# copyright (c) 2020 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 unittest import os import sys import argparse import logging import struct import six import numpy as np import time import paddle import paddle.fluid as fluid from paddle.fluid.framework import IrGraph from paddle.fluid import core logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s') _logger = logging.getLogger(__name__) _logger.setLevel(logging.INFO) def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--batch_size', type=int, default=1, help='Batch size.') parser.add_argument( '--skip_batch_num', type=int, default=0, help='Number of the first minibatches to skip in performance statistics.' ) parser.add_argument( '--infer_model', type=str, default='', help='A path to an Inference model.') parser.add_argument('--infer_data', type=str, default='', help='Data file.') parser.add_argument( '--batch_num', type=int, default=0, help='Number of batches to process. 0 or less means whole dataset. Default: 0.' ) parser.add_argument( '--with_accuracy_layer', type=bool, default=False, help='The model is with accuracy or without accuracy layer') test_args, args = parser.parse_known_args(namespace=unittest) return test_args, sys.argv[:1] + args class SampleTester(unittest.TestCase): def _reader_creator(self, data_file='data.bin'): def reader(): with open(data_file, 'rb') as fp: num = fp.read(8) num = struct.unpack('q', num)[0] imgs_offset = 8 img_ch = 3 img_w = 224 img_h = 224 img_pixel_size = 4 img_size = img_ch * img_h * img_w * img_pixel_size label_size = 8 labels_offset = imgs_offset + num * img_size step = 0 while step < num: fp.seek(imgs_offset + img_size * step) img = fp.read(img_size) img = struct.unpack_from( '{}f'.format(img_ch * img_w * img_h), img) img = np.array(img) img.shape = (img_ch, img_w, img_h) fp.seek(labels_offset + label_size * step) label = fp.read(label_size) label = struct.unpack('q', label)[0] yield img, int(label) step += 1 return reader def _get_batch_accuracy(self, batch_output=None, labels=None): total = 0 correct = 0 correct_5 = 0 for n, result in enumerate(batch_output): index = result.argsort() top_1_index = index[-1] top_5_index = index[-5:] total += 1 if top_1_index == labels[n]: correct += 1 if labels[n] in top_5_index: correct_5 += 1 acc1 = float(correct) / float(total) acc5 = float(correct_5) / float(total) return acc1, acc5 def _prepare_for_fp32_mkldnn(self, graph): ops = graph.all_op_nodes() for op_node in ops: name = op_node.name() if name in ['depthwise_conv2d']: input_var_node = graph._find_node_by_name( op_node.inputs, op_node.input("Input")[0]) weight_var_node = graph._find_node_by_name( op_node.inputs, op_node.input("Filter")[0]) output_var_node = graph._find_node_by_name( graph.all_var_nodes(), op_node.output("Output")[0]) attrs = { name: op_node.op().attr(name) for name in op_node.op().attr_names() } conv_op_node = graph.create_op_node( op_type='conv2d', attrs=attrs, inputs={ 'Input': input_var_node, 'Filter': weight_var_node }, outputs={'Output': output_var_node}) graph.link_to(input_var_node, conv_op_node) graph.link_to(weight_var_node, conv_op_node) graph.link_to(conv_op_node, output_var_node) graph.safe_remove_nodes(op_node) return graph def _predict(self, test_reader=None, model_path=None, with_accuracy_layer=False, batch_size=1, batch_num=1, skip_batch_num=0): place = fluid.CPUPlace() exe = fluid.Executor(place) inference_scope = fluid.executor.global_scope() with fluid.scope_guard(inference_scope): if os.path.exists(os.path.join(model_path, '__model__')): [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(model_path, exe) else: [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model( model_path, exe, 'model', 'params') graph = IrGraph(core.Graph(inference_program.desc), for_test=True) graph = self._prepare_for_fp32_mkldnn(graph) inference_program = graph.to_program() dshape = [3, 224, 224] outputs = [] infer_accs1 = [] infer_accs5 = [] batch_acc1 = 0.0 batch_acc5 = 0.0 fpses = [] batch_times = [] batch_time = 0.0 total_samples = 0 iters = 0 infer_start_time = time.time() for data in test_reader(): if batch_num > 0 and iters >= batch_num: break if iters == skip_batch_num: total_samples = 0 infer_start_time = time.time() if six.PY2: images = map(lambda x: x[0].reshape(dshape), data) if six.PY3: images = list(map(lambda x: x[0].reshape(dshape), data)) images = np.array(images).astype('float32') labels = np.array([x[1] for x in data]).astype('int64') if (with_accuracy_layer == False): # models that do not have accuracy measuring layers start = time.time() out = exe.run(inference_program, feed={feed_target_names[0]: images}, fetch_list=fetch_targets) batch_time = (time.time() - start) * 1000 # in miliseconds outputs.append(out[0]) # Calculate accuracy result batch_acc1, batch_acc5 = self._get_batch_accuracy(out[0], labels) else: # models have accuracy measuring layers labels = labels.reshape([-1, 1]) start = time.time() out = exe.run(inference_program, feed={ feed_target_names[0]: images, feed_target_names[1]: labels }, fetch_list=fetch_targets) batch_time = (time.time() - start) * 1000 # in miliseconds batch_acc1, batch_acc5 = out[1][0], out[2][0] outputs.append(batch_acc1) infer_accs1.append(batch_acc1) infer_accs5.append(batch_acc5) samples = len(data) total_samples += samples batch_times.append(batch_time) fps = samples / batch_time * 1000 fpses.append(fps) iters += 1 appx = ' (warm-up)' if iters <= skip_batch_num else '' _logger.info('batch {0}{5}, acc1: {1:.4f}, acc5: {2:.4f}, ' 'latency: {3:.4f} ms, fps: {4:.2f}'.format( iters, batch_acc1, batch_acc5, batch_time / batch_size, fps, appx)) # Postprocess benchmark data batch_latencies = batch_times[skip_batch_num:] batch_latency_avg = np.average(batch_latencies) latency_avg = batch_latency_avg / batch_size fpses = fpses[skip_batch_num:] fps_avg = np.average(fpses) infer_total_time = time.time() - infer_start_time acc1_avg = np.mean(infer_accs1) acc5_avg = np.mean(infer_accs5) _logger.info('Total inference run time: {:.2f} s'.format( infer_total_time)) return outputs, acc1_avg, acc5_avg, fps_avg, latency_avg def test_graph_transformation(self): if not fluid.core.is_compiled_with_mkldnn(): return infer_model_path = test_case_args.infer_model assert infer_model_path, 'The model path cannot be empty. Please, use the --infer_model option.' data_path = test_case_args.infer_data assert data_path, 'The dataset path cannot be empty. Please, use the --infer_data option.' batch_size = test_case_args.batch_size batch_num = test_case_args.batch_num skip_batch_num = test_case_args.skip_batch_num with_accuracy_layer = test_case_args.with_accuracy_layer _logger.info('Inference model: {0}'.format(infer_model_path)) _logger.info('Dataset: {0}'.format(data_path)) _logger.info('Batch size: {0}'.format(batch_size)) _logger.info('Batch number: {0}'.format(batch_num)) _logger.info('--- Inference prediction start ---') val_reader = paddle.batch( self._reader_creator(data_path), batch_size=batch_size) fp32_output, fp32_acc1, fp32_acc5, fp32_fps, fp32_lat = self._predict( val_reader, infer_model_path, with_accuracy_layer, batch_size, batch_num, skip_batch_num) _logger.info( 'Inference: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'. format(fp32_acc1, fp32_acc5)) _logger.info('Inference: avg fps: {0:.2f}, avg latency: {1:.4f} ms'. format(fp32_fps, fp32_lat)) if __name__ == '__main__': global test_case_args test_case_args, remaining_args = parse_args() unittest.main(argv=remaining_args)