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config.py
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import os
import numpy as np
class Config:
def __init__(self):
self._configs = {}
self._configs["dataset"] = None
# Training Config
self._configs["display"] = 5
self._configs["decay_step"] = 12000
self._configs["epoch_num"] = 100
self._configs["stepsize"] = 450000
self._configs["learning_rate"] = 0.00025
self._configs["decay_rate"] = 0.95
self._configs["max_iter"] = 500000
self._configs["val_iter"] = 100
self._configs["batch_size"] = 5
self._configs["snapshot_name"] = 'corner_net'
self._configs["prefetch_size"] = 100
self._configs["weight_decay"] = False
self._configs["weight_decay_rate"] = 1e-5
self._configs["weight_decay_type"] = "l2"
self._configs["pretrain"] = None
self._configs["opt_algo"] = "adam"
self._configs["chunk_sizes"] = [4, 5, 5, 5, 5, 5, 5, 5, 5, 5]
# Directories
self._configs["data_dir"] = "./data"
self._configs["cache_dir"] = "./cache"
self._configs["config_dir"] = "./config"
self._configs["result_dir"] = "./results"
self._configs["debug_dir"] = "./debug"
# Split
self._configs["train_split"] = "trainval"
self._configs["val_split"] = "minival"
self._configs["test_split"] = "testdev"
# Rng
self._configs["data_rng"] = np.random.RandomState(123)
self._configs["nnet_rng"] = np.random.RandomState(317)
#data_config
self._configs["categories"] =80
self._configs["rand_scale_min"] =0.6
self._configs["rand_scale_max"] =1.4
self._configs["rand_scale_step"] =0.1
self._configs["rand_scales"] =[0.5,0.75,1,1.25,1.5]
self._configs["rand_crop"] =True
self._configs["rand_color"] =True
self._configs["border"] =128
self._configs["gaussian_bump"] =True
self._configs["input_size"] =[511,511]
self._configs["output_sizes"] =[[128,128]]
self._configs["test_scales"] =[0.5,0.75,1,1.25,1.5]
self._configs["top_k"] =100
self._configs["ae_threshold"] =0.5
self._configs["nms_threshold"] =0.5
self._configs["merge_bbox"] =True
self._configs["weight_exp"] =10
self._configs["max_per_image"] =100
self._configs["gaussian_radius"] =-1
self._configs["gaussian_iou"] =0.7
@property
def gaussian_iou(self):
return self._configs["gaussian_iou"]
@property
def gaussian_radius(self):
return self._configs["gaussian_radius"]
@property
def categories(self):
return self._configs["categories"]
@property
def rand_scale_min(self):
return self._configs["rand_scale_min"]
@property
def rand_scale_max(self):
return self._configs["rand_scale_max"]
@property
def rand_scale_step(self):
return self._configs["rand_scale_step"]
@property
def rand_scales(self):
return self._configs["rand_scales"]
@property
def rand_crop(self):
return self._configs["rand_crop"]
@property
def rand_color(self):
return self._configs["rand_color"]
@property
def border(self):
return self._configs["border"]
@property
def gaussian_bump(self):
return self._configs["gaussian_bump"]
@property
def input_size(self):
return self._configs["input_size"]
@property
def output_sizes(self):
return self._configs["output_sizes"]
@property
def test_scales(self):
return self._configs["test_scales"]
@property
def top_k(self):
return self._configs["top_k"]
@property
def ae_threshold(self):
return self._configs["ae_threshold"]
@property
def nms_threshold(self):
return self._configs["nms_threshold"]
@property
def merge_bbox(self):
return self._configs["merge_bbox"]
@property
def weight_exp(self):
return self._configs["weight_exp"]
@property
def max_per_image(self):
return self._configs["max_per_image"]
@property
def chunk_sizes(self):
return self._configs["chunk_sizes"]
@property
def train_split(self):
return self._configs["train_split"]
@property
def val_split(self):
return self._configs["val_split"]
@property
def test_split(self):
return self._configs["test_split"]
@property
def full(self):
return self._configs
@property
def sampling_function(self):
return self._configs["sampling_function"]
@property
def data_rng(self):
return self._configs["data_rng"]
@property
def nnet_rng(self):
return self._configs["nnet_rng"]
@property
def opt_algo(self):
return self._configs["opt_algo"]
@property
def weight_decay_type(self):
return self._configs["weight_decay_type"]
@property
def prefetch_size(self):
return self._configs["prefetch_size"]
@property
def pretrain(self):
return self._configs["pretrain"]
@property
def weight_decay_rate(self):
return self._configs["weight_decay_rate"]
@property
def weight_decay(self):
return self._configs["weight_decay"]
@property
def result_dir(self):
result_dir = os.path.join(self._configs["result_dir"], self.snapshot_name)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
return result_dir
@property
def debug_dir(self):
debug_dir = os.path.join(self.cache_dir, "debug")
if not os.path.exists(debug_dir):
os.makedirs(debug_dir)
return debug_dir
@property
def dataset(self):
return self._configs["dataset"]
@property
def snapshot_name(self):
return self._configs["snapshot_name"]
@property
def snapshot_dir(self):
snapshot_dir = os.path.join(self.cache_dir, "nnet", self.snapshot_name)
if not os.path.exists(snapshot_dir):
os.makedirs(snapshot_dir)
return snapshot_dir
@property
def snapshot_file(self):
snapshot_file = os.path.join(self.snapshot_dir, self.snapshot_name + ".ckpt")
return snapshot_file
@property
def config_dir(self):
return self._configs["config_dir"]
@property
def batch_size(self):
return self._configs["batch_size"]
@property
def epoch_num(self):
return self._configs["epoch_num"]
@property
def max_iter(self):
return self._configs["max_iter"]
@property
def learning_rate(self):
return self._configs["learning_rate"]
@property
def decay_rate(self):
return self._configs["decay_rate"]
@property
def decay_step(self):
return self._configs["decay_step"]
@property
def stepsize(self):
return self._configs["stepsize"]
@property
def snapshot(self):
return self._configs["snapshot"]
@property
def display(self):
return self._configs["display"]
@property
def val_iter(self):
return self._configs["val_iter"]
@property
def data_dir(self):
return self._configs["data_dir"]
@property
def cache_dir(self):
if not os.path.exists(self._configs["cache_dir"]):
os.makedirs(self._configs["cache_dir"])
return self._configs["cache_dir"]
def update_config(self, new):
for key in new:
if key in self._configs:
self._configs[key] = new[key]
cfg = Config()