| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
|
|
| """ |
| Plotting utilities to visualize training logs. |
| """ |
| import cv2 |
| import torch |
| import pandas as pd |
| import numpy as np |
| import seaborn as sns |
| import matplotlib.pyplot as plt |
|
|
| from torch import Tensor |
|
|
| 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" |
|
|
| |
| |
|
|
| 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)}") |
|
|
| |
| 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}") |
|
|
| |
| 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': |
| |
| 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 = data['precision'] |
| recall = data['params'].recThrs |
| scores = data['scores'] |
| |
| 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 |
|
|
|
|
| def draw_boxes(image: Tensor, boxes: Tensor, color=(0, 255, 0), texts=None) -> np.ndarray: |
| if isinstance(image, Tensor): |
| cv_image = image.detach().cpu().numpy() |
| else: |
| cv_image = image |
| if isinstance(boxes, Tensor): |
| cv_boxes = boxes.detach().cpu().numpy() |
| else: |
| cv_boxes = boxes |
|
|
| tl = round(0.002 * max(image.shape[0:2])) + 1 |
| tf = max(tl - 1, 1) |
| for i in range(len(boxes)): |
| box = cv_boxes[i] |
| x1, y1 = box[0:2] |
| x2, y2 = box[2:4] |
| cv2.rectangle(cv_image, (int(x1), int(y1)), (int(x2), int(y2)), color=color) |
| if texts is not None: |
| cv2.putText(cv_image, texts[i], (int(x1), int(y1+10)), 0, tl/3, [225, 255, 255], |
| thickness=tf, |
| lineType=cv2.LINE_AA) |
| return cv_image |
|
|
|
|
| def draw_ref_pts(image: Tensor, ref_pts: Tensor) -> np.ndarray: |
| if isinstance(image, Tensor): |
| cv_image = image.detach().cpu().numpy() |
| else: |
| cv_image = image |
| if isinstance(ref_pts, Tensor): |
| cv_pts = ref_pts.detach().cpu().numpy() |
| else: |
| cv_pts = ref_pts |
| for i in range(len(cv_pts)): |
| x, y, is_pos = cv_pts[i] |
| color = (0, 1, 0) if is_pos else (1, 1, 1) |
| cv2.circle(cv_image, (int(x), int(y)), 2, color) |
| return cv_image |
|
|
|
|
| def image_hwc2chw(image: np.ndarray): |
| image = np.ascontiguousarray(image.transpose(2, 0, 1)) |
| return image |
|
|
