Merge remote-tracking branch 'origin/master' into ofa/finetune

modelscope/hub/api.py

@@ -12,6 +12,7 @@ from http.cookiejar import CookieJar
 from os.path import expanduser
 from typing import List, Optional, Tuple, Union
 
+import attrs
 import requests
 
 from modelscope.hub.constants import (API_RESPONSE_FIELD_DATA,
@@ -21,9 +22,14 @@ from modelscope.hub.constants import (API_RESPONSE_FIELD_DATA,
                                       API_RESPONSE_FIELD_USERNAME,
                                       DEFAULT_CREDENTIALS_PATH, Licenses,
                                       ModelVisibility)
+from modelscope.hub.deploy import (DeleteServiceParameters,
+                                   DeployServiceParameters,
+                                   GetServiceParameters, ListServiceParameters,
+                                   ServiceParameters, ServiceResourceConfig,
+                                   Vendor)
 from modelscope.hub.errors import (InvalidParameter, NotExistError,
-                                   NotLoginException, RequestError,
-                                   datahub_raise_on_error,
+                                   NotLoginException, NotSupportError,
+                                   RequestError, datahub_raise_on_error,
                                    handle_http_post_error,
                                    handle_http_response, is_ok, raise_on_error)
 from modelscope.hub.git import GitCommandWrapper
@@ -306,6 +312,169 @@ class HubApi:
             r.raise_for_status()
         return None
 
