def get_cfg( nopause=False ):
cfg = {}
# params
cfg['num_epochs'] = 30
cfg['model_path'] = None
# logging
config_dir = os.path.dirname(os.path.realpath( __file__ ))
task_name = os.path.basename( config_dir )
# model
cfg['model_type'] = architectures.EncoderDecoderWithCGAN
cfg['n_g_steps_before_d'] = 1
cfg['n_d_steps_after_g'] = 1
cfg['init_g_steps'] = 25000
cfg['l_norm_weight_prop'] = 0.996
cfg['weight_decay'] = 2e-6
cfg['instance_noise_sigma'] = 0.1
cfg['instance_noise_anneal_fn'] = tf.train.inverse_time_decay
cfg['instance_noise_anneal_fn_kwargs'] = {
'decay_rate': 0.2,
'decay_steps': 1000
}
cfg['encoder'] = resnet_encoder_fully_convolutional_16x16x8
cfg['hidden_size'] = 1024
cfg['encoder_kwargs'] = {
'resnet_build_fn' : resnet_v1_50_16x16,
'weight_decay': cfg['weight_decay']
}
cfg['decoder'] = decoder_fc_15_layer_256_resolution_fully_convolutional_16x16x4
cfg['decoder_kwargs'] = {
'activation_fn': leaky_relu( 0.2 ),
'weight_decay': cfg['weight_decay']
}
cfg['discriminator'] = pix2pix_discriminator
cfg['discriminator_kwargs'] = {
'n_layers': 5,
'stride': 4,
'n_channel_multiplier': 64,
'weight_decay': 10.*cfg['weight_decay']
}
# loss
cfg['gan_loss_kwargs'] = {
'real_label': 0.9, # Positive labels 1 -> 0.9
'fake_label': 0.0
}
# learning
cfg['initial_learning_rate'] = 1e-4
cfg[ 'optimizer' ] = tf.train.AdamOptimizer
cfg[ 'optimizer_kwargs' ] = {}
cfg[ 'discriminator_learning_args' ] = {
'initial_learning_rate':1e-5,
'optimizer': tf.train.GradientDescentOptimizer,
'optimizer_kwargs': {}
}
def pwc(initial_lr, **kwargs):
global_step = kwargs['global_step']
del kwargs['global_step']
return tf.train.piecewise_constant(global_step, **kwargs)
cfg['learning_rate_schedule'] = pwc
cfg['learning_rate_schedule_kwargs' ] = {
'boundaries': [np.int64(0), np.int64(80000)], # need to be int64 since global step is...
'values': [cfg['initial_learning_rate'], cfg['initial_learning_rate']/10]
}
# inputs
cfg['input_dim'] = (256, 256) # (1024, 1024)
cfg['input_num_channels'] = 3
cfg['input_dtype'] = tf.float32
cfg['input_domain_name'] = 'rgb'
cfg['input_preprocessing_fn'] = load_ops.resize_rescale_image
cfg['input_preprocessing_fn_kwargs'] = {
'new_dims': cfg['input_dim'],
'new_scale': [-1, 1]
}
# outputs
cfg['target_num_channels'] = 1
cfg['target_dim'] = (256, 256) # (1024, 1024)
cfg['target_dtype'] = tf.float32
cfg['target_domain_name'] = 'keypoint'
cfg['target_preprocessing_fn'] = load_ops.resize_rescale_image
cfg['target_preprocessing_fn_kwargs'] = {
'new_dims': cfg['target_dim'],
'new_scale': [-1, 1]
}
# masks
cfg['depth_mask'] = True
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
cfg['randomize'] = False
cfg['num_read_threads'] = 300
cfg['batch_size'] = 32
cfg['inputs_queue_capacity'] = 4096
# Checkpoints and summaries
cfg['summary_save_every_secs'] = 300
cfg['checkpoint_save_every_secs'] = 600
RuntimeDeterminedEnviromentVars.register_dict( cfg ) # These will be loaded at runtime
print_cfg( cfg, nopause=nopause )
return cfg
def get_cfg( nopause=False ):
cfg = {}
# params
cfg['num_epochs'] = 30
cfg['model_path'] = None
# logging
config_dir = os.path.dirname(os.path.realpath( __file__ ))
task_name = os.path.basename( config_dir )
# model
cfg['model_type'] = architectures.SegmentationEncoderDecoder
cfg['weight_decay'] = 2e-6
cfg['instance_noise_sigma'] = 0.1
cfg['instance_noise_anneal_fn'] = tf.train.inverse_time_decay
cfg['instance_noise_anneal_fn_kwargs'] = {
'decay_rate': 0.2,
'decay_steps': 1000
}
cfg['encoder'] = resnet_encoder_fully_convolutional_16x16x8
cfg['hidden_size'] = 1024
cfg['encoder_kwargs'] = {
'resnet_build_fn' : resnet_v1_50_16x16,
'weight_decay': cfg['weight_decay']
}
cfg['decoder'] = decoder_fc_15_layer_256_resolution_fully_convolutional_16x16x4
cfg['decoder_kwargs'] = {
'activation_fn': leaky_relu( 0.2 ),
'weight_decay': cfg['weight_decay']
}
# learning
cfg['initial_learning_rate'] = 1e-4
cfg[ 'optimizer' ] = tf.train.AdamOptimizer
cfg[ 'optimizer_kwargs' ] = {}
def pwc(initial_lr, **kwargs):
global_step = kwargs['global_step']
del kwargs['global_step']
return tf.train.piecewise_constant(global_step, **kwargs)
cfg['learning_rate_schedule'] = pwc
cfg['learning_rate_schedule_kwargs' ] = {
'boundaries': [np.int64(0), np.int64(80000)], # need to be int64 since global step is...
'values': [cfg['initial_learning_rate'], cfg['initial_learning_rate']/10]
}
# inputs
cfg['input_dim'] = (256, 256) # (1024, 1024)
cfg['input_num_channels'] = 3
cfg['input_dtype'] = tf.float32
cfg['input_domain_name'] = 'rgb'
cfg['input_preprocessing_fn'] = load_ops.resize_rescale_image
cfg['input_preprocessing_fn_kwargs'] = {
'new_dims': cfg['input_dim'],
'new_scale': [-1, 1]
}
# outputs
cfg['output_dim'] = (256,256)
cfg['num_pixels'] = 300
cfg['only_target_discriminative'] = True
cfg['target_num_channels'] = 64
cfg['target_dim'] = (cfg['num_pixels'], 3) # (1024, 1024)
cfg['target_dtype'] = tf.int32
cfg['target_domain_name'] = 'segment25d'
cfg['target_from_filenames'] = load_ops.segment_pixel_sample
cfg['target_from_filenames_kwargs'] = {
'new_dims': cfg['output_dim'],
'num_pixels': cfg['num_pixels'],
'domain': cfg['target_domain_name']
}
cfg['return_accuracy'] = False
# input pipeline
cfg['preprocess_fn'] = load_and_specify_preprocessors
cfg['randomize'] = False
cfg['num_read_threads'] = 300
cfg['batch_size'] = 32
cfg['inputs_queue_capacity'] = 4096
# Checkpoints and summaries
cfg['summary_save_every_secs'] = 300
cfg['checkpoint_save_every_secs'] = 600
RuntimeDeterminedEnviromentVars.register_dict( cfg ) # These will be loaded at runtime
print_cfg( cfg, nopause=nopause )
return cfg