ModuleNotFoundError: No module named 'petrel_client'

[Bolvvv]

Hello, I installed Shikra according to the readme and downloaded the dataset. I tried to perform inference but encountered an error during runtime. The error is "ModuleNotFoundError: No module named 'petrel_client".

My python version is 3.9.2, torch vesion is 2.0.1. The following is the error message

╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /root/shikra/mllm/pipeline/finetune.py:141 in │ │ │ │ 138 │ │ 139 │ │ 140 if name == "main": │ │ ❱ 141 │ main() │ │ 142 │ │ │ │ /root/shikra/mllm/pipeline/finetune.py:127 in main │ │ │ │ 124 │ │ │ prefix = f"multitest{k}" │ │ 125 │ │ │ │ │ 126 │ │ │ trainer.compute_metrics = _compute_metrics │ │ ❱ 127 │ │ │ _pred_results = trainer.predict(_ds, metric_key_prefix=_prefix, *gen_kwargs │ │ 128 │ │ │ trainer.log_metrics(_prefix, _pred_results.metrics) # noqa │ │ 129 │ │ │ trainer.save_metrics(_prefix, _pred_results.metrics) # noqa │ │ 130 │ │ │ trainer.save_prediction(_pred_results, file_key_prefix=_prefix) │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/transformers/trainer_seq2seq.py:135 in predict │ │ │ │ 132 │ │ ) │ │ 133 │ │ self._gen_kwargs = gen_kwargs │ │ 134 │ │ │ │ ❱ 135 │ │ return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix= │ │ 136 │ │ │ 137 │ def prediction_step( │ │ 138 │ │ self, │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/transformers/trainer.py:3020 in predict │ │ │ │ 3017 │ │ start_time = time.time() │ │ 3018 │ │ │ │ 3019 │ │ eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else se │ │ ❱ 3020 │ │ output = eval_loop( │ │ 3021 │ │ │ test_dataloader, description="Prediction", ignore_keys=ignore_keys, metric_k │ │ 3022 │ │ ) │ │ 3023 │ │ total_batch_size = self.args.eval_batch_size self.args.world_size │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/transformers/trainer.py:3115 in evaluation_loop │ │ │ │ 3112 │ │ │ │ 3113 │ │ observed_num_examples = 0 │ │ 3114 │ │ # Main evaluation loop │ │ ❱ 3115 │ │ for step, inputs in enumerate(dataloader): │ │ 3116 │ │ │ # Update the observed num examples │ │ 3117 │ │ │ observed_batch_size = find_batch_size(inputs) │ │ 3118 │ │ │ if observed_batch_size is not None: │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py:633 in next │ │ │ │ 630 │ │ │ if self._sampler_iter is None: │ │ 631 │ │ │ │ # TODO(https://github.com/pytorch/pytorch/issues/76750) │ │ 632 │ │ │ │ self._reset() # type: ignore[call-arg] │ │ ❱ 633 │ │ │ data = self._next_data() │ │ 634 │ │ │ self._num_yielded += 1 │ │ 635 │ │ │ if self._dataset_kind == _DatasetKind.Iterable and \ │ │ 636 │ │ │ │ │ self._IterableDataset_len_called is not None and \ │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py:677 in _next_data │ │ │ │ 674 │ │ │ 675 │ def _next_data(self): │ │ 676 │ │ index = self._next_index() # may raise StopIteration │ │ ❱ 677 │ │ data = self._dataset_fetcher.fetch(index) # may raise StopIteration │ │ 678 │ │ if self._pin_memory: │ │ 679 │ │ │ data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) │ │ 680 │ │ return data │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:51 in fetch │ │ │ │ 48 │ │ │ if hasattr(self.dataset, "getitems") and self.dataset.getitems: │ │ 49 │ │ │ │ data = self.dataset.getitems(possibly_batched_index) │ │ 50 │ │ │ else: │ │ ❱ 51 │ │ │ │ data = [self.dataset[idx] for idx in possibly_batched_index] │ │ 52 │ │ else: │ │ 53 │ │ │ data = self.dataset[possibly_batched_index] │ │ 54 │ │ return self.collate_fn(data) │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:51 in │ │ │ │ 48 │ │ │ if hasattr(self.dataset, "getitems") and self.dataset.getitems: │ │ 49 │ │ │ │ data = self.dataset.getitems(possibly_batched_index) │ │ 50 │ │ │ else: │ │ ❱ 51 │ │ │ │ data = [self.dataset[idx] for idx in possibly_batched_index] │ │ 52 │ │ else: │ │ 53 │ │ │ data = self.dataset[possibly_batched_index] │ │ 54 │ │ return self.collate_fn(data) │ │ │ │ /root/shikra/mllm/dataset/single_image_convsation.py:42 in getitem │ │ │ │ 39 │ │ │ 40 │ def getitem(self, index, debug_mode=False) -> Dict[str, Any]: │ │ 41 │ │ # getitem │ │ ❱ 42 │ │ item = self.get_raw_item(index) │ │ 43 │ │ image: Image.Image = item.get('image', None) │ │ 44 │ │ target: Dict[str, Any] = item.get('target', None) │ │ 45 │ │ raw_conv: List[Dict[str, Any]] = item['conversations'] │ │ │ │ /root/shikra/mllm/dataset/single_image_convsation.py:267 in get_raw_item │ │ │ │ 264 │ │ │ 265 │ def get_raw_item(self, index) -> Dict[str, Any]: │ │ 266 │ │ self.initialize_if_needed() │ │ ❱ 267 │ │ return self.dataset[index] │ │ 268 │ │ │ 269 │ def repr(self) -> str: │ │ 270 │ │ head = "Dataset " + self.class.name │ │ │ │ /root/shikra/mllm/dataset/single_image_dataset/pope.py:16 in getitem │ │ │ │ 13 │ │ │ 14 │ def getitem(self, index): │ │ 15 │ │ item = self.get_raw_item(index) │ │ ❱ 16 │ │ image = self.get_image(image_path=item['image']) │ │ 17 │ │ │ │ 18 │ │ question = item['text'] │ │ 19 │ │ final_question = self.get_template().replace(QUESTION_PLACEHOLDER, question) │ │ │ │ /root/shikra/mllm/dataset/utils/mixin.py:72 in get_image │ │ │ │ 69 │ def get_image(self, image_path): │ │ 70 │ │ if self.image_folder is not None: │ │ 71 │ │ │ image_path = os.path.join(self.image_folder, image_path) │ │ ❱ 72 │ │ image = read_img_general(image_path) │ │ 73 │ │ return image │ │ 74 │ │ │ 75 │ def get_template(self): │ │ │ │ /root/shikra/mllm/dataset/utils/io.py:20 in read_img_general │ │ │ │ 17 │ │ 18 def read_img_general(img_path): │ │ 19 │ if "s3://" in img_path: │ │ ❱ 20 │ │ cv_img = read_img_ceph(img_path) │ │ 21 │ │ # noinspection PyUnresolvedReferences │ │ 22 │ │ return Image.fromarray(cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)) │ │ 23 │ else: │ │ │ │ /root/shikra/mllm/dataset/utils/io.py:31 in read_img_ceph │ │ │ │ 28 │ │ 29 │ │ 30 def read_img_ceph(img_path): │ │ ❱ 31 │ init_ceph_client_if_needed() │ │ 32 │ img_bytes = client.get(img_path) │ │ 33 │ assert img_bytes is not None, f"Please check image at {img_path}" │ │ 34 │ img_mem_view = memoryview(img_bytes) │ │ │ │ /root/shikra/mllm/dataset/utils/io.py:46 in init_ceph_client_if_needed │ │ │ │ 43 │ if client is None: │ │ 44 │ │ logger.info(f"initializing ceph client ...") │ │ 45 │ │ st = time.time() │ │ ❱ 46 │ │ from petrel_client.client import Client # noqa │ │ 47 │ │ client = Client(enable_mc=True) │ │ 48 │ │ ed = time.time() │ │ 49 │ │ logger.info(f"initialize client cost {ed - st:.2f} s") │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ModuleNotFoundError: No module named 'petrel_client' ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /root/shikra/venv/bin/accelerate:8 in │ │ │ │ 5 from accelerate.commands.accelerate_cli import main │ │ 6 if name == 'main': │ │ 7 │ sys.argv[0] = re.sub(r'(-script.pyw|.exe)?$', '', sys.argv[0]) │ │ ❱ 8 │ sys.exit(main()) │ │ 9 │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/accelerate/commands/accelerate_cli.py:45 in main │ │ │ │ 42 │ │ exit(1) │ │ 43 │ │ │ 44 │ # Run │ │ ❱ 45 │ args.func(args) │ │ 46 │ │ 47 │ │ 48 if name == "main": │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/accelerate/commands/launch.py:941 in │ │ launch_command │ │ │ │ 938 │ elif defaults is not None and defaults.compute_environment == ComputeEnvironment.AMA │ │ 939 │ │ sagemaker_launcher(defaults, args) │ │ 940 │ else: │ │ ❱ 941 │ │ simple_launcher(args) │ │ 942 │ │ 943 │ │ 944 def main(): │ │ │ │ /root/shikra/venv/lib/python3.9/site-packages/accelerate/commands/launch.py:603 in │ │ simple_launcher │ │ │ │ 600 │ process.wait() │ │ 601 │ if process.returncode != 0: │ │ 602 │ │ if not args.quiet: │ │ ❱ 603 │ │ │ raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) │ │ 604 │ │ else: │ │ 605 │ │ │ sys.exit(1) │ │ 606 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ CalledProcessError: Command '['/root/shikra/venv/bin/python', 'mllm/pipeline/finetune.py', 'config/shikra_eval_multi_pope.py', '--cfg-options', 'model_args.model_name_or_path=/root/shikra/shikra-7b', '--per_device_eval_batch_size', '1']' returned non-zero exit status 1.