+    def deploy_model(self, model_id: str, revision: str, instance_name: str,
+                     resource: ServiceResourceConfig,
+                     provider: ServiceParameters):
+        """Deploy model to cloud, current we only support PAI EAS, this is asynchronous
+        call , please check instance status through the console or query the instance status.
+        At the same time, this call may take a long time.
+
+        Args:
+            model_id (str): The deployed model id
+            revision (str): The model revision
+            instance_name (str): The deployed model instance name.
+            resource (DeployResource): The resource information.
+            provider (CreateParameter): The cloud service provider parameter
+
+        Raises:
+            NotLoginException: To use this api, you need login first.
+            NotSupportError: Not supported platform.
+            RequestError: The server return error.
+
+        Returns:
+            InstanceInfo: The instance information.
+        """
+        cookies = ModelScopeConfig.get_cookies()
+        if cookies is None:
+            raise NotLoginException(
+                'Token does not exist, please login first.')
+        if provider.vendor != Vendor.EAS:
+            raise NotSupportError(
+                'Not support vendor: %s ,only support EAS current.' %
+                (provider.vendor))
+        create_params = DeployServiceParameters(
+            instance_name=instance_name,
+            model_id=model_id,
+            revision=revision,
+            resource=resource,
+            provider=provider)
+        path = f'{self.endpoint}/api/v1/deployer/endpoint'
+        body = attrs.asdict(create_params)
+        r = requests.post(
+            path,
+            json=body,
+            cookies=cookies,
+        )
+        handle_http_response(r, logger, cookies, 'create_eas_instance')
+        if r.status_code >= HTTPStatus.OK and r.status_code < HTTPStatus.MULTIPLE_CHOICES:
+            if is_ok(r.json()):
+                data = r.json()[API_RESPONSE_FIELD_DATA]
+                return data
+            else:
+                raise RequestError(r.json()[API_RESPONSE_FIELD_MESSAGE])
+        else:
+            r.raise_for_status()
+        return None
+
+    def list_deployed_model_instances(self,
+                                      provider: ServiceParameters,
+                                      skip: int = 0,
+                                      limit: int = 100):
+        """List deployed model instances.
+
+        Args:
+            provider (ListServiceParameter): The cloud service provider parameter,
+            for eas, need access_key_id and access_key_secret.
+            skip: start of the list, current not support.
+            limit: maximum number of instances return, current not support
+        Raises:
+            NotLoginException: To use this api, you need login first.
+            RequestError: The request is failed from server.
+
+        Returns:
+            List: List of instance information
+        """
+        cookies = ModelScopeConfig.get_cookies()
+        if cookies is None:
+            raise NotLoginException(
+                'Token does not exist, please login first.')
+        params = ListServiceParameters(
+            provider=provider, skip=skip, limit=limit)
+        path = '%s/api/v1/deployer/endpoint?%s' % (self.endpoint,
+                                                   params.to_query_str())
+        r = requests.get(path, cookies=cookies)
+        handle_http_response(r, logger, cookies, 'list_deployed_model')
+        if r.status_code == HTTPStatus.OK:
+            if is_ok(r.json()):
+                data = r.json()[API_RESPONSE_FIELD_DATA]
+                return data
+            else:
+                raise RequestError(r.json()[API_RESPONSE_FIELD_MESSAGE])
+        else:
+            r.raise_for_status()
+        return None
+
+    def get_deployed_model_instance(self, instance_name: str,
+                                    provider: ServiceParameters):
+        """Query the specified instance information.
+
+        Args:
+            instance_name (str): The deployed instance name.
+            provider (GetParameter): The cloud provider information, for eas
+            need region(eg: ch-hangzhou), access_key_id and access_key_secret.
+
+        Raises:
+            NotLoginException: To use this api, you need login first.
+            RequestError: The request is failed from server.
+
+        Returns:
+            Dict: The request instance information
+        """
+        cookies = ModelScopeConfig.get_cookies()
+        if cookies is None:
+            raise NotLoginException(
+                'Token does not exist, please login first.')
+        params = GetServiceParameters(provider=provider)
+        path = '%s/api/v1/deployer/endpoint/%s?%s' % (
+            self.endpoint, instance_name, params.to_query_str())
+        r = requests.get(path, cookies=cookies)
+        handle_http_response(r, logger, cookies, 'get_deployed_model')
+        if r.status_code == HTTPStatus.OK:
+            if is_ok(r.json()):
+                data = r.json()[API_RESPONSE_FIELD_DATA]
+                return data
+            else:
+                raise RequestError(r.json()[API_RESPONSE_FIELD_MESSAGE])
+        else:
+            r.raise_for_status()
+        return None
+
+    def delete_deployed_model_instance(self, instance_name: str,
+                                       provider: ServiceParameters):
+        """Delete deployed model, this api send delete command and return, it will take
+        some to delete, please check through the cloud console.
+
+        Args:
+            instance_name (str): The instance name you want to delete.
+            provider (DeleteParameter): The cloud provider information, for eas
+            need region(eg: ch-hangzhou), access_key_id and access_key_secret.
+
+        Raises:
+            NotLoginException: To call this api, you need login first.
+            RequestError: The request is failed.
+
+        Returns:
+            Dict: The deleted instance information.
+        """
+        cookies = ModelScopeConfig.get_cookies()
+        if cookies is None:
+            raise NotLoginException(
+                'Token does not exist, please login first.')
+        params = DeleteServiceParameters(provider=provider)
+        path = '%s/api/v1/deployer/endpoint/%s?%s' % (
+            self.endpoint, instance_name, params.to_query_str())
+        r = requests.delete(path, cookies=cookies)
+        handle_http_response(r, logger, cookies, 'delete_deployed_model')
+        if r.status_code == HTTPStatus.OK:
+            if is_ok(r.json()):
+                data = r.json()[API_RESPONSE_FIELD_DATA]
+                return data
+            else:
+                raise RequestError(r.json()[API_RESPONSE_FIELD_MESSAGE])
+        else:
+            r.raise_for_status()
+        return None
+
     def _check_cookie(self,
                       use_cookies: Union[bool,
                                          CookieJar] = False) -> CookieJar:

