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grad_check.py
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grad_check.py
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"""
Copyright 2021 Jingkai Zhou
"""
import torch
from torch.autograd import gradcheck
from ipdb import set_trace
from ddf import ddf
def py_forward(feat, channel_att, spatial_att, kernel_size, dilation, stride, kernel_combine):
assert stride == 1
B, C, H, W = feat.shape
out_python = torch.zeros(B, C, H, W, requires_grad=True, device='cuda:0').double()
for b in range(B):
for c in range(C):
for y in range(H):
for x in range(W):
for iy in range(kernel_size):
for ix in range(kernel_size):
if y+(iy-kernel_size//2)*dilation < 0 or y+(iy-kernel_size//2)*dilation >= H or x+(ix-kernel_size//2)*dilation < 0 or x+(ix-kernel_size//2)*dilation >= W:
continue
if kernel_combine == 'mul':
_combined_kernel = channel_att[b, c, iy, ix] * spatial_att[
b, iy * kernel_size + ix, y, x]
else:
_combined_kernel = channel_att[b, c, iy, ix] + spatial_att[
b, iy * kernel_size + ix, y, x]
out_python[b, c, y, x] += _combined_kernel * feat[b, c, y+(iy-kernel_size//2)*dilation, x+(ix-kernel_size//2)*dilation]
return out_python
feat = torch.randn(1, 10, 15, 15, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(1, 10, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(1, 9, 15, 15, requires_grad=True, device='cuda:0').double()
# check forward
out_py = py_forward(feat, channel_att, spatial_att, 3, 2, 1, 'mul')
out_ddf_o = ddf(feat, channel_att, spatial_att, 3, 2, 1, 'mul', 'o')
out_ddf_f = ddf(feat, channel_att, spatial_att, 3, 2, 1, 'mul', 'f')
if(float(((out_py-out_ddf_o) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
if(float(((out_py-out_ddf_f) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
torch.cuda.empty_cache()
out_py = py_forward(feat, channel_att, spatial_att, 3, 1, 1, 'mul')
out_ddf_o = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'o')
out_ddf_f = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'f')
if(float(((out_py-out_ddf_o) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
if(float(((out_py-out_ddf_f) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
torch.cuda.empty_cache()
feat = torch.randn(96, 64, 56, 56, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(96, 64, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(96, 9, 56, 56, requires_grad=True, device='cuda:0').double()
out_ddf_o = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'o')
out_ddf_f = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'f')
if(float(((out_ddf_o-out_ddf_f) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
torch.cuda.empty_cache()
feat = torch.randn(96, 128, 28, 28, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(96, 128, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(96, 9, 28, 28, requires_grad=True, device='cuda:0').double()
out_ddf_o = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'o')
out_ddf_f = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'f')
if(float(((out_ddf_o-out_ddf_f) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
torch.cuda.empty_cache()
feat = torch.randn(96, 256, 14, 14, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(96, 256, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(96, 9, 14, 14, requires_grad=True, device='cuda:0').double()
out_ddf_o = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'o')
out_ddf_f = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'f')
if(float(((out_ddf_o-out_ddf_f) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
torch.cuda.empty_cache()
feat = torch.randn(96, 512, 7, 7, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(96, 512, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(96, 9, 7, 7, requires_grad=True, device='cuda:0').double()
out_ddf_o = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'o')
out_ddf_f = ddf(feat, channel_att, spatial_att, 3, 1, 1, 'mul', 'f')
if(float(((out_ddf_o-out_ddf_f) > 1e-10).sum()) == 0):
print(True)
else:
set_trace()
exit(1)
torch.cuda.empty_cache()
print('All forward pass')
feat = torch.randn(1, 70, 10, 10, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(1, 70, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(1, 9, 10, 10, requires_grad=True, device='cuda:0').double()
# check backwoard
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
spatial_att = torch.randn(1, 9, 5, 5, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
feat = torch.randn(35, 1, 10, 10, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(35, 1, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(35, 9, 10, 10, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
# check backwoard
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
spatial_att = torch.randn(35, 9, 5, 5, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
feat = torch.randn(1, 1, 50, 50, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(1, 1, 3, 3, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(1, 9, 50, 50, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
# check backwoard
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
spatial_att = torch.randn(1, 9, 25, 25, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 1, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 3, 2, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
feat = torch.randn(1, 70, 10, 10, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(1, 70, 5, 5, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(1, 25, 10, 10, requires_grad=True, device='cuda:0').double()
# check backwoard
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
spatial_att = torch.randn(1, 25, 5, 5, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
feat = torch.randn(20, 1, 10, 10, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(20, 1, 5, 5, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(20, 25, 10, 10, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
# check backwoard
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
spatial_att = torch.randn(20, 25, 5, 5, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
feat = torch.randn(1, 1, 40, 40, requires_grad=True, device='cuda:0').double()
channel_att = torch.randn(1, 1, 5, 5, requires_grad=True, device='cuda:0').double()
spatial_att = torch.randn(1, 25, 40, 40, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
# check backwoard
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 1, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 1, 'add'), atol=1e-5, eps=1e-3)
print(test)
spatial_att = torch.randn(1, 25, 20, 20, requires_grad=True, device='cuda:0').double()
torch.cuda.empty_cache()
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 2, 'mul'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 1, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
test = gradcheck(ddf, (feat, channel_att, spatial_att, 5, 2, 2, 'add'), atol=1e-5, eps=1e-3)
print(test)
print('All backward pass')
exit(1)