forked from mindspore-Ecosystem/mindspore
74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
# Copyright 2019 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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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import ms_function
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from mindspore.ops import operations as P
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context.set_context(device_target="Ascend")
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class Net(nn.Cell):
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def __init__(self, is_grad=False):
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super(Net, self).__init__()
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self.SparseSoftmaxCrossEntropyWithLogits = P.SparseSoftmaxCrossEntropyWithLogits(is_grad=is_grad)
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@ms_function
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def construct(self, features, labels):
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return self.SparseSoftmaxCrossEntropyWithLogits(features, labels)
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def np_sparse_softmax_cross_entropy_with_logits(labels_shape, logits_shape, logits_dtype):
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num_class = logits_shape[1]
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labels = np.random.randint(low=0, high=num_class - 1, size=labels_shape).astype(np.int32)
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logits = np.random.rand(*logits_shape).astype(logits_dtype)
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features = logits
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features_reshape = np.reshape(features, [-1, num_class])
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labels_reshape = np.reshape(labels, [-1])
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batch_dim = 0
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class_dim = 1
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batch_size = features_reshape.shape[batch_dim]
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e = np.exp(features_reshape - np.reshape(np.amax(features_reshape, axis=class_dim), [batch_size, 1]))
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probs = e / np.reshape(np.sum(e, axis=class_dim), [batch_size, 1])
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labels_mat = np.zeros_like(probs).astype(probs.dtype)
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labels_mat[np.arange(batch_size), labels_reshape] = 1.0
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bp = (probs - labels_mat)
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loss = -np.sum(labels_mat * np.log(probs + 1.0e-20), axis=1)
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bp_res = np.reshape(bp, features.shape)
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loss_res = np.reshape(loss, labels.shape)
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loss_res = np.sum(loss_res, axis=0) / loss_res.shape[0]
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return labels, logits, loss_res, bp_res
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def test_net():
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'''Compare Numpy with MS type is float32'''
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labels_shape = (32,)
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logits_shape = [32, 1001]
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labels, logits, loss_np, _ = np_sparse_softmax_cross_entropy_with_logits(labels_shape, logits_shape, np.float32)
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expect = loss_np
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SparseSoftmaxCrossEntropyWithLogits = Net()
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loss_me = SparseSoftmaxCrossEntropyWithLogits(Tensor(logits), Tensor(labels))
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# assert
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assert np.allclose(expect.flatten(), loss_me.asnumpy().flatten(), 0.01, 0.01)
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print(loss_me.asnumpy().flatten())
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print("-------------------------")
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print(expect)
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test_net()
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