mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
dprint(
'Average number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
dprint(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Results will not be satisfactory. Keep training.'
)
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(P_rpn[0],
P_rpn[1],
C,
use_regr=True,
overlap_thresh=0.7,
max_boxes=300)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2 = roi_helpers.calc_iou(R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
' this implementation relies on multiprocessing,\n',
' you should not pass\n',
' non picklable arguments to the generator\n',
" as they can't be passed\n",
' easily to children processes.\n',
' verbose: verbosity mode, 0 or 1.\n',
'\n',
' Returns:\n',
' Numpy array(s) of predictions.\n',
'\n',
' Raises:\n',
' ValueError: In case the generator yields\n',
' data in an invalid format.\n',
"""
preds_batch = model.predict_on_batch(val_img_array)
"""
([' def predict_on_batch(self, x):\n',
' Returns predictions for a single batch of samples.\n',
'\n',
' Arguments:\n',
' x: Input samples, as a Numpy array.\n',
'\n',
' Returns:\n',
' Numpy array(s) of predictions.\n',
"""
# how weights of model are saved
model.save_weights("/Users/Natsume/Downloads/data_for_all/dogscats/experiments/weights.h5")
