def __call__(self, trainer):
if _available:
# Dynamically import pyplot so that the backend of matplotlib
# can be configured after importing chainercv.
import matplotlib.pyplot as plt
else:
return
if hasattr(self.iterator, 'reset'):
self.iterator.reset()
it = self.iterator
else:
it = copy.copy(self.iterator)
idx = 0
while True:
try:
batch = next(it)
except StopIteration:
break
imgs = [img for img, _, _ in batch]
pred_bboxes, pred_labels, pred_scores = self.target.predict(imgs)
for (img, gt_bbox, gt_label), pred_bbox, pred_label, pred_score \
in zip(batch, pred_bboxes, pred_labels, pred_scores):
pred_bbox = chainer.backends.cuda.to_cpu(pred_bbox)
pred_label = chainer.backends.cuda.to_cpu(pred_label)
pred_score = chainer.backends.cuda.to_cpu(pred_score)
out_file = self.filename.format(
index=idx, iteration=trainer.updater.iteration)
out_file = os.path.join(trainer.out, out_file)
fig = plt.figure()
ax_gt = fig.add_subplot(2, 1, 1)
ax_gt.set_title('ground truth')
vis_bbox(img,
gt_bbox,
gt_label,
label_names=self.label_names,
ax=ax_gt)
ax_pred = fig.add_subplot(2, 1, 2)
ax_pred.set_title('prediction')
vis_bbox(img,
pred_bbox,
pred_label,
pred_score,
label_names=self.label_names,
ax=ax_pred)
plt.savefig(out_file)
plt.close()
idx += 1
def __call__(self, trainer):
if _available:
# Dynamically import pyplot so that the backend of matplotlib
# can be configured after importing chainercv.
import matplotlib.pyplot as plot
else:
return
if hasattr(self.iterator, 'reset'):
self.iterator.reset()
it = self.iterator
else:
it = copy.copy(self.iterator)
idx = 0
while True:
try:
batch = next(it)
except StopIteration:
break
imgs = [img for img, _, _ in batch]
pred_bboxes, pred_labels, pred_scores = self.target.predict(imgs)
for (img, gt_bbox, gt_label), pred_bbox, pred_label, pred_score \
in zip(batch, pred_bboxes, pred_labels, pred_scores):
pred_bbox = chainer.cuda.to_cpu(pred_bbox)
pred_label = chainer.cuda.to_cpu(pred_label)
pred_score = chainer.cuda.to_cpu(pred_score)
out_file = self.filename.format(
index=idx, iteration=trainer.updater.iteration)
out_file = os.path.join(trainer.out, out_file)
fig = plot.figure()
ax_gt = fig.add_subplot(2, 1, 1)
ax_gt.set_title('ground truth')
vis_bbox(
img, gt_bbox, gt_label,
label_names=self.label_names, ax=ax_gt)
ax_pred = fig.add_subplot(2, 1, 2)
ax_pred.set_title('prediction')
vis_bbox(
img, pred_bbox, pred_label, pred_score,
label_names=self.label_names, ax=ax_pred)
plot.savefig(out_file)
plot.close()
idx += 1