modelscope/hub/deploy.py

@@ -0,0 +1,189 @@
+import urllib
+from abc import ABC, abstractmethod
+from typing import Optional, Union
+
+import json
+from attr import fields
+from attrs import asdict, define, field, validators
+
+
+class Accelerator(object):
+    CPU = 'cpu'
+    GPU = 'gpu'
+
+
+class Vendor(object):
+    EAS = 'eas'
+
+
+class EASRegion(object):
+    beijing = 'cn-beijing'
+    hangzhou = 'cn-hangzhou'
+
+
+class EASCpuInstanceType(object):
+    """EAS Cpu Instance TYpe, ref(https://help.aliyun.com/document_detail/144261.html)
+    """
+    tiny = 'ecs.c6.2xlarge'
+    small = 'ecs.c6.4xlarge'
+    medium = 'ecs.c6.6xlarge'
+    large = 'ecs.c6.8xlarge'
+
+
+class EASGpuInstanceType(object):
+    """EAS Cpu Instance TYpe, ref(https://help.aliyun.com/document_detail/144261.html)
+    """
+    tiny = 'ecs.gn5-c28g1.7xlarge'
+    small = 'ecs.gn5-c8g1.4xlarge'
+    medium = 'ecs.gn6i-c24g1.12xlarge'
+    large = 'ecs.gn6e-c12g1.3xlarge'
+
+
+def min_smaller_than_max(instance, attribute, value):
+    if value > instance.max_replica:
+        raise ValueError(
+            "'min_replica' value: %s has to be smaller than 'max_replica' value: %s!"
+            % (value, instance.max_replica))
+
+
+@define
+class ServiceScalingConfig(object):
+    """Resource scaling config
+       Currently we ignore max_replica
+    Args:
+        max_replica: maximum replica
+        min_replica: minimum replica
+    """
+    max_replica: int = field(default=1, validator=validators.ge(1))
+    min_replica: int = field(
+        default=1, validator=[validators.ge(1), min_smaller_than_max])
+
+
+@define
+class ServiceResourceConfig(object):
+    """Eas Resource request.
+
+    Args:
+        accelerator: the accelerator(cpu|gpu)
+        instance_type: the instance type.
+        scaling: The instance scaling config.
+    """
+    instance_type: str
+    scaling: ServiceScalingConfig
+    accelerator: str = field(
+        default=Accelerator.CPU,
+        validator=validators.in_([Accelerator.CPU, Accelerator.GPU]))
+
+
+@define
+class ServiceParameters(ABC):
+    pass
+
+
+@define
+class EASDeployParameters(ServiceParameters):
+    """Parameters for EAS Deployment.
+
+    Args:
+        resource_group: the resource group to deploy, current default.
+        region: The eas instance region(eg: cn-hangzhou).
+        access_key_id: The eas account access key id.
+        access_key_secret: The eas account access key secret.
+        vendor: must be 'eas'
+    """
+    region: str
+    access_key_id: str
+    access_key_secret: str
+    resource_group: Optional[str] = None
+    vendor: str = field(
+        default=Vendor.EAS, validator=validators.in_([Vendor.EAS]))
+    """
+    def __init__(self,
+                 instance_name: str,
+                 access_key_id: str,
+                 access_key_secret: str,
+                 region = EASRegion.beijing,
+                 instance_type: str  = EASCpuInstances.small,
+                 accelerator: str =  Accelerator.CPU,
+                 resource_group: Optional[str] = None,
+                 scaling: Optional[str] = None):
+        self.instance_name=instance_name
+        self.access_key_id=self.access_key_id
+        self.access_key_secret = access_key_secret
+        self.region = region
+        self.instance_type = instance_type
+        self.accelerator = accelerator
+        self.resource_group = resource_group
+        self.scaling = scaling
+    """
+
+
+@define
+class EASListParameters(ServiceParameters):
+    """EAS instance list parameters.
+
+    Args:
+        resource_group: the resource group to deploy, current default.
+        region: The eas instance region(eg: cn-hangzhou).
+        access_key_id: The eas account access key id.
+        access_key_secret: The eas account access key secret.
+        vendor: must be 'eas'
+    """
+    access_key_id: str
+    access_key_secret: str
+    region: str = None
+    resource_group: str = None
+    vendor: str = field(
+        default=Vendor.EAS, validator=validators.in_([Vendor.EAS]))
+
+
+@define
+class DeployServiceParameters(object):
+    """Deploy service parameters
+
+    Args:
+        instance_name: the name of the service.
+        model_id: the modelscope model_id
+        revision: the modelscope model revision
+        resource: the resource requirement.
+        provider: the cloud service provider.
+    """
+    instance_name: str
+    model_id: str
+    revision: str
+    resource: ServiceResourceConfig
+    provider: ServiceParameters
+
+
+class AttrsToQueryString(ABC):
+    """Convert the attrs class to json string.
+
+    Args:
+    """
+
+    def to_query_str(self):
+        self_dict = asdict(
+            self.provider, filter=lambda attr, value: value is not None)
+        json_str = json.dumps(self_dict)
+        print(json_str)
+        safe_str = urllib.parse.quote_plus(json_str)
+        print(safe_str)
+        query_param = 'provider=%s' % safe_str
+        return query_param
+
+
+@define
+class ListServiceParameters(AttrsToQueryString):
+    provider: ServiceParameters
+    skip: int = 0
+    limit: int = 100
+
+
+@define
+class GetServiceParameters(AttrsToQueryString):
+    provider: ServiceParameters
+
+
+@define
+class DeleteServiceParameters(AttrsToQueryString):
+    provider: ServiceParameters

modelscope/hub/errors.py

@@ -9,6 +9,10 @@ from modelscope.utils.logger import get_logger
 logger = get_logger()
 
