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import json
import os
import six
from configs.example_configs import test_data_base, test_img_id, do_inv, gen_time, example_info_map, gpu_id
from configs.model_configs import model_config_map
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_id
os.environ["KMP_WARNINGS"] = "0"
import model4_ctrlpoint_train as sketch_ctrlpoint_model
from utils4_ctrlpoint_train import load_dataset, load_real_dataset
def trainer(model_params):
print('Hyperparams:')
for key, val in six.iteritems(model_params.values()):
print('%s = %s' % (key, str(val)))
print('-' * 100)
datasets = load_dataset(model_params)
sub_log_root = os.path.join(model_params.log_root, model_params.workspace)
sub_log_img_root = os.path.join(model_params.log_img_root, model_params.workspace)
sub_snapshot_root = os.path.join(model_params.snapshot_root, model_params.workspace)
os.makedirs(sub_log_root, exist_ok=True)
os.makedirs(sub_log_img_root, exist_ok=True)
os.makedirs(sub_snapshot_root, exist_ok=True)
train_set = datasets[0]
val_set = datasets[1]
train_model_params = datasets[2]
val_model_params = datasets[3]
# Write config file to json file.
with open(os.path.join(sub_snapshot_root, 'model_config.json'), 'w') as f:
json.dump(train_model_params.values(), f, indent=True)
model = sketch_ctrlpoint_model.FullModel(model_params, train_set, val_set,
sub_log_root, sub_snapshot_root, sub_log_img_root)
model.train()
model.evaluate()
def tester(model_params, mode):
print('Hyperparams:')
for key, val in six.iteritems(model_params.values()):
print('%s = %s' % (key, str(val)))
print('-' * 100)
# TODO: change parameters here
occluded_only = False
stroke_fixing = False
if stroke_fixing: assert occluded_only
datasets = load_dataset(model_params, test_only=True, stroke_fixing=stroke_fixing)
train_set = datasets[0]
val_set = datasets[1]
sub_snapshot_root = os.path.join(model_params.snapshot_root, model_params.workspace)
model = sketch_ctrlpoint_model.FullModel(model_params, train_set, val_set,
None, sub_snapshot_root, None)
if mode == 'inference':
sub_inference_root = os.path.join(model_params.inference_root, model_params.workspace)
sub_inference_root += '---' + model_params.transform_local_model_name
os.makedirs(sub_inference_root, exist_ok=True)
model.inference(sub_inference_root, show_data='selected') # ['selected', 'all', 'occluded']
elif mode == 'inference_full':
sub_inference_root = os.path.join(model_params.inference_full_root, model_params.workspace)
sub_inference_root += '---' + model_params.transform_local_model_name
sub_inference_root += '-[c_min=' + str(model_params.window_size_min_comp) + ']'
if model_params.use_optical_flow:
sub_inference_root += '-[optical]'
os.makedirs(sub_inference_root, exist_ok=True)
model.inference_full(sub_inference_root, show_data='all', occluded_only=occluded_only) # ['selected', 'all']
else:
model.evaluate(load_trained_weights=True, occluded_only=occluded_only)
def tester_real(model_params, mode):
print('Hyperparams:')
for key, val in six.iteritems(model_params.values()):
print('%s = %s' % (key, str(val)))
print('-' * 100)
data_base = test_data_base
datasets = load_real_dataset(model_params, data_base=data_base, generation_time=gen_time)
val_set = datasets[0]
sub_snapshot_root = os.path.join(model_params.snapshot_root, model_params.workspace)
model = sketch_ctrlpoint_model.FullModel(model_params, None, val_set,
None, sub_snapshot_root, None)
if mode == 'inference_full_real':
inference_full_real_root = model_params.inference_full_real_root
if gen_time > 0:
inference_full_real_root += '-Gen%d' % gen_time
os.makedirs(inference_full_real_root, exist_ok=True)
model.inference_full_real(inference_full_real_root)
else:
raise Exception('Unknown mode:', mode)
def tester_real_inv(model_params, mode):
print('Hyperparams:')
for key, val in six.iteritems(model_params.values()):
print('%s = %s' % (key, str(val)))
print('-' * 100)
data_base = test_data_base
data_base_extra = "outputs/stroke_correspondence_results"
if gen_time > 0:
data_base_extra += '-Gen%d' % gen_time
data_base = os.path.join(data_base, '[0inv]')
datasets = load_real_dataset(model_params, data_base=data_base, data_base_extra=data_base_extra,
generation_time=gen_time)
val_set = datasets[0]
sub_snapshot_root = os.path.join(model_params.snapshot_root, model_params.workspace)
model = sketch_ctrlpoint_model.FullModel(model_params, None, val_set,
None, sub_snapshot_root, None)
inference_full_real_root = model_params.inference_full_real_root
if gen_time > 0:
inference_full_real_root += '-Gen%d' % gen_time
inference_full_real_root = os.path.join(inference_full_real_root, '[0inv]')
os.makedirs(inference_full_real_root, exist_ok=True)
model.inference_full_real(inference_full_real_root, do_inv=True)
if __name__ == '__main__':
mode = 'inference_full_real' # ['train', 'test', 'inference', 'inference_full', 'inference_full_real']
if do_inv:
mode += '_inv'
model_params = sketch_ctrlpoint_model.get_default_hparams()
if 'real' in mode:
# Add params for real inference
model_params.add_hparam('use_optical_flow', example_info_map[str(test_img_id)]['use_optical_flow'])
if not do_inv:
model_params.add_hparam('use_target_layer', example_info_map[str(test_img_id)]['use_target_layer'])
model_params.add_hparam('use_target_layer_mask', example_info_map[str(test_img_id)]['use_target_layer_mask'])
else:
model_params.add_hparam('use_target_layer', False)
model_params.add_hparam('use_target_layer_mask', 'stroke')
model_params.add_hparam('target_layer_method', example_info_map[str(test_img_id)]['target_layer_method'])
else:
model_params.add_hparam('use_optical_flow', True)
model_params.add_hparam('raster_size', model_config_map['raster_size'])
model_params.add_hparam('window_size_scaling_ref_comp', model_config_map['window_size_scaling_ref_comp'])
model_params.add_hparam('window_size_min_comp', model_config_map['window_size_min_comp'])
model_params.add_hparam('window_size_scaling_ref_comp_local', model_config_map['window_size_scaling_ref_comp_local'])
model_params.add_hparam('window_size_min_comp_local', model_config_map['window_size_min_comp_local'])
model_params.add_hparam('window_size_scaling_ref', model_config_map['window_size_scaling_ref_ctrl'])
model_params.add_hparam('window_size_min', model_config_map['window_size_min_ctrl'])
if mode == 'train':
trainer(model_params)
elif mode == 'test' or mode == 'inference' or mode == 'inference_full':
tester(model_params, mode)
elif mode == 'inference_full_real':
tester_real(model_params, mode)
elif mode == 'inference_full_real_inv':
tester_real_inv(model_params, mode)
else:
raise Exception('Unknown mode:', mode)