[to #42322933]update ofa caption model

将caption pipeline的实现从pipeline下沉到model，并拆解preprocessor
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9081211

    * [to #41669377] docs and tools refinement and release 

1. add build_doc linter script
2. add sphinx-docs support
3. add development doc and api doc
4. change version to 0.1.0 for the first internal release version

Link: https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8775307

* [to #41669377] add pipeline tutorial and fix bugs 

1. add pipleine tutorial
2. fix bugs when using pipeline with certain model and preprocessor

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8814301

* refine doc

* refine doc

* upload ofa for caption(with source code but not whl)

* remove data in gitignore

* append uncommitted data dir in ofa

* remove ofa_dir , use ofa.whl instead.

* update BPE

* rollback changes used in debugging.

* Merge branch 'master' into ofa/image_caption

# Conflicts:
#	docs/README.md
#	docs/source/conf.py
#	docs/source/index.rst
#	docs/source/tutorials/pipeline.md
#	maas_lib/models/nlp/sequence_classification_model.py
#	maas_lib/pipelines/builder.py
#	maas_lib/version.py
#	setup.py
#	tests/pipelines/test_text_classification.py

* 1. fix a bug in pipelines/builder.py.
2. modify model_path to model in image_captioning.py.

* 1. rename test_image_captioning.py.

* format all files using pre-commit.

* add fairseq in requirements.txt

* add fairseq in requirements.txt

* change fairseq path to git repo to a whl on oss in ofa.txt.

* change module_name to 'ofa'

* Merge remote-tracking branch 'origin/master' into ofa/image_caption

# Conflicts:
#	maas_lib/pipelines/builder.py

* optim requirements for ofa / refine image_captioning.py

* uncommited change.

* feat: Fix confilct, auto commit by WebIDE

* Merge remote-tracking branch 'origin/master' into ofa/image_caption

# Conflicts:
#	maas_lib/pipelines/multi_modal/__init__.py
#	modelscope/pipelines/multi_modal/image_captioning.py
#	tests/pipelines/test_image_captioning.py

* merge master

* merge master

* merge master

* rename

* Merge remote-tracking branch 'origin/master' into ofa/nlu

* add caption model

* Merge remote-tracking branch 'origin/master' into ofa/nlu

* update ofa caption model

* fix some typo, update unittest

* use local test image

* use local test image

* refactor, ofa -> multi_model

* merge master

* 删除 image_caption_pipeline.py

data/test/images/image_captioning.png

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af83a94899a6d23339c3ecc5c4c58c57c835af57b531a2f4c50461184f820141
+size 603621

modelscope/models/__init__.py

@@ -4,4 +4,5 @@ from .audio.tts.am import SambertNetHifi16k
 from .audio.tts.vocoder import Hifigan16k
 from .base import Model
 from .builder import MODELS, build_model
+from .multi_model import OfaForImageCaptioning
 from .nlp import BertForSequenceClassification, SbertForSentenceSimilarity

modelscope/models/multi_model/__init__.py

@@ -0,0 +1 @@
+from .image_captioning_model import OfaForImageCaptioning

modelscope/models/multi_model/image_captioning_model.py

@@ -0,0 +1,80 @@
+import os.path as osp
+from typing import Any, Dict
+
+from PIL import Image
+
+from modelscope.utils.constant import ModelFile, Tasks
+from ..base import Model
+from ..builder import MODELS
+
+__all__ = ['OfaForImageCaptioning']
+
+
+@MODELS.register_module(
+    Tasks.image_captioning, module_name=r'ofa-image-captioning')
+class OfaForImageCaptioning(Model):
+
+    def __init__(self, model_dir, *args, **kwargs):
+        super().__init__(model_dir=model_dir, *args, **kwargs)
+        ckpt_name = ModelFile.TORCH_MODEL_FILE
+        local_model = osp.join(model_dir, ckpt_name)
+        bpe_dir = model_dir
+        # turn on cuda if GPU is available
+        from fairseq import checkpoint_utils, tasks, utils
+        from ofa.tasks.mm_tasks import CaptionTask
+        from ofa.utils.eval_utils import eval_caption
+        self.eval_caption = eval_caption
+
+        tasks.register_task('caption', CaptionTask)
+        use_cuda = kwargs['use_cuda'] if 'use_cuda' in kwargs else False
+        use_fp16 = kwargs[
+            'use_fp16'] if 'use_fp16' in kwargs and use_cuda else False
+        overrides = {
+            'bpe_dir': bpe_dir,
+            'eval_cider': False,
+            'beam': 5,
+            'max_len_b': 16,
+            'no_repeat_ngram_size': 3,
+            'seed': 7
+        }
+        models, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+            utils.split_paths(local_model), arg_overrides=overrides)
+
+        # Move models to GPU
+        for model in models:
+            model.eval()
+            if use_cuda:
+                model.cuda()
+            if use_fp16:
+                model.half()
+            model.prepare_for_inference_(cfg)
+        self.models = models
+        # Initialize generator
+        self.generator = task.build_generator(models, cfg.generation)
+
+        # Initialize transform
+        from torchvision import transforms
+        mean = [0.5, 0.5, 0.5]
+        std = [0.5, 0.5, 0.5]
+
+        self.patch_resize_transform = transforms.Compose([
+            lambda image: image.convert('RGB'),
+            transforms.Resize(
+                (cfg.task.patch_image_size, cfg.task.patch_image_size),
+                interpolation=Image.BICUBIC),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=mean, std=std),
+        ])
+        self.task = task
+
+    def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
+        results, _ = self.eval_caption(self.task, self.generator, self.models,
+                                       input)
+        return {
+            'image_id': results[0]['image_id'],
+            'caption': results[0]['caption']
+        }
+
+    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        # What should we do here ?
+        return inputs

