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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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11
examples/images/resnet/eval.py
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@@ -1,7 +1,6 @@
import argparse
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
@@ -9,15 +8,15 @@ import torchvision.transforms as transforms
# Parse Arguments
# ==============================
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--epoch', type=int, default=80, help="resume from the epoch's checkpoint")
parser.add_argument('-c', '--checkpoint', type=str, default='./checkpoint', help="checkpoint directory")
parser.add_argument("-e", "--epoch", type=int, default=80, help="resume from the epoch's checkpoint")
parser.add_argument("-c", "--checkpoint", type=str, default="./checkpoint", help="checkpoint directory")
args = parser.parse_args()
# ==============================
# Prepare Test Dataset
# ==============================
# CIFAR-10 dataset
test_dataset = torchvision.datasets.CIFAR10(root='./data/', train=False, transform=transforms.ToTensor())
test_dataset = torchvision.datasets.CIFAR10(root="./data/", train=False, transform=transforms.ToTensor())
# Data loader
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=128, shuffle=False)
@@ -26,7 +25,7 @@ test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=128,
# Load Model
# ==============================
model = torchvision.models.resnet18(num_classes=10).cuda()
state_dict = torch.load(f'{args.checkpoint}/model_{args.epoch}.pth')
state_dict = torch.load(f"{args.checkpoint}/model_{args.epoch}.pth")
model.load_state_dict(state_dict)
# ==============================
@@ -45,4 +44,4 @@ with torch.no_grad():
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
print("Accuracy of the model on the test images: {} %".format(100 * correct / total))