def test_detection_demo(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # it runs very slow in CPU
try_set_default_device(cntk_device(device_id))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
prepare_alexnet_v0_model()
from FastRCNN.install_data_and_model import create_grocery_mappings
create_grocery_mappings(grocery_path)
from DetectionDemo import get_configuration
import utils.od_utils as od
cfg = get_configuration('FasterRCNN')
cfg["CNTK"].FORCE_DETERMINISTIC = True
cfg["CNTK"].DEBUG_OUTPUT = False
cfg["CNTK"].MAKE_MODE = False
cfg["CNTK"].FAST_MODE = False
cfg.CNTK.E2E_MAX_EPOCHS = 3
cfg.CNTK.RPN_EPOCHS = 2
cfg.CNTK.FRCN_EPOCHS = 2
cfg.IMAGE_WIDTH = 400
cfg.IMAGE_HEIGHT = 400
cfg["CNTK"].TRAIN_E2E = True
cfg.USE_GPU_NMS = False
cfg.VISUALIZE_RESULTS = False
cfg["DATA"].MAP_FILE_PATH = grocery_path
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
cfg['BASE_MODEL_PATH'] = os.path.join(extPath, "PreTrainedModels",
"AlexNet", "v1",
"AlexNet_ImageNet_Caffe.model")
else:
cfg['BASE_MODEL_PATH'] = os.path.join(
abs_path,
*"../../../../PretrainedModels/AlexNet_ImageNet_Caffe.model".split(
"/"))
# train and test
eval_model = od.train_object_detector(cfg)
eval_results = od.evaluate_test_set(eval_model, cfg)
meanAP = np.nanmean(list(eval_results.values()))
print('meanAP={}'.format(meanAP))
assert meanAP > 0.01
# detect objects in single image
img_path = os.path.join(grocery_path, "testImages",
"WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(
eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
assert bboxes.shape[0] == labels.shape[0]
def test_detection_demo(device_id):
if cntk_device(device_id).type() != DeviceKind_GPU:
pytest.skip('test only runs on GPU') # it runs very slow in CPU
try_set_default_device(cntk_device(device_id))
from prepare_test_data import prepare_Grocery_data, prepare_alexnet_v0_model
grocery_path = prepare_Grocery_data()
prepare_alexnet_v0_model()
from FastRCNN.install_data_and_model import create_grocery_mappings
create_grocery_mappings(grocery_path)
from DetectionDemo import get_configuration
import utils.od_utils as od
cfg = get_configuration('FasterRCNN')
cfg["CNTK"].FORCE_DETERMINISTIC = True
cfg["CNTK"].DEBUG_OUTPUT = False
cfg["CNTK"].MAKE_MODE = False
cfg["CNTK"].FAST_MODE = False
cfg.CNTK.E2E_MAX_EPOCHS = 3
cfg.CNTK.RPN_EPOCHS = 2
cfg.CNTK.FRCN_EPOCHS = 2
cfg.IMAGE_WIDTH = 400
cfg.IMAGE_HEIGHT = 400
cfg["CNTK"].TRAIN_E2E = True
cfg.USE_GPU_NMS = False
cfg.VISUALIZE_RESULTS = False
cfg["DATA"].MAP_FILE_PATH = grocery_path
externalData = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY' in os.environ
if externalData:
extPath = os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY']
cfg['BASE_MODEL_PATH'] = os.path.join(extPath, "PreTrainedModels", "AlexNet", "v1", "AlexNet_ImageNet_Caffe.model")
else:
cfg['BASE_MODEL_PATH'] = os.path.join(abs_path, *"../../../../PretrainedModels/AlexNet_ImageNet_Caffe.model".split("/"))
# train and test
eval_model = od.train_object_detector(cfg)
eval_results = od.evaluate_test_set(eval_model, cfg)
meanAP = np.nanmean(list(eval_results.values()))
print('meanAP={}'.format(meanAP))
assert meanAP > 0.01
# detect objects in single image
img_path = os.path.join(grocery_path, "testImages", "WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
assert bboxes.shape[0] == labels.shape[0]
return merge_configs(
[detector_cfg, network_cfg, dataset_cfg, {
'DETECTOR': detector_name
}])
if __name__ == '__main__':
# Currently supported detectors: 'FastRCNN', 'FasterRCNN'
args = sys.argv
detector_name = get_detector_name(args)
cfg = get_configuration(detector_name)
# train and test
eval_model = od.train_object_detector(cfg)
eval_results = od.evaluate_test_set(eval_model, cfg)
# write AP results to output
for class_name in eval_results:
print('AP for {:>15} = {:.4f}'.format(class_name,
eval_results[class_name]))
print('Mean AP = {:.4f}'.format(np.nanmean(list(eval_results.values()))))
# detect objects in single image
img_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
r"../DataSets/Grocery/testImages/WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(
eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
from utils.configs.AlexNet_config import cfg as network_cfg
# for Pascal VOC 2007 data set use: from utils.configs.Pascal_config import cfg as dataset_cfg
# for the Grocery data set use: from utils.configs.Grocery_config import cfg as dataset_cfg
from utils.configs.Grocery_config import cfg as dataset_cfg
return merge_configs([detector_cfg, network_cfg, dataset_cfg, {'DETECTOR': detector_name}])
if __name__ == '__main__':
# Currently supported detectors: 'FastRCNN', 'FasterRCNN'
args = sys.argv
detector_name = get_detector_name(args)
cfg = get_configuration(detector_name)
# train and test
eval_model = od.train_object_detector(cfg)
eval_results = od.evaluate_test_set(eval_model, cfg)
# write AP results to output
for class_name in eval_results: print('AP for {:>15} = {:.4f}'.format(class_name, eval_results[class_name]))
print('Mean AP = {:.4f}'.format(np.nanmean(list(eval_results.values()))))
# detect objects in single image
img_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), r"../DataSets/Grocery/testImages/WIN_20160803_11_28_42_Pro.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(eval_model, img_path, cfg)
bboxes, labels, scores = od.filter_results(regressed_rois, cls_probs, cfg)
# write detection results to output
fg_boxes = np.where(labels > 0)
print("#bboxes: before nms: {}, after nms: {}, foreground: {}".format(len(regressed_rois), len(bboxes), len(fg_boxes[0])))
for i in fg_boxes[0]: print("{:<12} (label: {:<2}), score: {:.3f}, box: {}".format(
cfg["DATA"].CLASSES[labels[i]], labels[i], scores[i], [int(v) for v in bboxes[i]]))
from utils.configs.NO3310_config import cfg as dataset_cfg
return merge_configs(
[detector_cfg, network_cfg, dataset_cfg, {
'DETECTOR': 'FasterRCNN'
}])
if __name__ == '__main__':
# Get Configuration
cfg = get_configuration()
# train
trained_model = od.train_object_detector(cfg)
# test model with some images
eval_results = od.evaluate_test_set(trained_model, cfg)
# Plot results on test set images
if cfg.VISUALIZE_RESULTS:
num_eval = min(cfg["DATA"].NUM_TEST_IMAGES, 100)
results_folder = os.path.join(cfg.OUTPUT_PATH, cfg["DATA"].DATASET)
evaluator = FasterRCNN_Evaluator(trained_model, cfg)
plot_test_set_results(evaluator, num_eval, results_folder, cfg)
if cfg.STORE_EVAL_MODEL_WITH_NATIVE_UDF:
store_eval_model_with_native_udf(trained_model, cfg)
# detect objects in single image
img_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
r"DataSets/NO3310/testImages/img30.jpg")
regressed_rois, cls_probs = od.evaluate_single_image(