forked from mindspore-Ecosystem/mindspore
116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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""" test nn.Dense """
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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# pylint: disable=E1123
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def test_dense_defaultbias_noactivation():
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weight = Tensor(np.array([[0.1, 0.3, 0.4], [0.1, 0.3, 0.4]], dtype=np.float32))
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dense = nn.Dense(3, 2, weight)
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assert dense.activation is None
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input_data = Tensor(np.random.randint(0, 255, [1, 3]).astype(np.float32))
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output = dense(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0], (np.float32, np.float64))
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def test_dense_defaultweight():
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bias = Tensor(np.array([0.5, 0.3], dtype=np.float32))
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dense = nn.Dense(3, 2, bias_init=bias)
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# batch_size 1 && 3-channel RGB
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input_data = Tensor(np.random.randint(0, 255, [1, 3]).astype(np.float32))
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output = dense(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0], (np.float32, np.float64))
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def test_dense_bias():
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weight = Tensor(np.array([[0.1, 0.3, 0.6], [0.4, 0.5, 0.2]], dtype=np.float32))
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bias = Tensor(np.array([0.5, 0.3], dtype=np.float32))
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dense = nn.Dense(3, 2, weight, bias)
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input_data = Tensor(np.random.randint(0, 255, [2, 3]).astype(np.float32))
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output = dense(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0], (np.float32, np.float64))
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def test_dense_nobias():
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weight = Tensor(np.array([[0.1, 0.3, 0.6], [0.4, 0.5, 0.2]], dtype=np.float32))
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dense = nn.Dense(3, 2, weight, has_bias=False)
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input_data = Tensor(np.random.randint(0, 255, [2, 3]).astype(np.float32))
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output = dense(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0], (np.float32, np.float64))
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def test_dense_none():
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with pytest.raises(TypeError):
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nn.Dense(3, 2, None, None)
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def test_dense_str_activation():
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dense = nn.Dense(1, 1, activation='relu')
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assert isinstance(dense.activation, nn.ReLU)
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input_data = Tensor(np.random.randint(0, 255, [1, 1]).astype(np.float32))
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output = dense(input_data)
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0], np.float32)
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def test_dense_weight_error():
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dim_error = Tensor(np.array([[[0.1], [0.3], [0.6]], [[0.4], [0.5], [0.2]]]))
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with pytest.raises(ValueError):
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nn.Dense(3, 2, dim_error)
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shape_error = Tensor(np.array([[0.1, 0.3, 0.6], [0.4, 0.5, 0.2]]))
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with pytest.raises(ValueError):
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nn.Dense(2, 2, shape_error)
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with pytest.raises(ValueError):
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nn.Dense(3, 3, shape_error)
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def test_dense_bias_error():
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dim_error = Tensor(np.array([[0.5, 0.3]]))
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with pytest.raises(ValueError):
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nn.Dense(3, 2, bias_init=dim_error)
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shape_error = Tensor(np.array([0.5, 0.3, 0.4]))
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with pytest.raises(ValueError):
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nn.Dense(3, 2, bias_init=shape_error)
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def test_dense_dtype_error():
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with pytest.raises(TypeError):
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nn.Dense(3, 2, dtype=3)
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def test_dense_channels_error():
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with pytest.raises(ValueError):
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nn.Dense(3, 0)
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with pytest.raises(ValueError):
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nn.Dense(-1, 2)
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