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Update plot_study_txt() (#1533)

5.0
Glenn Jocher GitHub 3 years ago
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1 changed files with 5 additions and 6 deletions
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    -6
      utils/plots.py

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utils/plots.py View File

# Plotting utils # Plotting utils


import glob import glob
import math
import os import os
import random import random
from copy import copy from copy import copy
from pathlib import Path from pathlib import Path


import cv2 import cv2
import math
import matplotlib import matplotlib
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
plt.savefig('targets.jpg', dpi=200) plt.savefig('targets.jpg', dpi=200)




def plot_study_txt(f='study.txt', x=None): # from utils.plots import *; plot_study_txt()
def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt()
# Plot study.txt generated by test.py # Plot study.txt generated by test.py
fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
ax = ax.ravel() ax = ax.ravel()


fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
for f in ['study/study_coco_%s.txt' % x for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]:
for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]:
y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
x = np.arange(y.shape[1]) if x is None else np.array(x) x = np.arange(y.shape[1]) if x is None else np.array(x)
s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']


j = y[3].argmax() + 1 j = y[3].argmax() + 1
ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))


ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
ax2.set_xlabel('GPU Speed (ms/img)') ax2.set_xlabel('GPU Speed (ms/img)')
ax2.set_ylabel('COCO AP val') ax2.set_ylabel('COCO AP val')
ax2.legend(loc='lower right') ax2.legend(loc='lower right')
plt.savefig('study_mAP_latency.png', dpi=300)
plt.savefig(f.replace('.txt', '.png'), dpi=300)
plt.savefig('test_study.png', dpi=300)




def plot_labels(labels, save_dir=''): def plot_labels(labels, save_dir=''):

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