!6639 add monilenetv2_quant and resnet50_quant st
Merge pull request !6639 from hwjiaorui/masterpull/6639/MERGE
mindspore-ci-bot
4 years ago
committed by
Gitee
commit
3e885c0bc1
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# 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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"""learning rate generator"""
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import math
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import numpy as np
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def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
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"""
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generate learning rate array
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Args:
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global_step(int): total steps of the training
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lr_init(float): init learning rate
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lr_end(float): end learning rate
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lr_max(float): max learning rate
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warmup_epochs(int): number of warmup epochs
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total_epochs(int): total epoch of training
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steps_per_epoch(int): steps of one epoch
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Returns:
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np.array, learning rate array
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"""
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lr_each_step = []
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total_steps = steps_per_epoch * total_epochs
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warmup_steps = steps_per_epoch * warmup_epochs
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for i in range(total_steps):
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if i < warmup_steps:
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lr = lr_init + (lr_max - lr_init) * i / warmup_steps
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else:
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lr = lr_end + \
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(lr_max - lr_end) * \
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(1. + math.cos(math.pi * (i - warmup_steps) /
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(total_steps - warmup_steps))) / 2.
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if lr < 0.0:
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lr = 0.0
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lr_each_step.append(lr)
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current_step = global_step
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lr_each_step = np.array(lr_each_step).astype(np.float32)
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learning_rate = lr_each_step[current_step:]
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return learning_rate
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# 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 Mobilenetv2_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 import nn
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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.common import set_seed
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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, CrossEntropyWithLabelSmooth
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from mobilenetV2 import mobilenetV2
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config_ascend_quant = ed({
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"num_classes": 10,
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"image_height": 224,
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"image_width": 224,
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"batch_size": 200,
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"step_threshold": 10,
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"data_load_mode": "mindata",
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"epoch_size": 1,
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"start_epoch": 200,
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"warmup_epochs": 1,
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"lr": 0.3,
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"momentum": 0.9,
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"weight_decay": 4e-5,
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"label_smooth": 0.1,
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"loss_scale": 1024,
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"save_checkpoint": True,
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"save_checkpoint_epochs": 1,
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"keep_checkpoint_max": 300,
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"save_checkpoint_path": "./checkpoint",
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"quantization_aware": True,
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})
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dataset_path = "/dataset/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 train_on_ascend():
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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_ascend_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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network = mobilenetV2(num_classes=config.num_classes)
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# define loss
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if config.label_smooth > 0:
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loss = CrossEntropyWithLabelSmooth(
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smooth_factor=config.label_smooth, num_classes=config.num_classes)
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else:
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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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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network = quant.convert_quant_network(network,
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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(global_step=config.start_epoch * step_size,
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lr_init=0,
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lr_end=0,
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lr_max=config.lr,
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warmup_epochs=config.warmup_epochs,
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total_epochs=epoch_size + config.start_epoch,
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steps_per_epoch=step_size))
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# define optimization
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opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr, config.momentum,
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config.weight_decay)
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# define model
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model = Model(network, 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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callback = [monitor]
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model.train(epoch_size, dataset, callbacks=callback,
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dataset_sink_mode=False)
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print("============== End Training ==============")
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expect_avg_step_loss = 2.32
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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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train_on_ascend()
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# 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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"""MobileNetV2 utils"""
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import time
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import numpy as np
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from mindspore.train.callback import Callback
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from mindspore import Tensor
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from mindspore import nn
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import operations as P
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from mindspore.ops import functional as F
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from mindspore.common import dtype as mstype
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class Monitor(Callback):
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"""
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Monitor loss and time.
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Args:
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lr_init (numpy array): train lr
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Returns:
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None
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Examples:
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>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
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"""
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def __init__(self, lr_init=None, step_threshold=10):
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super(Monitor, self).__init__()
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self.lr_init = lr_init
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self.lr_init_len = len(lr_init)
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self.step_threshold = step_threshold
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def epoch_begin(self, run_context):
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self.losses = []
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self.epoch_time = time.time()
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def epoch_end(self, run_context):
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cb_params = run_context.original_args()
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epoch_mseconds = (time.time() - self.epoch_time) * 1000
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per_step_mseconds = epoch_mseconds / cb_params.batch_num
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print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:8.6f}".format(epoch_mseconds,
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per_step_mseconds,
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np.mean(self.losses)))
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self.epoch_mseconds = epoch_mseconds
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def step_begin(self, run_context):
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self.step_time = time.time()
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def step_end(self, run_context):
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cb_params = run_context.original_args()
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step_mseconds = (time.time() - self.step_time) * 1000
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step_loss = cb_params.net_outputs
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if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
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step_loss = step_loss[0]
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if isinstance(step_loss, Tensor):
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step_loss = np.mean(step_loss.asnumpy())
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self.losses.append(step_loss)
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cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
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print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:8.6f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.5f}]".format(
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cb_params.cur_epoch_num, cb_params.epoch_num, cur_step_in_epoch +
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1, cb_params.batch_num, step_loss,
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np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
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if cb_params.cur_step_num == self.step_threshold:
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run_context.request_stop()
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class CrossEntropyWithLabelSmooth(_Loss):
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"""
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CrossEntropyWith LabelSmooth.
