def test_random_color_py(degrees=(0.1, 1.9), plot=False):
"""
Test Python RandomColor
"""
logger.info("Test RandomColor")
# Original Images
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = data.map(operations=transforms_original,
input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
# Random Color Adjusted Images
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_random_color = mindspore.dataset.transforms.py_transforms.Compose(
[
F.Decode(),
F.Resize((224, 224)),
F.RandomColor(degrees=degrees),
F.ToTensor()
])
ds_random_color = data.map(operations=transforms_random_color,
input_columns="image")
ds_random_color = ds_random_color.batch(512)
for idx, (image, _) in enumerate(ds_random_color):
if idx == 0:
images_random_color = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_random_color = np.append(images_random_color,
np.transpose(
image.asnumpy(), (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_color[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_color)
def test_HWC2CHW(plot=False):
"""
Test HWC2CHW
"""
logger.info("Test HWC2CHW")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = c_vision.Decode()
hwc2chw_op = c_vision.HWC2CHW()
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=hwc2chw_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=decode_op, input_columns=["image"])
image_transposed = []
image = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
transposed_item = item1["image"].copy()
original_item = item2["image"].copy()
image_transposed.append(transposed_item.transpose(1, 2, 0))
image.append(original_item)
# check if the shape of data is transposed correctly
# transpose the original image from shape (H,W,C) to (C,H,W)
mse = diff_mse(transposed_item, original_item.transpose(2, 0, 1))
assert mse == 0
if plot:
visualize_list(image, image_transposed)
def test_center_crop_comp(height=375, width=375, plot=False):
"""
Test CenterCrop between python and c image augmentation
"""
logger.info("Test CenterCrop")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = vision.Decode()
center_crop_op = vision.CenterCrop([height, width])
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=center_crop_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.CenterCrop([height, width]),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = data2.map(operations=transform, input_columns=["image"])
image_c_cropped = []
image_py_cropped = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
c_image = item1["image"]
py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
# Note: The images aren't exactly the same due to rounding error
assert diff_mse(py_image, c_image) < 0.001
image_c_cropped.append(c_image.copy())
image_py_cropped.append(py_image.copy())
if plot:
visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
def test_resize_md5_parameters(test_name, size, filename, seed, plot):
"""
Test Resize with md5 check
"""
logger.info("Test Resize with md5 check: {0}".format(test_name))
original_seed = config_get_set_seed(seed)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = vision.Decode()
resize_op = vision.Resize(size)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data2 = data1.map(operations=resize_op, input_columns=["image"])
image_original = []
image_resized = []
# Compare with expected md5 from images
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image_1 = item1["image"]
image_2 = item2["image"]
image_original.append(image_1)
image_resized.append(image_2)
if plot:
visualize_list(image_original, image_resized)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_grayscale_valid_prob(plot=False):
"""
Test RandomGrayscale Op: valid input, expect to pass
"""
logger.info("test_random_grayscale_valid_prob")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms1 = [
py_vision.Decode(),
# Note: prob is 1 so the output should always be grayscale images
py_vision.RandomGrayscale(1),
py_vision.ToTensor()
]
transform1 = py_vision.ComposeOp(transforms1)
data1 = data1.map(input_columns=["image"], operations=transform1())
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms2 = [
py_vision.Decode(),
py_vision.ToTensor()
]
transform2 = py_vision.ComposeOp(transforms2)
data2 = data2.map(input_columns=["image"], operations=transform2())
image_gray = []
image = []
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_gray.append(image1)
image.append(image2)
if plot:
visualize_list(image, image_gray)
def test_random_affine_op(plot=False):
"""
Test RandomAffine in python transformations
"""
logger.info("test_random_affine_op")
# define map operations
transforms1 = [
py_vision.Decode(),
py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)),
py_vision.ToTensor()
