import os
import argparse
from collections import Counter

import torch
from PIL import Image
from tqdm import tqdm
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader

from resnet import get_resnet, name_to_params


class ImagenetValidationDataset(Dataset):
    def __init__(self, val_path):
        super().__init__()
        self.val_path = val_path
        self.transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()])
        with open(os.path.join(val_path, 'ILSVRC2012_validation_ground_truth.txt')) as f:
            self.labels = [int(l) - 1 for l in f.readlines()]

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, item):
        img = Image.open(os.path.join(self.val_path, f'ILSVRC2012_val_{item + 1:08d}.JPEG')).convert('RGB')
        return self.transform(img), self.labels[item]


def accuracy(output, target, topk=(1,)):
    maxk = max(topk)
    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t().cpu()
    correct = pred.eq(target.view(1, -1).expand_as(pred))
    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum().item()
        res.append(correct_k)
    return res


@torch.no_grad()
def run(pth_path):
    device = 'cuda'
    dataset = ImagenetValidationDataset('./val/')
    data_loader = DataLoader(dataset, batch_size=64, shuffle=False, pin_memory=True, num_workers=8)
    model, _ = get_resnet(*name_to_params(pth_path))
    model.load_state_dict(torch.load(pth_path)['resnet'])
    model = model.to(device).eval()
    preds = []
    target = []
    for images, labels in tqdm(data_loader):
        _, pred = model(images.to(device), apply_fc=True).topk(1, dim=1)
        preds.append(pred.squeeze(1).cpu())
        target.append(labels)
    p = torch.cat(preds).numpy()
    t = torch.cat(target).numpy()
    all_counters = [Counter() for i in range(1000)]
    for i in range(50000):
        all_counters[t[i]][p[i]] += 1
    total_correct = 0
    for i in range(1000):
        total_correct += all_counters[i].most_common(1)[0][1]
    print(f'ACC: {total_correct / 50000 * 100}')


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='SimCLR verifier')
    parser.add_argument('pth_path', type=str, help='path of the input checkpoint file')
    args = parser.parse_args()
    run(args.pth_path)

import os
import argparse
from collections import Counter

import torch
from PIL import Image
from tqdm import tqdm
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader

from resnet import get_resnet, name_to_params


class ImagenetValidationDataset(Dataset):
    def __init__(self, val_path):
        super().__init__()
        self.val_path = val_path
        self.transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()])
        with open(os.path.join(val_path, 'ILSVRC2012_validation_ground_truth.txt')) as f:
            self.labels = [int(l) - 1 for l in f.readlines()]

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, item):
        img = Image.open(os.path.join(self.val_path, f'ILSVRC2012_val_{item + 1:08d}.JPEG')).convert('RGB')
        return self.transform(img), self.labels[item]


def accuracy(output, target, topk=(1,)):
    maxk = max(topk)
    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t().cpu()
    correct = pred.eq(target.view(1, -1).expand_as(pred))
    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum().item()
        res.append(correct_k)
    return res


@torch.no_grad()
def run(pth_path):
    device = 'cuda'
    dataset = ImagenetValidationDataset('./val/')
    data_loader = DataLoader(dataset, batch_size=64, shuffle=False, pin_memory=True, num_workers=8)
    model, _ = get_resnet(*name_to_params(pth_path))
    model.load_state_dict(torch.load(pth_path)['resnet'])
    model = model.to(device).eval()
    preds = []
    target = []
    for images, labels in tqdm(data_loader):
        _, pred = model(images.to(device), apply_fc=True).topk(1, dim=1)
        preds.append(pred.squeeze(1).cpu())
        target.append(labels)
    p = torch.cat(preds).numpy()
    t = torch.cat(target).numpy()
    all_counters = [Counter() for i in range(1000)]
    for i in range(50000):
        all_counters[t[i]][p[i]] += 1
    total_correct = 0
    for i in range(1000):
        total_correct += all_counters[i].most_common(1)[0][1]
    print(f'ACC: {total_correct / 50000 * 100}')


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='SimCLR verifier')
    parser.add_argument('pth_path', type=str, help='path of the input checkpoint file')
    args = parser.parse_args()
    run(args.pth_path)
