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348 lines
12 KiB
348 lines
12 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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import paddle.nn as nn
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class TestLayerPrint(unittest.TestCase):
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def test_layer_str(self):
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module = nn.ELU(0.2)
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self.assertEqual(str(module), 'ELU(alpha=0.2)')
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module = nn.GELU(True)
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self.assertEqual(str(module), 'GELU(approximate=True)')
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module = nn.Hardshrink()
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self.assertEqual(str(module), 'Hardshrink(threshold=0.5)')
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module = nn.Hardswish(name="Hardswish")
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self.assertEqual(str(module), 'Hardswish(name=Hardswish)')
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module = nn.Tanh(name="Tanh")
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self.assertEqual(str(module), 'Tanh(name=Tanh)')
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module = nn.Hardtanh(name="Hardtanh")
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self.assertEqual(
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str(module), 'Hardtanh(min=-1.0, max=1.0, name=Hardtanh)')
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module = nn.PReLU(1, 0.25, name="PReLU")
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self.assertEqual(
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str(module),
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'PReLU(num_parameters=1, init=0.25, dtype=float32, name=PReLU)')
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module = nn.ReLU()
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self.assertEqual(str(module), 'ReLU()')
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module = nn.ReLU6()
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self.assertEqual(str(module), 'ReLU6()')
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module = nn.SELU()
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self.assertEqual(
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str(module),
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'SELU(scale=1.0507009873554805, alpha=1.6732632423543772)')
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module = nn.LeakyReLU()
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self.assertEqual(str(module), 'LeakyReLU(negative_slope=0.01)')
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module = nn.Sigmoid()
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self.assertEqual(str(module), 'Sigmoid()')
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module = nn.Hardsigmoid()
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self.assertEqual(str(module), 'Hardsigmoid()')
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module = nn.Softplus()
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self.assertEqual(str(module), 'Softplus(beta=1, threshold=20)')
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module = nn.Softshrink()
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self.assertEqual(str(module), 'Softshrink(threshold=0.5)')
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module = nn.Softsign()
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self.assertEqual(str(module), 'Softsign()')
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module = nn.Swish()
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self.assertEqual(str(module), 'Swish()')
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module = nn.Tanhshrink()
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self.assertEqual(str(module), 'Tanhshrink()')
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module = nn.ThresholdedReLU()
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self.assertEqual(str(module), 'ThresholdedReLU(threshold=1.0)')
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module = nn.LogSigmoid()
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self.assertEqual(str(module), 'LogSigmoid()')
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module = nn.Softmax()
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self.assertEqual(str(module), 'Softmax(axis=-1)')
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module = nn.LogSoftmax()
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self.assertEqual(str(module), 'LogSoftmax(axis=-1)')
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module = nn.Maxout(groups=2)
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self.assertEqual(str(module), 'Maxout(groups=2, axis=1)')
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module = nn.Linear(2, 4, name='linear')
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self.assertEqual(
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str(module),
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'Linear(in_features=2, out_features=4, dtype=float32, name=linear)')
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module = nn.Upsample(size=[12, 12])
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self.assertEqual(
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str(module),
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'Upsample(size=[12, 12], mode=nearest, align_corners=False, align_mode=0, data_format=NCHW)'
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)
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module = nn.UpsamplingNearest2D(size=[12, 12])
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self.assertEqual(
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str(module), 'UpsamplingNearest2D(size=[12, 12], data_format=NCHW)')
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module = nn.UpsamplingBilinear2D(size=[12, 12])
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self.assertEqual(
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str(module),
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'UpsamplingBilinear2D(size=[12, 12], data_format=NCHW)')
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module = nn.Bilinear(in1_features=5, in2_features=4, out_features=1000)
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self.assertEqual(
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str(module),
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'Bilinear(in1_features=5, in2_features=4, out_features=1000, dtype=float32)'
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)
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module = nn.Dropout(p=0.5)
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self.assertEqual(
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str(module), 'Dropout(p=0.5, axis=None, mode=upscale_in_train)')
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module = nn.Dropout2D(p=0.5)
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self.assertEqual(str(module), 'Dropout2D(p=0.5, data_format=NCHW)')
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module = nn.Dropout3D(p=0.5)
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self.assertEqual(str(module), 'Dropout3D(p=0.5, data_format=NCDHW)')
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module = nn.AlphaDropout(p=0.5)
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self.assertEqual(str(module), 'AlphaDropout(p=0.5)')
