forked from mindspore-Ecosystem/mindspore
627 lines
22 KiB
Python
627 lines
22 KiB
Python
# Copyright 2020-2022 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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"""Test Eager Support for Vision ops in Dataset"""
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import cv2
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import numpy as np
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from PIL import Image
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import pytest
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from mindspore import log as logger
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from mindspore import Tensor
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import mindspore.dataset.transforms as transforms
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import mindspore.dataset.vision as vision
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def test_eager_decode_c():
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"""
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Feature: Decode op
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Description: Test eager support for Decode Cpp implementation
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Expectation: Output image size from op is correct
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"""
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img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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img = vision.Decode()(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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assert img.shape == (2268, 4032, 3)
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fp = open("../data/dataset/apple.jpg", "rb")
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img2 = fp.read()
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img2 = vision.Decode()(img2)
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logger.info("Image.type: {}, Image.shape: {}".format(
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type(img2), img2.shape))
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assert img2.shape == (2268, 4032, 3)
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def test_eager_decode_py():
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"""
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Feature: Decode op
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Description: Test eager support for Decode Python implementation
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Expectation: Output image size from op is correct
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"""
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img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Decode(to_pil=True)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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assert img.size == (4032, 2268)
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fp = open("../data/dataset/apple.jpg", "rb")
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img2 = fp.read()
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img2 = vision.Decode(to_pil=True)(img2)
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logger.info("Image.type: {}, Image.shape: {}".format(
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type(img2), img2.size))
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assert img2.size == (4032, 2268)
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def test_eager_resize_c():
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"""
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Feature: Resize op
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Description: Test eager support for Resize C++ implementation
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Expectation: Output image size from op is correct
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"""
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img = cv2.imread("../data/dataset/apple.jpg")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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img = vision.Resize(size=(64, 32))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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assert img.shape == (64, 32, 3)
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def test_eager_resize_py():
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"""
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Feature: Resize op
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Description: Test eager support for Resize Python implementation
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Expectation: Output image info from op is correct
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Resize(size=(96, 64))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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assert img.size == (64, 96)
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def test_eager_rescale():
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"""
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Feature: Rescale op
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Description: Test eager support for Rescale op
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Expectation: Output image info from op is correct
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"""
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img = cv2.imread("../data/dataset/apple.jpg")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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pixel = img[0][0][0]
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rescale_factor = 0.5
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img = vision.Rescale(rescale=rescale_factor, shift=0)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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pixel_rescaled = img[0][0][0]
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assert pixel * rescale_factor == pixel_rescaled
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def test_eager_normalize_hwc():
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"""
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Feature: Normalize op
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Description: Test eager support for Normalize with HWC shape
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Expectation: Output image info from op is correct
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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pixel = img.getpixel((0, 0))[0]
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mean_vec = [100, 100, 100]
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std_vec = [2, 2, 2]
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img = vision.Normalize(mean=mean_vec, std=std_vec, is_hwc=True)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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pixel_normalized = img[0][0][0]
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assert (pixel - mean_vec[0]) / std_vec[0] == pixel_normalized
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def test_eager_normalize_chw():
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"""
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Feature: Normalize op
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Description: Test eager support for Normalize with CHW shape
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Expectation: Output image info from op is correct
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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pixel = img.getpixel((0, 0))[0]
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img = vision.ToTensor()(img)
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mean_vec = [.100, .100, .100]
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std_vec = [.2, .2, .2]
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img = vision.Normalize(mean=mean_vec, std=std_vec, is_hwc=False)(img)
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pixel_normalized = img[0][0][0]
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assert (pixel / 255 - mean_vec[0]) / \
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std_vec[0] == pytest.approx(pixel_normalized, 0.0001)
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def test_eager_resize_totensor_normalize_py():
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"""
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Feature: Eager Support
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Description: Test eager support for this sequence of Python ops: Resize, ToTensor and Normalize
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Expectation: Output image info from op is correct