]
transform1 = py_vision.ComposeOp(transforms1)
transforms2 = [
py_vision.Decode(),
py_vision.ToTensor()
]
transform2 = py_vision.ComposeOp(transforms2)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(input_columns=["image"], operations=transform1())
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(input_columns=["image"], operations=transform2())
image_affine = []
image_original = []
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_affine.append(image1)
image_original.append(image2)
if plot:
visualize_list(image_original, image_affine)
def test_random_solarize_op(threshold=None, plot=False):
"""
Test RandomSolarize
"""
logger.info("Test RandomSolarize")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
decode_op = vision.Decode()
if threshold is None:
solarize_op = vision.RandomSolarize()
else:
solarize_op = vision.RandomSolarize(threshold)
data1 = data1.map(input_columns=["image"], operations=decode_op)
data1 = data1.map(input_columns=["image"], operations=solarize_op)
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
data2 = data2.map(input_columns=["image"], operations=decode_op)
image_solarized = []
image = []
for item1, item2 in zip(data1.create_dict_iterator(),
data2.create_dict_iterator()):
image_solarized.append(item1["image"].copy())
image.append(item2["image"].copy())
if plot:
visualize_list(image, image_solarized)
def test_random_resize_op(plot=False):
"""
Test random_resize_op
"""
logger.info("Test resize")
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
# define map operations
decode_op = vision.Decode()
resize_op = vision.RandomResize(10)
# apply map operations on images
data1 = data1.map(input_columns=["image"], operations=decode_op)
data2 = data1.map(input_columns=["image"], operations=resize_op)
image_original = []
image_resized = []
num_iter = 0
for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
image_1 = item1["image"]
image_2 = item2["image"]
image_original.append(image_1)
image_resized.append(image_2)
num_iter += 1
if plot:
visualize_list(image_original, image_resized)
def test_center_crop_op(height=375, width=375, plot=False):
"""
Test CenterCrop
"""
logger.info("Test CenterCrop")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
decode_op = vision.Decode()
# 3 images [375, 500] [600, 500] [512, 512]
center_crop_op = vision.CenterCrop([height, width])
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=center_crop_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
data2 = data2.map(operations=decode_op, input_columns=["image"])
image_cropped = []
image = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image_cropped.append(item1["image"].copy())
image.append(item2["image"].copy())
if plot:
visualize_list(image, image_cropped)
def test_random_crop_comp(plot=False):
"""
Test RandomCrop and compare between python and c image augmentation
"""
logger.info("Test RandomCrop with c_transform and py_transform comparison")
cropped_size = 512
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
random_crop_op = c_vision.RandomCrop(cropped_size)
decode_op = c_vision.Decode()
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(cropped_size),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = data2.map(operations=transform, input_columns=["image"])
image_c_cropped = []
image_py_cropped = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
c_image = item1["image"]
py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_c_cropped.append(c_image)
image_py_cropped.append(py_image)
if plot:
visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
def test_resize_op_parameters(test_name, size, plot):
"""
Test resize_op
"""
logger.info("Test resize: {0}".format(test_name))
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
# define map operations
decode_op = vision.Decode()
resize_op = vision.Resize(size)
# apply map operations on images
data1 = data1.map(operations=decode_op, input_columns=["image"])
data2 = data1.map(operations=resize_op, input_columns=["image"])
image_original = []
image_resized = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image_1 = item1["image"]
image_2 = item2["image"]
image_original.append(image_1)
image_resized.append(image_2)
if plot:
visualize_list(image_original, image_resized)
def test_random_crop_op_c(plot=False):
"""
Test RandomCrop Op in c transforms
"""
logger.info("test_random_crop_op_c")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
random_crop_op = c_vision.RandomCrop([512, 512], [200, 200, 200, 200])
decode_op = c_vision.Decode()
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=decode_op, input_columns=["image"])
image_cropped = []
image = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image1 = item1["image"]
