if dataset == "50Salads":
features = "SpatialCNN_" + granularity
import sys
noise_level = int(sys.argv[1])
print(noise_level)
# ------------------------------------------------------------------
# Evaluate using different filter lengths
if 1:
# for conv in [5, 10, 15, 20]:
# Initialize dataset loader & metrics
data = datasets.Dataset(dataset, base_dir)
trial_metrics = metrics.ComputeMetrics(overlap=.1, bg_class=bg_class)
trial_metrics_30 = metrics.ComputeMetrics(overlap=.3, bg_class=bg_class)
trial_metrics_50 = metrics.ComputeMetrics(overlap=.5, bg_class=bg_class)
# Load data for each split
for split in data.splits:
if sensor_type == "video":
feature_type = "A" if model_type != "SVM" else "X"
else:
feature_type = "S"
X_train, y_train, X_test, y_test = data.load_split(
features,
split=split,
sample_rate=video_rate,
feature_type=feature_type)
conv = {'50Salads':25, "JIGSAWS":20, "MERL":5, "GTEA":25}[dataset]
# Which features for the given dataset
features = "SpatialCNN"
bg_class = 0 if dataset is not "JIGSAWS" else None
if dataset == "50Salads":
features = "SpatialCNN_" + granularity
# ------------------------------------------------------------------
# Evaluate using different filter lengths
if 1:
# for conv in [5, 10, 15, 20]:
# Initialize dataset loader & metrics
data = datasets.Dataset(dataset, base_dir)
trial_metrics = metrics.ComputeMetrics(overlap=.1, bg_class=bg_class)
# Load data for each split
for split in data.splits:
if sensor_type=="video":
feature_type = "A" if model_type != "SVM" else "X"
else:
feature_type = "S"
X_train, y_train, X_test, y_test = data.load_split(features, split=split,
sample_rate=video_rate,
feature_type=feature_type)
if trial_metrics.n_classes is None:
trial_metrics.set_classes(data.n_classes)
# conv = {'50Salads':50, "JIGSAWS":20, "MERL":5, "GTEA":25}[dataset]
# Which features for the given dataset
features = "SpatialCNN"
bg_class = 0 if dataset is not "JIGSAWS" else None
if dataset == "50Salads":
features = "SpatialCNN_" + granularity
# ------------------------------------------------------------------
# Evaluate using different filter lengths
if 1:
# for conv in [5, 10, 15, 20]:
# Initialize dataset loader & metrics
data = datasets.Dataset(dataset, base_dir)
trial_metrics = metrics.ComputeMetrics(overlap=.1, bg_class=bg_class)
trial_metrics_train = metrics.ComputeMetrics(overlap=.1,
bg_class=bg_class,
task='train')
# Load data for each split
for split in data.splits:
if sensor_type == "video":
feature_type = "A" if model_type != "SVM" else "X"
else:
feature_type = "S"
## stops training after the first split
if split != 'Split_1':
print('terminate')
sys.exit()