from torch import nn, optim
import torch

def handle_sigusr1(signum, frame):
    os.system(f'scontrol requeue {os.getenv("SLURM_JOB_ID")}')
    exit()


def handle_sigterm(signum, frame):
    pass


def off_diagonal(x):
    # return a flattened view of the off-diagonal elements of a square matrix
    n, m = x.shape
    assert n == m
    return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()


class BarlowTwins(nn.Module):
    def __init__(self, args):
        super().__init__()
        self.args = args
        self.backbone = torchvision.models.resnet50(zero_init_residual=True)
        self.backbone.fc = nn.Identity()

        # projector
        sizes = [2048] + list(map(int, args.projector.split('-')))
        layers = []
        for i in range(len(sizes) - 2):
            layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=False))
            layers.append(nn.BatchNorm1d(sizes[i + 1]))
            layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Linear(sizes[-2], sizes[-1], bias=False))
        self.projector = nn.Sequential(*layers)

        # normalization layer for the representations z1 and z2
        self.bn = nn.BatchNorm1d(sizes[-1], affine=False)

    def forward(self, y1, y2):
        z1 = self.projector(self.backbone(y1))
        z2 = self.projector(self.backbone(y2))

        # empirical cross-correlation matrix
        c = self.bn(z1).T @ self.bn(z2)

        # sum the cross-correlation matrix between all gpus
        c.div_(self.args.batch_size)
        torch.distributed.all_reduce(c)

        on_diag = torch.diagonal(c).add_(-1).pow_(2).sum()
        off_diag = off_diagonal(c).pow_(2).sum()
        loss = on_diag + self.args.lambd * off_diag
        return loss

    

class LARS(optim.Optimizer):
    def __init__(self, params, lr, weight_decay=0, momentum=0.9, eta=0.001,
                 weight_decay_filter=False, lars_adaptation_filter=False):
        defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum,
                        eta=eta, weight_decay_filter=weight_decay_filter,
                        lars_adaptation_filter=lars_adaptation_filter)
        super().__init__(params, defaults)


    def exclude_bias_and_norm(self, p):
        return p.ndim == 1

    @torch.no_grad()
    def step(self):
        for g in self.param_groups:
            for p in g['params']:
                dp = p.grad

                if dp is None:
                    continue

                if not g['weight_decay_filter'] or not self.exclude_bias_and_norm(p):
                    dp = dp.add(p, alpha=g['weight_decay'])

                if not g['lars_adaptation_filter'] or not self.exclude_bias_and_norm(p):
                    param_norm = torch.norm(p)
                    update_norm = torch.norm(dp)
                    one = torch.ones_like(param_norm)
                    q = torch.where(param_norm > 0.,
                                    torch.where(update_norm > 0,
                                                (g['eta'] * param_norm / update_norm), one), one)
                    dp = dp.mul(q)

                param_state = self.state[p]
                if 'mu' not in param_state:
                    param_state['mu'] = torch.zeros_like(p)
                mu = param_state['mu']
                mu.mul_(g['momentum']).add_(dp)

                p.add_(mu, alpha=-g['lr'])



class GaussianBlur(object):
    def __init__(self, p):
        self.p = p

    def __call__(self, img):
        if random.random() < self.p:
            sigma = random.random() * 1.9 + 0.1
            return img.filter(ImageFilter.GaussianBlur(sigma))
        else:
            return img


class Solarization(object):
    def __init__(self, p):
        self.p = p

    def __call__(self, img):
        if random.random() < self.p:
            return ImageOps.solarize(img)
        else:
            return img


class Transform:
    def __init__(self):
        self.transform = transforms.Compose([
            transforms.RandomResizedCrop(224, interpolation=Image.BICUBIC),
            transforms.RandomHorizontalFlip(p=0.5),
            transforms.RandomApply(
                [transforms.ColorJitter(brightness=0.4, contrast=0.4,
                                        saturation=0.2, hue=0.1)],
                p=0.8
            ),
            transforms.RandomGrayscale(p=0.2),
            GaussianBlur(p=1.0),
            Solarization(p=0.0),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ])
        self.transform_prime = transforms.Compose([
            transforms.RandomResizedCrop(224, interpolation=Image.BICUBIC),
            transforms.RandomHorizontalFlip(p=0.5),
            transforms.RandomApply(
                [transforms.ColorJitter(brightness=0.4, contrast=0.4,
                                        saturation=0.2, hue=0.1)],
                p=0.8
            ),
            transforms.RandomGrayscale(p=0.2),
            GaussianBlur(p=0.1),
            Solarization(p=0.2),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ])

    def __call__(self, x):
        y1 = self.transform(x)
        y2 = self.transform_prime(x)
        return y1, y2

torch.load('./checkpoint.pth')

