#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. # #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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import sys import math import numpy as np import argparse import functools import paddle import paddle.fluid as fluid import reader import models from utils import * parser = argparse.ArgumentParser(description=__doc__) add_arg = functools.partial(add_arguments, argparser=parser) # yapf: disable add_arg('data_dir', str, "./data/ILSVRC2012/", "The ImageNet datset") add_arg('batch_size', int, 256, "batch size on all the devices.") add_arg('use_gpu', bool, True, "Whether to use GPU or not.") add_arg('class_dim', int, 1000, "Class number.") parser.add_argument("--pretrained_model", default=None, required=True, type=str, help="The path to load pretrained model") add_arg('model', str, "ResNet50", "Set the network to use.") add_arg('resize_short_size', int, 256, "Set resize short size") add_arg('reader_thread', int, 8, "The number of multi thread reader") add_arg('reader_buf_size', int, 2048, "The buf size of multi thread reader") parser.add_argument('--image_mean', nargs='+', type=float, default=[0.485, 0.456, 0.406], help="The mean of input image data") parser.add_argument('--image_std', nargs='+', type=float, default=[0.229, 0.224, 0.225], help="The std of input image data") parser.add_argument('--image_shape', nargs="+", type=int, default=[3,224,224], help=" The shape of image") add_arg('interpolation', int, None, "The interpolation mode") add_arg('padding_type', str, "SAME", "Padding type of convolution") add_arg('use_se', bool, True, "Whether to use Squeeze-and-Excitation module for EfficientNet.") add_arg('save_json_path', str, None, "Whether to save output in json file.") add_arg('same_feed', int, 0, "Whether to feed same images") add_arg('print_step', int, 1, "the batch step to print info") # yapf: enable logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def eval(args): model_list = [m for m in dir(models) if "__" not in m] assert args.model in model_list, "{} is not in lists: {}".format(args.model, model_list) assert os.path.isdir( args.pretrained_model ), "{} doesn't exist, please load right pretrained model path for eval".format( args.pretrained_model) assert args.image_shape[ 1] <= args.resize_short_size, "Please check the args:image_shape and args:resize_short_size, The croped size(image_shape[1]) must smaller than or equal to the resized length(resize_short_size) " # check gpu: when using gpu, the number of visible cards should divide batch size if args.use_gpu: assert args.batch_size % fluid.core.get_cuda_device_count( ) == 0, "please support correct batch_size({}), which can be divided by available cards({}), you can change the number of cards by indicating: export CUDA_VISIBLE_DEVICES= ".format( args.batch_size, fluid.core.get_cuda_device_count()) image = fluid.data( name='image', shape=[None] + args.image_shape, dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') # model definition if args.model.startswith('EfficientNet'): model = models.__dict__[args.model](is_test=True, padding_type=args.padding_type, use_se=args.use_se) else: model = models.__dict__[args.model]() if args.model == "GoogLeNet": out0, out1, out2 = model.net(input=image, class_dim=args.class_dim) cost0 = fluid.layers.cross_entropy(input=out0, label=label) cost1 = fluid.layers.cross_entropy(input=out1, label=label) cost2 = fluid.layers.cross_entropy(input=out2, label=label) avg_cost0 = fluid.layers.mean(x=cost0) avg_cost1 = fluid.layers.mean(x=cost1) avg_cost2 = fluid.layers.mean(x=cost2) avg_cost = avg_cost0 + 0.3 * avg_cost1 + 0.3 * avg_cost2 acc_top1 = fluid.layers.accuracy(input=out0, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5) else: out = model.net(input=image, class_dim=args.class_dim) cost, pred = fluid.layers.softmax_with_cross_entropy( out, label, return_softmax=True) avg_cost = fluid.layers.mean(x=cost) acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1) acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5) test_program = fluid.default_main_program().clone(for_test=True) fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name, pred.name] gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0)) place = fluid.CUDAPlace(gpu_id) if args.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) if args.use_gpu: places = fluid.framework.cuda_places() compiled_program = fluid.compiler.CompiledProgram( test_program).with_data_parallel(places=places) fluid.io.load_persistables(exe, args.pretrained_model) imagenet_reader = reader.ImageNetReader() val_reader = imagenet_reader.val(settings=args) # set places to run on the multi-card feeder = fluid.DataFeeder(place=places, feed_list=[image, label]) test_info = [[], [], []] cnt = 0 parallel_data = [] parallel_id = [] place_num = paddle.fluid.core.get_cuda_device_count() real_iter = 0 info_dict = {} for batch_id, data in enumerate(val_reader()): #image data and label image_data = [items[0:2] for items in data] image_id = [items[2] for items in data] parallel_id.append(image_id) parallel_data.append(image_data) if place_num == len(parallel_data): t1 = time.time() loss_set, acc1_set, acc5_set, pred_set = exe.run( compiled_program, fetch_list=fetch_list, feed=list(feeder.feed_parallel(parallel_data, place_num))) t2 = time.time() period = t2 - t1 loss = np.mean(loss_set) acc1 = np.mean(acc1_set) acc5 = np.mean(acc5_set) test_info[0].append(loss * len(data)) test_info[1].append(acc1 * len(data)) test_info[2].append(acc5 * len(data)) cnt += len(data) if batch_id % args.print_step == 0: info = "Testbatch {0},loss {1}, acc1 {2},acc5 {3},time {4}".format(real_iter, \ "%.5f"%loss,"%.5f"%acc1, "%.5f"%acc5, \ "%2.2f sec" % period) logger.info(info) sys.stdout.flush() parallel_id = [] parallel_data = [] real_iter += 1 test_loss = np.sum(test_info[0]) / cnt test_acc1 = np.sum(test_info[1]) / cnt test_acc5 = np.sum(test_info[2]) / cnt info = "Test_loss {0}, test_acc1 {1}, test_acc5 {2}".format( "%.5f" % test_loss, "%.5f" % test_acc1, "%.5f" % test_acc5) if args.save_json_path: info_dict = { "Test_loss": test_loss, "test_acc1": test_acc1, "test_acc5": test_acc5 } save_json(info_dict, args.save_json_path) logger.info(info) sys.stdout.flush() def main(): args = parser.parse_args() print_arguments(args) check_gpu() check_version() eval(args) if __name__ == '__main__': main()