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Resize(size=(96, 64))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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assert img.size == (64, 96)
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pixel = img.getpixel((0, 0))[0]
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img = vision.ToTensor()(img)
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mean_vec = [.100, .100, .100]
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std_vec = [.2, .2, .2]
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img = vision.Normalize(mean=mean_vec, std=std_vec, is_hwc=False)(img)
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pixel_normalized = img[0][0][0]
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assert img.size == 64 * 96 * 3
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assert (pixel / 255 - mean_vec[0]) / std_vec[0] == pytest.approx(pixel_normalized, 0.0001)
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def test_eager_compose_py():
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"""
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Feature: Eager Support
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Description: Test eager support for this sequence of Python ops: Resize, Compose with ToTensor and Normalize
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Expectation: Output image info from op is correct
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Resize(size=(96, 64))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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assert img.size == (64, 96)
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pixel = img.getpixel((0, 0))[0]
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mean_vec = [.100, .100, .100]
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std_vec = [.2, .2, .2]
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transform = transforms.Compose([
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vision.ToTensor(),
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vision.Normalize(mean=mean_vec, std=std_vec, is_hwc=False)])
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# Convert to NumPy array
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img = np.array(img)
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output_size = 64 * 96 * 3
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assert img.size == output_size
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# Use Compose to apply transformation with ToTensor and Normalize
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# Note: Output of Compose is a NumPy array
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img = transform(img)
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assert img.size == output_size
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assert isinstance(img, np.ndarray)
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pixel_normalized = img[0][0][0]
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assert (pixel / 255 - mean_vec[0]) / std_vec[0] == pytest.approx(pixel_normalized, 0.0001)
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def test_eager_hwc2chw():
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"""
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Feature: HWC2CHW op
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Description: Test eager support for HWC2CHW op
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Expectation: Output image size from op is correct
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"""
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img = cv2.imread("../data/dataset/apple.jpg")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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channel = img.shape
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img = vision.HWC2CHW()(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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channel_swapped = img.shape
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assert channel == (channel_swapped[1],
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channel_swapped[2], channel_swapped[0])
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def test_eager_pad_c():
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"""
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Feature: Pad op
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Description: Test eager support for Pad Cpp implementation
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Expectation: Output image size info from op is correct
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"""
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img = cv2.imread("../data/dataset/apple.jpg")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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img = vision.Resize(size=(32, 32))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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shape_org = img.shape
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pad = 4
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img = vision.Pad(padding=pad)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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shape_padded = img.shape
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assert shape_padded == (
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shape_org[0] + 2 * pad, shape_org[1] + 2 * pad, shape_org[2])
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def test_eager_pad_py():
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"""
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Feature: Pad op
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Description: Test eager support for Pad Python implementation
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Expectation: Output image size info from op is correct
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Resize(size=(32, 32))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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size = img.size
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pad = 4
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img = vision.Pad(padding=pad)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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size_padded = img.size
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assert size_padded == (size[0] + 2 * pad, size[1] + 2 * pad)
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def test_eager_cutout_hwc_pil():
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"""
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Feature: CutOut op
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Description: Test eager support for CutOut with HWC shape and PIL input
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Expectation: Output image size info from op is correct
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Resize(size=(32, 32))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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size = img.size
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img = vision.CutOut(2, 4)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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size_cutout = img.shape
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assert (size_cutout[0], size_cutout[1]) == size
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def test_eager_cutout_chw_pil():
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"""
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Feature: CutOut op
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Description: Test eager support for CutOut with CHW shape and PIL input
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Expectation: Receive non-None output image from op
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"""
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Resize(size=(32, 32))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.ToTensor()(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.CutOut(2, 4, is_hwc=False)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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assert img is not None
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def test_eager_cutout_hwc_cv():
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"""
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Feature: CutOut op
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Description: Test eager support for CutOut with HWC shape and CV input
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Expectation: Output image size info from op is correct
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"""
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img = cv2.imread("../data/dataset/apple.jpg")