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_26630/2396659620.py in <module>
----> 1 torch.load('./checkpoint.pth')

~/.conda/envs/thesis/lib/python3.9/site-packages/torch/serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
    605                     opened_file.seek(orig_position)
    606                     return torch.jit.load(opened_file)
--> 607                 return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
    608         return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
    609 

~/.conda/envs/thesis/lib/python3.9/site-packages/torch/serialization.py in _load(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)
    880     unpickler = UnpicklerWrapper(data_file, **pickle_load_args)
    881     unpickler.persistent_load = persistent_load
--> 882     result = unpickler.load()
    883 
    884     torch._utils._validate_loaded_sparse_tensors()

~/.conda/envs/thesis/lib/python3.9/site-packages/torch/serialization.py in find_class(self, mod_name, name)
    873         def find_class(self, mod_name, name):
    874             mod_name = load_module_mapping.get(mod_name, mod_name)
--> 875             return super().find_class(mod_name, name)
    876 
    877     # Load the data (which may in turn use `persistent_load` to load tensors)

AttributeError: Can't get attribute 'exclude_bias_and_norm' on <module '__main__'>

def exclude_bias_and_norm(p):
    return p.ndim == 1

torch.load('./checkpoint.pth')

{'epoch': 1000,
 'model': OrderedDict([('module.backbone.conv1.weight',
               tensor([[[[ 2.5191e-01,  4.0922e-01, -7.2669e-02,  ..., -6.4052e-01,
                          -1.1066e-02,  4.0831e-01],
                         [-5.2041e-01, -1.3853e+00, -2.4806e+00,  ..., -3.7158e+00,
                          -2.3665e+00, -1.5645e+00],
                         [ 4.2183e-01,  9.7686e-01,  2.3565e+00,  ...,  3.7117e+00,
                           2.6598e+00,  1.9411e+00],
                         ...,
                         [-8.6241e-01, -2.2307e+00, -4.4583e+00,  ..., -7.4381e+00,
                          -5.3399e+00, -2.9650e+00],
                         [-6.1035e-02, -7.8220e-01, -2.2777e+00,  ..., -4.4006e+00,
                          -1.4920e+00, -7.4665e-01],
                         [-3.1397e-01,  1.0959e+00,  2.4175e+00,  ...,  3.6965e+00,
                           2.8647e+00,  6.7018e-01]],
               
                        [[ 4.1280e-01,  5.8042e-01, -1.5425e-01,  ..., -9.6484e-01,
                          -6.6626e-02,  6.9789e-01],
                         [-8.2528e-01, -1.6618e+00, -2.7006e+00,  ..., -4.4289e+00,
                          -3.0250e+00, -2.1394e+00],
                         [ 4.1685e-01,  1.2428e+00,  3.1064e+00,  ...,  4.5680e+00,
                           3.1231e+00,  1.7739e+00],
                         ...,
                         [-1.1539e+00, -2.8626e+00, -5.2551e+00,  ..., -8.8027e+00,
                          -6.2876e+00, -3.1646e+00],
                         [-6.6414e-02, -1.1891e+00, -2.8664e+00,  ..., -5.6534e+00,
                          -2.2097e+00, -8.5040e-01],
                         [ 2.3680e-01,  1.5741e+00,  3.3157e+00,  ...,  4.3272e+00,
                           3.3939e+00,  1.3456e+00]],
               
                        [[-8.0329e-02, -2.9256e-02, -9.8408e-02,  ..., -1.3230e-01,
                           1.5961e-01,  7.1574e-01],
                         [ 1.3789e-01, -4.4711e-01, -1.0742e+00,  ..., -2.6502e+00,
                          -1.9514e+00, -1.2553e+00],
                         [ 9.3539e-02,  2.0351e-01,  1.5192e+00,  ...,  2.0887e+00,
                           8.4074e-01,  3.4803e-01],
                         ...,
                         [-3.4124e-01, -1.4572e+00, -2.9482e+00,  ..., -5.3057e+00,
                          -3.1230e+00, -9.5267e-01],
                         [-1.7457e-02, -7.0415e-01, -1.7256e+00,  ..., -3.6293e+00,
                          -1.1590e+00, -5.6111e-01],
                         [-3.8973e-02,  7.5807e-01,  2.0161e+00,  ...,  3.0020e+00,
                           1.6776e+00,  3.2620e-02]]],
               
