def create_mb_source(img_height, img_width, img_channels, n_classes, n_rois, data_path, data_set):
rois_dim = 4 * n_rois
label_dim = n_classes * n_rois
path = os.path.normpath(os.path.join(abs_path, data_path))
if data_set == 'test':
map_file = os.path.join(path, test_map_filename)
else:
map_file = os.path.join(path, train_map_filename)
roi_file = os.path.join(path, data_set + rois_filename_postfix)
label_file = os.path.join(path, data_set + roilabels_filename_postfix)
if not os.path.exists(map_file) or not os.path.exists(roi_file) or not os.path.exists(label_file):
raise RuntimeError("File '%s', '%s' or '%s' does not exist. "
"Please run install_fastrcnn.py from Examples/Image/Detection/FastRCNN to fetch them" %
(map_file, roi_file, label_file))
# read images
image_source = ImageDeserializer(map_file)
image_source.ignore_labels()
image_source.map_features(features_stream_name,
[ImageDeserializer.scale(width=img_width, height=img_height, channels=img_channels,
scale_mode="pad", pad_value=114, interpolations='linear')])
# read rois and labels
roi_source = CTFDeserializer(roi_file)
roi_source.map_input(roi_stream_name, dim=rois_dim, format="dense")
label_source = CTFDeserializer(label_file)
label_source.map_input(label_stream_name, dim=label_dim, format="dense")
# define a composite reader
return MinibatchSource([image_source, roi_source, label_source], epoch_size=sys.maxsize, randomize=data_set == "train")
def create_mb_source(image_height, image_width, num_channels, map_file):
transforms = [ImageDeserializer.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear')]
image_source = ImageDeserializer(map_file)
image_source.ignore_labels()
image_source.map_features('features', transforms)
return MinibatchSource(image_source, randomize=False)
def create_test_mb_source(features_stream_name, labels_stream_name,
image_height, image_width, num_channels, num_classes,
cifar_data_path):
path = os.path.normpath(os.path.join(abs_path, cifar_data_path))
map_file = os.path.join(path, TEST_MAP_FILENAME)
mean_file = os.path.join(path, MEAN_FILENAME)
if not os.path.exists(map_file) or not os.path.exists(mean_file):
cifar_py3 = "" if sys.version_info.major < 3 else "_py3"
raise RuntimeError(
"File '%s' or '%s' do not exist. Please run CifarDownload%s.py and CifarConverter%s.py from CIFAR-10 to fetch them"
% (map_file, mean_file, cifar_py3, cifar_py3))
image = ImageDeserializer(map_file)
image.map_features(features_stream_name, [
ImageDeserializer.crop(
crop_type='Random', ratio=0.8, jitter_type='uniRatio'),
ImageDeserializer.scale(width=image_width,
height=image_height,
channels=num_channels,
interpolations='linear'),
ImageDeserializer.mean(mean_file)
])
image.map_labels(labels_stream_name, num_classes)
rc = ReaderConfig(image, epoch_size=sys.maxsize)
return rc.minibatch_source()
def test_image():
from cntk.io import ReaderConfig, ImageDeserializer
map_file = "input.txt"
mean_file = "mean.txt"
epoch_size = 150
feature_name = "f"
image_width = 100
image_height = 200
num_channels = 3
label_name = "l"
num_classes = 7
image = ImageDeserializer(map_file)
image.map_features(
feature_name,
[
ImageDeserializer.crop(crop_type="Random", ratio=0.8, jitter_type="uniRatio"),
ImageDeserializer.scale(
width=image_width, height=image_height, channels=num_channels, interpolations="linear"
),
ImageDeserializer.mean(mean_file),
],
)
image.map_labels(label_name, num_classes)
rc = ReaderConfig(image, randomize=False, epoch_size=epoch_size)
