def __init__(self):
self.seq_set = -1
self.seq = ()
self.load = 0
self.save = 1
self.start = 0
self.load_prefix = MultiPath()
self.save_prefix = MultiPath()
self.results_dir_root = MultiPath()
self.results_dir = MultiPath()
def __init__(self):
self.cfg = ()
self.codec = 'H264'
self.del_patch_seq = 0
self.disp_resize_factor = 1.0
self.end_id = -1
self.fname_templ = 'img'
self.fps = 30
self.height = 720
self.images_ext = 'tif'
self.labels_ext = 'png'
self.labels_path = ''
self.method = 0
self.n_classes = 3
self.n_frames = 0
self.normalize_patches = 0
self.out_ext = 'png'
self.patch_ext = 'png'
self.patch_height = 32
self.patch_width = 0
self.resize_factor = 1.0
self.seq_name = 'Training'
self.show_img = 0
self.stacked = 0
self.start_id = 0
self.src_path = ''
self.patch_seq_path = ''
self.stitched_seq_path = ''
self.db_root_dir = '/data'
self.patch_seq_name = ''
self.patch_seq_type = 'images'
self.labels_dir = 'Labels'
self.images_dir = 'Images'
self.class_info_path = 'data/classes_ice.txt'
self.width = 1280
self.model_info = MultiPath()
self.train_info = MultiPath()
self.train_split = MultiPath()
self.dataset = ''
def __init__(self):
RunParams.__init__(self)
self.seq_set_info = MultiPath()
self.eval_dir = MultiPath()
self.mode = 1
self.evaluate = 1
self.eval_dist_type = 0
self.eval_file = 'mot_metrics.log'
self.subseq_postfix = 1
self._load_prefix = None
self._save_prefix = None
def __init__(self):
RunParams.__init__(self)
self.load_dir = ''
self.load_id = -1
self.seq_set = 4
self.seq = (5, )
self.active_pt = 1
self.active_pt_dir = MultiPath()
self.load_prefix = MultiPath('trained')
self.save_prefix = MultiPath('trained')
self.results_dir_root = MultiPath()
self.results_dir = MultiPath('log')
def __init__(self):
self.cfg_root = 'cfg'
self.cfg_ext = 'cfg'
self.cfg = ()
self.dataset = ''
self.seq_name = MultiPath()
self.image_dir = 'Images'
self.labels_dir = 'Labels'
self.seq_name = MultiPath()
self.n_classes = 3
self.db_root_dir = ''
self.src_path = ''
self.labels_path = ''
self.img_ext = 'tif'
self.labels_ext = 'jpg'
self.out_img_ext = 'jpg'
self.out_labels_ext = 'png'
self.py_exe = 'python36'
self.seq_id = -1
self.seq_start_id = 0
self.seq_end_id = -1
self.start_id = 0
self.end_id = -1
self.enable_flip = 0
self.max_rot = 345
self.max_stride = 0
self.min_rot = 15
self.min_stride = 10
self.n_frames = 0
self.n_rot = 3
self.patch_height = 32
self.patch_width = 0
self.show_img = 0
self.parallel = 1
self.log_to_file = 0
self.log_dir = 'log'
def __init__(self):
self.cfg = ()
self.end_id = -1
self.images_ext = 'png'
self.labels_ext = 'png'
self.log_dir = ''
self.normalize_labels = 1
self.out_ext = 'jpg'
self.save_path = ''
self.save_stitched = 1
self.seg_ext = 'png'
self.seg_path = ''
self.selective_mode = 0
self.show_img = 0
self.start_id = 0
self.stitch = 0
self.stitch_seg = 1
self.no_labels = 1
self.class_info_path = 'data/classes_ice.txt'
self.multi_sequence_db = 0
self.seg_on_subset = 0
self.add_border = 0
self.blended = 1
self.log_root_dir = 'log'
self.db_root_dir = '/data'
self.images_path = ''
self.labels_path = ''
self.labels_dir = 'labels'
self.images_dir = 'images'
self.dataset = ''
self.model_info = MultiPath()
self.train_split = MultiPath()
self.vis_split = MultiPath()
self.train_info = MultiPath()
self.vis_info = MultiPath()
def __init__(self, name):
self.path = ''
self.src_dir = MultiPath(name)
self.fix_frame_ids = 1
self.sort_by_frame_ids = 0
self.ignore_ioa_thresh = 0.5
self.allow_missing = 0
self.help = {
'path':
'path of the text file in MOT format from where the objects data is to be read;'
'if this is empty, then a default path is constructed from the sequence and dataset names',
'fix_frame_ids':
'convert the frame IDs in the annotations and detections from 1-based '
'(default MOT challenge format) to 0-based that is needed for internal'
' processing convenience',
'sort_by_frame_ids':
'sort data by frame IDs',
'ignored_regions':
'1: read ignored_regions from annotations; '
'2: discard the regions after reading'
}
def __init__(self):
self.cfg = ()
self.add_flipped_images = False
self.add_image_level_feature = False
self.also_save_raw_predictions = True
self.also_save_vis_predictions = 0
self.alsologtostderr = False
self.aspp_with_batch_norm = True
self.aspp_with_separable_conv = True
self.atrous_rates = []