               
                       [[[-8.9692e-02,  3.2251e+00,  1.2142e+00,  ..., -2.9515e+00,
                           9.0080e-01, -1.4247e-01],
                         [ 2.3453e+00,  3.5902e+00, -1.1578e+00,  ..., -2.0056e+00,
                           2.1578e+00,  5.2829e-01],
                         [ 1.0247e+00, -1.8667e-01, -6.6530e+00,  ..., -8.3165e-02,
                           4.0317e+00,  5.3877e-01],
                         ...,
                         [-1.6770e+00, -2.3302e+00, -2.4914e+00,  ...,  7.6225e+00,
                           2.8766e+00, -3.8522e+00],
                         [-2.7456e-01, -2.8197e-01,  1.1143e+00,  ...,  4.3267e+00,
                          -1.1914e+00, -4.8010e+00],
                         [-7.8956e-01,  2.2733e-01,  1.3688e+00,  ..., -1.2070e+00,
                          -4.8014e+00, -3.1817e+00]],
               
                        [[-1.3456e+00,  1.8257e+00,  1.2904e+00,  ..., -1.7344e+00,
                           1.1245e+00, -4.2477e-01],
                         [ 1.3038e+00,  3.1280e+00,  7.7193e-03,  ..., -1.2828e+00,
                           1.4337e+00, -2.9749e-01],
                         [ 6.6033e-01,  4.0264e-01, -4.7965e+00,  ..., -1.0769e+00,
                           2.0162e+00, -5.9825e-01],
                         ...,
                         [-7.7632e-01, -1.3503e+00, -2.5663e+00,  ...,  5.4909e+00,
                           2.0115e+00, -2.1628e+00],
                         [ 5.2943e-01, -1.3592e-01,  3.7521e-01,  ...,  3.3042e+00,
                          -8.4158e-01, -2.6059e+00],
                         [-3.3505e-01, -1.3344e-01,  1.6126e-01,  ..., -1.1911e+00,
                          -3.2970e+00, -1.0734e+00]],
               
                        [[-1.8053e+00, -1.1253e-01,  3.0891e-01,  ..., -2.5376e-01,
                           9.5538e-01,  1.4765e-01],
                         [-5.1867e-01,  1.1942e+00,  1.1856e+00,  ...,  3.2721e-01,
                           5.7661e-01, -6.5609e-01],
                         [-2.2089e-01,  1.3203e+00, -3.4832e-02,  ..., -7.3294e-01,
                          -1.2780e-01, -1.0192e+00],
                         ...,
                         [ 1.4057e-01,  5.6375e-01, -1.4303e+00,  ..., -2.2039e-01,
                          -8.4140e-01, -6.8928e-01],
                         [ 9.2775e-01,  2.0964e-01, -6.0748e-01,  ..., -1.9131e-01,
                          -6.4559e-01,  6.2669e-01],
                         [ 1.8681e-01, -3.5203e-01, -7.2755e-01,  ..., -1.0582e+00,
                          -3.8756e-01,  2.1985e+00]]],
               
               
                       [[[ 1.8560e-01,  5.6893e-01,  5.1143e-01,  ...,  1.2379e-01,
                          -1.3736e+00, -1.5662e+00],
                         [-4.3645e-01,  2.5362e-01,  4.1121e-01,  ...,  1.0769e+00,
                          -1.2763e+00, -3.0101e+00],
                         [-8.0284e-01, -7.4334e-01,  2.3192e-01,  ...,  2.9006e+00,
                           1.7561e+00, -1.9319e+00],
                         ...,
                         [-3.7080e-01, -1.2575e+00, -3.2034e+00,  ...,  1.5216e+00,
                           2.5932e+00,  1.2644e+00],
                         [ 1.0571e+00,  6.6774e-01, -2.2329e+00,  ..., -2.0950e+00,
                           5.7546e-01,  4.8884e-01],
                         [ 2.2446e+00,  2.5481e+00,  2.1628e-01,  ..., -2.0150e+00,
                          -5.5418e-01, -6.0265e-02]],
               