image2 = item2["image"]
image_cropped.append(image1)
image.append(image2)
if plot:
visualize_list(image, image_cropped)
def test_random_crop_and_resize_comp(plot=False):
"""
Test RandomCropAndResize and compare between python and c image augmentation
"""
logger.info("test_random_crop_and_resize_comp")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = c_vision.Decode()
random_crop_and_resize_op = c_vision.RandomResizedCrop(512, (1, 1), (0.5, 0.5))
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_and_resize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomResizedCrop(512, (1, 1), (0.5, 0.5)),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = data2.map(operations=transform, input_columns=["image"])
image_c_cropped = []
image_py_cropped = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
c_image = item1["image"]
py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_c_cropped.append(c_image)
image_py_cropped.append(py_image)
mse = diff_mse(c_image, py_image)
assert mse < 0.02 # rounding error
if plot:
visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
def test_random_crop_and_resize_op_c(plot=False):
"""
Test RandomCropAndResize op in c transforms
"""
logger.info("test_random_crop_and_resize_op_c")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = c_vision.Decode()
# With these inputs we expect the code to crop the whole image
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), (2, 2), (1, 3))
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_and_resize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=decode_op, input_columns=["image"])
num_iter = 0
crop_and_resize_images = []
original_images = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
crop_and_resize = item1["image"]
original = item2["image"]
# Note: resize the original image with the same size as the one applied RandomResizedCrop()
original = cv2.resize(original, (512, 256))
mse = diff_mse(crop_and_resize, original)
assert mse == 0
logger.info("random_crop_and_resize_op_{}, mse: {}".format(num_iter + 1, mse))
num_iter += 1
crop_and_resize_images.append(crop_and_resize)
original_images.append(original)
if plot:
visualize_list(original_images, crop_and_resize_images)
def test_config(plot, file_name, op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data1 = data1.map(operations=op_list, input_columns=["image"])
data2 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data2 = data2.map(operations=c_vision.Decode(),
input_columns=["image"])
original_images = []
transformed_images = []
for item in data1.create_dict_iterator(num_epochs=1,
output_numpy=True):
transformed_images.append(item["image"])
for item in data2.create_dict_iterator(num_epochs=1,
output_numpy=True):
original_images.append(item["image"])
if run_golden:
# Compare with expected md5 from images
save_and_check_md5(data1,
file_name,
generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(original_images, transformed_images)
def test_random_crop_op_py(plot=False):
"""
Test RandomCrop op in py transforms
"""
logger.info("test_random_crop_op_py")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms1 = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512], [200, 200, 200, 200]),
py_vision.ToTensor()
]
transform1 = mindspore.dataset.transforms.py_transforms.Compose(transforms1)
data1 = data1.map(operations=transform1, input_columns=["image"])
# Second dataset
# Second dataset for comparison
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms2 = [
py_vision.Decode(),
py_vision.ToTensor()
]
transform2 = mindspore.dataset.transforms.py_transforms.Compose(transforms2)
data2 = data2.map(operations=transform2, input_columns=["image"])
crop_images = []
original_images = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
crop = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
crop_images.append(crop)
original_images.append(original)
if plot:
visualize_list(original_images, crop_images)
def test_random_posterize_op_fixed_point_c(plot=False, run_golden=True):
"""
Test RandomPosterize in C transformations with fixed point
"""
logger.info("test_random_posterize_op_c")
# define map operations
transforms1 = [
c_vision.Decode(),
c_vision.RandomPosterize(1)
]
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(operations=transforms1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=[c_vision.Decode()], input_columns=["image"])
image_posterize = []
image_original = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image1 = item1["image"]
image2 = item2["image"]
image_posterize.append(image1)
image_original.append(image2)
if run_golden:
# check results with md5 comparison
filename = "random_posterize_fixed_point_01_result_c.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(image_original, image_posterize)
def test_random_affine_op_c(plot=False):
"""
Test RandomAffine in C transformations
"""
logger.info("test_random_affine_op_c")
# define map operations
transforms1 = [
c_vision.Decode(),
c_vision.RandomAffine(degrees=0, translate=(0.5, 0.5, 0, 0))
]
transforms2 = [
c_vision.Decode()
]
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(operations=transforms1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=transforms2, input_columns=["image"])
image_affine = []
image_original = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image1 = item1["image"]
image2 = item2["image"]
image_affine.append(image1)
image_original.append(image2)
if plot:
visualize_list(image_original, image_affine)
def test_random_sharpness_py(degrees=(0.7, 0.7), plot=False):
"""
Test RandomSharpness python op
"""
logger.info("Test RandomSharpness python op")
# Original Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = data.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# Random Sharpness Adjusted Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
py_op = F.RandomSharpness()
if degrees is not None:
py_op = F.RandomSharpness(degrees)
transforms_random_sharpness = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)), py_op,
F.ToTensor()])
ds_random_sharpness = data.map(input_columns="image",
operations=transforms_random_sharpness())
ds_random_sharpness = ds_random_sharpness.batch(512)
for idx, (image, _) in enumerate(ds_random_sharpness):
if idx == 0:
images_random_sharpness = np.transpose(image, (0, 2, 3, 1))
else:
images_random_sharpness = np.append(images_random_sharpness,
np.transpose(
image, (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_sharpness[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_sharpness)
def test_random_sharpness_c_py(degrees=(1.0, 1.0), plot=False):
"""
Test Random Sharpness C and python Op
"""
logger.info("Test RandomSharpness C and python Op")
# RandomSharpness Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((200, 300))])
python_op = F.RandomSharpness(degrees)
c_op = C.RandomSharpness(degrees)
transforms_op = F.ComposeOp(
[lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])()
ds_random_sharpness_py = data.map(input_columns="image",
operations=transforms_op)
ds_random_sharpness_py = ds_random_sharpness_py.batch(512)
for idx, (image, _) in enumerate(ds_random_sharpness_py):
if idx == 0:
images_random_sharpness_py = image
else:
images_random_sharpness_py = np.append(images_random_sharpness_py,
image,
axis=0)
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
data = data.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((200, 300))])
ds_images_random_sharpness_c = data.map(input_columns="image",
operations=c_op)
ds_images_random_sharpness_c = ds_images_random_sharpness_c.batch(512)
for idx, (image, _) in enumerate(ds_images_random_sharpness_c):
if idx == 0:
images_random_sharpness_c = image
else:
images_random_sharpness_c = np.append(images_random_sharpness_c,
image,
axis=0)
num_samples = images_random_sharpness_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_sharpness_c[i],
images_random_sharpness_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_random_sharpness_c,
images_random_sharpness_py,
visualize_mode=2)
def test_cpp_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test CPP UniformAugment")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = [C.Decode(), C.Resize(size=[224, 224]),
F.ToTensor()]
ds_original = ds.map(input_columns="image",
operations=transforms_original)
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# UniformAugment Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops)
transforms_all = [C.Decode(), C.Resize(size=[224, 224]),
uni_aug,
F.ToTensor()]
ds_ua = ds.map(input_columns="image",
operations=transforms_all, num_parallel_workers=1)
ds_ua = ds_ua.batch(512)
for idx, (image, _) in enumerate(ds_ua):
if idx == 0:
images_ua = np.transpose(image, (0, 2, 3, 1))
else:
images_ua = np.append(images_ua,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
if plot:
visualize_list(images_original, images_ua)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_ua[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_random_sharpness_c(degrees=(1.6, 1.6), plot=False):
"""
Test RandomSharpness cpp op
"""
print(degrees)
logger.info("Test RandomSharpness cpp op")
# Original Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = [C.Decode(),
C.Resize((224, 224))]
ds_original = data.map(input_columns="image",
operations=transforms_original)
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original,
image,
axis=0)