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module = nn.Pad1D(padding=[1, 2], mode='constant')
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self.assertEqual(
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str(module),
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'Pad1D(padding=[1, 2], mode=constant, value=0.0, data_format=NCL)')
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module = nn.Pad2D(padding=[1, 0, 1, 2], mode='constant')
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self.assertEqual(
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str(module),
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'Pad2D(padding=[1, 0, 1, 2], mode=constant, value=0.0, data_format=NCHW)'
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)
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module = nn.Pad3D(padding=[1, 0, 1, 2, 0, 0], mode='constant')
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self.assertEqual(
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str(module),
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'Pad3D(padding=[1, 0, 1, 2, 0, 0], mode=constant, value=0.0, data_format=NCDHW)'
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)
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module = nn.CosineSimilarity(axis=0)
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self.assertEqual(str(module), 'CosineSimilarity(axis=0, eps=1e-08)')
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module = nn.Embedding(10, 3, sparse=True)
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self.assertEqual(str(module), 'Embedding(10, 3, sparse=True)')
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module = nn.Conv1D(3, 2, 3)
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self.assertEqual(
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str(module), 'Conv1D(3, 2, kernel_size=[3], data_format=NCL)')
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module = nn.Conv1DTranspose(2, 1, 2)
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self.assertEqual(
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str(module),
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'Conv1DTranspose(2, 1, kernel_size=[2], data_format=NCL)')
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module = nn.Conv2D(4, 6, (3, 3))
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self.assertEqual(
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str(module), 'Conv2D(4, 6, kernel_size=[3, 3], data_format=NCHW)')
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module = nn.Conv2DTranspose(4, 6, (3, 3))
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self.assertEqual(
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str(module),
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'Conv2DTranspose(4, 6, kernel_size=[3, 3], data_format=NCHW)')
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module = nn.Conv3D(4, 6, (3, 3, 3))
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self.assertEqual(
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str(module),
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'Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)')
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module = nn.Conv3DTranspose(4, 6, (3, 3, 3))
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self.assertEqual(
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str(module),
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'Conv3DTranspose(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)')
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module = nn.PairwiseDistance()
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self.assertEqual(str(module), 'PairwiseDistance(p=2.0)')
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module = nn.InstanceNorm1D(2)
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self.assertEqual(
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str(module), 'InstanceNorm1D(num_features=2, epsilon=1e-05)')
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module = nn.InstanceNorm2D(2)
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self.assertEqual(
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str(module), 'InstanceNorm2D(num_features=2, epsilon=1e-05)')
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module = nn.InstanceNorm3D(2)
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self.assertEqual(
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str(module), 'InstanceNorm3D(num_features=2, epsilon=1e-05)')
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module = nn.GroupNorm(num_channels=6, num_groups=6)
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self.assertEqual(
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str(module),
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'GroupNorm(num_groups=6, num_channels=6, epsilon=1e-05)')
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module = nn.LayerNorm([2, 2, 3])
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self.assertEqual(
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str(module), 'LayerNorm(normalized_shape=[2, 2, 3], epsilon=1e-05)')
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module = nn.BatchNorm1D(1)
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self.assertEqual(
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str(module),
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'BatchNorm1D(num_features=1, momentum=0.9, epsilon=1e-05)')
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module = nn.BatchNorm2D(1)
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self.assertEqual(
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str(module),
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'BatchNorm2D(num_features=1, momentum=0.9, epsilon=1e-05)')
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module = nn.BatchNorm3D(1)
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self.assertEqual(
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str(module),
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'BatchNorm3D(num_features=1, momentum=0.9, epsilon=1e-05)')
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module = nn.SyncBatchNorm(2)
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self.assertEqual(
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str(module),
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'SyncBatchNorm(num_features=2, momentum=0.9, epsilon=1e-05)')
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module = nn.LocalResponseNorm(size=5)
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self.assertEqual(
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str(module),
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'LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=1.0)')
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module = nn.AvgPool1D(kernel_size=2, stride=2, padding=0)
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self.assertEqual(
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str(module), 'AvgPool1D(kernel_size=2, stride=2, padding=0)')
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module = nn.AvgPool2D(kernel_size=2, stride=2, padding=0)
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self.assertEqual(
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str(module), 'AvgPool2D(kernel_size=2, stride=2, padding=0)')
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module = nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
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self.assertEqual(
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str(module), 'AvgPool3D(kernel_size=2, stride=2, padding=0)')