assert rc["epochSize"] == epoch_size
assert rc["randomize"] == False
assert len(rc["deserializers"]) == 1
d = rc["deserializers"][0]
assert d["type"] == "ImageDeserializer"
assert d["file"] == map_file
assert set(d["input"].keys()) == {label_name, feature_name}
l = d["input"][label_name]
assert l["labelDim"] == num_classes
f = d["input"][feature_name]
assert set(f.keys()) == {"transforms"}
t0, t1, t2 = f["transforms"]
assert t0["type"] == "Crop"
assert t1["type"] == "Scale"
assert t2["type"] == "Mean"
t0["cropType"] == "Random"
t0["cropRatio"] == 0.8
t0["jitterType"] == "uniRatio"
t1["width"] == image_width
t1["height"] == image_height
t1["channels"] == num_channels
t1["interpolations"] == "linear"
t2["type"] == "mean"
t2["meanFile"] == mean_file
# TODO depends on ImageReader.dll
"""
def test_image():
from cntk.io import ReaderConfig, ImageDeserializer
map_file = "input.txt"
mean_file = "mean.txt"
epoch_size = 150
feature_name = "f"
image_width = 100
image_height = 200
num_channels = 3
label_name = "l"
num_classes = 7
image = ImageDeserializer(map_file)
image.map_features(feature_name, [
ImageDeserializer.crop(
crop_type='Random', ratio=0.8, jitter_type='uniRatio'),
ImageDeserializer.scale(width=image_width,
height=image_height,
channels=num_channels,
interpolations='linear'),
ImageDeserializer.mean(mean_file)
])
image.map_labels(label_name, num_classes)
rc = ReaderConfig(image, randomize=False, epoch_size=epoch_size)
assert rc['epochSize'] == epoch_size
assert rc['randomize'] == False
assert len(rc['deserializers']) == 1
d = rc['deserializers'][0]
assert d['type'] == 'ImageDeserializer'
assert d['file'] == map_file
assert set(d['input'].keys()) == {label_name, feature_name}
l = d['input'][label_name]
assert l['labelDim'] == num_classes
f = d['input'][feature_name]
assert set(f.keys()) == {'transforms'}
t0, t1, t2 = f['transforms']
assert t0['type'] == 'Crop'
assert t1['type'] == 'Scale'
assert t2['type'] == 'Mean'
t0['cropType'] == 'Random'
t0['cropRatio'] == 0.8
t0['jitterType'] == 'uniRatio'
t1['width'] == image_width
t1['height'] == image_height
t1['channels'] == num_channels
t1['interpolations'] == 'linear'
t2['type'] == 'mean'
t2['meanFile'] == mean_file
# TODO depends on ImageReader.dll
'''
def test_image():
from cntk.io import ReaderConfig, ImageDeserializer
map_file = "input.txt"
mean_file = "mean.txt"
epoch_size = 150
feature_name = "f"
image_width = 100
image_height = 200
num_channels = 3
label_name = "l"
num_classes = 7
image = ImageDeserializer(map_file)
image.map_features(feature_name,
[ImageDeserializer.crop(crop_type='Random', ratio=0.8,
jitter_type='uniRatio'),
ImageDeserializer.scale(width=image_width, height=image_height,
channels=num_channels, interpolations='linear'),
ImageDeserializer.mean(mean_file)])
image.map_labels(label_name, num_classes)
rc = ReaderConfig(image, randomize=False, epoch_size=epoch_size)
assert rc['epochSize'].value == epoch_size
assert rc['randomize'] == False
assert len(rc['deserializers']) == 1
d = rc['deserializers'][0]
assert d['type'] == 'ImageDeserializer'
assert d['file'] == map_file
assert set(d['input'].keys()) == {label_name, feature_name}
l = d['input'][label_name]
assert l['labelDim'] == num_classes
f = d['input'][feature_name]
assert set(f.keys()) == { 'transforms' }
t0, t1, t2 = f['transforms']
assert t0['type'] == 'Crop'
assert t1['type'] == 'Scale'
assert t2['type'] == 'Mean'
t0['cropType'] == 'Random'
t0['cropRatio'] == 0.8
