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Yuwen Xiong
2024-01-16 00:22:22 +08:00
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# --------------------------------------------------------
# DCNv4
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import time
import torch
import torch.nn as nn
import math
from torch.autograd import gradcheck
from functions.dcnv3_func import DCNv3Function, dcnv3_core_pytorch
H_in, W_in = 8, 8
N, M, D = 2, 4, 16
Kh, Kw = 3, 3
P = Kh * Kw
offset_scale = 2.0
pad = 1
dilation = 1
stride = 1
H_out = (H_in + 2 * pad - (dilation * (Kh - 1) + 1)) // stride + 1
W_out = (W_in + 2 * pad - (dilation * (Kw - 1) + 1)) // stride + 1
torch.manual_seed(3)
@torch.no_grad()
def check_forward_equal_with_pytorch_double():
input = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
offset = torch.rand(N, H_out, W_out, M*P*2).cuda() * 10
mask = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
mask /= mask.sum(-1, keepdim=True)
mask = mask.reshape(N, H_out, W_out, M*P)
output_pytorch = dcnv3_core_pytorch(
input.double(),
offset.double(),
mask.double(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale).detach().cpu()
im2col_step = 2
output_cuda = DCNv3Function.apply(
input.double(),
offset.double(),
mask.double(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
im2col_step).detach().cpu()
fwdok = torch.allclose(output_cuda, output_pytorch)
max_abs_err = (output_cuda - output_pytorch).abs().max()
max_rel_err = ((output_cuda - output_pytorch).abs() /
output_pytorch.abs()).max()
print('>>> forward double')
print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
@torch.no_grad()
def check_forward_equal_with_pytorch_float():
input = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
offset = torch.rand(N, H_out, W_out, M*P*2).cuda() * 10
mask = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
mask /= mask.sum(-1, keepdim=True)
mask = mask.reshape(N, H_out, W_out, M*P)
output_pytorch = dcnv3_core_pytorch(
input,
offset,
mask,
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale).detach().cpu()
im2col_step = 2
output_cuda = DCNv3Function.apply(
input,
offset,
mask,
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
im2col_step).detach().cpu()
fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
max_abs_err = (output_cuda - output_pytorch).abs().max()
max_rel_err = ((output_cuda - output_pytorch).abs() /
output_pytorch.abs()).max()
print('>>> forward float')
print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
def check_backward_equal_with_pytorch_double(channels=4, grad_input=True, grad_offset=True, grad_mask=True):
# H_in, W_in = 4, 4
N = 2
M = 2
H_out = (H_in + 2 * pad - (dilation * (Kh - 1) + 1)) // stride + 1
W_out = (W_in + 2 * pad - (dilation * (Kw - 1) + 1)) // stride + 1
D = channels
input0 = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
offset0 = torch.rand(N, H_out, W_out, M*P*2).cuda() * 10
mask0 = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
mask0 /= mask0.sum(-1, keepdim=True)
mask0 = mask0.reshape(N, H_out, W_out, M*P)
input0.requires_grad = grad_input
offset0.requires_grad = grad_offset
mask0.requires_grad = grad_mask
output_pytorch = dcnv3_core_pytorch(
input0.double(),
offset0.double(),
mask0.double(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale)
output_pytorch.sum().backward()
input1 = input0.detach()
offset1 = offset0.detach()
mask1 = mask0.detach()
input1.requires_grad = grad_input
offset1.requires_grad = grad_offset
mask1.requires_grad = grad_mask
im2col_step = 2
output_cuda = DCNv3Function.apply(
input1.double(),
offset1.double(),
mask1.double(),
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
im2col_step)
output_cuda.sum().backward()
print(f'>>> backward double: channels {D}')
bwdok = torch.allclose(input0.grad, input1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (input0.grad - input1.grad).abs().max()
max_rel_err = ((input0.grad - input1.grad).abs() /
input0.grad.abs()).max()
print(
f'* {bwdok} input_grad check_backward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