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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img = vision.Resize(size=(32, 32))(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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size = img.size
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img = vision.CutOut(2, 4)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
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size_cutout = img.size
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assert size_cutout == size
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def test_eager_exceptions_decode():
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"""
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Feature: Decode op
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Description: Exception eager support test for Decode
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Expectation: Error input image is detected
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"""
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with pytest.raises(TypeError) as error_info:
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img = "../data/dataset/apple.jpg"
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_ = vision.Decode()(img)
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assert "The type of the encoded image should be <class 'numpy.ndarray'>" in str(error_info.value)
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with pytest.raises(TypeError) as error_info:
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img = np.array(["a", "b", "c"])
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_ = vision.Decode()(img)
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assert "The data type of the encoded image can not be <class 'numpy.str_'>" in str(error_info.value)
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def test_eager_exceptions_resize():
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"""
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Feature: Resize op
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Description: Exception eager support test for Resize Python implementation
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Expectation: Error input image is detected
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"""
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try:
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img = cv2.imread("../data/dataset/apple.jpg")
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_ = vision.Resize(size=(-32, 32))(img)
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assert False
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except ValueError as e:
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assert "not within the required interval" in str(e)
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def test_eager_exceptions_normalize():
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"""
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Feature: Normalize op
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Description: Exception eager support test for Normalize Python implementation
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Expectation: Error input image is detected
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"""
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try:
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img = Image.open("../data/dataset/apple.jpg").convert("RGB")
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mean_vec = [.100, .100, .100]
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std_vec = [.2, .2, .2]
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_ = vision.Normalize(mean=mean_vec, std=std_vec, is_hwc=False)(img)
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assert False
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except RuntimeError as e:
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assert "Normalize: number of channels does not match the size of mean and std vectors" in str(
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e)
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def test_eager_exceptions_pad():
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"""
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Feature: Pad
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Description: Test Pad with invalid input of string
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Expectation: Raise TypeError
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"""
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try:
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img = "../data/dataset/apple.jpg"
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_ = vision.Pad(padding=4)(img)
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except TypeError as e:
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assert "Input should be NumPy or PIL image, got <class 'str'>." in str(e)
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def test_eager_invalid_image_randomadjustsharpness():
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"""
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Feature: RandomAdjustSharpness op
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Description: Exception eager support test for RandomAdjustSharpness op with invalid image input type
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Expectation: Error input image is detected
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"""
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def test_config(my_input, error_type, error_msg):
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with pytest.raises(error_type) as error_info:
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_ = vision.RandomAdjustSharpness(degree=0.5, prob=1)(my_input)
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assert error_msg in str(error_info.value)
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my_input = np.random.rand(128, 128, 3)
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test_config((10,), TypeError, "Input should be NumPy or PIL image, got <class 'tuple'>.")
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test_config(10, TypeError, "Input should be NumPy or PIL image, got <class 'int'>.")
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test_config(Tensor(my_input), TypeError,
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"Input should be NumPy or PIL image, got <class 'mindspore.common.tensor.Tensor'>.")
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def test_eager_invalid_image_hwc2chw():
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"""
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Feature: HWC2CHW op
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Description: Exception eager support test for HWC2CHW op with invalid image input type
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Expectation: Error input image is detected
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"""
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def test_config(my_input, error_type, error_msg):
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with pytest.raises(error_type) as error_info:
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_ = vision.HWC2CHW()(my_input)
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assert error_msg in str(error_info.value)
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my_input = np.random.randn(64, 32, 3).astype(np.int32).tolist()
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test_config(my_input, TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
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test_config(Tensor(my_input), TypeError,
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"Input should be NumPy or PIL image, got <class 'mindspore.common.tensor.Tensor'>.")
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def test_eager_invalid_image_invert():
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"""
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Feature: Invert op
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Description: Exception eager support test for Invert op with invalid image type
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Expectation: Error input image is detected
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"""
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def test_config(my_input, error_type, error_msg):
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with pytest.raises(error_type) as error_info:
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_ = vision.Invert()(my_input)
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assert error_msg in str(error_info.value)
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test_config((10,), TypeError, "Input should be NumPy or PIL image, got <class 'tuple'>.")
