mirror of https://github.com/alibaba/EasyCV.git
116 lines
4.8 KiB
Python
116 lines
4.8 KiB
Python
# ------------------------------------------------------------------------------------------------
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# Deformable DETR
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------------------------------
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# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
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# ------------------------------------------------------------------------------------------------
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# Copyright (c) Facebook, Inc. and its affiliates.
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# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
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from __future__ import absolute_import, division, print_function
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import time
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import torch
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import torch.nn as nn
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from functions.ms_deform_attn_func import (MSDeformAttnFunction,
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ms_deform_attn_core_pytorch)
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from torch.autograd import gradcheck
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N, M, D = 1, 2, 2
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Lq, L, P = 2, 2, 2
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shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
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level_start_index = torch.cat((shapes.new_zeros(
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(1, )), shapes.prod(1).cumsum(0)[:-1]))
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S = sum([(H * W).item() for H, W in shapes])
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torch.manual_seed(3)
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@torch.no_grad()
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def check_forward_equal_with_pytorch_double():
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value = torch.rand(N, S, M, D).cuda() * 0.01
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sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
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attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
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attention_weights /= attention_weights.sum(
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-1, keepdim=True).sum(
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-2, keepdim=True)
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im2col_step = 2
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output_pytorch = ms_deform_attn_core_pytorch(
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value.double(), shapes, sampling_locations.double(),
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attention_weights.double()).detach().cpu()
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output_cuda = MSDeformAttnFunction.apply(value.double(), shapes,
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level_start_index,
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sampling_locations.double(),
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attention_weights.double(),
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im2col_step).detach().cpu()
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fwdok = torch.allclose(output_cuda, output_pytorch)
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max_abs_err = (output_cuda - output_pytorch).abs().max()
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max_rel_err = ((output_cuda - output_pytorch).abs() /
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output_pytorch.abs()).max()
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print(
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f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}'
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)
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@torch.no_grad()
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def check_forward_equal_with_pytorch_float():
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value = torch.rand(N, S, M, D).cuda() * 0.01
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sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
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attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
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attention_weights /= attention_weights.sum(
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-1, keepdim=True).sum(
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-2, keepdim=True)
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im2col_step = 2
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output_pytorch = ms_deform_attn_core_pytorch(
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value, shapes, sampling_locations, attention_weights).detach().cpu()
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output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index,
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sampling_locations,
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attention_weights,
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im2col_step).detach().cpu()
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fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
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max_abs_err = (output_cuda - output_pytorch).abs().max()
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max_rel_err = ((output_cuda - output_pytorch).abs() /
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output_pytorch.abs()).max()
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print(
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f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}'
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)
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def check_gradient_numerical(channels=4,
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grad_value=True,
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grad_sampling_loc=True,
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grad_attn_weight=True):
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value = torch.rand(N, S, M, channels).cuda() * 0.01
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sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
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attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
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attention_weights /= attention_weights.sum(
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-1, keepdim=True).sum(
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-2, keepdim=True)
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im2col_step = 2
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func = MSDeformAttnFunction.apply
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value.requires_grad = grad_value
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sampling_locations.requires_grad = grad_sampling_loc
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attention_weights.requires_grad = grad_attn_weight
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gradok = gradcheck(
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func,
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(value.double(), shapes, level_start_index,
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sampling_locations.double(), attention_weights.double(), im2col_step))
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print(f'* {gradok} check_gradient_numerical(D={channels})')
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if __name__ == '__main__':
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check_forward_equal_with_pytorch_double()
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check_forward_equal_with_pytorch_float()
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# If out of memory occurs, reduce the number of channels
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for channels in [30, 32, 64, 71, 1025, 2048, 3096]:
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check_gradient_numerical(channels, True, True, True)
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