Spaces:
Runtime error
Runtime error
File size: 5,613 Bytes
3e99b05 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------------------------------
# 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/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
# ------------------------------------------------------------------------------------------------
from __future__ import absolute_import, division, print_function
import unittest
import torch
import torch.nn.functional as F
from torch.autograd import gradcheck
from detrex.layers.multi_scale_deform_attn import MultiScaleDeformableAttnFunction
N, M, D = 1, 2, 2
Lq, L, P = 2, 2, 2
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros((1,)), shapes.prod(1).cumsum(0)[:-1]))
S = sum([(H * W).item() for H, W in shapes])
class TestMsDeformAttn(unittest.TestCase):
def ms_deform_attn_core_pytorch(
self, 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()
def check_gradient_numerical(
self, channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True
):
value = torch.rand(N, S, M, channels).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
im2col_step = 2
func = MultiScaleDeformableAttnFunction.apply
value.requires_grad = grad_value
sampling_locations.requires_grad = grad_sampling_loc
attention_weights.requires_grad = grad_attn_weight
gradok = gradcheck(
func,
(
value.double(),
shapes,
level_start_index,
sampling_locations.double(),
attention_weights.double(),
im2col_step,
),
)
return gradok
@torch.no_grad()
def test_forward_equal_with_pytorch_double(self):
value = torch.rand(N, S, M, D).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
im2col_step = 2
output_pytorch = (
self.ms_deform_attn_core_pytorch(
value.double(), shapes, sampling_locations.double(), attention_weights.double()
)
.detach()
.cpu()
)
output_cuda = (
MultiScaleDeformableAttnFunction.apply(
value.double(),
shapes,
level_start_index,
sampling_locations.double(),
attention_weights.double(),
im2col_step,
)
.detach()
.cpu()
)
self.assertTrue(torch.allclose(output_cuda, output_pytorch))
def test_gradient_numerical(self):
for channels in [30, 32, 64, 71, 1025]:
self.assertTrue(self.check_gradient_numerical(channels, True, True, True))
|