Spaces:
Runtime error
Runtime error
File size: 4,664 Bytes
3672502 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
# ------------------------------------------------------------------------
# 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
|