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test_config(10, TypeError, "Input should be NumPy or PIL image, got <class 'int'>.")
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def test_eager_invalid_image_pad():
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"""
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Feature: Pad op
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Description: Exception eager support test for Pad op with invalid image input type
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Expectation: Error input image is detected
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"""
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def test_config(my_input, error_type, error_msg):
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with pytest.raises(error_type) as error_info:
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_ = vision.Pad(padding=10)(my_input)
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assert error_msg in str(error_info.value)
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my_input = np.random.randn(64, 32, 3).astype(np.int32).tolist()
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test_config(my_input, TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
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test_config(Tensor(my_input), TypeError,
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"Input should be NumPy or PIL image, got <class 'mindspore.common.tensor.Tensor'>.")
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|
|
|
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def test_eager_invalid_image_randomcrop():
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"""
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Feature: RandomCrop op
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Description: Exception eager support test for RandomCrop op with invalid image input type
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Expectation: Error input image is detected
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|
"""
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|
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def test_config(my_input, error_type, error_msg):
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with pytest.raises(error_type) as error_info:
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_ = vision.RandomCrop(size=200)(my_input)
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assert error_msg in str(error_info.value)
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|
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my_input = np.random.randint(0, 255, (987, 654, 3)).astype(np.uint8).tolist()
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test_config(my_input, TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
|
|
|
|
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|
def test_eager_invalid_image_randomhorizontalflip():
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|
"""
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|
Feature: RandomHorizontalFlip op
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|
Description: Exception eager support test for RandomHorizontalFlip op with invalid image input type
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|
Expectation: Error input image is detected
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|
"""
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|
|
|
def test_config(my_input, error_type, error_msg):
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|
with pytest.raises(error_type) as error_info:
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_ = vision.RandomHorizontalFlip(prob=1)(my_input)
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|
assert error_msg in str(error_info.value)
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|
|
|
img = cv2.imread("../data/dataset/apple.jpg")
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my_input = img.tolist()
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test_config(my_input, TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
|
|
|
|
|
|
def test_eager_invalid_image_randomsolarize():
|
|
"""
|
|
Feature: RandomSolarize op
|
|
Description: Exception eager support test for RandomSolarize op with invalid image input type
|
|
Expectation: Error input image is detected
|
|
"""
|
|
|
|
def test_config(my_input, error_type, error_msg):
|
|
with pytest.raises(error_type) as error_info:
|
|
_ = vision.RandomSolarize(threshold=(0, 120))(my_input)
|
|
assert error_msg in str(error_info.value)
|
|
|
|
my_input = np.random.randint(0, 255, (500, 600, 3)).astype(np.uint8).tolist()
|
|
test_config(my_input, TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
|
|
|
|
|
|
def test_eager_invalid_image_cutout():
|
|
"""
|
|
Feature: CutOut op
|
|
Description: Exception eager support test for CutOut op with invalid image input type
|
|
Expectation: Error input image is detected
|
|
"""
|
|
|
|
def test_config(my_input, error_type, error_msg):
|
|
with pytest.raises(error_type) as error_info:
|
|
_ = vision.CutOut(length=120, num_patches=1)(my_input)
|
|
assert error_msg in str(error_info.value)
|
|
|
|
my_input = np.random.randn(60, 50)
|
|
test_config(my_input, RuntimeError, "CutOut: shape is invalid.")
|
|
|
|
test_config(1, TypeError, "Input should be NumPy or PIL image, got <class 'int'>.")
|
|
test_config(1.0, TypeError, "Input should be NumPy or PIL image, got <class 'float'>.")
|
|
test_config((10, 20), TypeError, "Input should be NumPy or PIL image, got <class 'tuple'>.")
