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Update model.py #2715

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Update model.py #2715

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10 changes: 5 additions & 5 deletions mrcnn/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -550,7 +550,7 @@ def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config)
# Positive ROIs
positive_count = int(config.TRAIN_ROIS_PER_IMAGE *
config.ROI_POSITIVE_RATIO)
positive_indices = tf.random_shuffle(positive_indices)[:positive_count]
positive_indices = tf.tf.random.shuffle(positive_indices)[:positive_count]
positive_count = tf.shape(positive_indices)[0]
# Negative ROIs. Add enough to maintain positive:negative ratio.
r = 1.0 / config.ROI_POSITIVE_RATIO
Expand Down Expand Up @@ -2155,6 +2155,7 @@ def compile(self, learning_rate, momentum):
metrics. Then calls the Keras compile() function.
"""
# Optimizer object
self.keras_model.metrics_tensors = []
optimizer = keras.optimizers.SGD(
lr=learning_rate, momentum=momentum,
clipnorm=self.config.GRADIENT_CLIP_NORM)
Expand All @@ -2167,8 +2168,8 @@ def compile(self, learning_rate, momentum):
"mrcnn_class_loss", "mrcnn_bbox_loss", "mrcnn_mask_loss"]
for name in loss_names:
layer = self.keras_model.get_layer(name)
if layer.output in self.keras_model.losses:
continue
# if layer.output in self.keras_model.losses:
# continue
loss = (
tf.reduce_mean(layer.output, keepdims=True)
* self.config.LOSS_WEIGHTS.get(name, 1.))
Expand Down Expand Up @@ -2264,8 +2265,7 @@ def set_log_dir(self, model_path=None):
print('Re-starting from epoch %d' % self.epoch)

# Directory for training logs
self.log_dir = os.path.join(self.model_dir, "{}{:%Y%m%dT%H%M}".format(
self.config.NAME.lower(), now))
self.log_dir = os.path.join(self.model_dir, "Object Detection\\Tutorial\\logs")

# Path to save after each epoch. Include placeholders that get filled by Keras.
self.checkpoint_path = os.path.join(self.log_dir, "mask_rcnn_{}_*epoch*.h5".format(
Expand Down