modelscope/pipelines/builder.py

@@ -24,7 +24,7 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     ('bert-sentiment-analysis', 'damo/bert-base-sst2'),
     Tasks.text_generation: ('palm2.0',
                             'damo/nlp_palm2.0_text-generation_chinese-base'),
-    Tasks.image_captioning: ('ofa', None),
+    Tasks.image_captioning: ('ofa', 'damo/ofa_image-caption_coco_large_en'),
     Tasks.image_generation:
     ('person-image-cartoon',
      'damo/cv_unet_person-image-cartoon_compound-models'),

modelscope/pipelines/multi_modal/__init__.py

@@ -1 +1 @@
-from .image_caption_pipeline import ImageCaptionPipeline
+from .image_captioning_pipeline import ImageCaptionPipeline

modelscope/pipelines/multi_modal/image_captioning_pipeline.py

@@ -0,0 +1,33 @@
+from typing import Any, Dict, Union
+
+from modelscope.preprocessors import OfaImageCaptionPreprocessor, Preprocessor
+from modelscope.utils.constant import Tasks
+from modelscope.utils.logger import get_logger
+from ..base import Model, Pipeline
+from ..builder import PIPELINES
+
+logger = get_logger()
+
+
+@PIPELINES.register_module(Tasks.image_captioning, module_name='ofa')
+class ImageCaptionPipeline(Pipeline):
+
+    def __init__(self,
+                 model: Union[Model, str],
+                 preprocessor: [Preprocessor] = None,
+                 **kwargs):
+        super().__init__()
+        assert isinstance(model, str) or isinstance(model, Model), \
+            'model must be a single str or OfaForImageCaptioning'
+        if isinstance(model, str):
+            pipe_model = Model.from_pretrained(model)
+        elif isinstance(model, Model):
+            pipe_model = model
+        else:
+            raise NotImplementedError
+        if preprocessor is None and pipe_model:
+            preprocessor = OfaImageCaptionPreprocessor(model_dir=model)
+        super().__init__(model=pipe_model, preprocessor=preprocessor, **kwargs)
+
+    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        return inputs

modelscope/preprocessors/__init__.py

@@ -5,5 +5,6 @@ from .base import Preprocessor
 from .builder import PREPROCESSORS, build_preprocessor
 from .common import Compose
 from .image import LoadImage, load_image
+from .multi_model import OfaImageCaptionPreprocessor
 from .nlp import *  # noqa F403
 from .text_to_speech import *  # noqa F403

modelscope/preprocessors/multi_model.py

@@ -1,32 +1,50 @@
-from typing import Any, Dict
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os.path as osp
+from typing import Any, Dict, Union
 
 import numpy as np
 import torch
+from maas_hub.snapshot_download import snapshot_download
 from PIL import Image
 
-from modelscope.pipelines.base import Input
-from modelscope.preprocessors import load_image
-from modelscope.utils.constant import Tasks
-from modelscope.utils.logger import get_logger
-from ..base import Pipeline
-from ..builder import PIPELINES
+from modelscope.utils.constant import Fields, ModelFile
+from modelscope.utils.hub import get_model_cache_dir
+from modelscope.utils.type_assert import type_assert
+from .base import Preprocessor
+from .builder import PREPROCESSORS
+from .image import load_image
 
-logger = get_logger()
+__all__ = [
+    'OfaImageCaptionPreprocessor',
+]
 