                        [[-4.1686e-01, -1.4638e+00, -1.2328e+00,  ...,  1.4917e-01,
                           2.8838e+00,  3.5382e+00],
                         [ 1.1321e+00,  2.9915e-02, -1.2463e+00,  ..., -1.6150e+00,
                           4.1837e+00,  7.4198e+00],
                         [ 1.5178e+00,  4.5107e-01, -7.5115e-01,  ..., -9.3576e+00,
                          -2.8495e+00,  3.6279e+00],
                         ...,
                         [ 1.4452e+00,  3.7269e+00,  8.0291e+00,  ..., -8.0128e-01,
                          -7.3354e+00, -3.6480e+00],
                         [-2.5798e+00, -1.4158e+00,  5.3240e+00,  ...,  5.8107e+00,
                          -8.9770e-02, -8.5261e-01],
                         [-5.0094e+00, -6.7237e+00, -2.0059e+00,  ...,  4.5151e+00,
                           1.3696e+00,  2.2111e-01]],
               
                        [[ 2.7206e-01,  6.7343e-01,  9.8231e-01,  ..., -7.2013e-02,
                          -1.8549e+00, -1.9343e+00],
                         [-7.6852e-01,  1.0288e-01,  5.4191e-01,  ...,  8.3842e-01,
                          -2.7022e+00, -4.7878e+00],
                         [-5.6594e-01, -2.2163e-01,  1.4951e+00,  ...,  5.4976e+00,
                           2.2751e+00, -2.5023e+00],
                         ...,
                         [-1.0044e+00, -2.6808e+00, -5.6874e+00,  ...,  1.6336e+00,
                           4.1365e+00,  2.0790e+00],
                         [ 1.4284e+00,  1.3326e+00, -2.3869e+00,  ..., -4.2554e+00,
                           2.2567e-01,  5.6704e-01],
                         [ 2.9544e+00,  3.9427e+00,  2.0316e+00,  ..., -2.9206e+00,
                          -9.3095e-01, -2.3322e-01]]],
               
               
                       ...,
               
               
                       [[[-2.4209e-01, -2.6249e-01,  6.1877e-01,  ...,  1.1661e+00,
                           1.5875e+00, -3.6234e+00],
                         [-1.5799e-01, -1.2104e+00, -6.7903e-01,  ..., -2.9879e-01,
                           2.6826e+00,  2.1865e+00],
                         [ 5.3539e-01, -1.9313e-01,  7.5569e-01,  ..., -3.6565e+00,
                          -4.6224e-01,  1.6064e+00],
                         ...,
                         [ 1.1123e+00, -7.3400e-01, -3.0353e+00,  ...,  1.3072e+00,
                           5.7798e-01,  1.7498e-01],
                         [ 1.2093e+00,  1.8917e+00, -6.0431e-01,  ..., -1.0639e+00,
                          -3.0071e-01, -9.5373e-02],
                         [-3.0303e+00,  2.2435e+00,  1.8081e+00,  ..., -3.3269e-01,
                           2.9252e-01, -6.9156e-01]],
               
                        [[-4.9166e-01,  7.9051e-02,  3.2756e-01,  ..., -4.1966e-01,
                           2.0339e+00, -3.8106e-01],
                         [ 2.7587e-01,  2.0969e-01,  6.8963e-01,  ..., -3.8425e+00,
                           7.2870e-01,  4.2289e+00],
                         [ 3.3893e-01,  1.3925e+00,  4.1376e+00,  ..., -5.8050e+00,
                          -4.7140e+00, -9.3494e-02],
                         ...,
                         [-8.8865e-01, -4.5216e+00, -4.8841e+00,  ...,  6.1337e+00,
                           2.1862e+00, -8.5566e-01],
                         [ 2.0842e+00,  8.3129e-02, -4.5867e+00,  ...,  1.0407e+00,
                           2.0134e+00,  6.7563e-01],
                         [ 2.8539e-01,  4.1572e+00,  1.6899e-01,  ..., -1.4035e+00,
                           7.6838e-01, -7.0165e-01]],
               
