# 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. from collections import namedtuple import tvm from tvm import te from tvm import relay from tvm.relay import quantize as qtz import mxnet as mx from mxnet import gluon import logging import os logging.basicConfig(level=logging.INFO) Config = namedtuple('Config', ['model', 'nbit_input', 'dtype_input', 'nbit_output', 'dtype_output', 'global_scale', 'expected_acc']) def get_val_data(model_name, rec_val, batch_size, num_workers=4): rec_val = os.path.expanduser(rec_val) mean_rgb = [123.68, 116.779, 103.939] std_rgb = [58.393, 57.12, 57.375] def batch_fn(batch, ctx): data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0) label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0) return data, label img_size = 299 if model_name == 'inceptionv3' else 224 val_data = mx.io.ImageRecordIter( path_imgrec = rec_val, preprocess_threads = num_workers, shuffle = False, batch_size = batch_size, resize = 256, data_shape = (3, img_size, img_size), mean_r = mean_rgb[0], mean_g = mean_rgb[1], mean_b = mean_rgb[2], std_r = std_rgb[0], std_g = std_rgb[1], std_b = std_rgb[2], ) return val_data, batch_fn def get_model(model_name, batch_size, qconfig, target=None, original=False, simulated=False): gluon_model = gluon.model_zoo.vision.get_model(model_name, pretrained=True) img_size = 299 if model_name == 'inceptionv3' else 224 data_shape = (batch_size, 3, img_size, img_size) mod, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape}) net = mod['main'] with relay.build_config(opt_level=3): qfunc = relay.quantize.prerequisite_optimize(net, params=params) logging.debug('original') logging.debug(qfunc.astext(show_meta_data=False)) if original: return qfunc with qconfig: logging.debug('current quantize config') logging.debug(qtz.current_qconfig()) qfunc = qtz.quantize(qfunc) logging.debug('after quantize') logging.debug(qfunc.astext(show_meta_data=False)) return qfunc def eval_acc(model, dataset, batch_fn, target=tvm.target.cuda(), ctx=tvm.gpu(), log_interval=100): with relay.build_config(opt_level=3): graph, lib, params = relay.build(model, target) # create runtime module m = tvm.contrib.graph_runtime.create(graph, lib, ctx) m.set_input(**params) # setup evaluaiton metric dataset.reset() batch_size = dataset.batch_size acc_top1 = mx.metric.Accuracy() acc_top5 = mx.metric.TopKAccuracy(5) acc_top1.reset() acc_top5.reset() # Execute for i, batch in enumerate(dataset): data, label = batch_fn(batch, [mx.cpu(0)]) m.run(data=data[0].asnumpy()) out_arr = m.get_output(0) acc_top1.update(label, [mx.nd.array(out_arr.asnumpy())]) acc_top5.update(label, [mx.nd.array(out_arr.asnumpy())]) if not (i + 1) % log_interval: _, top1 = acc_top1.get() _, top5 = acc_top5.get() nsamples = (i + 1) * batch_size logging.info('[%d samples] validation: acc-top1=%f acc-top5=%f', nsamples, top1, top5) logging.info('[final] validation: acc-top1=%f acc-top5=%f', top1, top5) return top1 def test_quantize_acc(cfg, rec_val): qconfig = qtz.qconfig(skip_conv_layers=[0], nbit_input=cfg.nbit_input, nbit_weight=cfg.nbit_input, global_scale=cfg.global_scale, dtype_input=cfg.dtype_input, dtype_weight=cfg.dtype_input, dtype_activation=cfg.dtype_output, debug_enabled_ops=None) model = get_model(cfg.model, 32, qconfig, tvm.target.cuda()) val_data, batch_fn = get_val_data(cfg.model, rec_val=rec_val, batch_size=32) acc = eval_acc(model, val_data, batch_fn) assert acc > cfg.expected_acc return acc if __name__ == "__main__": #TODO(for user): replace the line with the path to imagenet validation dataset rec_val = "/scratch/tqchen/imagenet/val.rec" results = [] configs = [ Config('mobilenetv2_1.0', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=4.0, expected_acc=0.666), Config('resnet18_v1', nbit_input=8, dtype_input='int8', nbit_output=16, dtype_output='int16', global_scale=8.0, expected_acc=0.692), Config('resnet18_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.692), Config('resnet34_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.733), Config('resnet50_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.747), Config('resnet101_v1', nbit_input=8, dtype_input='int8', nbit_output=32, dtype_output='int32', global_scale=8.0, expected_acc=0.756), # TODO: need to fix accuracy # Config('mobilenetv2_1.0', nbit_input=8, dtype_input='int8', nbit_output=16, dtype_output='int16', global_scale=4.0), ] for config in configs: acc = test_quantize_acc(config, rec_val) results.append((config, acc)) for res in results: print(res)