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You are reading the documentation for MMEditing 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMEditing 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMEditing 1.0 for more details.

Source code for mmedit.models.common.model_utils

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch


[docs]def set_requires_grad(nets, requires_grad=False): """Set requires_grad for all the networks. Args: nets (nn.Module | list[nn.Module]): A list of networks or a single network. requires_grad (bool): Whether the networks require gradients or not """ if not isinstance(nets, list): nets = [nets] for net in nets: if net is not None: for param in net.parameters(): param.requires_grad = requires_grad
[docs]def extract_bbox_patch(bbox, img, channel_first=True): """Extract patch from a given bbox. Args: bbox (torch.Tensor | numpy.array): Bbox with (top, left, h, w). If `img` has batch dimension, the `bbox` must be stacked at first dimension. The shape should be (4,) or (n, 4). img (torch.Tensor | numpy.array): Image data to be extracted. If organized in batch dimension, the batch dimension must be the first order like (n, h, w, c) or (n, c, h, w). channel_first (bool): If True, the channel dimension of img is before height and width, e.g. (c, h, w). Otherwise, the img shape (samples in the batch) is like (h, w, c). Returns: (torch.Tensor | numpy.array): Extracted patches. The dimension of the \ output should be the same as `img`. """ def _extract(bbox, img): assert len(bbox) == 4 t, l, h, w = bbox if channel_first: img_patch = img[..., t:t + h, l:l + w] else: img_patch = img[t:t + h, l:l + w, ...] return img_patch input_size = img.shape assert len(input_size) == 3 or len(input_size) == 4 bbox_size = bbox.shape assert bbox_size == (4, ) or (len(bbox_size) == 2 and bbox_size[0] == input_size[0]) # images with batch dimension if len(input_size) == 4: output_list = [] for i in range(input_size[0]): img_patch_ = _extract(bbox[i], img[i:i + 1, ...]) output_list.append(img_patch_) if isinstance(img, torch.Tensor): img_patch = torch.cat(output_list, dim=0) else: img_patch = np.concatenate(output_list, axis=0) # standardize image else: img_patch = _extract(bbox, img) return img_patch
[docs]def scale_bbox(bbox, target_size): """Modify bbox to target size. The original bbox will be enlarged to the target size with the original bbox in the center of the new bbox. Args: bbox (np.ndarray | torch.Tensor): Bboxes to be modified. Bbox can be in batch or not. The shape should be (4,) or (n, 4). target_size (tuple[int]): Target size of final bbox. Returns: (np.ndarray | torch.Tensor): Modified bboxes. """ def _mod(bbox, target_size): top_ori, left_ori, h_ori, w_ori = bbox h, w = target_size assert h >= h_ori and w >= w_ori top = int(max(0, top_ori - (h - h_ori) // 2)) left = int(max(0, left_ori - (w - w_ori) // 2)) if isinstance(bbox, torch.Tensor): bbox_new = torch.Tensor([top, left, h, w]).type_as(bbox) else: bbox_new = np.asarray([top, left, h, w]) return bbox_new if isinstance(bbox, torch.Tensor): bbox_new = torch.zeros_like(bbox) elif isinstance(bbox, np.ndarray): bbox_new = np.zeros_like(bbox) else: raise TypeError('bbox mush be torch.Tensor or numpy.ndarray' f'but got type {type(bbox)}') bbox_shape = list(bbox.shape) if len(bbox_shape) == 2: for i in range(bbox_shape[0]): bbox_new[i, :] = _mod(bbox[i], target_size) else: bbox_new = _mod(bbox, target_size) return bbox_new
[docs]def extract_around_bbox(img, bbox, target_size, channel_first=True): """Extract patches around the given bbox. Args: bbox (np.ndarray | torch.Tensor): Bboxes to be modified. Bbox can be in batch or not. target_size (List(int)): Target size of final bbox. Returns: (torch.Tensor | numpy.array): Extracted patches. The dimension of the \ output should be the same as `img`. """ bbox_new = scale_bbox(bbox, target_size) img_patch = extract_bbox_patch(bbox_new, img, channel_first=channel_first) return img_patch, bbox_new
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