merge with master

modelscope/metainfo.py

@@ -16,6 +16,7 @@ class Models(object):
     palm = 'palm-v2'
     structbert = 'structbert'
     veco = 'veco'
+    translation = 'csanmt-translation'
     space = 'space'
 
     # audio models
@@ -56,6 +57,7 @@ class Pipelines(object):
     sentiment_analysis = 'sentiment-analysis'
     sentiment_classification = 'sentiment-classification'
     fill_mask = 'fill-mask'
+    csanmt_translation = 'csanmt-translation'
     nli = 'nli'
     dialog_intent_prediction = 'dialog-intent-prediction'
     dialog_modeling = 'dialog-modeling'

modelscope/models/__init__.py

@@ -16,7 +16,8 @@ try:
     from .audio.kws import GenericKeyWordSpotting
     from .multi_modal import OfaForImageCaptioning
     from .nlp import (BertForMaskedLM, BertForSequenceClassification,
-                      SbertForNLI, SbertForSentenceSimilarity,
+                      CsanmtForTranslation, SbertForNLI,
+                      SbertForSentenceSimilarity,
                       SbertForSentimentClassification,
                       SbertForTokenClassification,
                       SbertForZeroShotClassification, SpaceForDialogIntent,

modelscope/models/nlp/__init__.py

@@ -1,4 +1,5 @@
 from .bert_for_sequence_classification import *  # noqa F403
+from .csanmt_for_translation import *  # noqa F403
 from .masked_language_model import *  # noqa F403
 from .palm_for_text_generation import *  # noqa F403
 from .sbert_for_nli import *  # noqa F403

modelscope/pipelines/builder.py

@@ -21,12 +21,12 @@ DEFAULT_MODEL_FOR_PIPELINE = {
     Tasks.sentence_similarity:
     (Pipelines.sentence_similarity,
      'damo/nlp_structbert_sentence-similarity_chinese-base'),
+    Tasks.translation: (Pipelines.csanmt_translation,
+                        'damo/nlp_csanmt_translation'),
     Tasks.nli: (Pipelines.nli, 'damo/nlp_structbert_nli_chinese-base'),
     Tasks.sentiment_classification:
     (Pipelines.sentiment_classification,
      'damo/nlp_structbert_sentiment-classification_chinese-base'),
-    Tasks.text_classification: ('bert-sentiment-analysis',
-                                'damo/bert-base-sst2'),
     Tasks.image_matting: (Pipelines.image_matting,
                           'damo/cv_unet_image-matting'),
     Tasks.text_classification: (Pipelines.sentiment_analysis,

modelscope/pipelines/nlp/__init__.py

@@ -8,6 +8,7 @@ try:
     from .sentiment_classification_pipeline import *  # noqa F403
     from .sequence_classification_pipeline import *  # noqa F403
     from .text_generation_pipeline import *  # noqa F403
+    from .translation_pipeline import *  # noqa F403
     from .word_segmentation_pipeline import *  # noqa F403
     from .zero_shot_classification_pipeline import *  # noqa F403
 except ModuleNotFoundError as e:

modelscope/pipelines/nlp/translation_pipeline.py

@@ -0,0 +1,119 @@
+import os.path as osp
+from typing import Any, Dict, Optional, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ...hub.snapshot_download import snapshot_download
+from ...metainfo import Pipelines
+from ...models import Model
+from ...models.nlp import CsanmtForTranslation
+from ...utils.constant import ModelFile, Tasks
+from ...utils.logger import get_logger
+from ..base import Pipeline, Tensor
+from ..builder import PIPELINES
+from ..outputs import OutputKeys
+
+if tf.__version__ >= '2.0':
+    tf = tf.compat.v1
+tf.disable_eager_execution()
+
+logger = get_logger()
+
+__all__ = ['TranslationPipeline']
+
+# constant
+PARAMS = {
+    'hidden_size': 512,
+    'filter_size': 2048,
+    'num_heads': 8,
+    'num_encoder_layers': 6,
+    'num_decoder_layers': 6,
+    'attention_dropout': 0.0,
+    'residual_dropout': 0.0,
+    'relu_dropout': 0.0,
+    'layer_preproc': 'none',
+    'layer_postproc': 'layer_norm',
+    'shared_embedding_and_softmax_weights': True,
+    'shared_source_target_embedding': True,
+    'initializer_scale': 0.1,
+    'train_max_len': 100,
+    'confidence': 0.9,
+    'position_info_type': 'absolute',
+    'max_relative_dis': 16,
+    'beam_size': 4,
+    'lp_rate': 0.6,
+    'num_semantic_encoder_layers': 4,
+    'max_decoded_trg_len': 100,
+    'src_vocab_size': 37006,
+    'trg_vocab_size': 37006,
+    'vocab_src': 'src_vocab.txt',
+    'vocab_trg': 'trg_vocab.txt'
+}
+
+
+@PIPELINES.register_module(
+    Tasks.translation, module_name=Pipelines.csanmt_translation)
+class TranslationPipeline(Pipeline):
+
+    def __init__(self, model: str, **kwargs):
+        if not osp.exists(model):
+            model = snapshot_download(model)
+        tf.reset_default_graph()
+        model_path = osp.join(
+            osp.join(model, ModelFile.TF_CHECKPOINT_FOLDER), 'ckpt-0')
+
+        self.params = PARAMS
+        self._src_vocab_path = osp.join(model, self.params['vocab_src'])
+        self._src_vocab = dict([
+            (w.strip(), i) for i, w in enumerate(open(self._src_vocab_path))
+        ])
+        self._trg_vocab_path = osp.join(model, self.params['vocab_trg'])
+        self._trg_rvocab = dict([
+            (i, w.strip()) for i, w in enumerate(open(self._trg_vocab_path))
+        ])
+
+        config = tf.ConfigProto(allow_soft_placement=True)
+        config.gpu_options.allow_growth = True
+        self._session = tf.Session(config=config)
+
+        self.input_wids = tf.placeholder(
+            dtype=tf.int64, shape=[None, None], name='input_wids')
+        self.output = {}
+
+        # model
+        csanmt_model = CsanmtForTranslation(model, params=self.params)
+        output = csanmt_model(self.input_wids)
+        self.output.update(output)
+
+        with self._session.as_default() as sess:
+            logger.info(f'loading model from {model_path}')
+            # load model
+            model_loader = tf.train.Saver(tf.global_variables())
+            model_loader.restore(sess, model_path)
+
+    def preprocess(self, input: str) -> Dict[str, Any]:
+        input_ids = np.array([[
+            self._src_vocab[w]
+            if w in self._src_vocab else self.params['src_vocab_size']
+            for w in input.strip().split()
+        ]])
+        result = {'input_ids': input_ids}
+        return result
+
+    def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
+        with self._session.as_default():
+            feed_dict = {self.input_wids: input['input_ids']}
+            sess_outputs = self._session.run(self.output, feed_dict=feed_dict)
+            return sess_outputs
+
+    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
+        output_seqs = inputs['output_seqs'][0]
+        wids = list(output_seqs[0]) + [0]
+        wids = wids[:wids.index(0)]
+        translation_out = ' '.join([
+            self._trg_rvocab[wid] if wid in self._trg_rvocab else '<unk>'
+            for wid in wids
+        ]).replace('@@ ', '').replace('@@', '')
+        result = {OutputKeys.TRANSLATION: translation_out}
+        return result

modelscope/pipelines/outputs.py

@@ -18,6 +18,7 @@ class OutputKeys(object):
     OUTPUT_PCM = 'output_pcm'
     IMG_EMBEDDING = 'img_embedding'
     TEXT_EMBEDDING = 'text_embedding'
+    TRANSLATION = 'translation'
     RESPONSE = 'response'
     PREDICTION = 'prediction'
     DIALOG_STATES = 'dialog_states'
@@ -124,6 +125,12 @@ TASK_OUTPUTS = {
     #   }
     Tasks.sentence_similarity: [OutputKeys.SCORES, OutputKeys.LABELS],
 
+    # translation result for a source sentence
+    #   {
+    #       "translation": “北京是中国的首都”
+    #   }
+    Tasks.translation: [OutputKeys.TRANSLATION],
+
     # sentiment classification result for single sample
     #   {
     #       "labels": ["happy", "sad", "calm", "angry"],

tests/pipelines/test_csanmt_translation.py

@@ -0,0 +1,22 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import shutil
+import unittest
+
+from modelscope.hub.snapshot_download import snapshot_download
+from modelscope.pipelines import TranslationPipeline, pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import test_level
+
+
+class TranslationTest(unittest.TestCase):
+    model_id = 'damo/nlp_csanmt_translation'
+    inputs = 'Gut@@ ach : Incre@@ ased safety for pedestri@@ ans'
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_run_with_model_name(self):
+        pipeline_ins = pipeline(task=Tasks.translation, model=self.model_id)
+        print(pipeline_ins(input=self.inputs))
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/pipelines/test_text_to_image_synthesis.py

@@ -15,7 +15,7 @@ class TextToImageSynthesisTest(unittest.TestCase):
     model_id = 'damo/cv_imagen_text-to-image-synthesis_tiny'
     test_text = {'text': '宇航员'}
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
         model = Model.from_pretrained(self.model_id)
         pipe_line_text_to_image_synthesis = pipeline(
@@ -24,7 +24,7 @@ class TextToImageSynthesisTest(unittest.TestCase):
             self.test_text)[OutputKeys.OUTPUT_IMG]
         print(np.sum(np.abs(img)))
 
-    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_model_name(self):
         pipe_line_text_to_image_synthesis = pipeline(
             task=Tasks.text_to_image_synthesis, model=self.model_id)