search

matterport / Mask_RCNN

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Other
24.63k stars 11.7k forks source link

Layer #389 (named "mrcnn_bbox_fc"), weight <tf.Variable 'mrcnn_bbox_fc/kernel:0' shape=(1024, 8) dtype=float32_ref> has shape (1024, 8), but the saved weight has shape (1024, 324) #849

Open 694376965 opened 6 years ago

694376965 commented 6 years ago

hi, when I loaded all the files down, I can run the balloon demo to detect balloon correctly. But, when I want to train a new model with the given balloon data set according to the steps given, there was an error: Layer #389 (named "mrcnn_bbox_fc"), weight <tf.Variable 'mrcnn_bbox_fc/kernel:0' shape=(1024, 8) dtype=float32_ref> has shape (1024, 8), but the saved weight has shape (1024, 324)

Can some one help me? I used the mask_rcnn_coco.h5 as the pretrained model.

python3 balloon.py train --dataset=/opt/projects/samples/balloon/balloonImages/datasets/ --weights=/opt/projects/samples/balloon/mask_rcnn_coco.h5 Using TensorFlow backend. Weights: /opt/projects/samples/balloon/mask_rcnn_coco.h5 Dataset: /opt/projects/samples/balloon/balloonImages/datasets/ Logs: /opt/projects/logs_balloon <main.BalloonConfig object at 0x7efb9d8cc898>

Configurations: BACKBONE resnet101 BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 2 BBOX_STD_DEV [0.1 0.1 0.2 0.2] COMPUTE_BACKBONE_SHAPE None DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.9 DETECTION_NMS_THRESHOLD 0.3 FPN_CLASSIF_FC_LAYERS_SIZE 1024 GPU_COUNT 1 GRADIENT_CLIP_NORM 5.0 IMAGES_PER_GPU 2 IMAGE_MAX_DIM 1024 IMAGE_META_SIZE 14 IMAGE_MIN_DIM 800 IMAGE_MIN_SCALE 0 IMAGE_RESIZE_MODE square IMAGE_SHAPE [1024 1024 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME balloon NUM_CLASSES 2 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (32, 64, 128, 256, 512) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 100 TOP_DOWN_PYRAMID_SIZE 256 TRAIN_BN False TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001

Loading weights /opt/projects/samples/balloon/mask_rcnn_coco.h5 <HDF5 file "mask_rcnn_coco.h5" (mode r)> Traceback (most recent call last): File "balloon.py", line 357, in model.load_weights(weights_path, by_name=True) File "/opt/projects/mrcnn/model.py", line 2140, in load_weights reshape=False) File "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py", line 1017, in load_weights_from_hdf5_group_by_name str(weight_values[i].shape) + '.') ValueError: Layer #389 (named "mrcnn_bbox_fc"), weight <tf.Variable 'mrcnn_bbox_fc/kernel:0' shape=(1024, 8) dtype=float32_ref> has shape (1024, 8), but the saved weight has shape (1024, 324).