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Args:
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smooth_factor (float): smooth factor, default=0.
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num_classes (int): num classes
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Returns:
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None.
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Examples:
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>>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
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"""
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def __init__(self, smooth_factor=0., num_classes=1000):
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super(CrossEntropyWithLabelSmooth, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor /
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(num_classes - 1), mstype.float32)
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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self.cast = P.Cast()
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def construct(self, logit, label):
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one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
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self.on_value, self.off_value)
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out_loss = self.ce(logit, one_hot_label)
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out_loss = self.mean(out_loss, 0)
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return out_loss
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# 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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"""learning rate generator"""
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import math
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import numpy as np
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def get_lr(lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_decay_mode):
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"""
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generate learning rate array
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Args:
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lr_init(float): init learning rate
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lr_end(float): end learning rate
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lr_max(float): max learning rate
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warmup_epochs(int): number of warmup epochs
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total_epochs(int): total epoch of training
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steps_per_epoch(int): steps of one epoch
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lr_decay_mode(string): learning rate decay mode, including steps, poly, cosine or default
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Returns:
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np.array, learning rate array
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"""
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lr_each_step = []
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total_steps = steps_per_epoch * total_epochs
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warmup_steps = steps_per_epoch * warmup_epochs
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if lr_decay_mode == 'steps':
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decay_epoch_index = [0.3 * total_steps,
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0.6 * total_steps, 0.8 * total_steps]
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for i in range(total_steps):
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if i < decay_epoch_index[0]:
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lr = lr_max
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elif i < decay_epoch_index[1]:
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lr = lr_max * 0.1
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elif i < decay_epoch_index[2]:
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lr = lr_max * 0.01
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else:
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lr = lr_max * 0.001
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lr_each_step.append(lr)
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elif lr_decay_mode == 'poly':
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if warmup_steps != 0:
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inc_each_step = (float(lr_max) - float(lr_init)) / \
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float(warmup_steps)
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else:
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inc_each_step = 0
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for i in range(total_steps):
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if i < warmup_steps:
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lr = float(lr_init) + inc_each_step * float(i)
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else:
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base = (1.0 - (float(i) - float(warmup_steps)) /
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(float(total_steps) - float(warmup_steps)))
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lr = float(lr_max) * base * base
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if lr < 0.0:
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lr = 0.0
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lr_each_step.append(lr)
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elif lr_decay_mode == 'cosine':
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decay_steps = total_steps - warmup_steps
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for i in range(total_steps):
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if i < warmup_steps:
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lr_inc = (float(lr_max) - float(lr_init)) / float(warmup_steps)
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lr = float(lr_init) + lr_inc * (i + 1)
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else:
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linear_decay = (total_steps - i) / decay_steps
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cosine_decay = 0.5 * \
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(1 + math.cos(math.pi * 2 * 0.47 * i / decay_steps))
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decayed = linear_decay * cosine_decay + 0.00001
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lr = lr_max * decayed
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lr_each_step.append(lr)
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else:
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for i in range(total_steps):
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if i < warmup_steps:
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lr = lr_init + (lr_max - lr_init) * i / warmup_steps
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else:
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lr = lr_max - (lr_max - lr_end) * \
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(i - warmup_steps) / (total_steps - warmup_steps)
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lr_each_step.append(lr)
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learning_rate = np.array(lr_each_step).astype(np.float32)
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return learning_rate
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# 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.