 
+class NotSupportError(Exception):
+    pass
+
+
 class NotExistError(Exception):
     pass
 
@@ -66,6 +70,7 @@ def handle_http_response(response, logger, cookies, model_id):
             logger.error(
                 f'Authentication token does not exist, failed to access model {model_id} which may not exist or may be \
                 private. Please login first.')
+        logger.error('Response details: %s' % response.content)
         raise error
 
 

modelscope/metainfo.py

@@ -67,8 +67,9 @@ class Models(object):
     space_dst = 'space-dst'
     space_intent = 'space-intent'
     space_modeling = 'space-modeling'
-    star = 'star'
-    star3 = 'star3'
+    space_T_en = 'space-T-en'
+    space_T_cn = 'space-T-cn'
+
     tcrf = 'transformer-crf'
     transformer_softmax = 'transformer-softmax'
     lcrf = 'lstm-crf'

modelscope/models/cv/realtime_object_detection/realtime_video_detector.py

@@ -16,6 +16,7 @@ from modelscope.models.builder import MODELS
 from modelscope.preprocessors import LoadImage
 from modelscope.utils.config import Config
 from modelscope.utils.constant import ModelFile, Tasks
+from .utils import timestamp_format
 from .yolox.data.data_augment import ValTransform
 from .yolox.exp import get_exp_by_name
 from .yolox.utils import postprocess
@@ -99,14 +100,17 @@ class RealtimeVideoDetector(TorchModel):
     def inference_video(self, v_path):
         outputs = []
         desc = 'Detecting video: {}'.format(v_path)
-        for frame, result in tqdm(
-                self.inference_video_iter(v_path), desc=desc):
+        for frame_idx, (frame, result) in enumerate(
+                tqdm(self.inference_video_iter(v_path), desc=desc)):
+            result = result + (timestamp_format(seconds=frame_idx
+                                                / self.fps), )
             outputs.append(result)
 
         return outputs
 
     def inference_video_iter(self, v_path):
         capture = cv2.VideoCapture(v_path)
+        self.fps = capture.get(cv2.CAP_PROP_FPS)
         while capture.isOpened():
             ret, frame = capture.read()
             if not ret:

modelscope/models/cv/realtime_object_detection/utils.py

@@ -0,0 +1,9 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import math
+
+
+def timestamp_format(seconds):
+    m, s = divmod(seconds, 60)
+    h, m = divmod(m, 60)
+    time = '%02d:%02d:%06.3f' % (h, m, s)
+    return time

modelscope/models/nlp/space_T_cn/configuration_space_T_cn.py

@@ -24,8 +24,8 @@ import json
 logger = logging.getLogger(__name__)
 
 
-class Star3Config(object):
-    """Configuration class to store the configuration of a `Star3Model`.
+class SpaceTCnConfig(object):
+    """Configuration class to store the configuration of a `SpaceTCnModel`.
     """
 
     def __init__(self,
@@ -40,10 +40,10 @@ class Star3Config(object):
                  max_position_embeddings=512,
                  type_vocab_size=2,
                  initializer_range=0.02):
-        """Constructs Star3Config.
+        """Constructs SpaceTCnConfig.
 
         Args:
-            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `Star3Model`.
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `SpaceTCnConfig`.
             hidden_size: Size of the encoder layers and the pooler layer.
             num_hidden_layers: Number of hidden layers in the Transformer encoder.
             num_attention_heads: Number of attention heads for each attention layer in
@@ -59,7 +59,7 @@ class Star3Config(object):
             max_position_embeddings: The maximum sequence length that this model might
                 ever be used with. Typically set this to something large just in case
                 (e.g., 512 or 1024 or 2048).
-            type_vocab_size: The vocabulary size of the `token_type_ids` passed into `Star3Model`.
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into `SpaceTCnConfig`.
             initializer_range: The sttdev of the truncated_normal_initializer for
                 initializing all weight matrices.
         """
@@ -89,15 +89,15 @@ class Star3Config(object):
 
