Bienqq
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car-detection-bayes
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GIoU to default

This commit is contained in:
glenn-jocher 2019-07-07 23:24:34 +02:00
parent 32a52dfb02
commit 70f6379601
3 changed files with 7 additions and 4 deletions
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2
test.py
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@ -64,7 +64,7 @@ def test(
# Plot images with bounding boxes # Plot images with bounding boxes
if batch_i == 0 and not os.path.exists('test_batch0.jpg'): if batch_i == 0 and not os.path.exists('test_batch0.jpg'):
plot_images(imgs=imgs, targets=targets, fname='test_batch0.jpg') plot_images(imgs=imgs, targets=targets, paths=paths, fname='test_batch0.jpg')
# Run model # Run model
inf_out, train_out = model(imgs) # inference and training outputs inf_out, train_out = model(imgs) # inference and training outputs

4
train.py
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@ -178,7 +178,7 @@ def train(
mloss = torch.zeros(5).to(device) # mean losses mloss = torch.zeros(5).to(device) # mean losses
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, _, _) in pbar: for i, (imgs, targets, paths, _) in pbar:
imgs = imgs.to(device) imgs = imgs.to(device)
targets = targets.to(device) targets = targets.to(device)
@ -192,7 +192,7 @@ def train(
# Plot images with bounding boxes # Plot images with bounding boxes
if epoch == 0 and i == 0: if epoch == 0 and i == 0:
plot_images(imgs=imgs, targets=targets, fname='train_batch%g.jpg' % i) plot_images(imgs=imgs, targets=targets, paths=paths, fname='train_batch%g.jpg' % i)
# SGD burn-in # SGD burn-in
if epoch == 0 and i <= n_burnin: if epoch == 0 and i <= n_burnin:

5
utils/utils.py
View File

@ -9,6 +9,7 @@ import torch
import torch.nn as nn import torch.nn as nn
from PIL import Image from PIL import Image
from tqdm import tqdm from tqdm import tqdm
from pathlib import Path
from . import torch_utils from . import torch_utils
from . import google_utils from . import google_utils
@ -611,7 +612,7 @@ def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
fig.savefig('comparison.png', dpi=300) fig.savefig('comparison.png', dpi=300)
def plot_images(imgs, targets, fname='images.jpg'): def plot_images(imgs, targets, paths=None, fname='images.jpg'):
# Plots training images overlaid with targets # Plots training images overlaid with targets
imgs = imgs.cpu().numpy() imgs = imgs.cpu().numpy()
targets = targets.cpu().numpy() targets = targets.cpu().numpy()
@ -627,6 +628,8 @@ def plot_images(imgs, targets, fname='images.jpg'):
plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0)) plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-') plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
plt.axis('off') plt.axis('off')
if paths is not None:
plt.title(Path(paths[i]).name, fontdict={'size': 8})
fig.tight_layout() fig.tight_layout()
fig.savefig(fname, dpi=300) fig.savefig(fname, dpi=300)
plt.close() plt.close()