file_ptr = open(mapping_file, 'r')
actions = file_ptr.read().split('\n')[:-1]
file_ptr.close()
actions_dict = dict()
for a in actions:
actions_dict[a.split()[1]] = int(a.split()[0])
num_classes = len(actions_dict)
trainer = Trainer(num_stages,
num_layers,
num_f_maps,
features_dim,
num_classes,
pooling_type=pooling_type,
dropout=dropout)
if args.action == "train":
batch_gen = BatchGenerator(num_classes, actions_dict, gt_path,
features_path, sample_rate)
batch_gen.read_data(vid_list_file)
trainer.train(model_dir,
batch_gen,
num_epochs=num_epochs,
batch_size=bz,
learning_rate=lr,
device=device)
if args.action == "predict":
trainer.predict(model_dir, results_dir, features_path, vid_list_file_tst,
num_epochs, actions_dict, device, sample_rate)
def main(args, device, model_load_dir, model_save_dir, results_save_dir):
if args.action == 'train' and args.extract_save_pseudo_labels == 0:
# load train dataset and test dataset
print(f'Load train data: {args.train_data}')
train_loader = DataLoader(args, args.train_data, 'train')
print(f'Load test data: {args.test_data}')
test_loader = DataLoader(args, args.test_data, 'test')
print(f'Start training.')
trainer = Trainer(
args.num_stages,
args.num_layers,
args.num_f_maps,
args.features_dim,
train_loader.num_classes,
device,
train_loader.weights,
model_save_dir
)
eval_args = [
args,
model_save_dir,
results_save_dir,
test_loader.features_dict,
test_loader.gt_dict,
test_loader.eval_gt_dict,
test_loader.vid_list,
args.num_epochs,
device,
'eval',
args.classification_threshold,
]
batch_gen = BatchGenerator(
train_loader.num_classes,
train_loader.gt_dict,
train_loader.features_dict,
train_loader.eval_gt_dict
)
batch_gen.read_data(train_loader.vid_list)
trainer.train(
model_save_dir,
batch_gen,
args.num_epochs,
args.bz,
args.lr,
device,
eval_args,
pretrained=model_load_dir)
elif args.extract_save_pseudo_labels and args.pseudo_label_type != 'PL':
# extract/ generate pseudo labels and save in "data/pseudo_labels"
print(f'Load test data: {args.test_data}')
test_loader = DataLoader(args, args.test_data, args.extract_set, results_dir=results_save_dir)
print(f'Extract {args.pseudo_label_type}')
if args.pseudo_label_type == 'local':
get_save_local_fusion(args, test_loader.features_dict, test_loader.gt_dict)
elif args.pseudo_label_type == 'merge':
merge_PL_CP(args, test_loader.features_dict, test_loader.gt_dict)
elif args.pseudo_label_type == 'CMPL':
CMPL(args, test_loader.features_dict, test_loader.gt_dict)
elif args.pseudo_label_type == 'CP':
extract_CP(args, test_loader.features_dict)
print('Self labelling process finished')
else:
print(f'Load test data: {args.test_data}')
test_loader = DataLoader(args, args.test_data, args.extract_set, results_dir=results_save_dir)
if args.extract_save_pseudo_labels and args.pseudo_label_type == 'PL':
print(f'Extract {args.pseudo_label_type}')
extract_save_PL = 1
else:
print(f'Start inference.')
extract_save_PL = 0
trainer = Trainer(
args.num_stages,
args.num_layers,
args.num_f_maps,
args.features_dim,
test_loader.num_classes,
device,
test_loader.weights,
results_save_dir)
trainer.predict(
args,
model_load_dir,
results_save_dir,
test_loader.features_dict,
test_loader.gt_dict,
test_loader.eval_gt_dict,
test_loader.vid_list,
args.num_epochs,
device,
'test',
args.classification_threshold,
uniform=args.uniform,
save_pslabels=extract_save_PL,
CP_dict=test_loader.CP_dict,
)
separator = ','
reader = open(target_file, 'r')
header = reader.readline().split(separator)
count = 0
print('Number of columns: %d' % len(header))
print('Reading %s\n' % target_file)
model = Trainer()
model.load_model('./SVM_classifier.sav')
while True:
row = reader.readline()
if not row:
time.sleep(0.1)
continue
count += 1
# sys.stdout.write('\r' + str(count))
# sys.stdout.flush()
# Preprocess
row = row.split(separator)[:-1]
row = np.delete(np.array(row), [0, 1, 2, 3, 5, 6], 0)
row = row.astype(np.float32)
# Classify
label = model.predict([row])
print(label)
dataset, target = readTrainCSV(filecsv, fea_sel=0)
# Split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
dataset, target, test_size=0.2, random_state=0)
X_train = np.asarray(X_train).astype(np.float)
X_test = np.asarray(X_test).astype(np.float)
y_train = np.asarray(y_train).astype(np.float)
y_test = np.asarray(y_test).astype(np.float)
model_name = 'SVM'
classifer = Trainer(model_name, kernel='rbf', C=1, gamma='scale')
# classifer.load_model('kNN_classifier.txt')
# parameters = {'kernel':('linear', 'rbf'), 'C':[1,2]}
# classifer.gridsearchCV(parameters)
classifer.fit(X_train, y_train)
classifer.predict(X_test)
classifer.report(y_test)
print(classifer.model)
classifer.save_model('SVM_classifier.sav')