# Random Sharpness Adjusted Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
c_op = C.RandomSharpness()
if degrees is not None:
c_op = C.RandomSharpness(degrees)
transforms_random_sharpness = [C.Decode(),
C.Resize((224, 224)),
c_op]
ds_random_sharpness = data.map(input_columns="image",
operations=transforms_random_sharpness)
ds_random_sharpness = ds_random_sharpness.batch(512)
for idx, (image, _) in enumerate(ds_random_sharpness):
if idx == 0:
images_random_sharpness = image
else:
images_random_sharpness = np.append(images_random_sharpness,
image,
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_sharpness[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_sharpness)
def test_auto_contrast_py(plot=False):
"""
Test AutoContrast
"""
logger.info("Test AutoContrast Python Op")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
F.Resize((224, 224)),
F.ToTensor()])
ds_original = data_set.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
# AutoContrast Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_auto_contrast = \
mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=10.0, ignore=[10, 20]),
F.ToTensor()])
ds_auto_contrast = data_set.map(operations=transforms_auto_contrast, input_columns="image")
ds_auto_contrast = ds_auto_contrast.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast):
if idx == 0:
images_auto_contrast = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_auto_contrast = np.append(images_auto_contrast,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
# Compare with expected md5 from images
filename = "autocontrast_01_result_py.npz"
save_and_check_md5(ds_auto_contrast, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(images_original, images_auto_contrast)
def test_equalize_py(plot=False):
"""
Test Equalize py op
"""
logger.info("Test Equalize")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = data_set.map(operations=transforms_original,
input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
# Color Equalized Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_equalize = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(),
F.Resize((224, 224)),
F.Equalize(),
F.ToTensor()])
ds_equalize = data_set.map(operations=transforms_equalize,
input_columns="image")
ds_equalize = ds_equalize.batch(512)
for idx, (image, _) in enumerate(ds_equalize):
if idx == 0:
images_equalize = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_equalize = np.append(images_equalize,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_equalize[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_equalize)
def test_compare_random_color_op(degrees=None, plot=False):
"""
Compare Random Color op in Python and Cpp
"""
logger.info("test_random_color_op")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Decode with rgb format set to True
data1 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = ds.TFRecordDataset(C_DATA_DIR,
C_SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
if degrees is None:
c_op = vision.RandomColor()
p_op = F.RandomColor()
else:
c_op = vision.RandomColor(degrees)
p_op = F.RandomColor(degrees)
transforms_random_color_py = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: img.astype(np.uint8),
F.ToPIL(), p_op, np.array])
data1 = data1.map(operations=[vision.Decode(), c_op],
input_columns=["image"])
data2 = data2.map(operations=[vision.Decode()], input_columns=["image"])
data2 = data2.map(operations=transforms_random_color_py,
input_columns=["image"])
image_random_color_op = []
image = []
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
actual = item1["image"]
expected = item2["image"]
image_random_color_op.append(actual)
image.append(expected)
assert actual.shape == expected.shape
mse = diff_mse(actual, expected)
logger.info("MSE= {}".format(str(np.mean(mse))))
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
if plot:
visualize_list(image, image_random_color_op)
def test_equalize_py_c(plot=False):
"""
Test Equalize Cpp op and python op
"""
logger.info("Test Equalize cpp and python op")
# equalize Images in cpp
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(),
C.Resize((224, 224))],
input_columns=["image"])
ds_c_equalize = data_set.map(operations=C.Equalize(),
input_columns="image")
ds_c_equalize = ds_c_equalize.batch(512)
for idx, (image, _) in enumerate(ds_c_equalize):
if idx == 0:
images_c_equalize = image.asnumpy()
else:
images_c_equalize = np.append(images_c_equalize,
image.asnumpy(),
axis=0)
# Equalize images in python
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(),
C.Resize((224, 224))],
input_columns=["image"])
transforms_p_equalize = mindspore.dataset.transforms.py_transforms.Compose(
[lambda img: img.astype(np.uint8),
