wehub-resource-sync
141 lines
3.7 KiB
Python
141 lines
3.7 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 argparse
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import ast
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import random
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import numpy as np
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import paddle
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from paddle import Model, set_device
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from paddle.metric import Accuracy
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from paddle.static import (
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InputSpec as Input,
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amp,
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)
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from paddle.vision.datasets import MNIST
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from paddle.vision.models import LeNet
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SEED = 2
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paddle.seed(SEED)
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paddle.framework.random._manual_program_seed(SEED)
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np.random.seed(SEED)
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random.seed(SEED)
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paddle.enable_static()
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set_device('cpu')
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def parse_args():
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parser = argparse.ArgumentParser("Lenet BF16 train static script")
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parser.add_argument(
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'-bf16',
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'--bf16',
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type=ast.literal_eval,
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default=False,
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help="whether use bf16",
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)
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args = parser.parse_args()
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return args
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class MnistDataset(MNIST):
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def __init__(self, mode, return_label=True):
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super().__init__(mode=mode)
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self.return_label = return_label
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def __getitem__(self, idx):
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img = np.reshape(self.images[idx], [1, 28, 28])
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if self.return_label:
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return img, np.array(self.labels[idx]).astype('int64')
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return (img,)
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def __len__(self):
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return len(self.images)
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def compute_accuracy(pred, gt):
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pred = np.argmax(pred, -1)
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gt = np.array(gt)
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correct = pred[:, np.newaxis] == gt
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return np.sum(correct) / correct.shape[0]
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def main(args):
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print('download training data and load training data')
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train_dataset = MnistDataset(
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mode='train',
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)
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val_dataset = MnistDataset(
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mode='test',
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)
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test_dataset = MnistDataset(mode='test', return_label=False)
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im_shape = (-1, 1, 28, 28)
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batch_size = 64
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inputs = [Input(im_shape, 'float32', 'image')]
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labels = [Input([None, 1], 'int64', 'label')]
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model = Model(LeNet(), inputs, labels)
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optim = paddle.optimizer.SGD(learning_rate=0.001)
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if args.bf16:
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optim = amp.bf16.decorate_bf16(
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optim,
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amp_lists=amp.bf16.AutoMixedPrecisionListsBF16(
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custom_bf16_list={
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'matmul_v2',
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'pool2d',
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'relu',
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'scale',
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'elementwise_add',
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'reshape2',
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'slice',
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'reduce_mean',
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'conv2d',
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},
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),
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)
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# Configuration model
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model.prepare(optim, paddle.nn.CrossEntropyLoss(), Accuracy())
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# Training model #
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if args.bf16:
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print('Training BF16')
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else:
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print('Training FP32')
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model.fit(train_dataset, epochs=2, batch_size=batch_size, verbose=1)
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eval_result = model.evaluate(val_dataset, batch_size=batch_size, verbose=1)
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output = model.predict(
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test_dataset, batch_size=batch_size, stack_outputs=True
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)
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np.testing.assert_equal(output[0].shape[0], len(test_dataset))
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acc = compute_accuracy(output[0], val_dataset.labels)
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print("acc", acc)
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print("eval_result['acc']", eval_result['acc'])
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np.testing.assert_allclose(acc, eval_result['acc'])
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if __name__ == "__main__":
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args = parse_args()
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main(args)
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