forked from mindspore-Ecosystem/mindspore
104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
# Copyright 2022 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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This example mainly illustrates the usage of mindspore data processing pipeline.
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"""
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import mindspore as ms
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import mindspore.dataset as ds
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import mindspore.dataset.vision as vision
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import mindspore.dataset.transforms as transforms
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from PIL import Image, ImageDraw
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class MyDataset:
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"""User-defined Loader"""
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def __init__(self):
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self.data = [] # user-defined data
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# draw ellipse
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img = Image.new("RGB", (300, 300), (255, 255, 255))
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draw = ImageDraw.Draw(img)
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draw.ellipse(((0, 0), (100, 100)), fill=(255, 0, 0), outline=(255, 0, 0), width=5)
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img.save("./1.jpg")
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with open("./1.jpg", "rb") as f:
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data = f.read()
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self.data.append(data)
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# draw triangle
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img = Image.new("RGB", (300, 300), (255, 255, 255))
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draw = ImageDraw.Draw(img)
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draw.polygon([(50, 50), (150, 50), (100, 150)], fill=(0, 255, 0), outline=(0, 255, 0))
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img.save("./2.jpg")
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with open("./2.jpg", "rb") as f:
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data = f.read()
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self.data.append(data)
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# draw square
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img = Image.new("RGB", (300, 300), (255, 255, 255))
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draw = ImageDraw.Draw(img)
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draw.rectangle(((200, 200), (300, 300)), fill=(0, 0, 255), outline=(0, 0, 255), width=5)
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img.save("./3.jpg")
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with open("./3.jpg", "rb") as f:
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data = f.read()
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self.data.append(data)
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# draw rectangle
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img = Image.new("RGB", (300, 300), (255, 255, 255))
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draw = ImageDraw.Draw(img)
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draw.rectangle(((50, 50), (250, 150)), fill=(0, 255, 255), outline=(0, 255, 255), width=5)
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img.save("./4.jpg")
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with open("./4.jpg", "rb") as f:
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data = f.read()
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self.data.append(data)
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# label
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self.label = [1, 2, 3, 4]
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def __getitem__(self, index):
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"""get item"""
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return self.data[index], self.label[index]
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def __len__(self):
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"""dataset length"""
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return len(self.data)
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# initialize dataset class
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dataset_generator = MyDataset()
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# load dataset by GeneratorDataset
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dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"])
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# transform the data
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dataset = dataset.map(operations=vision.Decode(), input_columns="data")
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dataset = dataset.map(operations=vision.RandomCrop(size=(250, 250)), input_columns="data")
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dataset = dataset.map(operations=vision.Resize(size=(224, 224)), input_columns="data")
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dataset = dataset.map(operations=vision.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
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std=[0.229 * 255, 0.224 * 255, 0.225 * 255]),
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input_columns="data")
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dataset = dataset.map(operations=vision.HWC2CHW(), input_columns="data")
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# transform the label
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dataset = dataset.map(operations=transforms.TypeCast(ms.int32), input_columns="label")
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# batch
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dataset = dataset.batch(batch_size=2)
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# create iterator
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epochs = 2
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ds_iter = dataset.create_dict_iterator(output_numpy=True, num_epochs=epochs)
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for _ in range(epochs):
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for item in ds_iter:
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print("item: {}".format(item), flush=True)
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