reminisce commented on a change in pull request #11251: [WIP] Graph partitioner and subgraph op URL: https://github.com/apache/incubator-mxnet/pull/11251#discussion_r195511375
########## File path: example/subgraph_op/imagenet_inference.py ########## @@ -0,0 +1,166 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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 argparse +import logging +import os +import time +import mxnet as mx +from common import modelzoo +from mxnet import nd +from mxnet.contrib.quantization import * +from mxnet.base import _LIB + + +def download_dataset(dataset_url, dataset_dir, logger=None): + if logger is not None: + logger.info('Downloading dataset for inference from %s to %s' % (dataset_url, dataset_dir)) + mx.test_utils.download(dataset_url, dataset_dir) + + +def download_model(model_name, logger=None): + dir_path = os.path.dirname(os.path.realpath(__file__)) + model_path = os.path.join(dir_path, 'model') + if logger is not None: + logger.info('Downloading model %s... into path %s' % (model_name, model_path)) + return modelzoo.download_model(args.model, os.path.join(dir_path, 'model')) + + +def advance_data_iter(data_iter, n): + assert n >= 0 + if n == 0: + return data_iter + has_next_batch = True + while has_next_batch: + try: + data_iter.next() + n -= 1 + if n == 0: + return data_iter + except StopIteration: + has_next_batch = False + + +def score(sym, arg_params, aux_params, data, devs, label_name, max_num_examples, logger=None): + metrics = [mx.metric.create('acc'), + mx.metric.create('top_k_accuracy', top_k=5)] + if not isinstance(metrics, list): + metrics = [metrics, ] + mod = mx.mod.Module(symbol=sym, context=devs, label_names=[label_name, ]) + mod.bind(for_training=False, + data_shapes=data.provide_data, + label_shapes=data.provide_label) + mod.set_params(arg_params, aux_params) + + tic = time.time() + num = 0 + for batch in data: + mod.forward(batch, is_train=False) + for m in metrics: + mod.update_metric(m, batch.label) + num += batch_size + if max_num_examples is not None and num >= max_num_examples: + break + + speed = num / (time.time() - tic) + + if logger is not None: + logger.info('Finished inference with %d images' % num) + logger.info('Finished with %f images per second', speed) + for m in metrics: + logger.info(m.get()) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Score a model on a dataset') + parser.add_argument('--model', type=str, choices=['imagenet1k-resnet-152', 'imagenet1k-inception-bn'], + help='currently only supports imagenet1k-resnet-152 or imagenet1k-inception-bn') + parser.add_argument('--batch-size', type=int, default=32) + parser.add_argument('--label-name', type=str, default='softmax_label') + parser.add_argument('--dataset', type=str, required=True, help='dataset path') + parser.add_argument('--rgb-mean', type=str, default='0,0,0') + parser.add_argument('--image-shape', type=str, default='3,224,224') + parser.add_argument('--data-nthreads', type=int, default=60, help='number of threads for data decoding') + parser.add_argument('--num-skipped-batches', type=int, default=0, help='skip the number of batches for inference') + parser.add_argument('--num-inference-batches', type=int, required=True, help='number of images used for inference') + parser.add_argument('--shuffle-dataset', action='store_true', default=True, + help='shuffle the calibration dataset') + parser.add_argument('--shuffle-chunk-seed', type=int, default=3982304, + help='shuffling chunk seed, see' + ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter' + ' for more details') + parser.add_argument('--shuffle-seed', type=int, default=48564309, + help='shuffling seed, see' + ' https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imager#mxnet.io.ImageRecordIter' + ' for more details') + + args = parser.parse_args() + + logging.basicConfig() + logger = logging.getLogger('logger') + logger.setLevel(logging.INFO) + data_nthreads = args.data_nthreads + batch_size = args.batch_size + logger.info('batch size = %d for inference' % batch_size) + + rgb_mean = args.rgb_mean + logger.info('rgb_mean = %s' % rgb_mean) + rgb_mean = [float(i) for i in rgb_mean.split(',')] + mean_args = {'mean_r': rgb_mean[0], 'mean_g': rgb_mean[1], 'mean_b': rgb_mean[2]} + + label_name = args.label_name + logger.info('label_name = %s' % label_name) + + image_shape = args.image_shape + data_shape = tuple([int(i) for i in image_shape.split(',')]) + logger.info('Input data shape = %s' % str(data_shape)) + + dataset = args.dataset + download_dataset('http://data.mxnet.io/data/val_256_q90.rec', dataset) + logger.info('Dataset for inference: %s' % dataset) + + # creating data iterator + data = mx.io.ImageRecordIter(path_imgrec=dataset, + label_width=1, + preprocess_threads=data_nthreads, + batch_size=batch_size, + data_shape=data_shape, + label_name=label_name, + rand_crop=False, + rand_mirror=False, + shuffle=True, + shuffle_chunk_seed=3982304, + seed=48564309, + **mean_args) + + # download model + prefix, epoch = download_model(model_name=args.model, logger=logger) + sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) + op_names = ['BatchNorm', 'Convolution', 'Pooling', 'Activation'] + out = SymbolHandle() + check_call(_LIB.MXPartitionGraph(sym.handle, mx_uint(len(op_names)), c_str_array(op_names), Review comment: We will hide this from users. 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