126 lines
4.2 KiB
Python
126 lines
4.2 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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"""
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Testing Pad op in DE
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from util import diff_mse
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as c_vision
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import mindspore.dataset.transforms.vision.py_transforms as py_vision
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from mindspore import log as logger
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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def test_pad_op():
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"""
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Test Pad op
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"""
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logger.info("test_random_color_jitter_op")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = c_vision.Decode()
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pad_op = c_vision.Pad((100, 100, 100, 100))
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ctrans = [decode_op,
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pad_op,
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]
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data1 = data1.map(input_columns=["image"], operations=ctrans)
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# Second dataset
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transforms = [
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py_vision.Decode(),
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py_vision.Pad(100),
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py_vision.ToTensor(),
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]
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transform = py_vision.ComposeOp(transforms)
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(input_columns=["image"], operations=transform())
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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c_image = item1["image"]
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py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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logger.info("shape of c_image: {}".format(c_image.shape))
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logger.info("shape of py_image: {}".format(py_image.shape))
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logger.info("dtype of c_image: {}".format(c_image.dtype))
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logger.info("dtype of py_image: {}".format(py_image.dtype))
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mse = diff_mse(c_image, py_image)
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logger.info("mse is {}".format(mse))
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assert mse < 0.01
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def test_pad_grayscale():
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"""
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Tests that the pad works for grayscale images
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"""
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def channel_swap(image):
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"""
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Py func hack for our pytransforms to work with c transforms
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"""
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return (image.transpose(1, 2, 0) * 255).astype(np.uint8)
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transforms = [
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py_vision.Decode(),
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py_vision.Grayscale(1),
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py_vision.ToTensor(),
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(lambda image: channel_swap(image))
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]
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transform = py_vision.ComposeOp(transforms)
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(input_columns=["image"], operations=transform())
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# if input is grayscale, the output dimensions should be single channel
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pad_gray = c_vision.Pad(100, fill_value=(20, 20, 20))
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data1 = data1.map(input_columns=["image"], operations=pad_gray)
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dataset_shape_1 = []
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for item1 in data1.create_dict_iterator():
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c_image = item1["image"]
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dataset_shape_1.append(c_image.shape)
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# Dataset for comparison
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = c_vision.Decode()
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# we use the same padding logic
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ctrans = [decode_op, pad_gray]
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dataset_shape_2 = []
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data2 = data2.map(input_columns=["image"], operations=ctrans)
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for item2 in data2.create_dict_iterator():
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c_image = item2["image"]
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dataset_shape_2.append(c_image.shape)
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for shape1, shape2 in zip(dataset_shape_1, dataset_shape_2):
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# validate that the first two dimensions are the same
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# we have a little inconsistency here because the third dimension is 1 after py_vision.Grayscale
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assert (shape1[0:1] == shape2[0:1])
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if __name__ == "__main__":
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test_pad_op()
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test_pad_grayscale()
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