TypeError Traceback (most recent call last)
Cell In[7], line 4
1 from autodistill_llava import LLaVA
3 base_model = LLaVA(ontology=ontology)
----> 4 dataset = base_model.label(
5 input_folder='/kaggle/input/distillation-test/traffic_dataset/test',
6 extension=".jpg",
7 output_folder='/kaggle/working/images'
8 )
File /opt/conda/lib/python3.10/site-packages/autodistill/detection/detection_base_model.py:52, in DetectionBaseModel.label(self, input_folder, extension, output_folder, human_in_the_loop, roboflow_project, roboflow_tags)
50 f_path_short = os.path.basename(f_path)
51 images_map[f_path_short] = image.copy()
---> 52 detections = self.predict(f_path)
53 detections_map[f_path_short] = detections
55 dataset = sv.DetectionDataset(
56 self.ontology.classes(), images_map, detections_map
57 )
File /opt/conda/lib/python3.10/site-packages/autodistill_llava/model.py:140, in LLaVA.predict(self, input)
137 streamer = TextStreamer(self.tokenizer, skip_prompt=True, skip_special_tokens=True)
139 with torch.inference_mode():
--> 140 output_ids = self.model.generate(
141 input_ids,
142 images=image_tensor,
143 do_sample=True,
144 temperature=0.2,
145 max_new_tokens=512,
146 streamer=streamer,
147 use_cache=True,
148 stopping_criteria=[stopping_criteria])
150 outputs = self.tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
152 self.conv.messages[-1][-1] = outputs
File /opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:1588, in GenerationMixin.generate(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, **kwargs)
1580 input_ids, model_kwargs = self._expand_inputs_for_generation(
1581 input_ids=input_ids,
1582 expand_size=generation_config.num_return_sequences,
1583 is_encoder_decoder=self.config.is_encoder_decoder,
1584 **model_kwargs,
1585 )
1587 # 13. run sample
-> 1588 return self.sample(
1589 input_ids,
1590 logits_processor=logits_processor,
1591 logits_warper=logits_warper,
1592 stopping_criteria=stopping_criteria,
1593 pad_token_id=generation_config.pad_token_id,
1594 eos_token_id=generation_config.eos_token_id,
1595 output_scores=generation_config.output_scores,
1596 return_dict_in_generate=generation_config.return_dict_in_generate,
1597 synced_gpus=synced_gpus,
1598 streamer=streamer,
1599 **model_kwargs,
1600 )
1602 elif is_beam_gen_mode:
1603 if generation_config.num_return_sequences > generation_config.num_beams:
File /opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:2642, in GenerationMixin.sample(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, streamer, **model_kwargs)
2639 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
2641 # forward pass to get next token
-> 2642 outputs = self(
2643 **model_inputs,
2644 return_dict=True,
2645 output_attentions=output_attentions,
2646 output_hidden_states=output_hidden_states,
2647 )
2649 if synced_gpus and this_peer_finished:
2650 continue # don't waste resources running the code we don't need
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.10/site-packages/accelerate/hooks.py:165, in add_hook_to_module.<locals>.new_forward(*args, **kwargs)
163 output = old_forward(*args, **kwargs)
164 else:
--> 165 output = old_forward(*args, **kwargs)
166 return module._hf_hook.post_forward(module, output)
File ~/.autodistill/LLaVA/llava/model/language_model/llava_llama.py:79, in LlavaLlamaForCausalLM.forward(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, images, return_dict)
56 def forward(
57 self,
58 input_ids: torch.LongTensor = None,
(...)
68 return_dict: Optional[bool] = None,
69 ) -> Union[Tuple, CausalLMOutputWithPast]:
71 if inputs_embeds is None:
72 (
73 input_ids,
74 position_ids,
75 attention_mask,
76 past_key_values,
77 inputs_embeds,
78 labels
---> 79 ) = self.prepare_inputs_labels_for_multimodal(
80 input_ids,
81 position_ids,
82 attention_mask,
83 past_key_values,
84 labels,
85 images
86 )
88 return super().forward(
89 input_ids=input_ids,
90 attention_mask=attention_mask,
(...)
98 return_dict=return_dict
99 )
File ~/.autodistill/LLaVA/llava/model/llava_arch.py:121, in LlavaMetaForCausalLM.prepare_inputs_labels_for_multimodal(self, input_ids, position_ids, attention_mask, past_key_values, labels, images)
119 image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
120 else:
--> 121 image_features = self.encode_images(images).to(self.device)
123 # TODO: image start / end is not implemented here to support pretraining.
124 if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
File ~/.autodistill/LLaVA/llava/model/llava_arch.py:96, in LlavaMetaForCausalLM.encode_images(self, images)
94 def encode_images(self, images):
95 image_features = self.get_model().get_vision_tower()(images)
---> 96 image_features = self.get_model().mm_projector(image_features)
97 return image_features
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.10/site-packages/accelerate/hooks.py:165, in add_hook_to_module.<locals>.new_forward(*args, **kwargs)
163 output = old_forward(*args, **kwargs)
164 else:
--> 165 output = old_forward(*args, **kwargs)
166 return module._hf_hook.post_forward(module, output)
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/container.py:217, in Sequential.forward(self, input)
215 def forward(self, input):
216 for module in self:
--> 217 input = module(input)
218 return input
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.10/site-packages/accelerate/hooks.py:165, in add_hook_to_module.<locals>.new_forward(*args, **kwargs)
163 output = old_forward(*args, **kwargs)
164 else:
--> 165 output = old_forward(*args, **kwargs)
166 return module._hf_hook.post_forward(module, output)
File /opt/conda/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:441, in Linear8bitLt.forward(self, x)
438 if self.bias is not None and self.bias.dtype != x.dtype:
439 self.bias.data = self.bias.data.to(x.dtype)
--> 441 out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
443 if not self.state.has_fp16_weights:
444 if self.state.CB is not None and self.state.CxB is not None:
445 # we converted 8-bit row major to turing/ampere format in the first inference pass
446 # we no longer need the row-major weight
File /opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:563, in matmul(A, B, out, state, threshold, bias)
561 if threshold > 0.0:
562 state.threshold = threshold
--> 563 return MatMul8bitLt.apply(A, B, out, bias, state)
File /opt/conda/lib/python3.10/site-packages/torch/autograd/function.py:506, in Function.apply(cls, *args, **kwargs)
503 if not torch._C._are_functorch_transforms_active():
504 # See NOTE: [functorch vjp and autograd interaction]
505 args = _functorch.utils.unwrap_dead_wrappers(args)
--> 506 return super().apply(*args, **kwargs) # type: ignore[misc]
508 if cls.setup_context == _SingleLevelFunction.setup_context:
509 raise RuntimeError(
510 'In order to use an autograd.Function with functorch transforms '
511 '(vmap, grad, jvp, jacrev, ...), it must override the setup_context '
512 'staticmethod. For more details, please see '
513 '[https://pytorch.org/docs/master/notes/extending.func.html](https://pytorch.org/docs/master/notes/extending.func.html%3C/span%3E%3Cspan) style="color:rgb(175,0,0)">')
File /opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:384, in MatMul8bitLt.forward(ctx, A, B, out, bias, state)
382 outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
383 else:
--> 384 outliers = state.CB[:, state.idx.long()].clone()
386 state.subB = (outliers * state.SCB.view(-1, 1) / 127.0).t().contiguous().to(A.dtype)
387 CA[:, state.idx.long()] = 0
TypeError: 'NoneType' object is not subscriptable
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