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from __future__ import division
from __future__ import print_function
import argparse
import numpy as np
import torch
from spatial_correlation_sampler import SpatialCorrelationSampler
def check_equal(first, second, verbose):
if verbose:
print()
for i, (x, y) in enumerate(zip(first, second)):
x = x.cpu().detach().numpy()
y = y.cpu().detach().numpy()
if verbose:
print("x = {}".format(x.flatten()))
print("y = {}".format(y.flatten()))
print('-' * 80)
np.testing.assert_allclose(x, y, err_msg="Index: {}".format(i))
def zero_grad(variables):
for variable in variables:
if variable.grad is not None: variable.grad.zero_()
def get_grads(variables):
return [var.grad.clone() for var in variables]
def check_forward(input1, input2, correlation_sampler, verbose, gpu_index=0):
device = torch.device(f"cuda:{gpu_index}")
cpu_values = correlation_sampler(input1, input2)
cuda_values = correlation_sampler(input1.to(device), input2.to(device))
print(f"Forward: CPU vs. CUDA device:{gpu_index} ... ", end='')
check_equal(cpu_values, cuda_values, verbose)
print('Ok')
def check_backward(input1, input2, correlation_sampler, verbose, gpu_index=0):
device = torch.device(f"cuda:{gpu_index}")
zero_grad([input1, input2])
cpu_values = correlation_sampler(input1, input2)
cpu_values.sum().backward()
grad_cpu = get_grads([input1, input2])
zero_grad([input1, input2])
cuda_values = correlation_sampler(input1.to(device), input2.to(device))
cuda_values.sum().backward()
grad_cuda = get_grads([input1, input2])
print(f"Backward: CPU vs. CUDA device:{gpu_index} ... ", end='')
check_equal(grad_cpu, grad_cuda, verbose)
print('Ok')
def check_multi_gpu_forward(correlation_sampler, verbose):
print("Multi-GPU forward")
total_gpus = torch.cuda.device_count()
for gpu in range(total_gpus):
check_forward(input1, input2, correlation_sampler, verbose, gpu_index=gpu)
def check_multi_gpu_backward(correlation_sampler, verbose):
print("Multi-GPU backward")
total_gpus = torch.cuda.device_count()
for gpu in range(total_gpus):
check_backward(input1, input2, correlation_sampler, verbose, gpu_index=gpu)
parser = argparse.ArgumentParser()
parser.add_argument('direction', choices=['forward', 'backward'], nargs='+')
parser.add_argument('-b', '--batch-size', type=int, default=1)
parser.add_argument('-k', '--kernel-size', type=int, default=3)
parser.add_argument('--patch', type=int, default=3)
parser.add_argument('--patch_dilation', type=int, default=2)
parser.add_argument('-c', '--channel', type=int, default=10)
parser.add_argument('--height', type=int, default=10)
parser.add_argument('-w', '--width', type=int, default=10)
parser.add_argument('-s', '--stride', type=int, default=2)
parser.add_argument('-p', '--pad', type=int, default=5)
parser.add_argument('-v', '--verbose', action='store_true', default=False)
parser.add_argument('-d', '--dilation', type=int, default=2)
args = parser.parse_args()
print(args)
assert(torch.cuda.is_available()), "no comparison to make"
input1 = torch.randn(args.batch_size,
args.channel,
args.height,
args.width).double()
input2 = torch.randn(args.batch_size,
args.channel,
args.height,
args.width).double()
input1.requires_grad = True
input2.requires_grad = True
correlation_sampler = SpatialCorrelationSampler(
args.kernel_size,
args.patch,
args.stride,
args.pad,
args.dilation,
args.patch_dilation)
if 'forward' in args.direction:
check_forward(input1, input2, correlation_sampler, args.verbose)
if torch.cuda.device_count() > 1: check_multi_gpu_forward(correlation_sampler, args.verbose)
if 'backward' in args.direction:
check_backward(input1, input2, correlation_sampler, args.verbose)
if torch.cuda.device_count() > 1: check_multi_gpu_backward(correlation_sampler, args.verbose)
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