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| # ------------------------------------------------------------------------------------------------ | |
| # Deformable DETR | |
| # Copyright (c) 2020 SenseTime. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------------------------------ | |
| # Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 | |
| # ------------------------------------------------------------------------------------------------ | |
| from __future__ import absolute_import | |
| from __future__ import print_function | |
| from __future__ import division | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.autograd import Function | |
| from torch.autograd.function import once_differentiable | |
| import MultiScaleDeformableAttention as MSDA | |
| class MSDeformAttnFunction(Function): | |
| def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step): | |
| ctx.im2col_step = im2col_step | |
| output = MSDA.ms_deform_attn_forward( | |
| value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step) | |
| ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights) | |
| return output | |
| def backward(ctx, grad_output): | |
| value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors | |
| grad_value, grad_sampling_loc, grad_attn_weight = \ | |
| MSDA.ms_deform_attn_backward( | |
| value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step) | |
| return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None | |
| def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): | |
| # for debug and test only, | |
| # need to use cuda version instead | |
| N_, S_, M_, D_ = value.shape | |
| _, Lq_, M_, L_, P_, _ = sampling_locations.shape | |
| value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) | |
| sampling_grids = 2 * sampling_locations - 1 | |
| sampling_value_list = [] | |
| for lid_, (H_, W_) in enumerate(value_spatial_shapes): | |
| # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ | |
| value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_) | |
| # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 | |
| sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) | |
| # N_*M_, D_, Lq_, P_ | |
| sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, | |
| mode='bilinear', padding_mode='zeros', align_corners=False) | |
| sampling_value_list.append(sampling_value_l_) | |
| # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) | |
| attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) | |
| output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) | |
| return output.transpose(1, 2).contiguous() | |