def parse_train_config(config=None): config = {} if not config else config c = AttrDict() c.DATASET_ROOT = config.get("DATASET_ROOT", DATASET_ROOT) c.JSON_PATH = config.get("JSON_PATH", "train.json") c.BATCH_SIZE = config.get("BATCH_SIZE", BATCH_SIZE) c.IMAGE_SIZE = config.get("IMAGE_SIZE", IMAGE_SIZE) c.WORKERS = config.get("WORKERS", WORKERS) c.PIN_MEMORY = config.get("PIN_MEMORY", PIN_MEMORY) c.SHUFFLE = config.get("SHUFFLE", True) c.LEARNING_RATE = config.get("LEARNING_RATE", LEARNING_RATE) c.MOMENTUM = config.get("MOMENTUM", MOMENTUM) c.DAMPENING = config.get("DAMPENING", DAMPENING) c.BETAS = config.get("BETAS", BETAS) c.EPS = config.get("EPS", EPS) c.WEIGHT_DECAY = config.get("WEIGHT_DECAY", WEIGHT_DECAY) c.MILESTONES = config.get("MILESTONES", MILESTONES) c.GAMMA = config.get("GAMMA", GAMMA) c.NUM_EPOCHS = config.get("NUM_EPOCHS", NUM_EPOCHS) c.TEST = config.get("TEST", TEST) c.OUT_PATH = config.get("OUT_PATH", OUT_PATH) c.LOAD_MODEL = config.get("LOAD_MODEL", LOAD_MODEL) c.SAVE_MODEL = config.get("SAVE_MODEL", SAVE_MODEL) c.CHECKPOINT_FILE = config.get("CHECKPOINT_FILE", CHECKPOINT_FILE) return c
def parse_detect_config(config=None): config = {} if not config else config c = AttrDict() c.JSON = config.get("JSON", JSON) c.IMAGE_SIZE = config.get("IMAGE_SIZE", IMAGE_SIZE) c.CHECKPOINT_FILE = config.get("CHECKPOINT_FILE", CHECKPOINT_FILE) return c
def parse_test_config(config=None): config = {} if not config else config c = AttrDict() c.DATASET_ROOT = config.get("DATASET_ROOT", DATASET_ROOT) c.JSON_PATH = config.get("JSON_PATH", "test.json") c.BATCH_SIZE = config.get("BATCH_SIZE", BATCH_SIZE) c.IMAGE_SIZE = config.get("IMAGE_SIZE", IMAGE_SIZE) c.WORKERS = config.get("WORKERS", WORKERS) c.PIN_MEMORY = config.get("PIN_MEMORY", PIN_MEMORY) c.SHUFFLE = config.get("SHUFFLE", False) c.OUT_PATH = config.get("OUT_PATH", OUT_PATH) c.LOAD_MODEL = config.get("LOAD_MODEL", True) c.CHECKPOINT_FILE = config.get("CHECKPOINT_FILE", CHECKPOINT_FILE) return c
def get_params(): checkpoint_dir = '/Users/Nolsigan/PycharmProjects/rlntm-tensorflow/checkpoints' max_length = 6 rnn_cell = rnn.BasicLSTMCell rnn_hidden = 128 learning_rate = 0.003 optimizer = tf.train.AdamOptimizer() gradient_clipping = 5 batch_size = 100 epochs = 30 epoch_size = 100 num_symbols = 10 dup_factor = 2 mem_dim = 128 mem_move_table = [-1, 0, 1] in_move_table = [-1, 0, 1] out_move_table = [0, 1] return AttrDict(**locals())
# Config ############################################################################## from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os import re import torch from attr_dict import AttrDict __C = AttrDict() cfg = __C __C.GLOBAL_RANK = 0 __C.EPOCH = 0 # Absolute path to a location to keep some large files, not in this dir. __C.ASSETS_PATH = '/home/dcg-adlr-atao-data.cosmos277/assets' # Use class weighted loss per batch to increase loss for low pixel count classes per batch __C.BATCH_WEIGHTING = False # Border Relaxation Count __C.BORDER_WINDOW = 1 # Number of epoch to use before turn off border restriction __C.REDUCE_BORDER_EPOCH = -1 # Comma Seperated List of class id to relax __C.STRICTBORDERCLASS = None