     @classmethod
     def from_dict(cls, json_object):
-        """Constructs a `Star3Config` from a Python dictionary of parameters."""
-        config = Star3Config(vocab_size_or_config_json_file=-1)
+        """Constructs a `SpaceTCnConfig` from a Python dictionary of parameters."""
+        config = SpaceTCnConfig(vocab_size_or_config_json_file=-1)
         for key, value in json_object.items():
             config.__dict__[key] = value
         return config
 
     @classmethod
     def from_json_file(cls, json_file):
-        """Constructs a `Star3Config` from a json file of parameters."""
+        """Constructs a `SpaceTCnConfig` from a json file of parameters."""
         with open(json_file, 'r', encoding='utf-8') as reader:
             text = reader.read()
         return cls.from_dict(json.loads(text))

modelscope/models/nlp/space_T_cn/modeling_space_T_cn.py

@@ -27,7 +27,8 @@ import numpy as np
 import torch
 from torch import nn
 
-from modelscope.models.nlp.star3.configuration_star3 import Star3Config
+from modelscope.models.nlp.space_T_cn.configuration_space_T_cn import \
+    SpaceTCnConfig
 from modelscope.utils.constant import ModelFile
 from modelscope.utils.logger import get_logger
 
@@ -609,9 +610,9 @@ class PreTrainedBertModel(nn.Module):
 
     def __init__(self, config, *inputs, **kwargs):
         super(PreTrainedBertModel, self).__init__()
-        if not isinstance(config, Star3Config):
+        if not isinstance(config, SpaceTCnConfig):
             raise ValueError(
-                'Parameter config in `{}(config)` should be an instance of class `Star3Config`. '
+                'Parameter config in `{}(config)` should be an instance of class `SpaceTCnConfig`. '
                 'To create a model from a Google pretrained model use '
                 '`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`'.format(
                     self.__class__.__name__, self.__class__.__name__))
@@ -676,7 +677,7 @@ class PreTrainedBertModel(nn.Module):
             serialization_dir = tempdir
         # Load config
         config_file = os.path.join(serialization_dir, CONFIG_NAME)
-        config = Star3Config.from_json_file(config_file)
+        config = SpaceTCnConfig.from_json_file(config_file)
         logger.info('Model config {}'.format(config))
         # Instantiate model.
         model = cls(config, *inputs, **kwargs)
@@ -742,11 +743,11 @@ class PreTrainedBertModel(nn.Module):
         return model
 
 
-class Star3Model(PreTrainedBertModel):
-    """Star3Model model ("Bidirectional Embedding Representations from a Transformer pretrained on STAR3.0").
+class SpaceTCnModel(PreTrainedBertModel):
+    """SpaceTCnModel model ("Bidirectional Embedding Representations from a Transformer pretrained on STAR-T-CN").
 
     Params:
-        config: a Star3Config class instance with the configuration to build a new model
+        config: a SpaceTCnConfig class instance with the configuration to build a new model
 
     Inputs:
         `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
@@ -780,16 +781,16 @@ class Star3Model(PreTrainedBertModel):
     input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
     token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
 
-    config = modeling.Star3Config(vocab_size_or_config_json_file=32000, hidden_size=768,
+    config = modeling.SpaceTCnConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
         num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
 
-    model = modeling.Star3Model(config=config)
+    model = modeling.SpaceTCnModel(config=config)
     all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
     ```
     """
 
     def __init__(self, config, schema_link_module='none'):
-        super(Star3Model, self).__init__(config)
+        super(SpaceTCnModel, self).__init__(config)
         self.embeddings = BertEmbeddings(config)
         self.encoder = BertEncoder(
             config, schema_link_module=schema_link_module)

modelscope/models/nlp/star_text_to_sql.py

@@ -20,7 +20,7 @@ __all__ = ['StarForTextToSql']
 
 
 @MODELS.register_module(
-    Tasks.conversational_text_to_sql, module_name=Models.star)
+    Tasks.table_question_answering, module_name=Models.space_T_en)
 class StarForTextToSql(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):

modelscope/models/nlp/table_question_answering.py

@@ -3,27 +3,25 @@
 import os
 from typing import Dict
 
-import json
 import numpy
 import torch
 import torch.nn.functional as F
-import tqdm
 from transformers import BertTokenizer
 
 from modelscope.metainfo import Models
 from modelscope.models.base import Model, Tensor
 from modelscope.models.builder import MODELS
-from modelscope.models.nlp.star3.configuration_star3 import Star3Config
-from modelscope.models.nlp.star3.modeling_star3 import Seq2SQL, Star3Model
-from modelscope.preprocessors.star3.fields.struct import Constant
+from modelscope.preprocessors.space_T_cn.fields.struct import Constant
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.device import verify_device
+from .space_T_cn.configuration_space_T_cn import SpaceTCnConfig
+from .space_T_cn.modeling_space_T_cn import Seq2SQL, SpaceTCnModel
 
