def fit_one_epoch(model_rpn, model_all, epoch, epoch_size, epoch_size_val, gen,
genval, Epoch, callback):
total_loss = 0
rpn_loc_loss = 0
rpn_cls_loss = 0
roi_loc_loss = 0
roi_cls_loss = 0
val_toal_loss = 0
with tqdm(total=epoch_size,
desc=f'Epoch {epoch + 1}/{Epoch}',
postfix=dict,
mininterval=0.3) as pbar:
for iteration, batch in enumerate(gen):
if iteration >= epoch_size:
break
X, Y, boxes = batch[0], batch[1], batch[2]
P_rpn = model_rpn.predict_on_batch(X)
height, width, _ = np.shape(X[0])
base_feature_width, base_feature_height = get_img_output_length(
width, height)
anchors = get_anchors([base_feature_width, base_feature_height],
width, height)
results = bbox_util.detection_out_rpn(P_rpn, anchors)
roi_inputs = []
out_classes = []
out_regrs = []
for i in range(len(X)):
R = results[i][:, 1:]
X2, Y1, Y2 = calc_iou(R, config, boxes[i], NUM_CLASSES)
roi_inputs.append(X2)
out_classes.append(Y1)
out_regrs.append(Y2)
loss_class = model_all.train_on_batch(
[X, np.array(roi_inputs)],
[Y[0], Y[1],
np.array(out_classes),
np.array(out_regrs)])
write_log(callback, [
'total_loss', 'rpn_cls_loss', 'rpn_reg_loss',
'detection_cls_loss', 'detection_reg_loss'
], loss_class, iteration)
rpn_cls_loss += loss_class[1]
rpn_loc_loss += loss_class[2]
roi_cls_loss += loss_class[3]
roi_loc_loss += loss_class[4]
total_loss = rpn_loc_loss + rpn_cls_loss + roi_loc_loss + roi_cls_loss
pbar.set_postfix(
**{
'total': total_loss / (iteration + 1),
'rpn_cls': rpn_cls_loss / (iteration + 1),
'rpn_loc': rpn_loc_loss / (iteration + 1),
'roi_cls': roi_cls_loss / (iteration + 1),
'roi_loc': roi_loc_loss / (iteration + 1),
'lr': K.get_value(model_rpn.optimizer.lr)
})
pbar.update(1)
print('Start Validation')
with tqdm(total=epoch_size_val,
desc=f'Epoch {epoch + 1}/{Epoch}',
postfix=dict,
mininterval=0.3) as pbar:
for iteration, batch in enumerate(genval):
if iteration >= epoch_size_val:
break
X, Y, boxes = batch[0], batch[1], batch[2]
P_rpn = model_rpn.predict_on_batch(X)
height, width, _ = np.shape(X[0])
base_feature_width, base_feature_height = get_img_output_length(
width, height)
anchors = get_anchors([base_feature_width, base_feature_height],
width, height)
results = bbox_util.detection_out_rpn(P_rpn, anchors)
roi_inputs = []
out_classes = []
out_regrs = []
for i in range(len(X)):
R = results[i][:, 1:]
X2, Y1, Y2 = calc_iou(R, config, boxes[i], NUM_CLASSES)
roi_inputs.append(X2)
out_classes.append(Y1)
out_regrs.append(Y2)
loss_class = model_all.test_on_batch(
[X, np.array(roi_inputs)],
[Y[0], Y[1],
np.array(out_classes),
np.array(out_regrs)])
val_toal_loss += loss_class[0]
pbar.set_postfix(**{'total': val_toal_loss / (iteration + 1)})
pbar.update(1)
loss_history.append_loss(total_loss / (epoch_size + 1),
val_toal_loss / (epoch_size_val + 1))
print('Finish Validation')
print('Epoch:' + str(epoch + 1) + '/' + str(Epoch))
print('Total Loss: %.4f || Val Loss: %.4f ' %
(total_loss / (epoch_size + 1), val_toal_loss /
(epoch_size_val + 1)))
print('Saving state, iter:', str(epoch + 1))
model_all.save_weights('logs/Epoch%d-Total_Loss%.4f-Val_Loss%.4f.h5' %
((epoch + 1), total_loss /
(epoch_size + 1), val_toal_loss /
(epoch_size_val + 1)))
return
print(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, EPOCH_LENGTH))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
X, Y, boxes = batch[0], batch[1], batch[2]
loss_rpn = model_rpn.train_on_batch(X, Y)
write_log(callback, ['rpn_cls_loss', 'rpn_reg_loss'], loss_rpn,
train_step)
P_rpn = model_rpn.predict_on_batch(X)
height, width, _ = np.shape(X[0])
anchors = get_anchors(get_img_output_length(width, height), width,
height)
# 将预测结果进行解码
results = bbox_util.detection_out(P_rpn,
anchors,
1,
confidence_threshold=0)
R = results[0][:, 2:]
X2, Y1, Y2, IouS = calc_iou(R, config, boxes[0], width, height,
NUM_CLASSES)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
