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import os, cv2 |
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import torch |
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import torch.nn.functional as F |
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import torch.distributed as dist |
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import math |
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import numpy as np |
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import PIL.Image as Image |
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import matplotlib.pyplot as plt |
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from torch.utils.data import Sampler |
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from torchvision import transforms |
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def tensor2disp(tensor, vmax=0.18, percentile=None, viewind=0): |
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cm = plt.get_cmap('magma') |
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tnp = tensor[viewind, 0, :, :].detach().cpu().numpy() |
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if percentile is not None: |
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if np.sum(tnp > 0) > 100: |
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vmax = np.percentile(tnp[tnp > 0], 95) |
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else: |
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vmax = 1.0 |
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tnp = tnp / vmax |
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tnp = (cm(tnp) * 255).astype(np.uint8) |
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return Image.fromarray(tnp[:, :, 0:3]) |
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def tensor2grad(gradtensor, percentile=95, pos_bar=0, neg_bar=0, viewind=0): |
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cm = plt.get_cmap('bwr') |
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gradnumpy = gradtensor.detach().cpu().numpy()[viewind, 0, :, :] |
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selector_pos = gradnumpy > 0 |
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if np.sum(selector_pos) > 1: |
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if pos_bar <= 0: |
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pos_bar = np.percentile(gradnumpy[selector_pos], percentile) |
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gradnumpy[selector_pos] = gradnumpy[selector_pos] / pos_bar / 2 |
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selector_neg = gradnumpy < 0 |
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if np.sum(selector_neg) > 1: |
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if neg_bar >= 0: |
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neg_bar = -np.percentile(-gradnumpy[selector_neg], percentile) |
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gradnumpy[selector_neg] = -gradnumpy[selector_neg] / neg_bar / 2 |
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disp_grad_numpy = gradnumpy + 0.5 |
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colorMap = cm(disp_grad_numpy)[:, :, 0:3] |
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return Image.fromarray((colorMap * 255).astype(np.uint8)) |
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def tensor2rgb(tensor, viewind=0): |
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tnp = tensor.detach().cpu().permute([0, 2, 3, 1]).contiguous()[viewind, :, :, :].numpy() |
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if np.max(tnp) <= 2: |
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tnp = tnp * 255 |
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tnp = np.clip(tnp, a_min=0, a_max=255).astype(np.uint8) |
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return Image.fromarray(tnp) |
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