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)
