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Hello! I trained the maskrcnn model from this repository on my sample:
https://github.com/matterport/Mask_RCNN
During conversion to the platform, the following errors occurred:
Command: python mo_tf.py --input_shape = [1,800,1365,3] --input = image_tensor --tensorflow_custom_operations_config_update = C: \ Users \ Anna \ Downloads \ maskrcnn1 \ maskrcnn \ frozen_model \ mask_rcnn_support.json_tensorflow_flow_object_line = C: \ Users \ Anna \ Downloads \ maskrcnn1 \ maskrcnn \ frozen_model \ pipeline.config --input_model = C: \ Users \ Anna \ Downloads \ maskrcnn1 \ maskrcnn \ frozen_model \ mask_frozen_graph.pb --data_type FP32
Error:[ ERROR ] Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.front.user_data_repack.UserDataRepack'>): No node with name image_tensor.
If I enter the following in input_node: mrcnn_mask / BiasAdd
ERROR: Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.front.input_cut.InputCut'>): Node mrcnn_mask/BiasAdd has more than 1 input and input shapes were provided. Try not to provide input shapes or specify input port with port:node notation, where port is an integer.
3 input(s) detected:
Name: input_image, type: float32, shape: (-1,-1,-1,3)
Name: input_image_meta, type: float32, shape: (-1,14)
Name: input_anchors, type: float32, shape: (-1,-1,4)
1 output(s) detected:
mrcnn_mask/Reshape_1
[UPDATE]
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_CHANNEL_COUNT 3
IMAGE_MAX_DIM 1024
IMAGE_META_SIZE 19
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 object
NUM_CLASSES 7
POOL_SIZE 7
POST_NMS_ROIS_INFERENCE 1000
POST_NMS_ROIS_TRAINING 2000
PRE_NMS_LIMIT 6000
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
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Hi Karmeo,
Thanks for reaching out. OpenVINO™ toolkit supports the Mask RCNN models from the Open Model Zoo (OMZ). The model you are using is not supported because the model architecture you are using seems to be different as the ones in OMZ. As the configuration file (.json) does not match the layer names, you can try to configure the json file to match each layer on your model, but we recommend you to retrain your network and dataset using one of the models on OMZ as the base model.
Best regards,
David C.
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Hi Karmeo,
Thanks for reaching out. OpenVINO™ toolkit supports the Mask RCNN models from the Open Model Zoo (OMZ). The model you are using is not supported because the model architecture you are using seems to be different as the ones in OMZ. As the configuration file (.json) does not match the layer names, you can try to configure the json file to match each layer on your model, but we recommend you to retrain your network and dataset using one of the models on OMZ as the base model.
Best regards,
David C.
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Hi Karmeo,
In case you need additional information, please post a new question as this thread will no longer be monitored.
Regards,
David C.
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