 __all__ = ['TableQuestionAnswering']
 
 
 @MODELS.register_module(
-    Tasks.table_question_answering, module_name=Models.star3)
+    Tasks.table_question_answering, module_name=Models.space_T_cn)
 class TableQuestionAnswering(Model):
 
     def __init__(self, model_dir: str, *args, **kwargs):
@@ -43,9 +41,9 @@ class TableQuestionAnswering(Model):
             os.path.join(self.model_dir, ModelFile.TORCH_MODEL_BIN_FILE),
             map_location='cpu')
 
-        self.backbone_config = Star3Config.from_json_file(
+        self.backbone_config = SpaceTCnConfig.from_json_file(
             os.path.join(self.model_dir, ModelFile.CONFIGURATION))
-        self.backbone_model = Star3Model(
+        self.backbone_model = SpaceTCnModel(
             config=self.backbone_config, schema_link_module='rat')
         self.backbone_model.load_state_dict(state_dict['backbone_model'])
 

modelscope/outputs.py

@@ -606,21 +606,12 @@ TASK_OUTPUTS = {
     # }
     Tasks.task_oriented_conversation: [OutputKeys.OUTPUT],
 
-    # conversational text-to-sql result for single sample
-    # {
-    #   "text": "SELECT shop.Name FROM shop."
-    # }
-    Tasks.conversational_text_to_sql: [OutputKeys.TEXT],
-
     # table-question-answering result for single sample
     # {
     #   "sql": "SELECT shop.Name FROM shop."
     #   "sql_history": {sel: 0, agg: 0, conds: [[0, 0, 'val']]}
     # }
-    Tasks.table_question_answering: [
-        OutputKeys.SQL_STRING, OutputKeys.SQL_QUERY, OutputKeys.HISTORY,
-        OutputKeys.QUERT_RESULT
-    ],
+    Tasks.table_question_answering: [OutputKeys.OUTPUT],
 
     # ============ audio tasks ===================
     # asr result for single sample

modelscope/pipelines/builder.py

@@ -69,9 +69,6 @@ DEFAULT_MODEL_FOR_PIPELINE = {
                                        'damo/nlp_space_dialog-modeling'),
     Tasks.dialog_state_tracking: (Pipelines.dialog_state_tracking,
                                   'damo/nlp_space_dialog-state-tracking'),
-    Tasks.conversational_text_to_sql:
-    (Pipelines.conversational_text_to_sql,
-     'damo/nlp_star_conversational-text-to-sql'),
     Tasks.table_question_answering:
     (Pipelines.table_question_answering_pipeline,
      'damo/nlp-convai-text2sql-pretrain-cn'),

modelscope/pipelines/cv/animal_recognition_pipeline.py

@@ -113,9 +113,8 @@ class AnimalRecognitionPipeline(Pipeline):
             label_mapping = f.readlines()
         score = torch.max(inputs['outputs'])
         inputs = {
-            OutputKeys.SCORES:
-            score.item(),
+            OutputKeys.SCORES: [score.item()],
             OutputKeys.LABELS:
-            label_mapping[inputs['outputs'].argmax()].split('\t')[1]
+            [label_mapping[inputs['outputs'].argmax()].split('\t')[1]]
         }
         return inputs

modelscope/pipelines/cv/general_recognition_pipeline.py

@@ -114,9 +114,8 @@ class GeneralRecognitionPipeline(Pipeline):
             label_mapping = f.readlines()
         score = torch.max(inputs['outputs'])
         inputs = {
-            OutputKeys.SCORES:
-            score.item(),
+            OutputKeys.SCORES: [score.item()],
             OutputKeys.LABELS:
-            label_mapping[inputs['outputs'].argmax()].split('\t')[1]
+            [label_mapping[inputs['outputs'].argmax()].split('\t')[1]]
         }
         return inputs

modelscope/pipelines/cv/realtime_video_object_detection_pipeline.py

@@ -45,15 +45,17 @@ class RealtimeVideoObjectDetectionPipeline(Pipeline):
                     **kwargs) -> str:
         forward_output = input['forward_output']
 
