def init_model(transform):
use_gpu = True
inference_engine = None
neural_network = None
postprocessors = None
if transform == 'gesture':
# Load feature extractor
feature_extractor = feature_extractors.StridedInflatedEfficientNet()
feature_extractor.load_weights_from_resources(
'backbone/strided_inflated_efficientnet.ckpt')
feature_extractor.eval()
# Load a logistic regression classifier
gesture_classifier = LogisticRegression(
num_in=feature_extractor.feature_dim, num_out=30)
checkpoint = load_weights_from_resources(
'gesture_detection/efficientnet_logistic_regression.ckpt')
gesture_classifier.load_state_dict(checkpoint)
gesture_classifier.eval()
# Concatenate feature extractor and met converter
neural_network = Pipe(feature_extractor, gesture_classifier)
postprocessors = [
PostprocessClassificationOutput(INT2LAB, smoothing=4)
]
elif transform == 'fitness':
weight = float(60)
height = float(170)
age = float(20)
gender = 'female'
# Load feature extractor
feature_extractor = feature_extractors.StridedInflatedMobileNetV2()
feature_extractor.load_weights_from_resources(
'backbone/strided_inflated_mobilenet.ckpt')
feature_extractor.eval()
# Load fitness activity classifier
gesture_classifier = LogisticRegression(
num_in=feature_extractor.feature_dim, num_out=81)
checkpoint = load_weights_from_resources(
'fitness_activity_recognition/mobilenet_logistic_regression.ckpt')
gesture_classifier.load_state_dict(checkpoint)
gesture_classifier.eval()
# Load MET value converter
met_value_converter = calorie_estimation.METValueMLPConverter()
checkpoint = load_weights_from_resources(
'calorie_estimation/mobilenet_features_met_converter.ckpt')
met_value_converter.load_state_dict(checkpoint)
met_value_converter.eval()
# Concatenate feature extractor with downstream nets
neural_network = Pipe(
feature_extractor,
feature_converter=[gesture_classifier, met_value_converter])
postprocessors = [
PostprocessClassificationOutput(INT2LAB, smoothing=8, indices=[0]),
calorie_estimation.CalorieAccumulator(weight=weight,
height=height,
age=age,
gender=gender,
smoothing=12,
indices=[1])
]
if neural_network is not None:
inference_engine = InferenceEngine(neural_network, use_gpu=use_gpu)
start_inference(inference_engine)
return (inference_engine, postprocessors), None
path_out = args['--path_out'] or None
title = args['--title'] or None
use_gpu = False
# Load feature extractor
feature_extractor = feature_extractors.StridedInflatedEfficientNet()
feature_extractor.load_weights_from_resources('backbone/strided_inflated_efficientnet.ckpt')
# feature_extractor = feature_extractors.StridedInflatedMobileNetV2()
# feature_extractor.load_weights_from_resources(r'../resources\backbone\strided_inflated_mobilenet.ckpt')
feature_extractor.eval()
# Load a logistic regression classifier
gesture_classifier = LogisticRegression(num_in=feature_extractor.feature_dim,
num_out=30)
checkpoint = load_weights_from_resources('gesture_detection/efficientnet_logistic_regression.ckpt')
# checkpoint = load_weights_from_resources('gesture_detection/mobilenet_logistic_regression.ckpt')
gesture_classifier.load_state_dict(checkpoint)
gesture_classifier.eval()
# Concatenate feature extractor and met converter
net = Pipe(feature_extractor, gesture_classifier)
postprocessor = [
PostprocessClassificationOutput(INT2LAB, smoothing=4)
]
display_ops = [
sense.display.DisplayFPS(expected_camera_fps=net.fps,
expected_inference_fps=net.fps / net.step_size),
def test_load_weights_from_resources_on_absolute_path(self):
_ = nn_utils.load_weights_from_resources(self.ABSOLUTE_PATH)
def test_load_weights_from_resources_on_relative_path(self):
_ = nn_utils.load_weights_from_resources(self.RELATIVE_PATH)
camera_id = int(args['--camera_id'] or 0)
path_in = args['--path_in'] or None
path_out = args['--path_out'] or None
title = args['--title'] or None
use_gpu = args['--use_gpu']
# Load feature extractor
feature_extractor = feature_extractors.StridedInflatedEfficientNet()
feature_extractor.load_weights_from_resources(
'backbone/strided_inflated_efficientnet.ckpt')
feature_extractor.eval()
# Load a logistic regression classifier
gesture_classifier = LogisticRegression(
num_in=feature_extractor.feature_dim, num_out=5)
checkpoint = load_weights_from_resources(
'fitness_rep_counting/efficientnet_logistic_regression.ckpt')
gesture_classifier.load_state_dict(checkpoint)
gesture_classifier.eval()
# Concatenate feature extractor and met converter
net = Pipe(feature_extractor, gesture_classifier)
postprocessor = [
PostprocessRepCounts(INT2LAB),
PostprocessClassificationOutput(INT2LAB, smoothing=1)
]
display_ops = [
sense.display.DisplayFPS(expected_camera_fps=net.fps,
expected_inference_fps=net.fps /
net.step_size),
use_gpu = args['--use_gpu']
camera_id = int(args['--camera_id'] or 0)
path_in = args['--path_in'] or None
path_out = args['--path_out'] or None
title = args['--title'] or None
# Load feature extractor
feature_extractor = feature_extractors.StridedInflatedMobileNetV2()
feature_extractor.load_weights_from_resources(
'backbone/strided_inflated_mobilenet.ckpt')
feature_extractor.eval()
# Load MET value converter
met_value_converter = calorie_estimation.METValueMLPConverter()
checkpoint = load_weights_from_resources(
'calorie_estimation/mobilenet_features_met_converter.ckpt')
met_value_converter.load_state_dict(checkpoint)
met_value_converter.eval()
# Concatenate feature extractor and met converter
net = Pipe(feature_extractor, met_value_converter)
post_processors = [
calorie_estimation.CalorieAccumulator(weight=weight,
height=height,
age=age,
gender=gender,
smoothing=12)
]
display_ops = [
camera_id = int(args['--camera_id'] or 0)
path_in = args['--path_in'] or None
path_out = args['--path_out'] or None
title = args['--title'] or None
use_gpu = args['--use_gpu']
# Load feature extractor
feature_extractor = feature_extractors.StridedInflatedMobileNetV2()
feature_extractor.load_weights_from_resources(
'backbone/strided_inflated_mobilenet.ckpt')
feature_extractor.eval()
# Load fitness activity classifier
gesture_classifier = LogisticRegression(
num_in=feature_extractor.feature_dim, num_out=81)
checkpoint = load_weights_from_resources(
'fitness_activity_recognition/mobilenet_logistic_regression.ckpt')
gesture_classifier.load_state_dict(checkpoint)
gesture_classifier.eval()
# Load MET value converter
met_value_converter = calorie_estimation.METValueMLPConverter()
checkpoint = load_weights_from_resources(
'calorie_estimation/mobilenet_features_met_converter.ckpt')
met_value_converter.load_state_dict(checkpoint)
met_value_converter.eval()
# Concatenate feature extractor with downstream nets
net = Pipe(feature_extractor,
feature_converter=[gesture_classifier, met_value_converter])
post_processors = [