35 lines
1.5 KiB
Python
35 lines
1.5 KiB
Python
# -*- coding: utf-8 -*-
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# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
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# holder of all proprietary rights on this computer program.
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# You can only use this computer program if you have closed
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# a license agreement with MPG or you get the right to use the computer
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# program from someone who is authorized to grant you that right.
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# Any use of the computer program without a valid license is prohibited and
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# liable to prosecution.
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#
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# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
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# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
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# for Intelligent Systems. All rights reserved.
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#
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# Contact: ps-license@tuebingen.mpg.de
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import torch
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def interpolate(feat, uv):
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'''
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:param feat: [B, C, H, W] image features
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:param uv: [B, 2, N] uv coordinates in the image plane, range [-1, 1]
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:return: [B, C, N] image features at the uv coordinates
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'''
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if uv.shape[-1] != 2:
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uv = uv.transpose(1, 2) # [B, N, 2]
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uv = uv.unsqueeze(2) # [B, N, 1, 2]
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# NOTE: for newer PyTorch, it seems that training results are degraded due to implementation diff in F.grid_sample
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# for old versions, simply remove the aligned_corners argument.
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if int(torch.__version__.split('.')[1]) < 4:
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samples = torch.nn.functional.grid_sample(feat, uv) # [B, C, N, 1]
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else:
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samples = torch.nn.functional.grid_sample(feat, uv, align_corners=True) # [B, C, N, 1]
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return samples[:, :, :, 0] # [B, C, N] |