InvalidArgumentError encountered while training my dataset

[KevinYijunQian]

I encountered the error when change train_shapes for my personal dataset and i only have one object to detect Epoch 1/2 Traceback (most recent call last): File "train_shapes.py", line 273, in layers="all") File "/home/zhiqi.cheng/Mask_RCNN-master/mrcnn/model.py", line 2328, in train use_multiprocessing=False, File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper return func(*args, kwargs) File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/keras/engine/training.py", line 2230, in fit_generator class_weight=class_weight) File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/keras/engine/training.py", line 1883, in train_on_batch outputs = self.train_function(ins) File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 2482, in call self.session_kwargs) File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 905, in run run_metadata_ptr) File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1140, in _run feed_dict_tensor, options, run_metadata) File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run run_metadata) File "/home/zhiqi.cheng/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [2,76,76,256] vs. [2,75,75,256] [[Node: training/SGD/gradients/fpn_p3add/add_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@fpn_p3add/add"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](training/SGD/gradients/fpn_p3add/add_grad/Shape, training/SGD/gradients/fpn_p3add/add_grad/Shape_1-0-1-VecPermuteNCHWToNHWC-LayoutOptimizer/_6005)]] [[Node: roi_align_mask/Cast_3/_6481 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_11117_roi_align_mask/Cast_3", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

And my changed train_shapes file are as followed:

coding: utf-8

Mask R-CNN - Train on Shapes Dataset

This notebook shows how to train Mask R-CNN on your own dataset. To keep things simple we use a synthetic dataset of shapes (squares, triangles, and circles) which enables fast training. You'd still need a GPU, though, because the network backbone is a Resnet101, which would be too slow to train on a CPU. On a GPU, you can start to get okay-ish results in a few minutes, and good results in less than an hour.

The code of the Shapes dataset is included below. It generates images on the fly, so it doesn't require downloading any data. And it can generate images of any size, so we pick a small image size to train faster.

In[1]:

import os import sys import random import math import re import time import numpy as np import cv2 import matplotlib import matplotlib.pyplot as plt from PIL import Image import yaml

Root directory of the project

ROOT_DIR = os.path.abspath("../../")

Import Mask RCNN

sys.path.append(ROOT_DIR) # To find local version of the library from mrcnn.config import Config from mrcnn import utils import mrcnn.model as modellib from mrcnn import visualize from mrcnn.model import log

get_ipython().magic(u'matplotlib inline')

Directory to save logs and trained model

MODEL_DIR = os.path.join(ROOT_DIR, "logs")

Local path to trained weights file

COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

Download COCO trained weights from Releases if needed

if not os.path.exists(COCO_MODEL_PATH): utils.download_trained_weights(COCO_MODEL_PATH)

Configurations

In[2]:

class ShapesConfig(Config): """Configuration for training on the toy shapes dataset. Derives from the base Config class and overrides values specific to the toy shapes dataset. """

Give the configuration a recognizable name

NAME = "shapes"

# Train on 1 GPU and 8 images per GPU. We can put multiple images on each
# GPU because the images are small. Batch size is 8 (GPUs * images/GPU).
GPU_COUNT = 1
IMAGES_PER_GPU = 2

# Number of classes (including background)
NUM_CLASSES = 1 + 1  # background + 3 shapes

# Use small images for faster training. Set the limits of the small side
# the large side, and that determines the image shape.
IMAGE_MIN_DIM = 600
IMAGE_MAX_DIM = 600

# Use smaller anchors because our image and objects are small
RPN_ANCHOR_SCALES = (8*3, 16*3, 32*3, 64*3, 128*3)  # anchor side in pixels

# Reduce training ROIs per image because the images are small and have
# few objects. Aim to allow ROI sampling to pick 33% positive ROIs.
TRAIN_ROIS_PER_IMAGE = 32

# Use a small epoch since the data is simple
STEPS_PER_EPOCH = 100

# use small validation steps since the epoch is small
VALIDATION_STEPS = 5

config = ShapesConfig() config.display()

Notebook Preferences

In[3]:

def get_ax(rows=1, cols=1, size=8): """Return a Matplotlib Axes array to be used in all visualizations in the notebook. Provide a central point to control graph sizes.

Change the default size attribute to control the size
of rendered images
"""
_, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
return ax

Dataset

Create a synthetic dataset

Extend the Dataset class and add a method to load the shapes dataset, load_shapes(), and override the following methods:

* load_image()

* load_mask()

* image_reference()

In[4]:

class BallDataset(utils.Dataset): def get_obj_index(self, image): n = np.max(image) return n def from_yaml_get_class(self,image_id): info=self.image_info[image_id] with open(info['yaml_path']) as f: temp=yaml.load(f.read()) labels=temp['label_names'] del labels[0] return labels def draw_mask(self, num_obj, mask, image): info = self.image_info[image_id] for index in range(num_obj): for i in range(info['width']): for j in range(info['height']): at_pixel = image.getpixel((i, j)) if at_pixel == index + 1: mask[j, i, index] =1 return mask def load_shapes(self, count, height, width, img_floder, mask_floder, imglist,dataset_root_path,yaml_floder): """Generate the requested number of synthetic images. count: number of images to generate. height, width: the size of the generated images. """

