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Increase plot_labels() speed (#1736)

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Glenn Jocher 2020-12-18 18:05:38 -08:00 committed by GitHub
parent 49abc722fc
commit 685d601308
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2 changed files with 10 additions and 18 deletions
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2
train.py
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@ -205,7 +205,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device)) # model._initialize_biases(cf.to(device))
if plots: if plots:
Thread(target=plot_labels, args=(labels, save_dir, loggers), daemon=True).start() plot_labels(labels, save_dir, loggers)
if tb_writer: if tb_writer:
tb_writer.add_histogram('classes', c, 0) tb_writer.add_histogram('classes', c, 0)

22
utils/plots.py
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@ -11,6 +11,8 @@ import cv2
import matplotlib import matplotlib
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
import pandas as pd
import seaborn as sns
import torch import torch
import yaml import yaml
from PIL import Image, ImageDraw from PIL import Image, ImageDraw
@ -253,34 +255,24 @@ def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_tx
def plot_labels(labels, save_dir=Path(''), loggers=None): def plot_labels(labels, save_dir=Path(''), loggers=None):
# plot dataset labels # plot dataset labels
print('Plotting labels... ')
c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
nc = int(c.max() + 1) # number of classes nc = int(c.max() + 1) # number of classes
colors = color_list() colors = color_list()
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
# seaborn correlogram # seaborn correlogram
try: sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
import seaborn as sns
import pandas as pd
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
plot_kws=dict(s=3, edgecolor=None, linewidth=1, alpha=0.02),
diag_kws=dict(bins=50))
plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
plt.close() plt.close()
except Exception as e:
pass
# matplotlib labels # matplotlib labels
matplotlib.use('svg') # faster matplotlib.use('svg') # faster
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
ax[0].set_xlabel('classes') ax[0].set_xlabel('classes')
ax[2].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet') sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
ax[2].set_xlabel('x') sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
ax[2].set_ylabel('y')
ax[3].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
ax[3].set_xlabel('width')
ax[3].set_ylabel('height')
# rectangles # rectangles
labels[:, 1:3] = 0.5 # center labels[:, 1:3] = 0.5 # center