ChainerCV input image data format

Question

I have an imageset of 250 images of shape (3, 320, 240) and 250 annotation files. 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.

EDIT: 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

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 import necessary libs

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

I want to know what is causing this. Is input data format incorrect in this case? And how to resolve this situation.

Answer 1

The issue was due to a small overlooked situation where the text files had gaps as the image list was cut, copied and pasted in the same file. The text files were created in notepad. In notepad index is not visible, but the gaps are visible once you view the text files in github where the initial indexing is still present and the indexing remains even though the list was cut down in size. Eg first a list of 182 images were created but later cut down to 170. So when we use the Dataset Creation object the code reads all the lines of the text file ie it will read 182 instead of 170. This has affected training of the model with the dataset that was incorrectly read. A new set of text files for train, val and test was created and now the training proceeded correctly.