t0['jitterType'] == 'uniRatio'
t1['width'] == image_width
t1['height'] == image_height
t1['channels'] == num_channels
t1['interpolations'] == 'linear'
t2['type'] == 'mean'
t2['meanFile'] == mean_file
# TODO depends on ImageReader.dll
'''
def create_mb_source(map_file,
image_width,
image_height,
num_channels,
num_classes,
randomize=True):
transforms = [
ImageDeserializer.scale(width=image_width,
height=image_height,
channels=num_channels,
interpolations='linear')
]
image_source = ImageDeserializer(map_file)
image_source.map_features(features_stream_name, transforms)
image_source.map_labels(label_stream_name, num_classes)
return MinibatchSource(image_source, randomize=randomize)
def create_mb_source(features_stream_name, labels_stream_name, image_height,
image_width, num_channels, num_classes, cifar_data_path):
map_file = os.path.join(cifar_data_path, TRAIN_MAP_FILENAME)
mean_file = os.path.join(cifar_data_path, MEAN_FILENAME)
if not os.path.exists(map_file) or not os.path.exists(mean_file):
cifar_py3 = "" if sys.version_info.major < 3 else "_py3"
raise RuntimeError(
"File '%s' or '%s' do not exist. Please run CifarDownload%s.py and CifarConverter%s.py from CIFAR-10 to fetch them"
% (map_file, mean_file, cifar_py3, cifar_py3))
image = ImageDeserializer(map_file)
image.map_features(feature_name, [
ImageDeserializer.crop(
crop_type='Random', ratio=0.8, jitter_type='uniRatio'),
ImageDeserializer.scale(width=image_width,
height=image_height,
channels=num_channels,
interpolations='linear'),
ImageDeserializer.mean(mean_file)
])
image.map_labels(label_name, num_classes)
rc = ReaderConfig(image, epoch_size=sys.maxsize)
input_streams_config = {
features_stream_name: features_stream_config,
labels_stream_name: labels_stream_config
}
deserializer_config = {
"type": "ImageDeserializer",
"file": map_file,
"input": input_streams_config
}
minibatch_config = {
"epochSize": sys.maxsize,
"deserializers": [deserializer_config]
}
print(minibatch_config)
return minibatch_source(minibatch_config)
def create_mb_source(features_stream_name, labels_stream_name, image_height,
image_width, num_channels, num_classes, cifar_data_path):
path = os.path.normpath(os.path.join(abs_path, cifar_data_path))
map_file = os.path.join(path, TRAIN_MAP_FILENAME)
mean_file = os.path.join(path, MEAN_FILENAME)
if not os.path.exists(map_file) or not os.path.exists(mean_file):
cifar_py3 = "" if sys.version_info.major < 3 else "_py3"
raise RuntimeError("File '%s' or '%s' do not exist. Please run CifarDownload%s.py and CifarConverter%s.py from CIFAR-10 to fetch them" %
(map_file, mean_file, cifar_py3, cifar_py3))
image = ImageDeserializer(map_file)
image.map_features(features_stream_name,
[ImageDeserializer.crop(crop_type='Random', ratio=0.8,
jitter_type='uniRatio'),
ImageDeserializer.scale(width=image_width, height=image_height,
channels=num_channels, interpolations='linear'),
ImageDeserializer.mean(mean_file)])
image.map_labels(labels_stream_name, num_classes)
rc = ReaderConfig(image, epoch_size=sys.maxsize)
return rc.minibatch_source()
def create_mb_source(map_file, image_width, image_height, num_channels, num_classes, randomize=True):
transforms = [ImageDeserializer.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear')]
image_source = ImageDeserializer(map_file)
image_source.map_features(features_stream_name, transforms)
image_source.map_labels(label_stream_name, num_classes)
return MinibatchSource(image_source, randomize=randomize)
