module.py

# coding=utf-8
from __future__ import absolute_import

import ast
import argparse
import os
from functools import partial

import yaml
import numpy as np
import paddle.fluid as fluid
import paddlehub as hub
from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
from paddlehub.module.module import moduleinfo, runnable, serving
from paddlehub.common.paddle_helper import add_vars_prefix

from ssd_vgg16_300_coco2017.vgg import VGG
from ssd_vgg16_300_coco2017.processor import load_label_info, postprocess, base64_to_cv2
from ssd_vgg16_300_coco2017.data_feed import reader


@moduleinfo(
    name="ssd_vgg16_300_coco2017",
    version="1.0.1",
    type="cv/object_detection",
    summary="SSD with backbone VGG16, trained with dataset COCO.",
    author="paddlepaddle",
    author_email="paddle-dev@baidu.com")
class SSDVGG16(hub.Module):
    def _initialize(self):
        self.default_pretrained_model_path = os.path.join(self.directory, "ssd_vgg16_300_model")
        self.label_names = load_label_info(os.path.join(self.directory, "label_file.txt"))
        self.model_config = None
        self._set_config()

    def _set_config(self):
        # predictor config setting.
        cpu_config = AnalysisConfig(self.default_pretrained_model_path)
        cpu_config.disable_glog_info()
        cpu_config.disable_gpu()
        cpu_config.switch_ir_optim(False)
        self.cpu_predictor = create_paddle_predictor(cpu_config)

        try:
            _places = os.environ["CUDA_VISIBLE_DEVICES"]
            int(_places[0])
            use_gpu = True
        except:
            use_gpu = False
        if use_gpu:
            gpu_config = AnalysisConfig(self.default_pretrained_model_path)
            gpu_config.disable_glog_info()
            gpu_config.enable_use_gpu(memory_pool_init_size_mb=500, device_id=0)
            self.gpu_predictor = create_paddle_predictor(gpu_config)

        # model config setting.
        if not self.model_config:
            with open(os.path.join(self.directory, 'config.yml')) as fp:
                self.model_config = yaml.load(fp.read(), Loader=yaml.FullLoader)

        self.multi_box_head_config = self.model_config['MultiBoxHead']
        self.output_decoder_config = self.model_config['SSDOutputDecoder']

    def context(self, trainable=True, pretrained=True, get_prediction=False):
        """
        Distill the Head Features, so as to perform transfer learning.

        Args:
            trainable (bool): whether to set parameters trainable.
            pretrained (bool): whether to load default pretrained model.
            get_prediction (bool): whether to get prediction.

        Returns:
             inputs(dict): the input variables.
             outputs(dict): the output variables.
             context_prog (Program): the program to execute transfer learning.
        """
        context_prog = fluid.Program()
        startup_program = fluid.Program()
        with fluid.program_guard(context_prog, startup_program):
            with fluid.unique_name.guard():
                # image
                image = fluid.layers.data(name='image', shape=[3, 300, 300], dtype='float32')
                # backbone
                backbone = VGG(depth=16, with_extra_blocks=True, normalizations=[20., -1, -1, -1, -1, -1])
                # body_feats
                body_feats = backbone(image)
                # im_size
                im_size = fluid.layers.data(name='im_size', shape=[2], dtype='int32')
                # var_prefix
                var_prefix = '@HUB_{}@'.format(self.name)
                # names of inputs
                inputs = {'image': var_prefix + image.name, 'im_size': var_prefix + im_size.name}
                # names of outputs
                if get_prediction:
                    locs, confs, box, box_var = fluid.layers.multi_box_head(
                        inputs=body_feats, image=image, num_classes=81, **self.multi_box_head_config)
                    pred = fluid.layers.detection_output(
                        loc=locs, scores=confs, prior_box=box, prior_box_var=box_var, **self.output_decoder_config)
                    outputs = {'bbox_out': [var_prefix + pred.name]}
                else:
                    outputs = {'body_features': [var_prefix + var.name for var in body_feats]}

                # add_vars_prefix
                add_vars_prefix(context_prog, var_prefix)
                add_vars_prefix(fluid.default_startup_program(), var_prefix)
                # inputs
                inputs = {key: context_prog.global_block().vars[value] for key, value in inputs.items()}
                outputs = {
                    out_key: [context_prog.global_block().vars[varname] for varname in out_value]
                    for out_key, out_value in outputs.items()
                }
                # trainable
                for param in context_prog.global_block().iter_parameters():
                    param.trainable = trainable

                place = fluid.CPUPlace()
                exe = fluid.Executor(place)
                # pretrained
                if pretrained:

                    def _if_exist(var):
                        return os.path.exists(os.path.join(self.default_pretrained_model_path, var.name))

                    fluid.io.load_vars(exe, self.default_pretrained_model_path, predicate=_if_exist)
                else:
                    exe.run(startup_program)

                return inputs, outputs, context_prog

    def object_detection(self,
                         paths=None,
                         images=None,
                         batch_size=1,
                         use_gpu=False,
                         output_dir='detection_result',
                         score_thresh=0.5,
                         visualization=True):
        """API of Object Detection.

