module_v1.py

# coding:utf-8
# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import functools
import os
from typing import Tuple, List

import paddle
from easydict import EasyDict

from paddlehub.compat import paddle_utils
from paddlehub.compat.module import module_v1_utils
from paddlehub.utils import utils, log


class ModuleV1(object):
    '''
    '''

    def __init__(self, name: str = None, directory: str = None, version: str = None):
        if not directory:
            return

        desc_file = os.path.join(directory, 'module_desc.pb')
        self.desc = module_v1_utils.convert_module_desc(desc_file)
        self.helper = self
        self.signatures = self.desc.signatures

        self.directory = directory
        self._load_model()
        self._load_parameters()
        self._load_processor()
        self._load_assets()
        self._load_extra_info()
        self._generate_func()

    def _load_processor(self):
        # Some module does not have a processor(e.g. ernie)
        if not 'processor_info' in self.desc:
            return

        python_path = os.path.join(self.directory, 'python')
        processor_name = self.desc.processor_info
        self.processor = utils.load_py_module(python_path, processor_name)
        self.processor = self.processor.Processor(module=self)

    def _load_assets(self):
        self.assets = []
        for file in os.listdir(self.assets_path()):
            filepath = os.path.join(self.assets_path(), file)
            self.assets.append(filepath)

    def _load_parameters(self):
        global_block = self.program.global_block()

        # record num parameters loaded by PaddleHub
        num_param_loaded = 0

        for param, attrs in self.desc.param_attrs.items():
            name = self.desc.name_prefix + param
            if not name in global_block.vars:
                continue

            num_param_loaded += 1
            var = global_block.vars[name]

            global_block.create_parameter(
                name=name,
                shape=var.shape,
                dtype=var.dtype,
                type=var.type,
                lod_level=var.lod_level,
                error_clip=var.error_clip,
                stop_gradient=var.stop_gradient,
                is_data=var.is_data,
                **attrs)

        log.logger.info('{} pretrained paramaters loaded by PaddleHub'.format(num_param_loaded))

    def _load_extra_info(self):
        for key, value in self.desc.extra_info.items():
            self.__dict__['get_{}'.format(key)] = value

    def _generate_func(self):
        for signature in self.desc.signatures:
            self.__dict__[signature] = functools.partial(self.__call__, sign_name=signature)

    def _load_model(self):
        model_path = os.path.join(self.directory, 'model')
        exe = paddle.static.Executor(paddle.CPUPlace())
        self.program, _, _ = paddle.io.load_inference_model(model_path, executor=exe)

        # Clear the callstack since it may leak the privacy of the creator.
        for block in self.program.blocks:
            for op in block.ops:
                if not 'op_callstack' in op.all_attrs():
                    continue
                op._set_attr('op_callstack', [''])

    def context(self, signature: str = None, for_test: bool = False,
                trainable: bool = True) -> Tuple[dict, dict, paddle.static.Program]:
        '''
        '''
        program = self.program.clone(for_test=for_test)
        paddle_utils.remove_feed_fetch_op(program)

        # generate feed vars and fetch vars from signatures
        feed_dict = {}
        fetch_dict = {}
        varinfos = [self.desc.signatures[signature]] if signature else self.desc.signatures.values()

        for info in varinfos:
            for feed_var in info.inputs:
                paddle_var = program.global_block().vars[feed_var.name]
                feed_dict[feed_var.alias] = paddle_var

            for fetch_var in info.outputs:
                paddle_var = program.global_block().vars[fetch_var.name]
                fetch_dict[fetch_var.alias] = paddle_var

        for param in program.all_parameters():
            param.trainable = trainable

        return feed_dict, fetch_dict, program

    def __call__(self, sign_name: str, data: dict, use_gpu: bool = False, batch_size: int = 1, **kwargs):
        '''
        '''

        def _get_reader_and_feeder(data_format, data, place):
            def _reader(process_data):
                for item in zip(*process_data):
                    yield item

            process_data = []
            feed_name_list = []
            for key in data_format:
                process_data.append([value['processed'] for value in data[key]])
                feed_name_list.append(data_format[key]['feed_key'])
            feeder = paddle.fluid.DataFeeder(feed_list=feed_name_list, place=place)
            return functools.partial(_reader, process_data=process_data), feeder

        _, fetch_dict, program = self.context(signature=sign_name, for_test=True)
        fetch_list = list([value for key, value in fetch_dict.items()])
        with paddle.static.program_guard(program):
            result = []
            index = 0
            place = paddle.CUDAPlace(0) if use_gpu else paddle.CPUPlace()

            exe = paddle.static.Executor(place=place)
            data = self.processor.preprocess(sign_name=sign_name, data_dict=data)
            data_format = self.processor.data_format(sign_name=sign_name)
            reader, feeder = _get_reader_and_feeder(data_format, data, place)
            reader = paddle.batch(reader, batch_size=batch_size)
            for batch in reader():
                data_out = exe.run(feed=feeder.feed(batch), fetch_list=fetch_list, return_numpy=False)
                sub_data = {key: value[index:index + len(batch)] for key, value in data.items()}
                result += self.processor.postprocess(sign_name, data_out, sub_data, **kwargs)
                index += len(batch)

        return result

    @classmethod
    def get_py_requirements(cls) -> List[str]:
        return []

    @classmethod
    def load(cls, desc_file):
        desc = module_v1_utils.convert_module_desc(desc_file)

        cls.author = desc.module_info.author
        cls.author_email = desc.module_info.author_email
        cls.summary = desc.module_info.summary
        cls.type = desc.module_info.type
        cls.name = desc.module_info.name
        cls.version = utils.Version(desc.module_info.version)
        return cls

    def assets_path(self):
        return os.path.join(self.directory, 'assets')