mirror of https://github.com/Jittor/Jittor
134 lines
5.3 KiB
Python
134 lines
5.3 KiB
Python
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# ***************************************************************
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# Copyright (c) 2020 Jittor. All Rights Reserved.
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# Maintainers:
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# Wenyang Zhou <576825820@qq.com>
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# Dun Liang <randonlang@gmail.com>.
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#
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# This file is subject to the terms and conditions defined in
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# file 'LICENSE.txt', which is part of this source code package.
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# ***************************************************************
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import unittest
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import jittor as jt
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import numpy as np
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import jittor.nn as jnn
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skip_this_test = False
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try:
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jt.dirty_fix_pytorch_runtime_error()
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import torch
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import torch.nn as tnn
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except:
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torch = None
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tnn = None
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skip_this_test = True
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def check_equal_with_istrain(arr, j_layer, p_layer, is_train=True, has_running=True, threshold=1e-5):
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jittor_arr = jt.array(arr)
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pytorch_arr = torch.Tensor(arr)
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if has_running:
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if is_train:
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assert np.allclose(p_layer.running_mean.detach().numpy(), j_layer.running_mean.numpy(), threshold)
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else:
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assert np.allclose(p_layer.layer.running_mean.detach().numpy(), j_layer.running_mean.numpy(), threshold)
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jittor_result = j_layer(jittor_arr)
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pytorch_result = p_layer(pytorch_arr)
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if has_running:
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if is_train:
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assert np.allclose(p_layer.running_mean.detach().numpy(), j_layer.running_mean.numpy(), threshold)
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else:
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assert np.allclose(p_layer.layer.running_mean.detach().numpy(), j_layer.running_mean.numpy(), threshold)
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assert np.allclose(pytorch_result.detach().numpy(), jittor_result.numpy(), 1e-2, threshold), \
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( np.abs(pytorch_result.detach().numpy() - jittor_result.numpy()).max() )
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def check_equal_without_istrain(arr, j_layer, p_layer, threshold=1e-5):
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jittor_arr = jt.array(arr)
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pytorch_arr = torch.Tensor(arr)
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jittor_result = j_layer(jittor_arr)
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pytorch_result = p_layer(pytorch_arr)
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assert np.allclose(pytorch_result.detach().numpy(), jittor_result.numpy(), threshold)
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@unittest.skipIf(skip_this_test, "No Torch found")
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class TestBatchNorm(unittest.TestCase):
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def test_batchnorm(self):
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# ***************************************************************
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# Test BatchNorm Layer
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# ***************************************************************
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arr = np.random.randn(16,10,224,224)
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check_equal_with_istrain(arr, jnn.BatchNorm(10, is_train=True), tnn.BatchNorm2d(10))
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class Model(tnn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.layer = tnn.BatchNorm2d(10)
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def forward(self, x):
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return self.layer(x)
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model = Model()
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model.eval()
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check_equal_with_istrain(arr, jnn.BatchNorm(10, is_train=False), model, False)
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# ***************************************************************
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# Test InstanceNorm2d Layer
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# ***************************************************************
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arr = np.random.randn(16,10,224,224)
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check_equal_without_istrain(arr, jnn.InstanceNorm2d(10, is_train=True), tnn.InstanceNorm2d(10))
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class Model(tnn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.layer = tnn.InstanceNorm2d(10)
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def forward(self, x):
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return self.layer(x)
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model = Model()
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model.eval()
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check_equal_without_istrain(arr, jnn.InstanceNorm2d(10, is_train=False), model)
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# ***************************************************************
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# Test BatchNorm1d Layer
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# ***************************************************************
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arr = np.random.randn(16,10)
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check_equal_with_istrain(arr, jnn.BatchNorm1d(10, is_train=True), tnn.BatchNorm1d(10), 1e-3)
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class Model(tnn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.layer = tnn.BatchNorm1d(10)
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def forward(self, x):
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return self.layer(x)
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model = Model()
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model.eval()
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check_equal_with_istrain(arr, jnn.BatchNorm1d(10, is_train=False), model, False)
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# ***************************************************************
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# Test GroupNorm Layer
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# ***************************************************************
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arr = np.random.randn(16,10,224,224)
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class Model(tnn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.layer = tnn.GroupNorm(2, 10)
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def forward(self, x):
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return self.layer(x)
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model = Model()
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model.eval()
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check_equal_with_istrain(arr, jnn.GroupNorm(2, 10, is_train=False), model, False, False)
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# ***************************************************************
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# Test LayerNorm Layer
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# ***************************************************************
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arr = np.random.randn(16,10,224,224)
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class Model(tnn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.layer = tnn.LayerNorm(224)
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def forward(self, x):
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return self.layer(x)
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model = Model()
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model.eval()
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check_equal_with_istrain(arr, jnn.LayerNorm(224), model, False, False)
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if __name__ == "__main__":
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unittest.main() |