bwdok = torch.allclose(offset0.grad, offset1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (offset0.grad - offset1.grad).abs().max()
max_rel_err = ((offset0.grad - offset1.grad).abs() /
offset0.grad.abs()).max()
print(
f'* {bwdok} offset_grad check_backward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
bwdok = torch.allclose(mask0.grad, mask1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (mask0.grad - mask1.grad).abs().max()
max_rel_err = ((mask0.grad - mask1.grad).abs() /
mask0.grad.abs()).max()
print(
f'* {bwdok} mask_grad check_backward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
def check_backward_equal_with_pytorch_float(channels=4, grad_input=True, grad_offset=True, grad_mask=True):
# H_in, W_in = 4, 4
N = 2
M = 2
H_out = (H_in + 2 * pad - (dilation * (Kh - 1) + 1)) // stride + 1
W_out = (W_in + 2 * pad - (dilation * (Kw - 1) + 1)) // stride + 1
D = channels
input0 = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
offset0 = torch.rand(N, H_out, W_out, M*P*2).cuda() * 10
mask0 = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
mask0 /= mask0.sum(-1, keepdim=True)
mask0 = mask0.reshape(N, H_out, W_out, M*P)
input0.requires_grad = grad_input
offset0.requires_grad = grad_offset
mask0.requires_grad = grad_mask
output_pytorch = dcnv3_core_pytorch(
input0,
offset0,
mask0,
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale)
output_pytorch.sum().backward()
input1 = input0.detach()
offset1 = offset0.detach()
mask1 = mask0.detach()
input1.requires_grad = grad_input
offset1.requires_grad = grad_offset
mask1.requires_grad = grad_mask
im2col_step = 2
output_cuda = DCNv3Function.apply(
input1,
offset1,
mask1,
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, offset_scale,
im2col_step)
output_cuda.sum().backward()
print(f'>>> backward float: channels {D}')
bwdok = torch.allclose(input0.grad, input1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (input0.grad - input1.grad).abs().max()
max_rel_err = ((input0.grad - input1.grad).abs() /
input0.grad.abs()).max()
print(
f'* {bwdok} input_grad check_backward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
bwdok = torch.allclose(offset0.grad, offset1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (offset0.grad - offset1.grad).abs().max()
max_rel_err = ((offset0.grad - offset1.grad).abs() /
offset0.grad.abs()).max()
print(
f'* {bwdok} offset_grad check_backward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
bwdok = torch.allclose(mask0.grad, mask1.grad, rtol=1e-2, atol=1e-3)
max_abs_err = (mask0.grad - mask1.grad).abs().max()
max_rel_err = ((mask0.grad - mask1.grad).abs() /
mask0.grad.abs()).max()
print(
f'* {bwdok} mask_grad check_backward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
@torch.no_grad()
def check_time_cost(im2col_step=128):
N = 512
H_in, W_in = 64, 64
H_out = (H_in + 2 * pad - (dilation * (Kh - 1) + 1)) // stride + 1
W_out = (W_in + 2 * pad - (dilation * (Kw - 1) + 1)) // stride + 1
input = torch.rand(N, H_in, W_in, M*D).cuda() * 0.01
offset = torch.rand(N, H_out, W_out, M*P*2).cuda() * 10
mask = torch.rand(N, H_out, W_out, M, P).cuda() + 1e-5
mask /= mask.sum(-1, keepdim=True)
mask = mask.reshape(N, H_out, W_out, M*P)
print(
f'>>> time cost: im2col_step {im2col_step}; input {input.shape}; points {P} ')
repeat = 100
for i in range(repeat):
output_cuda = DCNv3Function.apply(
input,
offset,
mask,
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, 1.0,
im2col_step)
torch.cuda.synchronize()
start = time.time()
for i in range(repeat):
output_cuda = DCNv3Function.apply(
input,
offset,
mask,
Kh, Kw, stride, stride, Kh // 2, Kw // 2, dilation, dilation, M, D, 1.0,
im2col_step)
torch.cuda.synchronize()
print(f'foward time cost: {(time.time() - start) / repeat}')
if __name__ == '__main__':
check_forward_equal_with_pytorch_double()
check_forward_equal_with_pytorch_float()
for channels in [1, 16, 30, 32, 64, 71, 1025]:
check_backward_equal_with_pytorch_double(channels, True, True, True)
for channels in [1, 16, 30, 32, 64, 71, 1025]:
check_backward_equal_with_pytorch_float(channels, True, True, True)
for i in range(3):
im2col_step = 128 * (2 ** i)
check_time_cost(im2col_step)