def run_faster_rcnn():
print("Running training")
base_folder = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(base_folder, "FasterRCNN"))
from cntk import load_model
from FasterRCNN_train import prepare
from FasterRCNN_eval import compute_test_set_aps
from FasterRCNN.FasterRCNN_eval import FasterRCNN_Evaluator
import numpy as np
import json
cfg = get_configuration()
prepare(cfg, False)
cfg["DATA"].NUM_TEST_IMAGES = args.num_test
cfg["CNTK"].MAKE_MODE = True
cfg["CNTK"].VISUALIZE_RESULTS = True
if args.gpu:
cfg["CNTK"].USE_GPU_NMS = True
else:
cfg["CNTK"].USE_GPU_NMS = False
if not (args.conf_threshold is None):
cfg.RESULTS_NMS_CONF_THRESHOLD = args.conf_threshold
trained_model = load_model(args.model_path)
eval_results = compute_test_set_aps(trained_model, cfg)
for class_name in eval_results:
print('Average precision (AP) for {:>15} = {:.4f}'.format(
class_name, eval_results[class_name]))
print('Mean average precision (AP) = {:.4f}'.format(
np.nanmean(list(eval_results.values()))))
if cfg["CNTK"].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)
with open(
r"/cntk/Examples/Image/Detection/FasterRCNN/Output/custom_images_output.json",
"w+") as resultFile:
print(
"Bounding boxes written to /cntk/Examples/Image/Detection/FasterRCNN/Output/custom_images_output.json"
)
resultFile.write(json.dumps(json_output))
def run_fasterrcnn_grocery_training(e2e):
from FasterRCNN_eval import compute_test_set_aps
from utils.config_helpers import merge_configs
from FasterRCNN_config import cfg as detector_cfg
from utils.configs.AlexNet_config import cfg as network_cfg
from utils.configs.Grocery_config import cfg as dataset_cfg
cfg = merge_configs([detector_cfg, network_cfg, dataset_cfg])
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 = e2e
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']
model_file = os.path.join(extPath, "PreTrainedModels", "AlexNet", "v1",
"AlexNet_ImageNet_Caffe.model")
else:
model_file = os.path.join(
abs_path,
*"../../../../PretrainedModels/AlexNet_ImageNet_Caffe.model".split(
"/"))
from FasterRCNN_train import prepare, train_faster_rcnn
np.random.seed(seed=3)
prepare(cfg, False)
cfg['BASE_MODEL_PATH'] = model_file
trained_model = train_faster_rcnn(cfg)
eval_results = compute_test_set_aps(trained_model, cfg)
meanAP = np.nanmean(list(eval_results.values()))
print('meanAP={}'.format(meanAP))
assert meanAP > 0.01
return trained_model, meanAP, cfg
def run_fasterrcnn_grocery_training(e2e):
from FasterRCNN_eval import compute_test_set_aps
from utils.config_helpers import merge_configs
from FasterRCNN_config import cfg as detector_cfg
from utils.configs.AlexNet_config import cfg as network_cfg
from utils.configs.Grocery_config import cfg as dataset_cfg
cfg = merge_configs([detector_cfg, network_cfg, dataset_cfg])
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 = e2e
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']
model_file = os.path.join(extPath, "PreTrainedModels", "AlexNet", "v1", "AlexNet_ImageNet_Caffe.model")
else:
model_file = os.path.join(abs_path, *"../../../../PretrainedModels/AlexNet_ImageNet_Caffe.model".split("/"))
from FasterRCNN_train import prepare, train_faster_rcnn
np.random.seed(seed=3)
prepare(cfg, False)
cfg['BASE_MODEL_PATH'] = model_file
trained_model = train_faster_rcnn(cfg)
eval_results = compute_test_set_aps(trained_model, cfg)
meanAP = np.nanmean(list(eval_results.values()))
print('meanAP={}'.format(meanAP))
assert meanAP > 0.01
return trained_model, meanAP, cfg
def reenable_once_sorting_is_stable_test_native_fasterrcnn_eval(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 FasterRCNN_eval import compute_test_set_aps
eval_model, meanAP_python, cfg = run_fasterrcnn_grocery_training(True)
cntk_py.always_allow_setting_default_device()
try_set_default_device(cpu())
sys.path.append(os.path.join(abs_path, "..", "..", "..", "..", "Examples", "Extensibility", "ProposalLayer"))
from native_proposal_layer import clone_with_native_proposal_layer
model_with_native_pl = clone_with_native_proposal_layer(eval_model)
eval_results = compute_test_set_aps(model_with_native_pl, cfg)
meanAP_native = np.nanmean(list(eval_results.values()))
# 0.2067 (python) vs 0.2251 (native) -- the difference stems
# from different sorting algorithms: quicksort in python and
# heapsort in c++ (both are not stable).
print("Python: {}, native: {}".format(meanAP_python, meanAP_native))
assert abs(meanAP_python - meanAP_native) < 0.1
def reenable_once_sorting_is_stable_test_native_fasterrcnn_eval(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 FasterRCNN_eval import compute_test_set_aps
eval_model, meanAP_python, cfg = run_fasterrcnn_grocery_training(True)
cntk_py.always_allow_setting_default_device()
try_set_default_device(cpu())
sys.path.append(
os.path.join(abs_path, "..", "..", "..", "..", "Examples",
"Extensibility", "ProposalLayer"))
from native_proposal_layer import clone_with_native_proposal_layer
model_with_native_pl = clone_with_native_proposal_layer(eval_model)
eval_results = compute_test_set_aps(model_with_native_pl, cfg)
meanAP_native = np.nanmean(list(eval_results.values()))
# 0.2067 (python) vs 0.2251 (native) -- the difference stems
# from different sorting algorithms: quicksort in python and
# heapsort in c++ (both are not stable).
print("Python: {}, native: {}".format(meanAP_python, meanAP_native))
assert abs(meanAP_python - meanAP_native) < 0.1
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.TNC_config import cfg as dataset_cfg
return merge_configs([detector_cfg, network_cfg, dataset_cfg])
# trains and evaluates a Fast R-CNN model.
if __name__ == '__main__':
cfg = get_configuration()
prepare(cfg, False)
cntk.device.try_set_default_device(cntk.device.gpu(cfg.GPU_ID))
# train and test
trained_model = train_faster_rcnn(cfg)
eval_results = compute_test_set_aps(trained_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()))))
# 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)
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])
# trains and evaluates a Fast R-CNN model.
if __name__ == '__main__':
cfg = get_configuration()
prepare(cfg, False)
# cntk.device.try_set_default_device(cntk.device.gpu(cfg.GPU_ID))
# train and test
trained_model = train_faster_rcnn(cfg)
eval_results = compute_test_set_aps(trained_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()))))
# 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)