-        scores, boxes, labels = [], [], []
+        scores, boxes, labels, timestamps = [], [], [], []
         for result in forward_output:
-            box, score, label = result
+            box, score, label, timestamp = result
             scores.append(score)
             boxes.append(box)
             labels.append(label)
+            timestamps.append(timestamp)
 
         return {
             OutputKeys.BOXES: boxes,
             OutputKeys.SCORES: scores,
             OutputKeys.LABELS: labels,
+            OutputKeys.TIMESTAMPS: timestamps,
         }

modelscope/pipelines/nlp/conversational_text_to_sql_pipeline.py

@@ -19,7 +19,7 @@ __all__ = ['ConversationalTextToSqlPipeline']
 
 
 @PIPELINES.register_module(
-    Tasks.conversational_text_to_sql,
+    Tasks.table_question_answering,
     module_name=Pipelines.conversational_text_to_sql)
 class ConversationalTextToSqlPipeline(Pipeline):
 
@@ -62,7 +62,7 @@ class ConversationalTextToSqlPipeline(Pipeline):
             Dict[str, str]: the prediction results
         """
         sql = Example.evaluator.obtain_sql(inputs['predict'][0], inputs['db'])
-        result = {OutputKeys.TEXT: sql}
+        result = {OutputKeys.OUTPUT: {OutputKeys.TEXT: sql}}
         return result
 
     def _collate_fn(self, data):

modelscope/pipelines/nlp/table_question_answering_pipeline.py

@@ -13,8 +13,9 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Pipeline
 from modelscope.pipelines.builder import PIPELINES
 from modelscope.preprocessors import TableQuestionAnsweringPreprocessor
-from modelscope.preprocessors.star3.fields.database import Database
-from modelscope.preprocessors.star3.fields.struct import Constant, SQLQuery
+from modelscope.preprocessors.space_T_cn.fields.database import Database
+from modelscope.preprocessors.space_T_cn.fields.struct import (Constant,
+                                                               SQLQuery)
 from modelscope.utils.constant import ModelFile, Tasks
 
 __all__ = ['TableQuestionAnsweringPipeline']
@@ -320,7 +321,7 @@ class TableQuestionAnsweringPipeline(Pipeline):
             OutputKeys.QUERT_RESULT: tabledata,
         }
 
-        return output
+        return {OutputKeys.OUTPUT: output}
 
     def _collate_fn(self, data):
         return data

modelscope/preprocessors/__init__.py

@@ -40,7 +40,7 @@ if TYPE_CHECKING:
                         DialogStateTrackingPreprocessor)
     from .video import ReadVideoData, MovieSceneSegmentationPreprocessor
     from .star import ConversationalTextToSqlPreprocessor
-    from .star3 import TableQuestionAnsweringPreprocessor
+    from .space_T_cn import TableQuestionAnsweringPreprocessor
 
 else:
     _import_structure = {
@@ -81,7 +81,7 @@ else:
             'DialogStateTrackingPreprocessor', 'InputFeatures'
         ],
         'star': ['ConversationalTextToSqlPreprocessor'],
-        'star3': ['TableQuestionAnsweringPreprocessor'],
+        'space_T_cn': ['TableQuestionAnsweringPreprocessor'],
     }
 
     import sys

modelscope/preprocessors/space_T_cn/fields/database.py

@@ -4,7 +4,7 @@ import sqlite3
 import json
 import tqdm
 
-from modelscope.preprocessors.star3.fields.struct import Trie
+from modelscope.preprocessors.space_T_cn.fields.struct import Trie
 
 
 class Database:

modelscope/preprocessors/space_T_cn/fields/schema_link.py

@@ -1,7 +1,7 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import re
 
-from modelscope.preprocessors.star3.fields.struct import TypeInfo
+from modelscope.preprocessors.space_T_cn.fields.struct import TypeInfo
 
 
 class SchemaLinker:

modelscope/preprocessors/space_T_cn/table_question_answering_preprocessor.py

@@ -8,8 +8,8 @@ from transformers import BertTokenizer
 from modelscope.metainfo import Preprocessors
 from modelscope.preprocessors.base import Preprocessor
 from modelscope.preprocessors.builder import PREPROCESSORS
-from modelscope.preprocessors.star3.fields.database import Database
-from modelscope.preprocessors.star3.fields.schema_link import SchemaLinker
+from modelscope.preprocessors.space_T_cn.fields.database import Database
+from modelscope.preprocessors.space_T_cn.fields.schema_link import SchemaLinker
 from modelscope.utils.config import Config
 from modelscope.utils.constant import Fields, ModelFile
 from modelscope.utils.type_assert import type_assert

