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yolov5
Unverified 提交 4250f84d authored 作者: Glenn Jocher's avatar Glenn Jocher 提交者： GitHub
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Update PR curve (#1428)

* Update PR curve * legend outside * list(Path().glob())
上级 8d2d6d23
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正在显示 包含 35 行增加 和 25 行删除
test.py
浏览文件 @ 4250f84d
......@@ -213,7 +213,7 @@ def test(data,
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png')
p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95]
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
......
utils/metrics.py
浏览文件 @ 4250f84d
# Model validation metrics
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
......@@ -10,7 +12,7 @@ def fitness(x):
return (x[:, :4] * w).sum(1)
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png'):
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
......@@ -19,7 +21,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-re
pred_cls: Predicted object classes (nparray).
target_cls: True object classes (nparray).
plot: Plot precision-recall curve at mAP@0.5
fname: Plot filename
save_dir: Plot save directory
# Returns
The average precision as computed in py-faster-rcnn.
"""
......@@ -66,17 +68,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-re
f1 = 2 * p * r / (p + r + 1e-16)
if plot:
py = np.stack(py, axis=1)
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.plot(px, py, linewidth=0.5, color='grey') # plot(recall, precision)
ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
ax.set_xlabel('Recall')
ax.set_ylabel('Precision')
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
plt.legend()
fig.tight_layout()
fig.savefig(fname, dpi=200)
plot_pr_curve(px, py, ap, save_dir, names)
return p, r, ap, f1, unique_classes.astype('int32')
......@@ -108,3 +100,23 @@ def compute_ap(recall, precision):
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
return ap, mpre, mrec
def plot_pr_curve(px, py, ap, save_dir='.', names=()):
fig, ax = plt.subplots(1, 1, figsize=(9, 6))
py = np.stack(py, axis=1)
if 0 < len(names) < 21: # show mAP in legend if < 10 classes
for i, y in enumerate(py.T):
ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0]) # plot(recall, precision)
else:
ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)
ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
ax.set_xlabel('Recall')
ax.set_ylabel('Precision')
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
fig.tight_layout()
fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250)
utils/plots.py
浏览文件 @ 4250f84d
......@@ -65,7 +65,7 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
def plot_wh_methods(): # from utils.general import *; plot_wh_methods()
def plot_wh_methods(): # from utils.plots import *; plot_wh_methods()
# Compares the two methods for width-height anchor multiplication
# https://github.com/ultralytics/yolov3/issues/168
x = np.arange(-4.0, 4.0, .1)
......@@ -200,7 +200,7 @@ def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
def plot_test_txt(): # from utils.general import *; plot_test()
def plot_test_txt(): # from utils.plots import *; plot_test()
# Plot test.txt histograms
x = np.loadtxt('test.txt', dtype=np.float32)
box = xyxy2xywh(x[:, :4])
......@@ -217,7 +217,7 @@ def plot_test_txt(): # from utils.general import *; plot_test()
plt.savefig('hist1d.png', dpi=200)
def plot_targets_txt(): # from utils.general import *; plot_targets_txt()
def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
# Plot targets.txt histograms
x = np.loadtxt('targets.txt', dtype=np.float32).T
s = ['x targets', 'y targets', 'width targets', 'height targets']
......@@ -230,7 +230,7 @@ def plot_targets_txt(): # from utils.general import *; plot_targets_txt()
plt.savefig('targets.jpg', dpi=200)
def plot_study_txt(f='study.txt', x=None): # from utils.general import *; plot_study_txt()
def plot_study_txt(f='study.txt', x=None): # from utils.plots import *; plot_study_txt()
# Plot study.txt generated by test.py
fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
ax = ax.ravel()
......@@ -294,7 +294,7 @@ def plot_labels(labels, save_dir=''):
pass
def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.general import *; plot_evolution()
def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution()
# Plot hyperparameter evolution results in evolve.txt
with open(yaml_file) as f:
hyp = yaml.load(f, Loader=yaml.FullLoader)
......@@ -318,7 +318,7 @@ def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.general im
print('\nPlot saved as evolve.png')
def plot_results_overlay(start=0, stop=0): # from utils.general import *; plot_results_overlay()
def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay()
# Plot training 'results*.txt', overlaying train and val losses
s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends
t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles
......@@ -342,20 +342,18 @@ def plot_results_overlay(start=0, stop=0): # from utils.general import *; plot_
def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
# from utils.general import *; plot_results(save_dir='runs/train/exp0')
# Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
# Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
fig, ax = plt.subplots(2, 5, figsize=(12, 6))
ax = ax.ravel()
s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
if bucket:
# os.system('rm -rf storage.googleapis.com')
# files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
files = ['results%g.txt' % x for x in id]
c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
os.system(c)
else:
files = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt')
files = list(Path(save_dir).glob('results*.txt'))
assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
for fi, f in enumerate(files):
try:
......@@ -367,7 +365,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
if i in [0, 1, 2, 5, 6, 7]:
y[y == 0] = np.nan # don't show zero loss values
# y /= y[0] # normalize
label = labels[fi] if len(labels) else Path(f).stem
label = labels[fi] if len(labels) else f.stem
ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
ax[i].set_title(s[i])
# if i in [5, 6, 7]: # share train and val loss y axes
......
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