Can't do model.fit(dataset)

Actually I got the following errors when I was trying this code on Google Colab.

from detecto import core, utils, visualize

dataset = core.Dataset('images/')
model = core.Model(['dog', 'cat'])

model.fit(dataset)

This is the error I got in the Google Colab terminal. Please help me, anyone

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. 
  warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-9-11e9a74e8844> in <module>()
      4 model = core.Model(['dog', 'cat'])
      5 
----> 6 model.fit(dataset)

/usr/local/lib/python3.6/dist-packages/detecto/core.py in fit(self, dataset, val_dataset, epochs, learning_rate, momentum, weight_decay, gamma, lr_step_size, verbose)
    467             # Training step
    468             self._model.train()
--> 469             for images, targets in dataset:
    470                 self._convert_to_int_labels(targets)
    471                 images, targets = self._to_device(images, targets)

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
    343 
    344     def __next__(self):
--> 345         data = self._next_data()
    346         self._num_yielded += 1
    347         if self._dataset_kind == _DatasetKind.Iterable and \

/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
    383     def _next_data(self):
    384         index = self._next_index()  # may raise StopIteration
--> 385         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    386         if self._pin_memory:
    387             data = _utils.pin_memory.pin_memory(data)

/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in <listcomp>(.0)
     42     def fetch(self, possibly_batched_index):
     43         if self.auto_collation:
---> 44             data = [self.dataset[idx] for idx in possibly_batched_index]
     45         else:
     46             data = self.dataset[possibly_batched_index]

/usr/local/lib/python3.6/dist-packages/detecto/core.py in __getitem__(self, idx)
    196                         box[0, 0], box[0, 2] = box[0, (2, 0)]
    197                 else:
--> 198                     image = t(image)
    199 
    200             # Scale down box if necessary

/usr/local/lib/python3.6/dist-packages/torchvision/transforms/transforms.py in __call__(self, tensor)
    164             Tensor: Normalized Tensor image.
    165         """
--> 166         return F.normalize(tensor, self.mean, self.std, self.inplace)
    167 
    168     def __repr__(self):

/usr/local/lib/python3.6/dist-packages/torchvision/transforms/functional.py in normalize(tensor, mean, std, inplace)
    206     if std.ndim == 1:
    207         std = std[:, None, None]
--> 208     tensor.sub_(mean).div_(std)
    209     return tensor
    210 

RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 0

[alankbi]

The error seems to be that the PNG images have 4 channels instead of 3, i.e. RGBA vs. RGB. You’ll want to convert your images from RGBA to RGB, which you can do by applying a custom transform when creating your Dataset. For example:

from detecto import core, utils
from torchvision import transforms

augmentations = transforms.Compose([
    transforms.Lambda(lambda x: x[:,:,:3]),
    transforms.ToTensor(),
    utils.normalize_transform(),
])

dataset = core.Dataset('images/', transform=augmentations)

Here, the transforms.Lambda(lambda x: x[:,:,:3]) takes in an image and returns only the first three channels (RGB), which should allow you to train your model as normal:

model = core.Model(['dog', 'cat'])
model.fit(dataset)

[alankbi]

I can’t seem to open your attachments, but here are the answers to your questions:

1. If you run the code I shared above and it works without any errors (and you can successfully print dataset[0] then your dataset should be valid.
2. Yes, that shouldn’t be an issue.
3. Yes, there is the visualize.detect_live function you can use. However, note that it doesn’t work on VMs like Google Colaboratory.