Gfyuki

I have an imageset of 250 images of shape (3, 320, 240) and 250 annotation files and a ImageSet/Main folder containing train, val, test text files which has lists of images for train, test and validation respectively.
I am using ChainerCV to detect and recognize two classes in the image: ball and player. Here we are using SSD300 model pre-trained on ImageNet dataset.

CLASS TO CREATE DATASET OBJECT

bball_labels = ('ball','player')

class BBall_dataset(VOCBboxDataset):

  def _get_annotations(self, i):

    id_ = self.ids[i]

    anno = ET.parse(os.path.join(self.data_dir, 'Annotations', id_ + 

'.xml'))

    bbox = 

    label = 

    difficult = 

    for obj in anno.findall('object'):

      bndbox_anno = obj.find('bndbox')

      bbox.append([int(bndbox_anno.find(tag).text) - 1 for tag in ('ymin', 

'xmin', 'ymax', 'xmax')])

      name = obj.find('name').text.lower().strip()

      label.append(bball_labels.index(name))

    bbox = np.stack(bbox).astype(np.float32)

    label = np.stack(label).astype(np.int32)

    difficult = np.array(difficult, dtype=np.bool)

    return bbox, label, difficult



valid_dataset = BBall_dataset('ExpDataset', 'val')

test_dataset = BBall_dataset('ExpDataset', 'test')

train_dataset = BBall_dataset('ExpDataset', 'train') 

Here train_dataset is an array containing img data((3,240,320),float32), bbox data((4,4),float32) and label data((4,),int32).

DOWNLOAD PRE-TRAINED MODEL

import chainer

from chainercv.links import SSD300

from chainercv.links.model.ssd import multibox_loss



class MultiboxTrainChain(chainer.Chain):

    def __init__(self, model, alpha=1, k=3):

        super(MultiboxTrainChain, self).__init__()

        with self.init_scope():

            self.model = model

        self.alpha = alpha

        self.k = k

    def forward(self, imgs, gt_mb_locs, gt_mb_labels):

        mb_locs, mb_confs = self.model(imgs)

        loc_loss, conf_loss = multibox_loss(

            mb_locs, mb_confs, gt_mb_locs, gt_mb_labels, self.k)

        loss = loc_loss * self.alpha + conf_loss



        chainer.reporter.report(

            {'loss': loss, 'loss/loc': loc_loss, 'loss/conf': conf_loss},

            self)

        return loss



model = SSD300(n_fg_class=len(bball_labels), pretrained_model='imagenet')

train_chain = MultiboxTrainChain(model)

TRANSFORM DATASET:

class Transform(object):

  def __init__(self, coder, size, mean):

    self.coder = copy.copy(coder)

    self.coder.to_cpu()



    self.size = size

    self.mean = mean

  def __call__(self, in_data):

    img, bbox, label = in_data

    img = random_distort(img)

    if np.random.randint(2):

      img, param = transforms.random_expand(img, fill=self.mean, 

 return_param=True)

      bbox = transforms.translate_bbox(bbox, y_offset=param['y_offset'], 

x_offset=param['x_offset'])

      img, param = random_crop_with_bbox_constraints(img, bbox, 

return_param=True)

      bbox, param = transforms.crop_bbox(bbox, y_slice=param['y_slice'], 

x_slice=param['x_slice'],allow_outside_center=False, return_param=True)

      label = label[param['index']]



    _, H, W = img.shape

    img = resize_with_random_interpolation(img, (self.size, self.size))

    bbox = transforms.resize_bbox(bbox, (H, W), (self.size, self.size))



    img, params = transforms.random_flip(img, x_random=True, 

return_param=True)

    bbox = transforms.flip_bbox(bbox, (self.size, self.size), 

x_flip=params['x_flip'])



    img -= self.mean

    mb_loc, mb_label = self.coder.encode(bbox, label)



    return img, mb_loc, mb_label

    transformed_train_dataset = TransformDataset(train_dataset, 

    Transform(model.coder, model.insize, model.mean))



    train_iter = 

    chainer.iterators.MultiprocessIterator(transformed_train_dataset, 

batchsize)

    valid_iter = chainer.iterators.SerialIterator(valid_dataset, 

batchsize, 

    repeat=False, shuffle=False) 

During training it throws the following error:

Exception in thread Thread-4:

Traceback (most recent call last):

  File "/usr/lib/python3.6/threading.py", line 916, in _bootstrap_inner

    self.run()

  File "/usr/lib/python3.6/threading.py", line 864, in run

    self._target(*self._args, **self._kwargs)

  File "/usr/local/lib/python3.6/dist- 

packages/chainer/iterators/multiprocess_iterator.py", line 401, in 

fetch_batch

    batch_ret[0] = [self.dataset[idx] for idx in indices]

  File "/usr/local/lib/python3.6/dist-

........................................................................

packages/chainer/iterators/multiprocess_iterator.py", line 401, in 

<listcomp>

    batch_ret[0] = [self.dataset[idx] for idx in indices]

  File "/usr/local/lib/python3.6/dist- 

packages/chainer/dataset/dataset_mixin.py", line 67, in __getitem__

    return self.get_example(index)

  File "/usr/local/lib/python3.6/dist- 

packages/chainer/datasets/transform_dataset.py", line 51, in get_example

    in_data = self._dataset[i]

  File "/usr/local/lib/python3.6/dist- 

packages/chainer/dataset/dataset_mixin.py", line 67, in __getitem__

    return self.get_example(index)

  File "/usr/local/lib/python3.6/dist-- 

packages/chainercv/utils/image/read_image.py", line 120, in read_image

    return _read_image_cv2(path, dtype, color, alpha)

  File "/usr/local/lib/python3.6/dist- 

packages/chainercv/utils/image/read_image.py", line 49, in _read_image_cv2

    if img.ndim == 2:

AttributeError: 'NoneType' object has no attribute 'ndim'

    TypeError: 'NoneType' object is not iterable

Is train_dataset format incorrect in this case? The errors say NoneType. I want to know the correct format for feeding the dataset into the model.