mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-10-30 17:29:13 +00:00
77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
import paddle
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import paddle.nn as nn
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from paddle.vision.transforms import Compose, Normalize
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from paddle.utils.cpp_extension import load
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from paddle.inference import Config
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from paddle.inference import create_predictor
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import numpy as np
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EPOCH_NUM = 4
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BATCH_SIZE = 64
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# jit compile custom op
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custom_ops = load(
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name="custom_jit_ops", sources=["custom_relu_op.cc", "custom_relu_op.cu"])
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class LeNet(nn.Layer):
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def __init__(self):
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super(LeNet, self).__init__()
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self.conv1 = nn.Conv2D(
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in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
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self.max_pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
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self.conv2 = nn.Conv2D(
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in_channels=6, out_channels=16, kernel_size=5, stride=1)
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self.max_pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
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self.linear1 = nn.Linear(in_features=16 * 5 * 5, out_features=120)
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self.linear2 = nn.Linear(in_features=120, out_features=84)
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self.linear3 = nn.Linear(in_features=84, out_features=10)
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def forward(self, x):
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x = self.conv1(x)
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x = custom_ops.custom_relu(x)
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x = self.max_pool1(x)
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x = custom_ops.custom_relu(x)
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x = self.conv2(x)
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x = self.max_pool2(x)
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x = paddle.flatten(x, start_axis=1, stop_axis=-1)
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x = self.linear1(x)
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x = custom_ops.custom_relu(x)
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x = self.linear2(x)
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x = custom_ops.custom_relu(x)
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x = self.linear3(x)
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return x
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# set device
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paddle.set_device("gpu")
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# model
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net = LeNet()
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loss_fn = nn.CrossEntropyLoss()
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opt = paddle.optimizer.Adam(learning_rate=0.001, parameters=net.parameters())
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# data loader
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transform = Compose([Normalize(mean=[127.5], std=[127.5], data_format='CHW')])
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train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
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train_loader = paddle.io.DataLoader(
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train_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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# train
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for epoch_id in range(EPOCH_NUM):
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for batch_id, (image, label) in enumerate(train_loader()):
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out = net(image)
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loss = loss_fn(out, label)
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loss.backward()
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if batch_id % 300 == 0:
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print("Epoch {} batch {}: loss = {}".format(epoch_id, batch_id,
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np.mean(loss.numpy())))
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opt.step()
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opt.clear_grad()
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