):
test_items.append(test_item)
original_dataset = CIFAR10(
root=config["dataset_root"],
train=False,
download=True,
transform=transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
)
original_targets = [original_dataset[i][1] for i in range(len(original_dataset))]
original_targets = torch.tensor(original_targets, dtype=torch.long)
for item in test_items:
state = torch.load(item, map_location="cpu", weights_only=True)
model.load_state_dict({key: value.to(torch.float32).to(device) for key, value in state.items()})
loss, acc, all_targets, all_predicts = test(model=model)
all_targets, all_predicts = torch.tensor(all_targets), torch.tensor(all_predicts)
for class_idx in range(10):
class_mask = torch.where(original_targets == class_idx, 1, 0)
total_number = torch.sum(class_mask)
correct = torch.where(all_targets == all_predicts, 1, 0)
class_correct = class_mask * correct
correct_number = torch.sum(class_correct)
class_acc = correct_number.item() / total_number.item()
print(f"class{class_idx}:", class_acc) |