                        [[-3.9596e-01,  1.8512e-01,  6.5185e-02,  ..., -2.1192e+00,
                           2.2617e+00,  2.9884e+00],
                         [ 7.3629e-01,  1.5169e+00,  1.8451e+00,  ..., -6.6169e+00,
                          -1.1040e+00,  5.8901e+00],
                         [ 4.2017e-01,  2.7938e+00,  7.0721e+00,  ..., -7.0604e+00,
                          -7.6267e+00, -1.2317e+00],
                         ...,
                         [-3.0444e+00, -7.2614e+00, -5.4320e+00,  ...,  9.4523e+00,
                           3.0574e+00, -2.1463e+00],
                         [ 2.1839e+00, -1.8361e+00, -7.0507e+00,  ...,  3.0629e+00,
                           3.8924e+00,  1.4990e+00],
                         [ 3.2505e+00,  5.1462e+00, -1.6025e+00,  ..., -2.2084e+00,
                           1.1787e+00, -1.8861e-01]]],
               
               
                       [[[ 6.4364e-02,  8.4842e-02,  1.5676e-01,  ...,  1.1374e-02,
                          -3.6531e-02,  3.1694e-02],
                         [ 5.8909e-02,  1.4904e-01, -1.3246e-02,  ..., -1.5876e-01,
                          -3.2752e-01,  9.0173e-02],
                         [ 2.1613e-01,  5.0102e-02,  4.1258e-01,  ...,  2.4377e-01,
                           8.6762e-01,  3.9654e-01],
                         ...,
                         [-2.2194e-02, -2.5954e-01, -7.0863e-02,  ..., -1.1059e+00,
                           2.2759e+00, -9.6511e-01],
                         [ 2.1445e-01,  4.6011e-01,  4.1945e-01,  ...,  1.8268e-01,
                          -2.0661e+00, -3.4853e-01],
                         [ 1.7134e-01,  2.1831e-01,  6.0229e-01,  ..., -6.1516e-01,
                          -3.0576e-01, -7.4636e-01]],
               
                        [[-2.8888e-02,  1.5219e-01,  4.7257e-01,  ...,  1.7102e-01,
                           2.7250e-01,  7.9105e-02],
                         [ 3.5980e-01,  5.9953e-01,  5.0411e-01,  ..., -2.2862e-01,
                          -2.5933e-01, -9.6651e-02],
                         [ 5.1401e-01,  1.8799e-01,  6.7081e-01,  ..., -4.5798e-01,
                          -2.0574e-01, -4.8034e-01],
                         ...,
                         [-1.7391e-01, -7.9602e-01, -1.4692e+00,  ..., -3.5578e+00,
                           1.2630e+00, -2.5508e+00],
                         [ 8.3798e-02,  1.3325e-01, -8.2562e-01,  ..., -2.0857e+00,
                          -4.1340e+00, -1.5358e+00],
                         [ 2.3483e-01, -1.2849e-01,  1.3248e-02,  ..., -1.7343e+00,
                          -1.0087e+00, -9.1316e-01]],
               
                        [[-2.0461e-01, -3.0005e-01, -2.8701e-01,  ...,  4.6097e-01,
                          -2.1267e-01, -2.9682e-01],
                         [-3.1793e-01, -2.8373e-01, -9.0486e-01,  ...,  3.1256e-01,
                           1.8729e-01,  1.1837e-02],
                         [-6.5169e-01, -1.6092e+00, -6.3664e-01,  ...,  7.6935e-01,
                           1.1029e-01,  1.1409e-01],
                         ...,
                         [ 2.4212e-01,  8.0666e-02,  4.8853e-01,  ...,  2.6837e+00,
                           8.8726e+00,  1.9568e+00],
                         [-3.3940e-01,  5.5016e-01,  3.8333e-01,  ...,  2.4911e+00,
                          -8.9695e-01,  2.0776e+00],
                         [-4.0179e-01, -9.5298e-01, -2.2034e-02,  ...,  1.8464e+00,
                           1.8866e+00,  1.9364e+00]]],
               
               
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              ('module.backbone.layer2.1.bn3.running_var',
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                      device='cuda:0')),
              ('module.backbone.layer4.2.bn2.running_mean',
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            ...,
    
    
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             ...,
    
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            ...,
    
    
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   11: {'mu': tensor([[[[-6.0262e-04]],
    
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            ...,
    
    
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             ...,
    
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            ...,
    
    
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             ...,
    
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             ...,
    
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             ...,
    
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   13: {'mu': tensor([[[[ 7.9742e-04]],
    
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            [[[ 2.5891e-05]],
    
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