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| import sys |
| import os |
| import numpy as np |
| import pydensecrf.densecrf as dcrf |
| import pydensecrf.utils as utils |
| import torch |
| import torch.nn.functional as F |
| import torchvision.transforms.functional as VF |
| sys.path.append(os.getcwd()) |
| from modules.transforms import UnNormalize as unnorm |
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| MAX_ITER = 10 |
| POS_W = 3 |
| POS_XY_STD = 1 |
| Bi_W = 4 |
| Bi_XY_STD = 67 |
| Bi_RGB_STD = 3 |
| BGR_MEAN = np.array([104.008, 116.669, 122.675]) |
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| def dense_crf(image_tensor: torch.FloatTensor, output_logits: torch.FloatTensor): |
| image = np.array(VF.to_pil_image(unnorm()(image_tensor)))[:, :, ::-1] |
| H, W = image.shape[:2] |
| image = np.ascontiguousarray(image) |
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| output_logits = F.interpolate(output_logits.unsqueeze(0), size=(H, W), mode="bilinear", |
| align_corners=False).squeeze() |
| output_probs = F.softmax(output_logits, dim=0).cpu().numpy() |
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| c = output_probs.shape[0] |
| h = output_probs.shape[1] |
| w = output_probs.shape[2] |
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| U = utils.unary_from_softmax(output_probs) |
| U = np.ascontiguousarray(U) |
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| d = dcrf.DenseCRF2D(w, h, c) |
| d.setUnaryEnergy(U) |
| d.addPairwiseGaussian(sxy=POS_XY_STD, compat=POS_W) |
| d.addPairwiseBilateral(sxy=Bi_XY_STD, srgb=Bi_RGB_STD, rgbim=image, compat=Bi_W) |
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| Q = d.inference(MAX_ITER) |
| Q = np.array(Q).reshape((c, h, w)) |
| return Q |
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| def _apply_crf(tup): |
| return dense_crf(tup[0], tup[1]) |
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| def batched_crf(pool, img_tensor, prob_tensor): |
| outputs = pool.map(_apply_crf, zip(img_tensor.detach().cpu(), prob_tensor.detach().cpu())) |
| return torch.cat([torch.from_numpy(arr).unsqueeze(0) for arr in outputs], dim=0) |
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