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 FasterRCNN_config import cfg as detector_cfg
from utils.configs.VGG16_config import cfg as network_cfg
from utils.configs.Building100_config import cfg as dataset_cfg
return merge_configs([detector_cfg, network_cfg, dataset_cfg])
if __name__ == '__main__':
cfg = get_configuration()
prepare(cfg, False)
#cntk.device.try_set_default_device(cntk.device.gpu(cfg.GPU_ID))
cntk.device.try_set_default_device(cntk.device.cpu())
model_path = cfg['MODEL_PATH']
print(model_path)
if os.path.exists(model_path) and cfg["CNTK"].MAKE_MODE:
print("Loading existing model from %s" % model_path)
eval_model = load_model(model_path)
else:
print("No trained model found.")
exit()
# Plot results on test set images
results_folder = os.path.join(cfg.OUTPUT_PATH, cfg["DATA"].DATASET)
evaluator = FasterRCNN_Evaluator(eval_model, cfg)
a = 'image.jpg'
plot_test_file_results(evaluator,
'D:\\src\\CNTK_Faster_RCNN\\test_img\\' + a,
'D:\\src\\CNTK_Faster_RCNN\\test_img\\', cfg)
