# coding=utf-8 from __future__ import absolute_import from __future__ import division import ast import argparse import os 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 efficientnetb0_small_imagenet.processor import postprocess, base64_to_cv2 from efficientnetb0_small_imagenet.data_feed import reader from efficientnetb0_small_imagenet.efficientnet import EfficientNetB0_small @moduleinfo( name="efficientnetb0_small_imagenet", type="CV/image_classification", author="baidu-vis", author_email="", summary= "ResNet18vd is a image classfication model, this module is trained with imagenet datasets.", version="1.0.0") class ResNet18vdImageNet(hub.Module): def _initialize(self): self.default_pretrained_model_path = os.path.join( self.directory, "efficientnetb0_small_imagenet_model") label_file = os.path.join(self.directory, "label_list.txt") with open(label_file, 'r', encoding='utf-8') as file: self.label_list = file.read().split("\n")[:-1] self.predictor_set = False def get_expected_image_width(self): return 224 def get_expected_image_height(self): return 224 def get_pretrained_images_mean(self): im_mean = np.array([0.485, 0.456, 0.406]).reshape(1, 3) return im_mean def get_pretrained_images_std(self): im_std = np.array([0.229, 0.224, 0.225]).reshape(1, 3) return im_std def _set_config(self): """ predictor config setting """ cpu_config = AnalysisConfig(self.default_pretrained_model_path) cpu_config.disable_glog_info() cpu_config.disable_gpu() 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=1000, device_id=0) self.gpu_predictor = create_paddle_predictor(gpu_config) def context(self, trainable=True, pretrained=True): """context for transfer learning. Args: trainable (bool): Set parameters in program to be trainable. pretrained (bool) : Whether to load pretrained model. Returns: inputs (dict): key is 'image', corresponding vaule is image tensor. outputs (dict): key is : 'classification', corresponding value is the result of classification. 'feature_map', corresponding value is the result of the layer before the fully connected layer. context_prog (fluid.Program): program for transfer learning. """ context_prog = fluid.Program() startup_prog = fluid.Program() with fluid.program_guard(context_prog, startup_prog): with fluid.unique_name.guard(): image = fluid.layers.data( name="image", shape=[3, 224, 224], dtype="float32") resnet_vd = EfficientNetB0_small() output, feature_map = resnet_vd.net( input=image, class_dim=len(self.label_list)) name_prefix = '@HUB_{}@'.format(self.name) inputs = {'image': name_prefix + image.name} outputs = { 'classification': name_prefix + output.name, 'feature_map': name_prefix + feature_map.name } add_vars_prefix(context_prog, name_prefix) add_vars_prefix(startup_prog, name_prefix) global_vars = context_prog.global_block().vars inputs = { key: global_vars[value] for key, value in inputs.items() } outputs = { key: global_vars[value] for key, value in outputs.items() } place = fluid.CPUPlace() exe = fluid.Executor(place) # pretrained if pretrained: def _if_exist(var): b = os.path.exists( os.path.join(self.default_pretrained_model_path, var.name)) return b fluid.io.load_vars( exe, self.default_pretrained_model_path, context_prog, predicate=_if_exist) print(inputs.keys()) fluid.io.save_inference_model( dirname=os.path.join( self.directory, 'efficientnetb0_small_imagenet_model'), feeded_var_names=[name_prefix + 'image'], target_vars=list(outputs.values()), executor=exe, main_program=context_prog) else: exe.run(startup_prog) # trainable for param in context_prog.global_block().iter_parameters(): param.trainable = trainable return inputs, outputs, context_prog def classification(self, images=None, paths=None, batch_size=1, use_gpu=False, top_k=1): """ API for image classification. Args: images (list[numpy.ndarray]): data of images, shape of each is [H, W, C], color space must be BGR. paths (list[str]): The paths of images. batch_size (int): batch size. use_gpu (bool): Whether to use gpu. top_k (int): Return top k results. Returns: res (list[dict]): The classfication results. """ if not self.predictor_set: self._set_config() self.predictor_set = True all_data = list() for yield_data in reader(images, paths): all_data.append(yield_data) total_num = len(all_data) loop_num = int(np.ceil(total_num / batch_size)) res = list() for iter_id in range(loop_num): batch_data = list() handle_id = iter_id * batch_size for image_id in range(batch_size): try: batch_data.append(all_data[handle_id + image_id]) except: pass # feed batch image batch_image = np.array([data['image'] for data in batch_data]) batch_image = PaddleTensor(batch_image.copy()) predictor_output = self.gpu_predictor.run([ batch_image ]) if use_gpu else self.cpu_predictor.run([batch_image]) out = postprocess( data_out=predictor_output[0].as_ndarray(), label_list=self.label_list, top_k=top_k) res += out 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.classification(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.classification( paths=[args.input_path], batch_size=args.batch_size, use_gpu=args.use_gpu) 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( '--batch_size', type=ast.literal_eval, default=1, help="batch size.") self.arg_config_group.add_argument( '--top_k', type=ast.literal_eval, default=1, help="Return top k results.") 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.")