        Args:
            paths (list[str]): The paths of images.
            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
            batch_size (int): batch size.
            use_gpu (bool): Whether to use gpu.
            output_dir (str): The path to store output images.
            visualization (bool): Whether to save image or not.
            score_thresh (float): threshold for object detecion.

        Returns:
            res (list[dict]): The result of coco2017 detecion. keys include 'data', 'save_path', the corresponding value is:
                data (dict): the result of object detection, keys include 'left', 'top', 'right', 'bottom', 'label', 'confidence', the corresponding value is:
                    left (float): The X coordinate of the upper left corner of the bounding box;
                    top (float): The Y coordinate of the upper left corner of the bounding box;
                    right (float): The X coordinate of the lower right corner of the bounding box;
                    bottom (float): The Y coordinate of the lower right corner of the bounding box;
                    label (str): The label of detection result;
                    confidence (float): The confidence of detection result.
                save_path (str, optional): The path to save output images.
        """
        paths = paths if paths else list()
        data_reader = partial(reader, paths, images)
        batch_reader = fluid.io.batch(data_reader, batch_size=batch_size)
        res = []
        for iter_id, feed_data in enumerate(batch_reader()):
            feed_data = np.array(feed_data)
            image_tensor = PaddleTensor(np.array(list(feed_data[:, 0])).copy())
            if use_gpu:
                data_out = self.gpu_predictor.run([image_tensor])
            else:
                data_out = self.cpu_predictor.run([image_tensor])

            output = postprocess(
                paths=paths,
                images=images,
                data_out=data_out,
                score_thresh=score_thresh,
                label_names=self.label_names,
                output_dir=output_dir,
                handle_id=iter_id * batch_size,
                visualization=visualization)
            res.extend(output)
        return res

    def save_inference_model(self, dirname, model_filename=None, params_filename=None, combined=True):
        if combined:
            model_filename = "__model__" if not model_filename else model_filename
            params_filename = "__params__" if not params_filename else params_filename
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)

        program, feeded_var_names, target_vars = fluid.io.load_inference_model(
            dirname=self.default_pretrained_model_path, executor=exe)

        fluid.io.save_inference_model(
            dirname=dirname,
            main_program=program,
            executor=exe,
            feeded_var_names=feeded_var_names,
            target_vars=target_vars,
            model_filename=model_filename,
            params_filename=params_filename)

    @serving
    def serving_method(self, images, **kwargs):
        """
        Run as a service.
        """
        images_decode = [base64_to_cv2(image) for image in images]
        results = self.object_detection(images=images_decode, **kwargs)
        return results

    @runnable
    def run_cmd(self, argvs):
        """
        Run as a command.
        """
        self.parser = argparse.ArgumentParser(
            description="Run the {} module.".format(self.name),
            prog='hub run {}'.format(self.name),
            usage='%(prog)s',
            add_help=True)
        self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
        self.arg_config_group = self.parser.add_argument_group(
            title="Config options", description="Run configuration for controlling module behavior, not required.")
        self.add_module_config_arg()
        self.add_module_input_arg()
        args = self.parser.parse_args(argvs)
        results = self.object_detection(
            paths=[args.input_path],
            batch_size=args.batch_size,
            use_gpu=args.use_gpu,
            output_dir=args.output_dir,
            visualization=args.visualization,
            score_thresh=args.score_thresh)
        return results

    def add_module_config_arg(self):
        """
        Add the command config options.
        """
        self.arg_config_group.add_argument(
            '--use_gpu', type=ast.literal_eval, default=False, help="whether use GPU or not")
        self.arg_config_group.add_argument(
            '--output_dir', type=str, default='detection_result', help="The directory to save output images.")
        self.arg_config_group.add_argument(
            '--visualization', type=ast.literal_eval, default=False, help="whether to save output as images.")

    def add_module_input_arg(self):
        """
        Add the command input options.
        """
        self.arg_input_group.add_argument('--input_path', type=str, help="path to image.")
        self.arg_input_group.add_argument('--batch_size', type=ast.literal_eval, default=1, help="batch size.")
        self.arg_input_group.add_argument(
            '--score_thresh', type=ast.literal_eval, default=0.5, help="threshold for object detecion.")