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Source code for mmedit.models.common.flow_warp

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F


[docs]def flow_warp(x, flow, interpolation='bilinear', padding_mode='zeros', align_corners=True): """Warp an image or a feature map with optical flow. Args: x (Tensor): Tensor with size (n, c, h, w). flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is a two-channel, denoting the width and height relative offsets. Note that the values are not normalized to [-1, 1]. interpolation (str): Interpolation mode: 'nearest' or 'bilinear'. Default: 'bilinear'. padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'. Default: 'zeros'. align_corners (bool): Whether align corners. Default: True. Returns: Tensor: Warped image or feature map. """ if x.size()[-2:] != flow.size()[1:3]: raise ValueError(f'The spatial sizes of input ({x.size()[-2:]}) and ' f'flow ({flow.size()[1:3]}) are not the same.') _, _, h, w = x.size() # create mesh grid grid_y, grid_x = torch.meshgrid(torch.arange(0, h), torch.arange(0, w)) grid = torch.stack((grid_x, grid_y), 2).type_as(x) # (h, w, 2) grid.requires_grad = False grid_flow = grid + flow # scale grid_flow to [-1,1] grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0 grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0 grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3) output = F.grid_sample( x, grid_flow, mode=interpolation, padding_mode=padding_mode, align_corners=align_corners) return output
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