Add classes

    self.add_class("shapes", 1, "ball")
    for i in range(count):
        filestr = imglist[i].split(".")[0]
        #filestr = filestr.split("_")[1]
        mask_path = mask_floder + "/" + filestr + ".png"
        yaml_path=yaml_floder
        self.add_image("shapes", image_id=i, path=img_floder + "/" + imglist[i],
                       width=width, height=height, mask_path=mask_path,yaml_path=yaml_path)
def load_mask(self, image_id):
    """Generate instance masks for shapes of the given image ID.
    """
    global iter_num
    info = self.image_info[image_id]
    count = 1  # number of object
    img = Image.open(info['mask_path'])
    num_obj = self.get_obj_index(img)
    mask = np.zeros([info['height'], info['width'], num_obj], dtype=np.uint8)
    mask = self.draw_mask(num_obj, mask, img)
    occlusion = np.logical_not(mask[:, :, -1]).astype(np.uint8)
    for i in range(count - 2, -1, -1):
        mask[:, :, i] = mask[:, :, i] * occlusion
        occlusion = np.logical_and(occlusion, np.logical_not(mask[:, :, i]))
    labels=[]
    labels=self.from_yaml_get_class(image_id)
    labels_form=[]
    for i in range(len(labels)):
        if labels[i].find("ball")!=-1:
            #print "box"
            labels_form.append("ball")
    class_ids = np.array([self.class_names.index(s) for s in labels_form])
    return mask, class_ids.astype(np.int32)

In[5]:

dataset_root_path="/home/zhiqi.cheng/Mask_RCNN-master/maskdataset/" img_floder = dataset_root_path+"pic" mask_floder = dataset_root_path+"mask" yaml_floder = dataset_root_path+"info.yaml" imglist = os.listdir(img_floder) count = len(imglist) width = 600 height = 600

Training dataset

dataset_train = BallDataset() dataset_train.load_shapes(count, 600, 600, img_floder, mask_floder, imglist,dataset_root_path,yaml_floder) dataset_train.prepare()

Validation dataset

dataset_val = BallDataset() dataset_val.load_shapes(count, 600, 600, img_floder, mask_floder, imglist,dataset_root_path,yaml_floder) dataset_val.prepare()

In[6]:

Load and display random samples

image_ids = np.random.choice(dataset_train.image_ids, 4) for image_id in image_ids: image = dataset_train.load_image(image_id) mask, class_ids = dataset_train.load_mask(image_id) visualize.display_top_masks(image, mask, class_ids, dataset_train.class_names)

Ceate Model

In[ ]:

Create model in training mode

model = modellib.MaskRCNN(mode="training", config=config, model_dir=MODEL_DIR)

In[7]:

Which weights to start with?

init_with = "coco" # imagenet, coco, or last

if init_with == "imagenet": model.load_weights(model.get_imagenet_weights(), by_name=True) elif init_with == "coco":

Load weights trained on MS COCO, but skip layers that

# are different due to the different number of classes
# See README for instructions to download the COCO weights
model.load_weights(COCO_MODEL_PATH, by_name=True,
                   exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", 
                            "mrcnn_bbox", "mrcnn_mask"])

elif init_with == "last":

Load the last model you trained and continue training

model.load_weights(model.find_last()[1], by_name=True)

Training

Train in two stages:

1. Only the heads. Here we're freezing all the backbone layers and training only the randomly initialized layers (i.e. the ones that we didn't use pre-trained weights from MS COCO). To train only the head layers, pass layers='heads' to the train() function.

2. Fine-tune all layers. For this simple example it's not necessary, but we're including it to show the process. Simply pass layers="all to train all layers.

In[8]:

Train the head branches

Passing layers="heads" freezes all layers except the head

layers. You can also pass a regular expression to select

which layers to train by name pattern.

model.train(dataset_train, dataset_val,

        #learning_rate=config.LEARNING_RATE, 
        #epochs=1, 
        #layers='heads')

In[9]:

Fine tune all layers

Passing layers="all" trains all layers. You can also

pass a regular expression to select which layers to

train by name pattern.

model.train(dataset_train, dataset_val, learning_rate=config.LEARNING_RATE / 10, epochs=2, layers="all")

In[10]:

Save weights

Typically not needed because callbacks save after every epoch

Uncomment to save manually

model_path = os.path.join(MODEL_DIR, "mask_rcnn_shapes.h5")

model.keras_model.save_weights(model_path)

Detection

In[11]:

[npitsillos]

Were you able to solve this? If yes are you willing to help me because more or less I have the same problem as well.

[keineahnung2345]

IMAGE_MIN_DIM and IMAGE_MIN_DIM should be multiple of 64. Ref: https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/config.py#L106.

[keineahnung2345]

You are using Python2, so this line: https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/model.py#L1850 fails to throw an exception.

[rsumit123]

I am facing the same issue ? and my image min dim and image max dim is also in multiples of 64 .. in my case Incompatible shapes: [1,38,26,256] vs. [1,38,25,256] [[{{node training_4/SGD/gradients/fpn_p4add_4/add_grad/BroadcastGradientArgs}}]]

please inform me of any solutions

[PaulMcht]

I am facing the same issue during training. The error can occur at any time during the training process.

The error does not come from parameter settings or image shape. It looks like at some point a rounding of shape is done incorrectly.

Does anyone has a solution ? Basically this error makes impossible the training

[GavrielHabib123]

Same here. Anyone has a solution?