forked from mindspore-Ecosystem/mindspore
53 lines
2.3 KiB
Python
53 lines
2.3 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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"""dataset base."""
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import os
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from mindspore import dataset as ds
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from mindspore.common import dtype as mstype
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from mindspore.dataset.transforms import c_transforms as C
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from mindspore.dataset.vision import Inter
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from mindspore.dataset.vision import c_transforms as CV
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def create_mnist_dataset(mode='train', num_samples=2, batch_size=2):
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"""create dataset for train or test"""
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mnist_path = '/home/workspace/mindspore_dataset/mnist'
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num_parallel_workers = 1
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# define dataset
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mnist_ds = ds.MnistDataset(os.path.join(mnist_path, mode), num_samples=num_samples, shuffle=False)
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resize_height, resize_width = 32, 32
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# define map operations
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resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode
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rescale_nml_op = CV.Rescale(1 / 0.3081, -1 * 0.1307 / 0.3081)
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rescale_op = CV.Rescale(1.0 / 255.0, shift=0.0)
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hwc2chw_op = CV.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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# apply map operations on images
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mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
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# apply DatasetOps
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mnist_ds = mnist_ds.batch(batch_size=batch_size, drop_remainder=True)
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return mnist_ds
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