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