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import importlib
import os
from collections import OrderedDict
def create_default_local_file():
""" Contains the path to all necessary datasets or useful folders (like workspace, pretrained models..)"""
path = os.path.join(os.path.dirname(__file__), 'local.py')
empty_str = '\'\''
default_settings = OrderedDict({
'workspace_dir': empty_str,
'tensorboard_dir': 'self.workspace_dir',
'pretrained_networks': 'self.workspace_dir',
'pre_trained_models_dir' : empty_str,
'hp': empty_str,
'eth3d': empty_str,
'training_cad_520': empty_str,
'validation_cad_520': empty_str,
'coco': empty_str,
'dataset_name': empty_str,
'nbr_objects': 4,
'min_area_objects': 1300,
'compute_object_reprojection_mask': True,
'n_threads': 16,
'initial_pretrained_model': None,
'data_dir': empty_str,
'schedule_sampler': "'uniform'",
'lr': empty_str,
'weight_decay': 0.0,
'lr_anneal_steps': 0,
'batch_size': 18,
'microbatch': -1,
'ema_rate': 0.9999,
'log_interval': 10,
'save_interval': 5000,
'resume_checkpoint': empty_str,
'train_mode': empty_str,
'use_fp16': False,
'fp16_scale_growth': 1e-3,
'image_size': 64,
'flow_size': (64,64),
'num_channels': 128,
'num_res_blocks': 3,
'num_heads': 4,
'num_heads_upsample': -1,
'attention_resolutions': '"16,8"',
'dropout': 0.0,
'learn_sigma': False,
'sigma_small': False,
'class_cond': False,
'diffusion_steps': 5,
'noise_schedule': "'cosine'",
'use_kl': False,
'predict_xstart': True,
'rescale_timesteps': True,
'rescale_learned_sigmas': True,
'use_checkpoint': False,
'use_scale_shift_norm': True,
'clip_denoised': False,
'num_samples': 10000,
'val_batch_size': 1,
'use_ddim': False,
'model_path': empty_str,
'model_path_sr': empty_str,
'timestep_respacing': "''",
'eval_dataset': empty_str,
'n_batch': empty_str,
'visualize': empty_str
})
comment = {'workspace_dir': 'Base directory for saving network checkpoints.',
'tensorboard_dir': 'Directory for tensorboard files.',
'dataset_name': 'Training dataset name ("DPED" or "COCO2014")',
'lr': 'learning rate for training (recommendation: 3e-5 for DPED and 1e-4 for COCO)',
'train_mode': 'Training mode ("stage_1" or "sr")',
'model_path': 'Pre-trained model path for evaluation',
'model_path_sr': 'Pre-trained super-resolution model path for evaluation',
'eval_dataset': 'Evaluation dataset ("hp" or "eth3d")',
'n_batch': 'The number of multiple hypotheses',
'visualize': 'Set True, if you want qualitative results.'}
with open(path, 'w') as f:
f.write('class EnvironmentSettings:\n')
f.write(' def __init__(self):\n')
for attr, attr_val in default_settings.items():
comment_str = None
if attr in comment:
comment_str = comment[attr]
if comment_str is None:
f.write(' self.{} = {}\n'.format(attr, attr_val))
else:
f.write(' self.{} = {} # {}\n'.format(attr, attr_val, comment_str))
def env_settings():
env_module_name = 'admin.local'
# env_module_name = 'admin.example_coco'
try:
env_module = importlib.import_module(env_module_name)
return env_module.EnvironmentSettings()
except:
env_file = os.path.join(os.path.dirname(__file__), 'local.py')
create_default_local_file()
raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. '
'Then try to run again.'.format(env_file))
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