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| # ------------------------------------------------------------------------ | |
| # Deformable DETR | |
| # Copyright (c) 2020 SenseTime. All Rights Reserved. | |
| # Licensed under the Apache License, Version 2.0 [see LICENSE for details] | |
| # ------------------------------------------------------------------------ | |
| # Modified from DETR (https://github.com/facebookresearch/detr) | |
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| # ------------------------------------------------------------------------ | |
| """ | |
| Plotting utilities to visualize training logs. | |
| """ | |
| import torch | |
| import pandas as pd | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| from pathlib import Path, PurePath | |
| def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'): | |
| ''' | |
| Function to plot specific fields from training log(s). Plots both training and test results. | |
| :: Inputs - logs = list containing Path objects, each pointing to individual dir with a log file | |
| - fields = which results to plot from each log file - plots both training and test for each field. | |
| - ewm_col = optional, which column to use as the exponential weighted smoothing of the plots | |
| - log_name = optional, name of log file if different than default 'log.txt'. | |
| :: Outputs - matplotlib plots of results in fields, color coded for each log file. | |
| - solid lines are training results, dashed lines are test results. | |
| ''' | |
| func_name = "plot_utils.py::plot_logs" | |
| # verify logs is a list of Paths (list[Paths]) or single Pathlib object Path, | |
| # convert single Path to list to avoid 'not iterable' error | |
| if not isinstance(logs, list): | |
| if isinstance(logs, PurePath): | |
| logs = [logs] | |
| print(f"{func_name} info: logs param expects a list argument, converted to list[Path].") | |
| else: | |
| raise ValueError(f"{func_name} - invalid argument for logs parameter.\n \ | |
| Expect list[Path] or single Path obj, received {type(logs)}") | |
| # verify valid dir(s) and that every item in list is Path object | |
| for i, dir in enumerate(logs): | |
| if not isinstance(dir, PurePath): | |
| raise ValueError(f"{func_name} - non-Path object in logs argument of {type(dir)}: \n{dir}") | |
| if dir.exists(): | |
| continue | |
| raise ValueError(f"{func_name} - invalid directory in logs argument:\n{dir}") | |
| # load log file(s) and plot | |
| dfs = [pd.read_json(Path(p) / log_name, lines=True) for p in logs] | |
| fig, axs = plt.subplots(ncols=len(fields), figsize=(16, 5)) | |
| for df, color in zip(dfs, sns.color_palette(n_colors=len(logs))): | |
| for j, field in enumerate(fields): | |
| if field == 'mAP': | |
| coco_eval = pd.DataFrame(pd.np.stack(df.test_coco_eval.dropna().values)[:, 1]).ewm(com=ewm_col).mean() | |
| axs[j].plot(coco_eval, c=color) | |
| else: | |
| df.interpolate().ewm(com=ewm_col).mean().plot( | |
| y=[f'train_{field}', f'test_{field}'], | |
| ax=axs[j], | |
| color=[color] * 2, | |
| style=['-', '--'] | |
| ) | |
| for ax, field in zip(axs, fields): | |
| ax.legend([Path(p).name for p in logs]) | |
| ax.set_title(field) | |
| def plot_precision_recall(files, naming_scheme='iter'): | |
| if naming_scheme == 'exp_id': | |
| # name becomes exp_id | |
| names = [f.parts[-3] for f in files] | |
| elif naming_scheme == 'iter': | |
| names = [f.stem for f in files] | |
| else: | |
| raise ValueError(f'not supported {naming_scheme}') | |
| fig, axs = plt.subplots(ncols=2, figsize=(16, 5)) | |
| for f, color, name in zip(files, sns.color_palette("Blues", n_colors=len(files)), names): | |
| data = torch.load(f) | |
| # precision is n_iou, n_points, n_cat, n_area, max_det | |
| precision = data['precision'] | |
| recall = data['params'].recThrs | |
| scores = data['scores'] | |
| # take precision for all classes, all areas and 100 detections | |
| precision = precision[0, :, :, 0, -1].mean(1) | |
| scores = scores[0, :, :, 0, -1].mean(1) | |
| prec = precision.mean() | |
| rec = data['recall'][0, :, 0, -1].mean() | |
| print(f'{naming_scheme} {name}: mAP@50={prec * 100: 05.1f}, ' + | |
| f'score={scores.mean():0.3f}, ' + | |
| f'f1={2 * prec * rec / (prec + rec + 1e-8):0.3f}' | |
| ) | |
| axs[0].plot(recall, precision, c=color) | |
| axs[1].plot(recall, scores, c=color) | |
| axs[0].set_title('Precision / Recall') | |
| axs[0].legend(names) | |
| axs[1].set_title('Scores / Recall') | |
| axs[1].legend(names) | |
| return fig, axs | |