modelscope/utils/constant.py

@@ -123,7 +123,6 @@ class NLPTasks(object):
     backbone = 'backbone'
     text_error_correction = 'text-error-correction'
     faq_question_answering = 'faq-question-answering'
-    conversational_text_to_sql = 'conversational-text-to-sql'
     information_extraction = 'information-extraction'
     document_segmentation = 'document-segmentation'
     feature_extraction = 'feature-extraction'

modelscope/utils/nlp/nlp_utils.py

@@ -20,7 +20,7 @@ def text2sql_tracking_and_print_results(
             results = p(case)
             print({'question': item})
             print(results)
-            last_sql = results['text']
+            last_sql = results[OutputKeys.OUTPUT][OutputKeys.TEXT]
             history.append(item)
 
 

requirements/framework.txt

@@ -1,4 +1,5 @@
 addict
+attrs
 datasets
 easydict
 einops

tests/pipelines/test_conversational_text_to_sql.py

@@ -16,7 +16,7 @@ from modelscope.utils.test_utils import test_level
 class ConversationalTextToSql(unittest.TestCase, DemoCompatibilityCheck):
 
     def setUp(self) -> None:
-        self.task = Tasks.conversational_text_to_sql
+        self.task = Tasks.table_question_answering
         self.model_id = 'damo/nlp_star_conversational-text-to-sql'
 
     model_id = 'damo/nlp_star_conversational-text-to-sql'
@@ -66,11 +66,6 @@ class ConversationalTextToSql(unittest.TestCase, DemoCompatibilityCheck):
         pipelines = [pipeline(task=self.task, model=self.model_id)]
         text2sql_tracking_and_print_results(self.test_case, pipelines)
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
-    def test_run_with_default_model(self):
-        pipelines = [pipeline(task=self.task)]
-        text2sql_tracking_and_print_results(self.test_case, pipelines)
-
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_demo_compatibility(self):
         self.compatibility_check()

tests/pipelines/test_table_question_answering.py

@@ -12,7 +12,7 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.pipelines.nlp import TableQuestionAnsweringPipeline
 from modelscope.preprocessors import TableQuestionAnsweringPreprocessor
-from modelscope.preprocessors.star3.fields.database import Database
+from modelscope.preprocessors.space_T_cn.fields.database import Database
 from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.test_utils import test_level
 
@@ -38,7 +38,7 @@ def tableqa_tracking_and_print_results_with_history(
             output_dict = p({
                 'question': question,
                 'history_sql': historical_queries
-            })
+            })[OutputKeys.OUTPUT]
             print('question', question)
             print('sql text:', output_dict[OutputKeys.SQL_STRING])
             print('sql query:', output_dict[OutputKeys.SQL_QUERY])
@@ -61,7 +61,7 @@ def tableqa_tracking_and_print_results_without_history(
     }
     for p in pipelines:
         for question in test_case['utterance']:
-            output_dict = p({'question': question})
+            output_dict = p({'question': question})[OutputKeys.OUTPUT]
             print('question', question)
             print('sql text:', output_dict[OutputKeys.SQL_STRING])
             print('sql query:', output_dict[OutputKeys.SQL_QUERY])
@@ -92,7 +92,7 @@ def tableqa_tracking_and_print_results_with_tableid(
                 'question': question,
                 'table_id': table_id,
                 'history_sql': historical_queries
-            })
+            })[OutputKeys.OUTPUT]
             print('question', question)
             print('sql text:', output_dict[OutputKeys.SQL_STRING])
             print('sql query:', output_dict[OutputKeys.SQL_QUERY])
@@ -147,11 +147,6 @@ class TableQuestionAnswering(unittest.TestCase):
         ]
         tableqa_tracking_and_print_results_with_tableid(pipelines)
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_run_with_model_from_task(self):
-        pipelines = [pipeline(Tasks.table_question_answering, self.model_id)]
-        tableqa_tracking_and_print_results_with_history(pipelines)
-
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_model_from_modelhub_with_other_classes(self):
         model = Model.from_pretrained(self.model_id)