Custom Training: Infer_shape error when training rcnn about mx-rcnn HOT 7 CLOSED

data = mx.symbol.Variable(name="data")
rois = mx.symbol.Variable(name='rois')
label = mx.symbol.Variable(name='label')
bbox_target = mx.symbol.Variable(name='bbox_target')
bbox_weight = mx.symbol.Variable(name='bbox_weight')

# reshape input
rois = mx.symbol.Reshape(data=rois, shape=(-1, 5), name='rois_reshape')
label = mx.symbol.Reshape(data=label, shape=(-1, ), name='label_reshape')
bbox_target = mx.symbol.Reshape(data=bbox_target, shape=(-1, 4 * num_classes), name='bbox_target_reshape')
bbox_weight = mx.symbol.Reshape(data=bbox_weight, shape=(-1, 4 * num_classes), name='bbox_weight_reshape')

# shared convolutional layers
relu5_3 = get_vgg_conv(data)

# Fast R-CNN
pool5 = mx.symbol.ROIPooling(
    name='roi_pool5', data=relu5_3, rois=rois, pooled_size=(7, 7), spatial_scale=1.0 / config.RCNN_FEAT_SRTIDE)
# group 6
flatten = mx.symbol.Flatten(data=pool5, name="flatten")
fc6 = mx.symbol.FullyConnected(data=flatten, num_hidden=4096, name="fc6")
relu6 = mx.symbol.Activation(data=fc6, act_type="relu", name="relu6")
drop6 = mx.symbol.Dropout(data=relu6, p=0.5, name="drop6")
# group 7
fc7 = mx.symbol.FullyConnected(data=drop6, num_hidden=4096, name="fc7")
relu7 = mx.symbol.Activation(data=fc7, act_type="relu", name="relu7")
drop7 = mx.symbol.Dropout(data=relu7, p=0.5, name="drop7")
# classification
cls_score = mx.symbol.FullyConnected(name='cls_score', data=drop7, num_hidden=num_classes)
cls_prob = mx.symbol.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='batch')
# bounding box regression
bbox_pred = mx.symbol.FullyConnected(name='bbox_pred', data=drop7, num_hidden=num_classes * 4)
bbox_loss_ = bbox_weight * mx.symbol.smooth_l1(name='bbox_loss_', scalar=1.0, data=(bbox_pred - bbox_target))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / config.TRAIN.BATCH_ROIS)

# reshape output
cls_prob = mx.symbol.Reshape(data=cls_prob, shape=(config.TRAIN.BATCH_IMAGES, -1, num_classes), name='cls_prob_reshape')
bbox_loss = mx.symbol.Reshape(data=bbox_loss, shape=(config.TRAIN.BATCH_IMAGES, -1, 4 * num_classes), name='bbox_loss_reshape')

# group output
group = mx.symbol.Group([cls_prob, bbox_loss])
return group

{
  "op": "null", 
  "param": {}, 
  "name": "bbox_target", 
  "inputs": [], 
  "backward_source_id": -1
}, 
{
  "op": "Reshape", 
  "param": {
    "keep_highest": "False", 
    "reverse": "False", 
    "shape": "(-1,84)", 
    "target_shape": "(0,0)"
  }, 
  "name": "bbox_target_reshape", 
  "inputs": [[83, 0]], 
  "backward_source_id": -1
}, 
{
  "op": "_Minus", 
  "param": {}, 
  _"name": "_minus2",_ 
  "inputs": [[82, 0], [84, 0]], 
  "backward_source_id": -1
}, 
{
  "op": "smooth_l1", 
  "param": {"scalar": "1"}, 
  "name": "bbox_loss_", 
  "inputs": [[85, 0]], 
  "backward_source_id": -1
}, 
{
  "op": "_Mul", 
  "param": {}, 
  "name": "_mul2", 
  "inputs": [[79, 0], [86, 0]], 
  "backward_source_id": -1
}, 
{
  "op": "MakeLoss", 
  "param": {
    "grad_scale": "0.0078125", 
    "normalization": "null", 
    "valid_thresh": "0"
  }, 
  "name": "bbox_loss", 
  "inputs": [[87, 0]], 
  "backward_source_id": -1
},