|
|
test_config([10, 20, 30], TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
|
|
|
|
|
|
def test_eager_invalid_image_randomcolor():
|
|
"""
|
|
Feature: RandomColor op
|
|
Description: Exception eager support test for RandomColor op with invalid image input type
|
|
Expectation: Error input image is detected
|
|
"""
|
|
|
|
def test_config(my_input, error_type, error_msg):
|
|
with pytest.raises(error_type) as error_info:
|
|
_ = vision.RandomColor(degrees=(0.2, 0.3))(my_input)
|
|
assert error_msg in str(error_info.value)
|
|
|
|
my_input = np.random.randn(1280, 1280, 3)
|
|
|
|
test_config(None, TypeError, "Input should be NumPy or PIL image, got <class 'NoneType'>.")
|
|
test_config(1, TypeError, "Input should be NumPy or PIL image, got <class 'int'>.")
|
|
test_config(1.0, TypeError, "Input should be NumPy or PIL image, got <class 'float'>.")
|
|
test_config((10, 20), TypeError, "Input should be NumPy or PIL image, got <class 'tuple'>.")
|
|
test_config([10, 20, 30], TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
|
|
test_config(Tensor(my_input), TypeError,
|
|
"Input should be NumPy or PIL image, got <class 'mindspore.common.tensor.Tensor'>.")
|
|
|
|
|
|
def test_eager_invalid_image_randomsharpness():
|
|
"""
|
|
Feature: RandomSharpness op
|
|
Description: Exception eager support test for RandomSharpness op with invalid image input type
|
|
Expectation: Error input image is detected
|
|
"""
|
|
|
|
def test_config(my_input, error_type, error_msg):
|
|
with pytest.raises(error_type) as error_info:
|
|
_ = vision.RandomSharpness(degrees=(0.2, 0.3))(my_input)
|
|
assert error_msg in str(error_info.value)
|
|
|
|
my_input = np.random.randn(1280, 1280, 3)
|
|
|
|
test_config(None, TypeError, "Input should be NumPy or PIL image, got <class 'NoneType'>.")
|
|
test_config(1, TypeError, "Input should be NumPy or PIL image, got <class 'int'>.")
|
|
test_config(1.0, TypeError, "Input should be NumPy or PIL image, got <class 'float'>.")
|
|
test_config((10, 20), TypeError, "Input should be NumPy or PIL image, got <class 'tuple'>.")
|
|
test_config([10, 20, 30], TypeError, "Input should be NumPy or PIL image, got <class 'list'>.")
|
|
test_config(Tensor(my_input), TypeError,
|
|
"Input should be NumPy or PIL image, got <class 'mindspore.common.tensor.Tensor'>.")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_eager_decode_c()
|
|
test_eager_decode_py()
|
|
test_eager_resize_c()
|
|
test_eager_resize_py()
|
|
test_eager_rescale()
|
|
test_eager_normalize_hwc()
|
|
test_eager_normalize_chw()
|
|
test_eager_resize_totensor_normalize_py()
|
|
test_eager_compose_py()
|
|
test_eager_hwc2chw()
|
|
test_eager_pad_c()
|
|
test_eager_pad_py()
|
|
test_eager_cutout_hwc_pil()
|
|
test_eager_cutout_chw_pil()
|
|
test_eager_cutout_hwc_cv()
|
|
test_eager_exceptions_decode()
|
|
test_eager_exceptions_resize()
|
|
test_eager_exceptions_normalize()
|
|
test_eager_exceptions_pad()
|
|
test_eager_invalid_image_randomadjustsharpness()
|
|
test_eager_invalid_image_hwc2chw()
|
|
test_eager_invalid_image_invert()
|
|
test_eager_invalid_image_pad()
|
|
test_eager_invalid_image_randomcrop()
|
|
test_eager_invalid_image_randomhorizontalflip()
|
|
test_eager_invalid_image_randomsolarize()
|
|
test_eager_invalid_image_cutout()
|
|
test_eager_invalid_image_randomcolor()
|
|
test_eager_invalid_image_randomsharpness()
|