def images_motivation(dnn_daemon):
"""
Plots the images from the motivation section
"""
# The image numbers of our motivation images
img_nums = img_num_motivation
# Initialise an image generator
img_dir = 'images/val/images/'
img_class_map_file = 'images/val/val.txt'
class_file = 'images/val/synset_words.txt'
image_generator = ImageGenerator(img_dir, img_class_map_file, class_file)
# Get a path and label for each of our images
img_paths = list()
img_labels = list()
for img in img_nums:
path, label = image_generator.get_image_data(img)
img_paths.append(path)
img_labels.append(label)
return img_paths
def inference(dnn_daemon, img_nums, model_names, n_img_to_infer=5):
"""
Allows to do inference using a list of images and models.
"""
# Check all models are available
for model in model_names:
if not model_available(dnn_daemon, model):
print("ERROR: Model", model, "not availale. Exiting.")
sys.exit()
# Initialise an image generator
img_dir = 'images/val/images/'
img_class_map_file = 'images/val/val.txt'
class_file = 'images/val/synset_words.txt'
image_generator = ImageGenerator(img_dir, img_class_map_file, class_file)
# Get a path and label for each of our images
img_paths = list()
img_labels = list()
for img in img_nums:
path, label = image_generator.get_image_data(img)
img_paths.append(path)
img_labels.append(label)
# Infer each image with each model, get the results
# results: dict, mapping (img_num, model) to (inference_times, prediciton)
print("Running inference on Jetson...")
img_model_to_results = dict()
img_model_to_img = []
img_model_to_models = []
img_model_to_inference = []
img_model_to_prediction = []
img_model_to_results = []
if(len(model_names) is 0):
print("Any model was selected!!!")
return img_model_to_img, img_model_to_models, img_model_to_results
percentage_of_each_execution=100/(len(img_paths)*len(model_names))
counter=0
first_time=0
for img_num, path in enumerate(img_paths):
img_model_to_inference = []
img_model_to_img.append("Image " + `img_num`)
for model in model_names:
results = dnn_daemon.inference(model, [path], 1)
inference_time, prediction_vals, prediction_classes = results
inference_time = inference_time[0]
ordered_prediction = convert_prediction(prediction_vals[0],
prediction_classes[0],
image_generator.class_names)
#img_model_to_results[(img_num, model)] = (inference_time, ordered_prediction)
if first_time is 0:
img_model_to_models.append(model)
img_model_to_inference.append(inference_time)
#img_model_to_prediction.append(ordered_prediction)
counter=counter+1
print(percentage_of_each_execution*counter, "%\n")
img_model_to_results.append(img_model_to_inference)
first_time=first_time+1
#print("All the images has been inferenced! ")
return img_model_to_img, img_model_to_models, img_model_to_results