F.ToPIL(),
F.Equalize(), np.array])
ds_p_equalize = data_set.map(operations=transforms_p_equalize,
input_columns="image")
ds_p_equalize = ds_p_equalize.batch(512)
for idx, (image, _) in enumerate(ds_p_equalize):
if idx == 0:
images_p_equalize = image.asnumpy()
else:
images_p_equalize = np.append(images_p_equalize,
image.asnumpy(),
axis=0)
num_samples = images_c_equalize.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_p_equalize[i], images_c_equalize[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_c_equalize, images_p_equalize, visualize_mode=2)
def test_auto_contrast_c(plot=False):
"""
Test AutoContrast C Op
"""
logger.info("Test AutoContrast C Op")
# AutoContrast Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
python_op = F.AutoContrast()
c_op = C.AutoContrast()
transforms_op = F.ComposeOp(
[lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])()
ds_auto_contrast_py = ds.map(input_columns="image",
operations=transforms_op)
ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_py):
if idx == 0:
images_auto_contrast_py = image
else:
images_auto_contrast_py = np.append(images_auto_contrast_py,
image,
axis=0)
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[C.Decode(), C.Resize((224, 224))])
ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op)
ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_c):
if idx == 0:
images_auto_contrast_c = image
else:
images_auto_contrast_c = np.append(images_auto_contrast_c,
image,
axis=0)
num_samples = images_auto_contrast_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast_c[i],
images_auto_contrast_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
np.testing.assert_equal(np.mean(mse), 0.0)
if plot:
visualize_list(images_auto_contrast_c,
images_auto_contrast_py,
visualize_mode=2)
def test_auto_contrast(plot=False):
"""
Test AutoContrast
"""
logger.info("Test AutoContrast")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# AutoContrast Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_auto_contrast = F.ComposeOp(
[F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(),
F.ToTensor()])
ds_auto_contrast = ds.map(input_columns="image",
operations=transforms_auto_contrast())
ds_auto_contrast = ds_auto_contrast.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast):
if idx == 0:
images_auto_contrast = np.transpose(image, (0, 2, 3, 1))
else:
images_auto_contrast = np.append(images_auto_contrast,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = np.mean((images_auto_contrast[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_auto_contrast)
def test_invert_py(plot=False):
"""
Test Invert python op
"""
logger.info("Test Invert Python op")
# Original Images
ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
# Color Inverted Images
ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_invert = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(),
F.Resize((224, 224)),
F.Invert(),
F.ToTensor()])
ds_invert = ds.map(operations=transforms_invert, input_columns="image")
ds_invert = ds_invert.batch(512)
for idx, (image, _) in enumerate(ds_invert):
if idx == 0:
images_invert = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_invert = np.append(images_invert,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = np.mean((images_invert[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_invert)
def test_auto_contrast_c(plot=False):
"""
Test AutoContrast C Op
"""
logger.info("Test AutoContrast C Op")
# AutoContrast Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"])
python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20])
c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20])
transforms_op = mindspore.dataset.transforms.py_transforms.Compose([lambda img: F.ToPIL()(img.astype(np.uint8)),
python_op,
np.array])
ds_auto_contrast_py = data_set.map(operations=transforms_op, input_columns="image")
ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_py):
if idx == 0:
images_auto_contrast_py = image.asnumpy()
else:
images_auto_contrast_py = np.append(images_auto_contrast_py,
image.asnumpy(),
axis=0)
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"])
ds_auto_contrast_c = data_set.map(operations=c_op, input_columns="image")
ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast_c):
if idx == 0:
images_auto_contrast_c = image.asnumpy()
else:
images_auto_contrast_c = np.append(images_auto_contrast_c,
image.asnumpy(),
axis=0)
num_samples = images_auto_contrast_c.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
np.testing.assert_equal(np.mean(mse), 0.0)
# Compare with expected md5 from images
filename = "autocontrast_01_result_c.npz"
save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
