def train(dt):
if not 'dataset_dir' in dt:
return "No dataset directory specified"
for key in dt:
d_a[key] = dt[key]
d_a['freeze_dict']['input_checkpoint'] = os.path.join(
d_a['train_dir'], 'model.ckpt-' + str(d_a['max_number_of_steps']))
start = time.time()
train_size, val_size, num_classes = convert_data.run(
dt['dataset_dir'], d_a['dataset_name'], d_a['validation_percentage'],
d_a['num_shards'])
start_train = time.time()
train_image_classifier_gen.main(
d_a['train_dir'], d_a['num_clones'], d_a['clone_on_cpu'], train_size,
val_size, num_classes, d_a['worker_replicas'],
d_a['log_every_n_steps'], d_a['save_interval_secs'],
d_a['weight_decay'], d_a['optimization'], d_a['learning_rate'],
d_a['moving_average_decay'], d_a['dataset'],
d_a['max_number_of_steps'], d_a['checkpoint'])
end_train = time.time()
export_inference_graph.main(d_a['export'], train_size, val_size,
num_classes)
freeze_graph.main(d_a['freeze_dict'])
end = time.time()
train_time = end_train - start_train
full_time = end - start
rest = full_time - train_time
return {'training_time': train_time, 'full_time': full_time, 'rest': rest}
'num_shards': args.num_shards,
'train_dir': args.train_dir,
'num_clones': args.num_clones,
'worker_replicas': args.worker_replicas,
'log_every_n_steps': args.log_every_n_steps,
'save_interval_secs': args.save_interval_secs,
'weight_decay': args.weight_decay,
'moving_average_decay':args.moving_average_decay,
'max_number_of_steps': args.max_number_of_steps,
'clone_on_cpu': args.clone_on_cpu,
'optimization': optimization,
'learning_rate': learning_rate,
'dataset': dataset,
'checkpoint': checkpoint,
'export': export,
'freeze_dict': freeze_dict
}
with open('default_arguments.json', 'w') as js:
json.dump(j, js)'''
train_size, val_size, num_classes = convert_data.run(
dataset_dir, dataset_name, validation_percentage, num_shards)
train_image_classifier_gen.main(
train_dir, num_clones, clone_on_cpu, train_size, val_size, num_classes,
worker_replicas, log_every_n_steps, save_interval_secs, weight_decay,
optimization, learning_rate, moving_average_decay, dataset,
max_number_of_steps, checkpoint)
export_inference_graph.main(export, train_size, val_size, num_classes)
freeze_graph.main(freeze_dict)
def test_freeze_graph(self):
print('test_freeze_graph')
argv = [self.pretrained_model, self.frozen_graph_filename]
args = freeze_graph.parse_arguments(argv)
freeze_graph.main(args)
def test_freeze_graph(self):
print('test_freeze_graph')
argv = [ self.pretrained_model,
self.frozen_graph_filename ]
args = freeze_graph.parse_arguments(argv)
freeze_graph.main(args)