def _load_net(self):
# Load feature extractor
feature_extractor = feature_extractors.StridedInflatedEfficientNet()
checkpoint = engine.load_weights(self.feature_extractor_weights)
feature_extractor.load_state_dict(checkpoint)
feature_extractor.eval()
# Load a logistic regression classifier
gesture_classifier = LogisticRegression(
num_in=feature_extractor.feature_dim, num_out=30)
checkpoint = engine.load_weights(self.gesture_classifier_weights)
gesture_classifier.load_state_dict(checkpoint)
gesture_classifier.eval()
# Combine both models
self.net = Pipe(feature_extractor, gesture_classifier)
if __name__ == "__main__":
# Parse arguments
args = docopt(__doc__)
weight = float(args['--weight'])
height = float(args['--height'])
age = float(args['--age'])
gender = args['--gender'] or None
camera_id = 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()
checkpoint = engine.load_weights(
'resources/backbone/strided_inflated_mobilenet.ckpt')
feature_extractor.load_state_dict(checkpoint)
feature_extractor.eval()
# Load fitness activity classifier
gesture_classifier = LogisticRegression(
num_in=feature_extractor.feature_dim, num_out=81)
checkpoint = engine.load_weights(
'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 = torch.load(
# Parse arguments
args = docopt(__doc__)
weight = float(args['--weight'])
height = float(args['--height'])
age = float(args['--age'])
gender = args['--gender'] or None
use_gpu = args['--use_gpu']
camera_id = 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()
checkpoint = engine.load_weights(
'resources/backbone/strided_inflated_mobilenet.ckpt')
feature_extractor.load_state_dict(checkpoint)
feature_extractor.eval()
# Load MET value converter
met_value_converter = calorie_estimation.METValueMLPConverter()
checkpoint = engine.load_weights(
'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,
from realtimenet.downstream_tasks.nn_utils import Pipe, LogisticRegression
from realtimenet.downstream_tasks.postprocess import PostprocessClassificationOutput
if __name__ == "__main__":
# Parse arguments
args = docopt(__doc__)
camera_id = 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()
checkpoint = engine.load_weights('resources/backbone/strided_inflated_efficientnet.ckpt')
feature_extractor.load_state_dict(checkpoint)
feature_extractor.eval()
# Load a logistic regression classifier
gesture_classifier = LogisticRegression(num_in=feature_extractor.feature_dim,
num_out=30)
checkpoint = engine.load_weights('resources/gesture_detection/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)
# Create inference engine, video streaming and display instances
inference_engine = engine.InferenceEngine(net, use_gpu=use_gpu)
from realtimenet.downstream_tasks.nn_utils import Pipe, LogisticRegression
from realtimenet.downstream_tasks.postprocess import PostprocessClassificationOutput
if __name__ == "__main__":
# Parse arguments
args = docopt(__doc__)
camera_id = args['--camera_id'] or 0
path_in = args['--path_in'] or None
path_out = args['--path_out'] or None
custom_classifier = args['--custom_classifier'] or None
title = args['--title'] or None
use_gpu = args['--use_gpu']
# Load original feature extractor
feature_extractor = feature_extractors.StridedInflatedEfficientNet()
checkpoint = engine.load_weights(
'resources/backbone/strided_inflated_efficientnet.ckpt')
# Load custom classifier
checkpoint_classifier = engine.load_weights(
os.path.join(custom_classifier, 'classifier.checkpoint'))
# Update original weights in case some intermediate layers have been finetuned
name_finetuned_layers = set(checkpoint.keys()).intersection(
checkpoint_classifier.keys())
for key in name_finetuned_layers:
checkpoint[key] = checkpoint_classifier.pop(key)
feature_extractor.load_state_dict(checkpoint)
feature_extractor.eval()
with open(os.path.join(custom_classifier, 'label2int.json')) as file:
class2int = json.load(file)
INT2LAB = {value: key for key, value in class2int.items()}
from realtimenet.downstream_tasks.fitness_rep_counting import INT2LAB
from realtimenet.downstream_tasks.nn_utils import Pipe, LogisticRegression
from realtimenet.downstream_tasks.postprocess import PostprocessRepCounts, PostprocessClassificationOutput
if __name__ == "__main__":
# Parse arguments
args = docopt(__doc__)
camera_id = 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()
checkpoint = engine.load_weights(
'resources/backbone/strided_inflated_efficientnet.ckpt')
feature_extractor.load_state_dict(checkpoint)
feature_extractor.eval()
# Load a logistic regression classifier
gesture_classifier = LogisticRegression(
num_in=feature_extractor.feature_dim, num_out=5)
checkpoint = engine.load_weights(
'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)
# Create inference engine, video streaming and display instances
