341 lines
11 KiB
Python
341 lines
11 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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import cv2
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import numpy as np
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import pytest
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from PIL import Image
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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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():
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"""
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Feature: Resize op
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Description: Test eager support for Resize 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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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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assert img.shape == (32, 32, 3)
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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_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 "Input should be an encoded image in 1-D NumPy format" 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 "Input should be an encoded image in 1-D NumPy format" 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 op
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Description: Exception eager support test for Pad 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 = "../data/dataset/apple.jpg"
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_ = vision.Pad(padding=4)(img)
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assert False
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except RuntimeError as e:
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assert "tensor should be in shape of <H,W,C> or <H,W>" in str(e)
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if __name__ == '__main__':
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test_eager_decode_c()
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test_eager_decode_py()
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test_eager_resize()
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test_eager_rescale()
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test_eager_normalize_hwc()
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test_eager_normalize_chw()
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test_eager_hwc2chw()
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test_eager_pad_c()
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test_eager_pad_py()
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test_eager_cutout_hwc_pil()
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test_eager_cutout_chw_pil()
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test_eager_cutout_hwc_cv()
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test_eager_exceptions_decode()
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test_eager_exceptions_resize()
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test_eager_exceptions_normalize()
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test_eager_exceptions_pad()
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