wehub-resource-sync
106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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import numpy as np
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import paddle
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from paddle.distributed import fleet
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from paddle.io import DataLoader, Dataset
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from paddle.vision.models import ResNet
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from paddle.vision.models.resnet import BottleneckBlock
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base_lr = 0.1
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momentum_rate = 0.9
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l2_decay = 1e-4
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epoch = 3
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batch_num = 1
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batch_size = 1
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class_dim = 102
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# define a random dataset
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class RandomDataset(Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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image = np.random.random([3, 224, 224]).astype('float32')
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label = np.random.randint(0, class_dim - 1, (1,)).astype('int64')
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return image, label
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def __len__(self):
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return self.num_samples
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def optimizer_setting(parameter_list=None):
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optimizer = paddle.optimizer.Momentum(
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learning_rate=base_lr,
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momentum=momentum_rate,
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weight_decay=paddle.regularizer.L2Decay(l2_decay),
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parameters=parameter_list,
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)
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return optimizer
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def train_resnet():
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fleet.init(is_collective=True)
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resnet = ResNet(BottleneckBlock, 18, num_classes=class_dim)
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optimizer = optimizer_setting(parameter_list=resnet.parameters())
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optimizer = fleet.distributed_optimizer(optimizer)
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resnet = fleet.distributed_model(resnet)
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dataset = RandomDataset(batch_num * batch_size)
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train_loader = DataLoader(
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dataset,
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batch_size=batch_size,
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shuffle=True,
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drop_last=True,
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num_workers=2,
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)
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print("Distributed training start...")
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for eop in range(epoch):
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resnet.train()
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for batch_id, data in enumerate(train_loader()):
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img, label = data
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label.stop_gradient = True
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out = resnet(img)
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loss = paddle.nn.functional.cross_entropy(input=out, label=label)
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avg_loss = paddle.mean(x=loss)
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acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
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acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
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avg_loss.backward()
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optimizer.step()
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resnet.clear_gradients()
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print(
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f"[Epoch {eop}, batch {batch_id}] loss: {avg_loss:.5f}, acc1: {acc_top1:.5f}, acc5: {acc_top5:.5f}"
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)
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print("Distributed training completed")
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if __name__ == '__main__':
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import os
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nnodes = os.getenv('PADDLE_NNODES')
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cn = os.getenv('PADDLE_LOCAL_SIZE')
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print(f"Prepare distributed training with {nnodes} nodes {cn} cards")
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train_resnet()
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