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https://github.com/open-mmlab/mmcv.git
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869dbf1bf2
* add bias_act * support bias_act * support filtered_lrelu * support filtered_lrelu and upfirdn2d * support conv2d_gradfix and fix filtered_lrelu * fix lint * fix lint * fix c++ lint * fix part comments * fix lint * rm redundant header * fix upgrade pip * fix as comment * fix c++ lint * fix ci * fix-ut * fix as comments * add grad check * remove redundant template * Update mmcv/ops/bias_act.py Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com> * add typehint * fix as comment: * complete type hints * fix lint * add test for conv_gradfix * add test for conv_gradfix * fix lint * modify licenses and ops.md * add zh op md * add torch version policy for conv2d_gradfix * fix lint * fix as comments * rename impl * rm redudant function and add ut * fix as comment * fix lint * fix lint * fix as comments * fix lint * fix ut * fix as comment * fix as comment * fix as comment --------- Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>
145 lines
5.3 KiB
Python
145 lines
5.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmcv.ops import bias_act
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from mmcv.ops.bias_act import EasyDict
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_USING_PARROTS = True
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try:
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from parrots.autograd import gradcheck
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except ImportError:
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from torch.autograd import gradcheck, gradgradcheck
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_USING_PARROTS = False
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class TestBiasAct:
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@classmethod
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def setup_class(cls):
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cls.input_tensor = torch.randn((1, 3), requires_grad=True)
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cls.bias = torch.randn(3, requires_grad=True)
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def test_bias_act_cpu(self):
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out = bias_act(self.input_tensor, self.bias)
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assert out.shape == (1, 3)
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# test with different dim
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input_tensor = torch.randn((1, 1, 3), requires_grad=True)
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bias = torch.randn(3, requires_grad=True)
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out = bias_act(input_tensor, bias, dim=2)
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assert out.shape == (1, 1, 3)
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# test with different act
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out = bias_act(self.input_tensor, self.bias, act='relu')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor, self.bias, act='lrelu')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor, self.bias, act='tanh')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor, self.bias, act='sigmoid')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor, self.bias, act='elu')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor, self.bias, act='selu')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor, self.bias, act='softplus')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor, self.bias, act='swish')
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assert out.shape == (1, 3)
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# test with different alpha
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out = bias_act(self.input_tensor, self.bias, act='lrelu', alpha=0.1)
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assert out.shape == (1, 3)
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# test with different gain
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out1 = bias_act(self.input_tensor, self.bias, act='lrelu', gain=0.2)
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out2 = bias_act(self.input_tensor, self.bias, act='lrelu', gain=0.1)
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assert torch.allclose(out1, out2 * 2)
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# test with different clamp
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out1 = bias_act(self.input_tensor, self.bias, act='lrelu', clamp=0.5)
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out2 = bias_act(self.input_tensor, self.bias, act='lrelu', clamp=0.2)
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assert out1.max() <= 0.5
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assert out2.max() <= 0.5
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@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
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def test_bias_act_cuda(self):
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if _USING_PARROTS:
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gradcheck(
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bias_act, (self.input_tensor.cuda(), self.bias.cuda()),
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delta=1e-4,
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pt_atol=1e-3)
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else:
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gradcheck(
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bias_act, (self.input_tensor.cuda(), self.bias.cuda()),
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eps=1e-4,
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atol=1e-3)
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gradgradcheck(
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bias_act, (self.input_tensor.cuda(), self.bias.cuda()),
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eps=1e-4,
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atol=1e-3)
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out = bias_act(self.input_tensor.cuda(), self.bias.cuda())
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assert out.shape == (1, 3)
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# test with different dim
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input_tensor = torch.randn((1, 1, 3), requires_grad=True).cuda()
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bias = torch.randn(3, requires_grad=True).cuda()
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out = bias_act(input_tensor, bias, dim=2)
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assert out.shape == (1, 1, 3)
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# test with different act
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out = bias_act(self.input_tensor.cuda(), self.bias.cuda(), act='relu')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor.cuda(), self.bias.cuda(), act='lrelu')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor.cuda(), self.bias.cuda(), act='tanh')
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assert out.shape == (1, 3)
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out = bias_act(
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self.input_tensor.cuda(), self.bias.cuda(), act='sigmoid')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor.cuda(), self.bias.cuda(), act='elu')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor.cuda(), self.bias.cuda(), act='selu')
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assert out.shape == (1, 3)
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out = bias_act(
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self.input_tensor.cuda(), self.bias.cuda(), act='softplus')
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assert out.shape == (1, 3)
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out = bias_act(self.input_tensor.cuda(), self.bias.cuda(), act='swish')
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assert out.shape == (1, 3)
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# test with different alpha
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out = bias_act(
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self.input_tensor.cuda(), self.bias.cuda(), act='lrelu', alpha=0.1)
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assert out.shape == (1, 3)
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# test with different gain
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out1 = bias_act(
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self.input_tensor.cuda(), self.bias.cuda(), act='lrelu', gain=0.2)
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out2 = bias_act(
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self.input_tensor.cuda(), self.bias.cuda(), act='lrelu', gain=0.1)
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assert torch.allclose(out1, out2 * 2)
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# test with different clamp
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out1 = bias_act(
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self.input_tensor.cuda(), self.bias.cuda(), act='lrelu', clamp=0.5)
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out2 = bias_act(
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self.input_tensor.cuda(), self.bias.cuda(), act='lrelu', clamp=0.2)
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assert out1.max() <= 0.5
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assert out2.max() <= 0.5
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def test_easy_dict(self):
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easy_dict = EasyDict(
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func=lambda x, **_: x,
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def_alpha=0,
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def_gain=1,
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cuda_idx=1,
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ref='',
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has_2nd_grad=False)
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_ = easy_dict.def_alpha
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easy_dict.def_alpha = 1
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del easy_dict.def_alpha
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