110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
import os
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import sys
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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import importlib
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import argparse
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--name', type=str)
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parser.add_argument('--config1', type=str)
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parser.add_argument('--config2', type=str, default='')
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parser.add_argument('--debug', action='store_true')
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parser.add_argument('--iteration1', type=str, default='')
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parser.add_argument('--iteration2', type=str, default='')
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parser.add_argument('-g', '--gpu', type=str)
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args = vars(parser.parse_args())
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if args['debug']:
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from configs.iccvw.pyr_64_128_cape_f_2 import config_1
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else:
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config_module = importlib.import_module(args['config1'])
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config_1 = config_module.config
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config_module = importlib.import_module(args['config2'] if args['config2'] != '' else args['config1'] + '_skirt')
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config_2 = config_module.config
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import time
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import argparse
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import tqdm
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import random
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as ex
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from data.utils import Frame
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def calculate(name):
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errors = dict()
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errors_mean = dict()
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with open(f'{results_folder}/errors/{name}.csv', 'r') as f:
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lines = f.readlines()
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for line in lines:
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line = line.split(',')
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category = line[2]
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if category not in errors:
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errors[category] = []
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errors[category].append(float(line[3]))
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for category in errors:
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errors_mean[category] = np.mean(errors[category])
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return errors_mean
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if __name__ == '__main__':
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name = config_1['name']
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if not config_1['is_cape']:
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frames_file = config_1['test_frames']
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with open(os.path.join(config_1['test_dir'], frames_file), 'r') as f:
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frames = f.readlines()
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frames = [Frame(frames.split()[0], int(frames.split()[1]), frames.split()[2]) for frames in frames]
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else:
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from data.dataset_cape import read_frames
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frames = read_frames(config_1['test_dir'], shuffle=False, is_cape=True)
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results_folder = '/data-local/127_2/results'
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if args['iteration1'] == '':
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folders = [f for f in os.listdir(results_folder) if os.path.isdir(os.path.join(results_folder, f)) and f.startswith(config_1['name'])]
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if not config_1['is_skirt']:
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folders = [f for f in folders if f.find('skirt') == -1]
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assert len(folders) == 1
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name = folders[0]
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else:
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name = f'{name}_{args["iteration1"]}'
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non_skirt = calculate(name)
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name = config_2['name']
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if not config_2['is_cape']:
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frames_file = config_2['test_frames']
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with open(os.path.join(config_2['test_dir'], frames_file), 'r') as f:
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frames = f.readlines()
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frames = [Frame(frames.split()[0], int(frames.split()[1]), frames.split()[2]) for frames in frames]
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else:
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from data.dataset_cape import read_frames
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frames = read_frames(config_2['test_dir'], shuffle=False, is_cape=True)
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results_folder = '/data-local/127_2/results'
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if args['iteration2'] == '':
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folders = [f for f in os.listdir(results_folder) if os.path.isdir(os.path.join(results_folder, f)) and f.startswith(config_2['name'])]
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if not config_2['is_skirt']:
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folders = [f for f in folders if f.find('skirt') == -1]
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assert len(folders) == 1
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name = folders[0]
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else:
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name = f'{name}_{args["iteration2"]}'
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skirt = calculate(name)
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all = {**non_skirt, **skirt}
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print(all)
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mean = np.mean(list(all.values()))
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print(f'Final error: {mean}')
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