 
-@PIPELINES.register_module(Tasks.image_captioning, module_name='ofa')
-class ImageCaptionPipeline(Pipeline):
-    # TODO: refine using modelhub
-    def __init__(self, model: str, bpe_dir: str):
-        super().__init__()
-        # turn on cuda if GPU is available
+@PREPROCESSORS.register_module(
+    Fields.multi_modal, module_name=r'ofa-image-caption')
+class OfaImageCaptionPreprocessor(Preprocessor):
+
+    def __init__(self, model_dir: str, *args, **kwargs):
+        """preprocess the data via the vocab.txt from the `model_dir` path
+
+        Args:
+            model_dir (str): model path
+        """
+        super().__init__(*args, **kwargs)
+
+        if osp.exists(model_dir):
+            local_model_dir = model_dir
+        else:
+            cache_path = get_model_cache_dir(model_dir)
+            local_model_dir = cache_path if osp.exists(
+                cache_path) else snapshot_download(model_dir)
+        local_model = osp.join(local_model_dir, ModelFile.TORCH_MODEL_FILE)
+        bpe_dir = local_model_dir
+
         from fairseq import checkpoint_utils, tasks, utils
         from ofa.tasks.mm_tasks import CaptionTask
 
         tasks.register_task('caption', CaptionTask)
-        use_cuda = False
-        # use fp16 only when GPU is available
-        use_fp16 = False
+
         overrides = {
             'bpe_dir': bpe_dir,
             'eval_cider': False,
@@ -35,21 +53,9 @@ class ImageCaptionPipeline(Pipeline):
             'no_repeat_ngram_size': 3,
             'seed': 7
         }
-        models, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-            utils.split_paths(model), arg_overrides=overrides)
-
-        # Move models to GPU
-        for model in models:
-            model.eval()
-            if use_cuda:
-                model.cuda()
-            if use_fp16:
-                model.half()
-            model.prepare_for_inference_(cfg)
-        self.models = models
-        # Initialize generator
-        self.generator = task.build_generator(models, cfg.generation)
-
+        model, cfg, task = checkpoint_utils.load_model_ensemble_and_task(
+            utils.split_paths(local_model), arg_overrides=overrides)
+        del model
         # Initialize transform
         from torchvision import transforms
         mean = [0.5, 0.5, 0.5]
@@ -69,7 +75,8 @@ class ImageCaptionPipeline(Pipeline):
         self.eos_item = torch.LongTensor([task.src_dict.eos()])
         self.pad_idx = task.src_dict.pad()
 
-    def preprocess(self, input: Input) -> Dict[str, Any]:
+    @type_assert(object, (str, tuple))
+    def __call__(self, data: Union[str, tuple]) -> Dict[str, Any]:
 
         def encode_text(text, length=None, append_bos=False, append_eos=False):
             s = self.task.tgt_dict.encode_line(
@@ -88,7 +95,7 @@ class ImageCaptionPipeline(Pipeline):
             patch_image = self.patch_resize_transform(input).unsqueeze(0)
         else:
             patch_image = self.patch_resize_transform(
-                load_image(input)).unsqueeze(0)
+                load_image(data)).unsqueeze(0)
         patch_mask = torch.tensor([True])
         text = 'what does the image describe?'
         src_text = encode_text(
@@ -105,17 +112,3 @@ class ImageCaptionPipeline(Pipeline):
             }
         }
         return sample
-
-    def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
-        from ofa.utils.eval_utils import eval_caption
-
-        results, _ = eval_caption(self.task, self.generator, self.models,
-                                  input)
-        return {
-            'image_id': results[0]['image_id'],
-            'caption': results[0]['caption']
-        }
-
-    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
-        # What should we do here ?
-        return inputs

tests/pipelines/test_image_captioning.py

@@ -1,10 +1,7 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
-import os
-import tempfile
 import unittest
 
-from modelscope.fileio import File
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
@@ -12,23 +9,13 @@ from modelscope.utils.test_utils import test_level
 
 class ImageCaptionTest(unittest.TestCase):
 
-    @unittest.skip('skip before model is restored in model hub')
+    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run(self):
-        model = 'https://ofa-beijing.oss-cn-beijing.aliyuncs.com/checkpoints/caption_large_best_clean.pt'
-
-        os.system(
-            'wget  https://jirenmr.oss-cn-zhangjiakou.aliyuncs.com/ofa/BPE.zip'
-        )
-        os.system('unzip BPE.zip')
-        bpe_dir = './BPE'
-
-        with tempfile.NamedTemporaryFile('wb', suffix='.pb') as ofile:
-            ofile.write(File.read(model))
-            img_captioning = pipeline(
-                Tasks.image_captioning, model=ofile.name, bpe_dir=bpe_dir)
-
-            result = img_captioning('data/test/images/image_matting.png')
-            print(result['caption'])
+        img_captioning = pipeline(
+            Tasks.image_captioning,
+            model='damo/ofa_image-caption_coco_large_en')
+        result = img_captioning('data/test/images/image_captioning.png')
+        print(result['caption'])
 
 
 if __name__ == '__main__':