105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
import ast
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import io
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import os
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import subprocess
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from glob import glob
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import numpy as np
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def call_subprocess(cmd):
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process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
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error = False
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for process_line_output in io.TextIOWrapper(process.stdout,
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encoding="utf-8"): # print output of training process to console
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if 'Traceback' in process_line_output:
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error = True
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print(process_line_output)
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if error:
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raise RuntimeError("An error occured during calling subprocess")
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def get_values_from_conff_matrix(path):
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lines = open(path, 'r').readlines()[:6]
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d = {}
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for l in lines:
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key, value, *_ = l.split("\t")
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d.update({key.replace(":", ""): int(value)})
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return d
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def get_bayes_params_as_dict(x):
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return {
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'epochs': int(x[:, 0]),
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'batch-size': int(x[:, 1]),
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'multi-scale': bool(x[:, 2]),
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'img-size': f"{int(x[:, 3])} {int(x[:, 4])}",
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'rect': bool(x[:, 5]),
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'adam': bool(x[:, 6]),
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'giou': float(x[:, 7]), # train hyps start index
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'cls': float(x[:, 8]),
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'cls_pw': float(x[:, 9]),
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'obj': float(x[:, 10]),
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'obj_pw': float(x[:, 11]),
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'iou_t': float(x[:, 12]),
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'lr0': float(x[:, 13]),
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'lrf': float(x[:, 14]),
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'momentum': float(x[:, 15]),
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'weight_decay': float(x[:, 16]),
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'fl_gamma': float(x[:, 17]),
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'hsv_h': float(x[:, 18]),
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'hsv_s': float(x[:, 19]),
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'hsv_v': float(x[:, 20]),
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'degrees': float(x[:, 21]),
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'translate': float(x[:, 22]),
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'scale': float(x[:, 23]),
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'shear': float(x[:, 24]), # train hyps end index
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}
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def load_previous_bayes_experiments(experiments_dir):
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paths = list(glob(os.path.join(experiments_dir, '*bayes_params.txt')))
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if len(paths) == 0:
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print("No bayes files found")
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return None, None
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y_values = []
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x_values = []
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for p in paths:
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file = open(p, 'r')
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lines = file.readlines()
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for line in lines:
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try:
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dict_str, y_val = line.split('\t')
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bayes_dict = ast.literal_eval(dict_str)
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bayes_values = dict_to_numpy(bayes_dict)
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x_values.append(bayes_values)
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y_values.append(float(y_val))
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print("Loaded values from prevous experiments ", dict_str, y_val)
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except:
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raise Exception(f"Cannot parse line {line} from file {p}")
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if not y_values or not x_values:
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print("No bayes files found")
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return None, None
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return np.array(x_values), np.array(y_values).reshape((len(y_values), 1))
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def dict_to_numpy(d):
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x = []
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for key, value in d.items():
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if type(value) == bool:
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x.append(int(value))
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elif key == 'img-size':
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start_img_size, end_img_size = value.split(" ")
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x.append(int(start_img_size))
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x.append(int(end_img_size))
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else:
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x.append(float(value))
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return x
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if __name__ == '__main__':
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get_values_from_conff_matrix('/home/tomekb/yolov3/experiments/2020-08-17_02-05-43/confussion-matrix.tsv') |