def get_cfg(nopause=False): cfg = get_inp_cfg() # outputs cfg['target_num_channels'] = 1 cfg['target_dim'] = (256, 256) # (1024, 1024) cfg['target_domain_name'] = 'depth_zbuffer' cfg['target_preprocessing_fn'] = load_ops.resize_and_rescale_image_log cfg['target_preprocessing_fn_kwargs'] = { 'new_dims': cfg['target_dim'], 'offset': 1., 'normalizer': np.log(2.**16.0) } # masks cfg['mask_fn'] = mask_if_channel_ge # given target image as input cfg['mask_fn_kwargs'] = { 'img': '<TARGET_IMG>', 'channel_idx': 0, 'threshhold': 64500, # roughly max value - 1000. This margin is for interpolation errors 'broadcast_to_dim': cfg['target_num_channels'] } #cfg['depth_mask'] = True # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg(nopause=False): cfg = get_inp_cfg() cfg['is_discriminative'] = True cfg['single_filename_to_multiple'] = True # outputs cfg['target_dim'] = 9 # (1024, 1024) cfg['target_from_filenames'] = load_ops.vanishing_point_well_defined # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg( nopause=False ): cfg = get_inp_cfg() cfg['is_discriminative'] = True cfg['single_filename_to_multiple']=True # outputs cfg['target_dim'] = 63 # (1024, 1024) cfg['target_from_filenames'] = load_ops.class_places_workspace_and_home cfg['mask_by_target_func'] = True # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg(nopause=False): cfg = get_inp_cfg() # outputs cfg['target_num_channels'] = 2 cfg['target_dim'] = (256, 256) # (1024, 1024) cfg['target_domain_name'] = 'principal_curvature' cfg['target_preprocessing_fn'] = load_ops.curvature_preprocess cfg['target_preprocessing_fn_kwargs'] = {'new_dims': cfg['target_dim']} # masks cfg['depth_mask'] = True # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg( nopause=False ): cfg = get_inp_cfg() # outputs cfg['target_num_channels'] = 3 cfg['target_dim'] = (256, 256) # (1024, 1024) cfg['target_domain_name'] = 'rgb' cfg['target_preprocessing_fn'] = load_ops.resize_rescale_image cfg['target_preprocessing_fn_kwargs'] = { 'new_dims': cfg['target_dim'], 'new_scale': [-1, 1] } # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg(nopause=False): cfg = get_inp_cfg() # outputs cfg['target_num_channels'] = 1 cfg['target_dim'] = (256, 256) # (1024, 1024) cfg['target_domain_name'] = 'depth_euclidean' cfg['target_preprocessing_fn'] = load_ops.resize_and_rescale_image_log cfg['target_preprocessing_fn_kwargs'] = { 'new_dims': cfg['target_dim'], 'offset': 1., 'normalizer': np.log(2.**16.0) } # masks cfg['depth_mask'] = True # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg(nopause=False): cfg = get_inp_cfg() # outputs cfg['target_num_channels'] = 1 cfg['target_dim'] = (256, 256) # (1024, 1024) cfg['target_domain_name'] = 'edge_occlusion' cfg['target_preprocessing_fn'] = load_ops.resize_rescale_image_gaussian_blur cfg['target_preprocessing_fn_kwargs'] = { 'new_dims': cfg['target_dim'], 'new_scale': [-1, 1], 'current_scale': [0.0, 0.00625], 'no_clip': True } cfg['depth_mask'] = True # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg(nopause=False): cfg = get_inp_cfg() # outputs cfg['target_num_channels'] = 1 cfg['target_dim'] = (256, 256) # (1024, 1024) cfg['target_domain_name'] = 'keypoints2d' cfg['target_preprocessing_fn'] = load_ops.resize_rescale_image cfg['target_preprocessing_fn_kwargs'] = { 'new_dims': cfg['target_dim'], 'new_scale': [-1, 1], 'current_scale': [0.0, 0.005] } # masks #cfg['depth_mask'] = True # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg(nopause=False): cfg = get_inp_cfg() # outputs 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_domain_name'] = 'segment_unsup2d' cfg['target_from_filenames'] = load_ops.segment_pixel_sample cfg['target_from_filenames_kwargs'] = { 'new_dims': (256, 256), 'num_pixels': cfg['num_pixels'], 'domain': cfg['target_domain_name'] } cfg['return_accuracy'] = False # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg
def get_cfg(nopause=False): cfg = get_inp_cfg() # outputs cfg['only_target_discriminative'] = True cfg['target_domain_name'] = 'segmentsemantic' cfg['return_accuracy'] = True cfg['target_from_filenames'] = load_ops.semantic_segment_rebalanced # outputs cfg['target_num_channels'] = 17 cfg['target_dim'] = (256, 256) # (1024, 1024) cfg['target_from_filenames_kwargs'] = { 'new_dims': (256, 256), 'domain': 'segmentsemantic' } cfg['mask_by_target_func'] = True # masks # input pipeline cfg['preprocess_fn'] = load_and_specify_preprocessors return cfg