[YekunF]

same error

[yueluhhxx]

I am also having the same problem. Can you give a little more detailed guide?

[pangpang97]

same error

[MacavityT]

@yueluhhxx This is a module which we used for loading images from a cluster. It's an internal module of Sensetime, so you cannot install it. Please modify the image loading process yourself if it is convenient for you.

[kq-chen]

I apologize for any confusion caused earlier.

As mentioned by @MacavityT, the petrel_client module is an internal component of Sensetime that is utilized for loading images from a distributed storage system. When attempting to load a file from an S3 path like balabala:s3://, it triggers the initialization of petrel_client, resulting in the error you encountered. To work with a dataset, you need to modify the image_folder field in the configuration file.

To illustrate, let's consider this pope testset as an example. You can update the image_folder field in the config/_base_/dataset/DEFAULT_TEST_POPE_VARIANT.py file as follows:

Before modification:

POPE_TEST_COMMON_CFG = dict(
    type='POPEVQADataset',
    image_folder=r'openmmlab1424:s3://openmmlab/datasets/detection/coco/val2014',
)

After modification:

POPE_TEST_COMMON_CFG = dict(
    type='POPEVQADataset',
    image_folder=r'path/to/coco/val2014/on/your/computer',
)

I hope this clarification helps. If you have any further questions or need additional assistance, please let me know.