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module = nn.MaxPool1D(kernel_size=2, stride=2, padding=0)
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self.assertEqual(
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str(module), 'MaxPool1D(kernel_size=2, stride=2, padding=0)')
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module = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
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self.assertEqual(
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str(module), 'MaxPool2D(kernel_size=2, stride=2, padding=0)')
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module = nn.MaxPool3D(kernel_size=2, stride=2, padding=0)
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self.assertEqual(
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str(module), 'MaxPool3D(kernel_size=2, stride=2, padding=0)')
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module = nn.AdaptiveAvgPool1D(output_size=16)
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self.assertEqual(str(module), 'AdaptiveAvgPool1D(output_size=16)')
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module = nn.AdaptiveAvgPool2D(output_size=3)
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self.assertEqual(str(module), 'AdaptiveAvgPool2D(output_size=3)')
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module = nn.AdaptiveAvgPool3D(output_size=3)
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self.assertEqual(str(module), 'AdaptiveAvgPool3D(output_size=3)')
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module = nn.AdaptiveMaxPool1D(output_size=16, return_mask=True)
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self.assertEqual(
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str(module), 'AdaptiveMaxPool1D(output_size=16, return_mask=True)')
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module = nn.AdaptiveMaxPool2D(output_size=3, return_mask=True)
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self.assertEqual(
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str(module), 'AdaptiveMaxPool2D(output_size=3, return_mask=True)')
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module = nn.AdaptiveMaxPool3D(output_size=3, return_mask=True)
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self.assertEqual(
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str(module), 'AdaptiveMaxPool3D(output_size=3, return_mask=True)')
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module = nn.SimpleRNNCell(16, 32)
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self.assertEqual(str(module), 'SimpleRNNCell(16, 32)')
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module = nn.LSTMCell(16, 32)
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self.assertEqual(str(module), 'LSTMCell(16, 32)')
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module = nn.GRUCell(16, 32)
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self.assertEqual(str(module), 'GRUCell(16, 32)')
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module = nn.PixelShuffle(3)
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self.assertEqual(str(module), 'PixelShuffle(upscale_factor=3)')
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module = nn.SimpleRNN(16, 32, 2)
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self.assertEqual(
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str(module),
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'SimpleRNN(16, 32, num_layers=2\n (0): RNN(\n (cell): SimpleRNNCell(16, 32)\n )\n (1): RNN(\n (cell): SimpleRNNCell(32, 32)\n )\n)'
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)
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module = nn.LSTM(16, 32, 2)
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self.assertEqual(
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str(module),
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'LSTM(16, 32, num_layers=2\n (0): RNN(\n (cell): LSTMCell(16, 32)\n )\n (1): RNN(\n (cell): LSTMCell(32, 32)\n )\n)'
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)
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module = nn.GRU(16, 32, 2)
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self.assertEqual(
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str(module),
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'GRU(16, 32, num_layers=2\n (0): RNN(\n (cell): GRUCell(16, 32)\n )\n (1): RNN(\n (cell): GRUCell(32, 32)\n )\n)'
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)
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module1 = nn.Sequential(
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('conv1', nn.Conv2D(1, 20, 5)), ('relu1', nn.ReLU()),
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('conv2', nn.Conv2D(20, 64, 5)), ('relu2', nn.ReLU()))
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self.assertEqual(
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str(module1),
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'Sequential(\n '\
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'(conv1): Conv2D(1, 20, kernel_size=[5, 5], data_format=NCHW)\n '\
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'(relu1): ReLU()\n '\
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'(conv2): Conv2D(20, 64, kernel_size=[5, 5], data_format=NCHW)\n '\
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'(relu2): ReLU()\n)'
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)
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module2 = nn.Sequential(
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nn.Conv3DTranspose(4, 6, (3, 3, 3)),
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nn.AvgPool3D(
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kernel_size=2, stride=2, padding=0),
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nn.Tanh(name="Tanh"),
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module1,
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nn.Conv3D(4, 6, (3, 3, 3)),
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nn.MaxPool3D(
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kernel_size=2, stride=2, padding=0),
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nn.GELU(True))
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self.assertEqual(
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str(module2),
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'Sequential(\n '\
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'(0): Conv3DTranspose(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)\n '\
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'(1): AvgPool3D(kernel_size=2, stride=2, padding=0)\n '\
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'(2): Tanh(name=Tanh)\n '\
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'(3): Sequential(\n (conv1): Conv2D(1, 20, kernel_size=[5, 5], data_format=NCHW)\n (relu1): ReLU()\n'\
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' (conv2): Conv2D(20, 64, kernel_size=[5, 5], data_format=NCHW)\n (relu2): ReLU()\n )\n '\
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'(4): Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)\n '\
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'(5): MaxPool3D(kernel_size=2, stride=2, padding=0)\n '\
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'(6): GELU(approximate=True)\n)'
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)
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if __name__ == '__main__':
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unittest.main()
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