self.colormap_type = 'pascal'
self.decoder_output_stride = None
self.decoder_use_separable_conv = True
self.dense_prediction_cell_json = ''
self.depth_multiplier = 1.0
self.divisible_by = None
self.eval_interval_secs = 0
self.eval_scales = [1.0, ]
self.image_pooling_crop_size = None
self.image_pooling_stride = [1, 1]
self.image_pyramid = None
self.logits_kernel_size = 1
self.logtostderr = False
self.master = ''
self.max_number_of_iterations = 1
self.max_resize_value = None
self.merge_method = 'max'
self.min_resize_value = None
self.model_variant = 'mobilenet_v2'
self.multi_grid = None
self.nas_stem_output_num_conv_filters = 20
self.only_check_args = False
self.op_conversion_fallback_to_while_loop = False
self.output_stride = 16
self.pdb_post_mortem = False
self.prediction_with_upsampled_logits = True
self.profile_file = None
self.quantize_delay_step = -1
self.resize_factor = None
self.run_with_pdb = False
self.run_with_profiling = False
self.showprefixforinfo = True
self.stderrthreshold = 'fatal'
self.test_random_seed = 301
self.test_randomize_ordering_seed = None
self.test_srcdir = ''
self.use_bounded_activation = False
self.use_cprofile_for_profiling = True
self.v = -1
self.verbosity = -1
self.vis_batch_size = 1
self.vis_crop_size = [513, 513]
self.model_info = MultiPath()
self.train_info = MultiPath()
self.train_split = MultiPath()
self.vis_info = MultiPath()
self.vis_split = MultiPath()
self.dataset = ''
self.vis_type = 'test'
self.dataset_dir = ''
self.db_root_dir = '/data'
self.xml_output_file = ''
self.log_dir = ''
self.checkpoint_dir = ''
self.vis_logdir = ''
self.class_info_path = 'data/classes_ice.txt'
def __init__(self):
self.logtostderr = False
self.alsologtostderr = False
self.stderrthreshold = 'fatal'
self.showprefixforinfo = True
self.v = -1
self.verbosity = -1
self.run_with_pdb = False
self.pdb_post_mortem = False
self.run_with_profiling = False
self.profile_file = None
self.use_cprofile_for_profiling = True
self.only_check_args = False
self.op_conversion_fallback_to_while_loop = False
self.test_random_seed = 301
self.test_randomize_ordering_seed = 0
self.xml_output_file = ''
self.num_clones = 1
self.clone_on_cpu = False
self.num_replicas = 1
self.startup_delay_steps = 15
self.num_ps_tasks = 0
self.master = ''
self.task = 0
self.log_steps = 10
self.save_interval_secs = 12
self.save_summaries_secs = 6
self.save_summaries_images = True
self.profile_logdir = None
self.learning_policy = 'poly'
self.base_learning_rate = 0.0001
self.learning_rate_decay_factor = 0.1
self.learning_rate_decay_step = 2000
self.learning_power = 0.9
self.train_steps = 5000
self.momentum = 0.9
self.weight_decay = 4e-05
self.train_crop_size = [513, 513]
self.last_layer_gradient_multiplier = 1.0
self.upsample_logits = True
self.drop_path_keep_prob = 1.0
self.tf_initial_checkpoint = None
self.initialize_last_layer = True
self.last_layers_contain_logits_only = False
self.slow_start_step = 0
self.slow_start_learning_rate = 0.0001
self.fine_tune_batch_norm = True
self.min_scale_factor = 0.5
self.max_scale_factor = 2.0
self.scale_factor_step_size = 0.25
self.atrous_rates = [6, 12, 18]
self.output_stride = 16
self.hard_example_mining_step = 0
self.top_k_percent_pixels = 1.0
self.quantize_delay_step = -1
self.allow_memory_growth = 1
self.gpu_memory_fraction = 1.0
self.min_resize_value = None
self.max_resize_value = None
self.resize_factor = None
self.logits_kernel_size = 1
self.model_variant = 'mobilenet_v2'
self.image_pyramid = []
self.add_image_level_feature = True
self.image_pooling_crop_size = None
self.image_pooling_stride = [1, 1]
self.aspp_with_batch_norm = True
self.aspp_with_separable_conv = True
self.multi_grid = None
self.depth_multiplier = 1.0
self.divisible_by = None
self.decoder_output_stride = None
self.decoder_use_separable_conv = True
self.merge_method = 'max'
self.prediction_with_upsampled_logits = True
self.dense_prediction_cell_json = ''
self.nas_stem_output_num_conv_filters = 20
self.use_bounded_activation = False
self.train_batch_size = 8
self.db_root_dir = '/data'
self.dataset = ''
self.dataset_dir = ''
self.model_info = MultiPath()
self.train_info = MultiPath()
self.train_split = MultiPath()
self.log_dir = ''
self.tb_dir = ''
self.checkpoint_dir = ''
self.class_info_path = 'data/classes_ice.txt'