mirror of
https://github.com/open-mmlab/mmcv.git
synced 2025-06-03 21:54:52 +08:00
c0f5492ee9
* add ext ops, support parrots * fix lint * fix lint * update op from mmdetection * support non-pytorch env * fix import bug * test not import mmcv.op * rename mmcv.op to mmcv.ops * fix compile warning * 1. fix syncbn warning in pytorch 1.5 2. support only cpu compile 3. add point_sample from mmdet * fix text bug * update docstrings * fix line endings * minor updates * remove non_local from ops * bug fix for nonlocal2d * rename ops_ext to _ext and _ext to _flow_warp_ext * update the doc * try clang-format github action * fix github action * add ops to api.rst * fix cpp format * fix clang format issues * remove .clang-format Co-authored-by: Kai Chen <chenkaidev@gmail.com>
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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class Loss(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, input, target):
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input = input.view(-1)
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target = target.view(-1)
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return torch.mean(input - target)
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class TestPSAMask(object):
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def test_psa_mask_collect(self):
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if not torch.cuda.is_available():
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return
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from mmcv.ops import PSAMask
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test_loss = Loss()
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input = np.fromfile(
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'tests/data/for_psa_mask/psa_input.bin', dtype=np.float32)
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output_collect = np.fromfile(
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'tests/data/for_psa_mask/psa_output_collect.bin', dtype=np.float32)
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input = input.reshape((4, 16, 8, 8))
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output_collect = output_collect.reshape((4, 64, 8, 8))
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label = torch.ones((4, 64, 8, 8))
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input = torch.FloatTensor(input)
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input.requires_grad = True
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psamask_collect = PSAMask('collect', (4, 4))
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# test collect cpu
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test_output = psamask_collect(input)
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loss = test_loss(test_output, label)
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loss.backward()
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test_output = test_output.detach().numpy()
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assert np.allclose(test_output, output_collect)
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assert test_output.shape == output_collect.shape
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psamask_collect.cuda()
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input = input.cuda()
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label = label.cuda()
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# test collect cuda
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test_output = psamask_collect(input)
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loss = test_loss(test_output, label)
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loss.backward()
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test_output = test_output.detach().cpu().numpy()
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assert np.allclose(test_output, output_collect)
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assert test_output.shape == output_collect.shape
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def test_psa_mask_distribute(self):
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if not torch.cuda.is_available():
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return
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from mmcv.ops import PSAMask
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test_loss = Loss()
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input = np.fromfile(
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'tests/data/for_psa_mask/psa_input.bin', dtype=np.float32)
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output_distribute = np.fromfile(
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'tests/data/for_psa_mask/psa_output_distribute.bin',
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dtype=np.float32)
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input = input.reshape((4, 16, 8, 8))
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output_distribute = output_distribute.reshape((4, 64, 8, 8))
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label = torch.ones((4, 64, 8, 8))
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input = torch.FloatTensor(input)
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input.requires_grad = True
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psamask_distribute = PSAMask('distribute', (4, 4))
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# test distribute cpu
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test_output = psamask_distribute(input)
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loss = test_loss(test_output, label)
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loss.backward()
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test_output = test_output.detach().numpy()
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assert np.allclose(test_output, output_distribute)
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assert test_output.shape == output_distribute.shape
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psamask_distribute.cuda()
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input = input.cuda()
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label = label.cuda()
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# test distribute cuda
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test_output = psamask_distribute(input)
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loss = test_loss(test_output, label)
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loss.backward()
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test_output = test_output.detach().cpu().numpy()
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assert np.allclose(test_output, output_distribute)
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assert test_output.shape == output_distribute.shape
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