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132 lines
4.3 KiB
132 lines
4.3 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Train Resnet50_quant on Cifar10"""
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import pytest
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import numpy as np
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from easydict import EasyDict as ed
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from mindspore import context
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from mindspore import Tensor
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train.model import Model
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from mindspore.train.quant import quant
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from mindspore import set_seed
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from resnet_quant_manual import resnet50_quant
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from dataset import create_dataset
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from lr_generator import get_lr
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from utils import Monitor, CrossEntropy
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config_quant = ed({
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"class_num": 10,
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"batch_size": 128,
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"step_threshold": 20,
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"loss_scale": 1024,
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"momentum": 0.9,
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"weight_decay": 1e-4,
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"epoch_size": 1,
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"pretrained_epoch_size": 90,
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"buffer_size": 1000,
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"image_height": 224,
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"image_width": 224,
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"data_load_mode": "mindata",
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 50,
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"save_checkpoint_path": "./",
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"warmup_epochs": 0,
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"lr_decay_mode": "cosine",
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"use_label_smooth": True,
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"label_smooth_factor": 0.1,
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"lr_init": 0,
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"lr_max": 0.005,
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})
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dataset_path = "/home/workspace/mindspore_dataset/cifar-10-batches-bin/"
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_resnet50_quant():
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set_seed(1)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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config = config_quant
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print("training configure: {}".format(config))
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epoch_size = config.epoch_size
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# define network
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net = resnet50_quant(class_num=config.class_num)
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net.set_train(True)
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# define loss
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropy(
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smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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#loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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# define dataset
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dataset = create_dataset(dataset_path=dataset_path,
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config=config,
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repeat_num=1,
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batch_size=config.batch_size)
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step_size = dataset.get_dataset_size()
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# convert fusion network to quantization aware network
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net = quant.convert_quant_network(net,
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bn_fold=True,
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per_channel=[True, False],
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symmetric=[True, False])
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# get learning rate
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lr = Tensor(get_lr(lr_init=config.lr_init,
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lr_end=0.0,
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lr_max=config.lr_max,
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warmup_epochs=config.warmup_epochs,
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total_epochs=config.epoch_size,
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steps_per_epoch=step_size,
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lr_decay_mode='cosine'))
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# define optimization
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opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
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config.weight_decay, config.loss_scale)
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# define model
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#model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
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model = Model(net, loss_fn=loss, optimizer=opt)
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print("============== Starting Training ==============")
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monitor = Monitor(lr_init=lr.asnumpy(),
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step_threshold=config.step_threshold)
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callbacks = [monitor]
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model.train(epoch_size, dataset, callbacks=callbacks,
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dataset_sink_mode=False)
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print("============== End Training ==============")
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expect_avg_step_loss = 2.40
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avg_step_loss = np.mean(np.array(monitor.losses))
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print("average step loss:{}".format(avg_step_loss))
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assert avg_step_loss < expect_avg_step_loss
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if __name__ == '__main__':
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test_resnet50_quant()
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