#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 logging 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()