|
||||
# 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.
|
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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 = "/dataset/workspace/mindspore_dataset/cifar-10-batches-bin/"
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||||
|
||||
|
||||
@pytest.mark.level1
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def train_on_ascend():
|
||||
set_seed(1)
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
config = config_quant
|
||||
print("training configure: {}".format(config))
|
||||
epoch_size = config.epoch_size
|
||||
|
||||
# define network
|
||||
net = resnet50_quant(class_num=config.class_num)
|
||||
net.set_train(True)
|
||||
|
||||
# define loss
|
||||
if not config.use_label_smooth:
|
||||
config.label_smooth_factor = 0.0
|
||||
loss = CrossEntropy(
|
||||
smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
|
||||
#loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
|
||||
|
||||
# define dataset
|
||||
dataset = create_dataset(dataset_path=dataset_path,
|
||||
config=config,
|
||||
repeat_num=1,
|
||||
batch_size=config.batch_size)
|
||||
step_size = dataset.get_dataset_size()
|
||||
|
||||
# convert fusion network to quantization aware network
|
||||
net = quant.convert_quant_network(net,
|
||||
bn_fold=True,
|
||||
per_channel=[True, False],
|
||||
symmetric=[True, False])
|
||||
|
||||
# get learning rate
|
||||
lr = Tensor(get_lr(lr_init=config.lr_init,
|
||||
lr_end=0.0,
|
||||
lr_max=config.lr_max,
|
||||
warmup_epochs=config.warmup_epochs,
|
||||
total_epochs=config.epoch_size,
|
||||
steps_per_epoch=step_size,
|
||||
lr_decay_mode='cosine'))
|
||||
|
||||
# define optimization
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
|
||||
config.weight_decay, config.loss_scale)
|
||||
|
||||
# define model
|
||||
#model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
|
||||
model = Model(net, loss_fn=loss, optimizer=opt)
|
||||
|
||||
print("============== Starting Training ==============")
|
||||
monitor = Monitor(lr_init=lr.asnumpy(),
|
||||
step_threshold=config.step_threshold)
|
||||
|
||||
callbacks = [monitor]
|
||||
model.train(epoch_size, dataset, callbacks=callbacks,
|
||||
dataset_sink_mode=False)
|
||||
print("============== End Training ==============")
|
||||
|
||||
expect_avg_step_loss = 2.40
|
||||
avg_step_loss = np.mean(np.array(monitor.losses))
|
||||
|
||||
print("average step loss:{}".format(avg_step_loss))
|
||||
assert avg_step_loss < expect_avg_step_loss
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train_on_ascend()
|
||||
@ -0,0 +1,105 @@
|
||||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""Resnet50 utils"""
|
||||
|
||||
import time
|
||||
import numpy as np
|
||||
|
||||
from mindspore.train.callback import Callback
|
||||
from mindspore import Tensor
|
||||
from mindspore import nn
|
||||
from mindspore.nn.loss.loss import _Loss
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
from mindspore.common import dtype as mstype
|
||||
|
||||
|
||||
class Monitor(Callback):
|
||||
"""
|
||||
Monitor loss and time.
|
||||
|
||||
Args:
|
||||
lr_init (numpy array): train lr
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
Examples:
|
||||
>>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
|
||||
"""
|
||||
|
||||
def __init__(self, lr_init=None, step_threshold=10):
|
||||
super(Monitor, self).__init__()
|
||||
self.lr_init = lr_init
|
||||
self.lr_init_len = len(lr_init)
|
||||
self.step_threshold = step_threshold
|
||||
|
||||
def epoch_begin(self, run_context):
|
||||
self.losses = []
|
||||
self.epoch_time = time.time()
|
||||
|
||||
def epoch_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
|
||||
epoch_mseconds = (time.time() - self.epoch_time) * 1000
|
||||
per_step_mseconds = epoch_mseconds / cb_params.batch_num
|
||||
print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:8.6f}".format(epoch_mseconds,
|
||||
per_step_mseconds,
|
||||
np.mean(self.losses)))
|
||||
self.epoch_mseconds = epoch_mseconds
|
||||
|
||||
def step_begin(self, run_context):
|
||||
self.step_time = time.time()
|
||||
|
||||
def step_end(self, run_context):
|
||||
cb_params = run_context.original_args()
|
||||
step_mseconds = (time.time() - self.step_time) * 1000
|
||||
step_loss = cb_params.net_outputs
|
||||
|
||||
if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
|
||||
step_loss = step_loss[0]
|
||||
if isinstance(step_loss, Tensor):
|
||||
step_loss = np.mean(step_loss.asnumpy())
|
||||
|
||||
self.losses.append(step_loss)
|
||||
cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num
|
||||
|
||||
print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:8.6f}/{:8.6f}], time:[{:5.3f}], lr:[{:5.5f}]".format(
|
||||
cb_params.cur_epoch_num, cb_params.epoch_num, cur_step_in_epoch +
|
||||
1, cb_params.batch_num, step_loss,
|
||||
np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
|
||||
|
||||
if cb_params.cur_step_num == self.step_threshold:
|
||||
run_context.request_stop()
|
||||
|
||||
|
||||
class CrossEntropy(_Loss):
|
||||
"""the redefined loss function with SoftmaxCrossEntropyWithLogits"""
|
||||
|
||||
def __init__(self, smooth_factor=0, num_classes=1001):
|
||||
super(CrossEntropy, self).__init__()
|
||||
self.onehot = P.OneHot()
|
||||
self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
|
||||
self.off_value = Tensor(1.0 * smooth_factor /
|
||||
(num_classes - 1), mstype.float32)
|
||||
self.ce = nn.SoftmaxCrossEntropyWithLogits()
|
||||
self.mean = P.ReduceMean(False)
|
||||
|
||||
def construct(self, logit, label):
|
||||
one_hot_label = self.onehot(label, F.shape(
|
||||
logit)[1], self.on_value, self.off_value)
|
||||
loss = self.ce(logit, one_hot_label)
|
||||
loss = self.mean(loss, 0)
|
||||
return loss
|
||||
Loading…
Reference in new issue