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
def run_calorie_estimation(model_name: str,
model_version: str,
weight: Optional[float] = 70.0,
height: Optional[float] = 170.0,
age: float = 30.0,
gender: Optional[str] = None,
title: Optional[str] = None,
display_fn: Optional[Callable] = None,
**kwargs):
"""
:param model_name:
Model from backbone (StridedInflatedEfficientNet or StridedInflatedMobileNetV2).
:param model_version:
Model version (pro or lite)
:param weight:
Weight (in kilograms). Will be used to convert MET values to calories. Default to 70.
:param height:
Height (in centimeters). Will be used to convert MET values to calories. Default to 170.
:param age:
Age (in years). Will be used to convert MET values to calories. Default to 30.
:param gender:
Gender ("male" or "female" or "other"). Will be used to convert MET values to calories
:param title:
Title of the image frame on display.
:param display_fn:
Optional function to further process displayed image
"""
# Load weights
selected_config, weights = get_relevant_weights(
SUPPORTED_MODEL_CONFIGURATIONS,
model_name,
model_version
)
# Load backbone network
backbone_network = build_backbone_network(selected_config, weights['backbone'])
# Load MET value converter
met_value_converter = calorie_estimation.METValueMLPConverter()
met_value_converter.load_state_dict(weights['met_converter'])
met_value_converter.eval()
# Concatenate backbone network and met converter
net = Pipe(backbone_network, met_value_converter)
post_processors = [
calorie_estimation.CalorieAccumulator(weight=weight,
height=height,
age=age,
gender=gender,
smoothing=12)
]
display_ops = [
sense.display.DisplayFPS(expected_camera_fps=net.fps,
expected_inference_fps=net.fps / net.step_size),
sense.display.DisplayDetailedMETandCalories(),
]
display_results = sense.display.DisplayResults(title=title, display_ops=display_ops, display_fn=display_fn)
# Run live inference
controller = Controller(
neural_network=net,
post_processors=post_processors,
results_display=display_results,
callbacks=[],
**kwargs
)
controller.run_inference()
SUPPORTED_MODEL_CONFIGURATIONS,
model_name,
model_version
)
# Load backbone network
backbone_network = build_backbone_network(selected_config, weights['backbone'])
# Create fitness activity classifier
gesture_classifier = LogisticRegression(num_in=backbone_network.feature_dim,
num_out=81)
gesture_classifier.load_state_dict(weights['fitness_activity_recognition'])
gesture_classifier.eval()
# Create MET value converter
met_value_converter = calorie_estimation.METValueMLPConverter()
met_value_converter.load_state_dict(weights['met_converter'])
met_value_converter.eval()
# Concatenate backbone network with downstream nets
net = Pipe(backbone_network, feature_converter=[gesture_classifier,
met_value_converter])
post_processors = [
PostprocessClassificationOutput(INT2LAB, smoothing=8,
indices=[0]),
calorie_estimation.CalorieAccumulator(weight=weight,
height=height,
age=age,
gender=gender,
smoothing=12,
def run_fitness_tracker(model_name: str,
model_version: str,
weight: Optional[float] = 70.0,
height: Optional[float] = 170.0,
age: float = 30.0,
gender: Optional[str] = None,
title: Optional[str] = None,
display_fn: Optional[Callable] = None,
**kwargs):
"""
:param model_name:
Model from backbone (StridedInflatedEfficientNet or StridedInflatedMobileNetV2).
:param model_version:
Model version (pro or lite)
:param weight:
Weight (in kilograms). Will be used to convert MET values to calories. Default to 70.
:param height:
Height (in centimeters). Will be used to convert MET values to calories. Default to 170.
:param age:
Age (in years). Will be used to convert MET values to calories. Default to 30.
:param gender:
Gender ("male" or "female" or "other"). Will be used to convert MET values to calories
:param title:
Title of the image frame on display.
:param display_fn:
Optional function to further process displayed image
"""
# Load weights
selected_config, weights = get_relevant_weights(
SUPPORTED_MODEL_CONFIGURATIONS, model_name, model_version)
# Load backbone network
backbone_network = build_backbone_network(selected_config,
weights['backbone'])
# Create fitness activity classifier
gesture_classifier = LogisticRegression(
num_in=backbone_network.feature_dim, num_out=81)
gesture_classifier.load_state_dict(weights['fitness_activity_recognition'])
gesture_classifier.eval()
# Create MET value converter
met_value_converter = calorie_estimation.METValueMLPConverter()
met_value_converter.load_state_dict(weights['met_converter'])
met_value_converter.eval()
# Concatenate backbone network with downstream nets
net = Pipe(backbone_network,
feature_converter=[gesture_classifier, met_value_converter])
post_processors = [
PostprocessClassificationOutput(INT2LAB, smoothing=8, indices=[0]),
calorie_estimation.CalorieAccumulator(weight=weight,
height=height,
age=age,
gender=gender,
smoothing=12,
indices=[1])
]
display_ops = [
sense.display.DisplayFPS(expected_camera_fps=net.fps,
expected_inference_fps=net.fps /
net.step_size),
sense.display.DisplayTopKClassificationOutputs(top_k=1, threshold=0.5),
sense.display.DisplayMETandCalories(y_offset=40),
]
display_results = sense.display.DisplayResults(title=title,
display_ops=display_ops,
border_size_top=50,
display_fn=display_fn)
# Run live inference
controller = Controller(neural_network=net,
post_processors=post_processors,
results_display=display_results,
callbacks=[],
**kwargs)
controller.run_inference()