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self.assertIsNotNone(loop.run_until_complete(Net().post(
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'https://httpbin.org/post', None, {'accept': 'application/json'}, None)))
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self.assertIsNotNone(loop.run_until_complete(Net().delete(
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'https://httpbin.org/delete', {'accept': 'application/json'}, None)))
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if __name__ == '__main__':
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unittest.main()
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# <FILESEP>
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import matplotlib.pyplot as plt
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import torch
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from skimage.metrics import structural_similarity as ssim
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from sklearn.metrics import auc, roc_curve
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from helpers import gridify_output
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def main():
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pass
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def heatmap(real: torch.Tensor, recon: torch.Tensor, mask, filename, save=True):
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mse = ((recon - real).square() * 2) - 1
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mse_threshold = mse > 0
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mse_threshold = (mse_threshold.float() * 2) - 1
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if save:
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output = torch.cat((real, recon.reshape(1, *recon.shape), mse, mse_threshold, mask))
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plt.imshow(gridify_output(output, 5)[..., 0], cmap="gray")
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plt.axis('off')
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plt.savefig(filename)
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plt.clf()
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# for anomalous dataset - metric of crossover
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def dice_coeff(real: torch.Tensor, recon: torch.Tensor, real_mask: torch.Tensor, smooth=0.000001, mse=None):
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# scale_img = lambda img: ((img + 1) * 127.5).clamp(0, 255).to(torch.uint8)
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# real = scale_img(real.clone().detach())
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# recon = scale_img(recon.clone().detach())
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# real_mask = scale_img(real_mask.clone().detach())
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if mse == None:
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mse = (real - recon).square()
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mse = (mse > 0.5).float()
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intersection = torch.sum(mse * real_mask, dim=[1, 2, 3])
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union = torch.sum(mse, dim=[1, 2, 3]) + torch.sum(real_mask, dim=[1, 2, 3])
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dice = torch.mean((2. * intersection + smooth) / (union + smooth), dim=0)
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return dice
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def PSNR(recon, real):
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se = (real - recon).square()
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mse = torch.mean(se, dim=list(range(len(real.shape))))
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psnr = 20 * torch.log10(torch.max(real) / torch.sqrt(mse))
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return psnr.detach().cpu().numpy()
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def SSIM(real, recon):
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return ssim(real.detach().cpu().numpy(), recon.detach().cpu().numpy(), channel_axis=2)
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def IoU(real, recon):
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import numpy as np
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real = real.cpu().numpy()
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recon = recon.cpu().numpy()
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intersection = np.logical_and(real, recon)
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union = np.logical_or(real, recon)
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return np.sum(intersection) / (np.sum(union) + 1e-8)
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def precision(real_mask, recon_mask):
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TP = ((real_mask == 1) & (recon_mask == 1))
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FP = ((real_mask == 1) & (recon_mask == 0))
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return torch.sum(TP).float() / ((torch.sum(TP) + torch.sum(FP)).float() + 1e-6)
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def recall(real_mask, recon_mask):
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TP = ((real_mask == 1) & (recon_mask == 1))
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FN = ((real_mask == 0) & (recon_mask == 1))
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return torch.sum(TP).float() / ((torch.sum(TP) + torch.sum(FN)).float() + 1e-6)
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def FPR(real_mask, recon_mask):
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FP = ((real_mask == 1) & (recon_mask == 0))
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TN = ((real_mask == 0) & (recon_mask == 0))
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return torch.sum(FP).float() / ((torch.sum(FP) + torch.sum(TN)).float() + 1e-6)
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def ROC_AUC(real_mask, square_error):
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if type(real_mask) == torch.Tensor:
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return roc_curve(real_mask.detach().cpu().numpy().flatten(), square_error.detach().cpu().numpy().flatten())
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else:
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return roc_curve(real_mask.flatten(), square_error.flatten())
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def AUC_score(fpr, tpr):
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return auc(fpr, tpr)
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def testing(testing_dataset_loader, diffusion, args, ema, model):
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