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未验证 提交 8c1aa4e6 编写于 作者: saxon_zh's avatar saxon_zh 提交者： GitHub
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[Bug Fix]change paddle.disable_static() position (#902) 

* upgrade code to 2.0-beta

* add high level api doc

* add define callback/metric/loss chapter

* add define callback/metric/loss chapter

* rerun code with 2.0-beta whl

* fix bug: modify PetModel to PetNet

* Fix BUG: change paddle.disable_static() position
上级 241823fc
隐藏空白更改
内联 并排
Showing with 235 addition and 97 deletion
.gitignore
浏览文件 @ 8c1aa4e6
......@@ -6,3 +6,4 @@ pandoc.template
py_env*
*.ipynb
build
.vscode
paddle2.0_docs/getting_started/getting_started.ipynb
浏览文件 @ 8c1aa4e6
......@@ -31,24 +31,22 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2.0.0-beta0'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
"name": "stdout",
"output_type": "stream",
"text": [
"2.0.0-beta0\n"
]
}
],
"source": [
"import paddle\n",
"\n",
"paddle.__version__"
"print(paddle.__version__)\n",
"paddle.disable_static()"
]
},
{
......@@ -71,7 +69,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
......@@ -90,7 +88,7 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
......@@ -113,7 +111,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 11,
"metadata": {
"scrolled": true
},
......@@ -123,15 +121,15 @@
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"step 1875/1875 [==============================] - loss: 0.2571 - acc: 0.9037 - 10ms/step \n",
"step 1875/1875 [==============================] - loss: 0.2250 - acc: 0.9025 - 9ms/step \n",
"Epoch 2/5\n",
"step 1875/1875 [==============================] - loss: 0.1880 - acc: 0.9458 - 14ms/step \n",
"step 1875/1875 [==============================] - loss: 0.0969 - acc: 0.9462 - 13ms/step \n",
"Epoch 3/5\n",
"step 1875/1875 [==============================] - loss: 0.0279 - acc: 0.9549 - 11ms/step \n",
"step 1875/1875 [==============================] - loss: 0.1035 - acc: 0.9550 - 12ms/step \n",
"Epoch 4/5\n",
"step 1875/1875 [==============================] - loss: 0.0505 - acc: 0.9608 - 13ms/step \n",
"step 1875/1875 [==============================] - loss: 0.0316 - acc: 0.9603 - 12ms/step \n",
"Epoch 5/5\n",
"step 1875/1875 [==============================] - loss: 0.2253 - acc: 0.9646 - 12ms/step \n"
"step 1875/1875 [==============================] - loss: 0.1771 - acc: 0.9637 - 12ms/step \n"
]
}
],
......
paddle2.0_docs/high_level_api/high_level_api.ipynb
浏览文件 @ 8c1aa4e6
......@@ -36,7 +36,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [
{
......@@ -45,7 +45,7 @@
"'2.0.0-beta0'"
]
},
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
......@@ -55,6 +55,9 @@
"import paddle.vision as vision\n",
"import paddle.text as text\n",
"\n",
"# 启动动态图训练模式\n",
"paddle.disable_static()\n",
"\n",
"paddle.__version__"
]
},
......@@ -90,7 +93,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 3,
"metadata": {
"tags": []
},
......@@ -118,7 +121,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
......@@ -140,7 +143,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 5,
"metadata": {
"tags": []
},
......@@ -207,8 +210,8 @@
" return len(self.data)\n",
"\n",
"# 测试定义的数据集\n",
"train_dataset = MyDataset(mode='train')\n",
"val_dataset = MyDataset(mode='test')\n",
"train_dataset_2 = MyDataset(mode='train')\n",
"val_dataset_2 = MyDataset(mode='test')\n",
"\n",
"print('=============train dataset=============')\n",
"for data, label in train_dataset:\n",
......@@ -232,7 +235,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -243,7 +246,7 @@
"transform = Compose([ColorJitter(), Resize(size=100)])\n",
"\n",
"# 通过transform参数传递定义好的数据增项方法即可完成对自带数据集的应用\n",
"train_dataset = vision.datasets.MNIST(mode='train', transform=transform)"
"train_dataset_3 = vision.datasets.MNIST(mode='train', transform=transform)"
]
},
{
......@@ -257,7 +260,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
......@@ -316,7 +319,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -340,7 +343,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
......@@ -364,7 +367,7 @@
"\n",
" return y\n",
"\n",
"mnist = Mnist()"
"mnist_2 = Mnist()"
]
},
{
......@@ -380,14 +383,12 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# 场景1：动态图模式\n",
"\n",
"# 启动动态图训练模式\n",
"paddle.disable_static()\n",
"# 使用GPU训练\n",
"paddle.set_device('gpu')\n",
"# 模型封装\n",
......@@ -412,9 +413,45 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 33,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------------------------------------------------------------------\n",
" Layer (type) Input Shape Output Shape Param #\n",
"================================================================================\n",
" Flatten-57509 [-1, 1, 28, 28] [-1, 784] 0\n",
" Linear-7 [-1, 784] [-1, 512] 401,920\n",
" ReLU-4 [-1, 512] [-1, 512] 0\n",
" Dropout-4 [-1, 512] [-1, 512] 0\n",
" Linear-8 [-1, 512] [-1, 10] 5,130\n",
"================================================================================\n",
"Total params: 407,050\n",
"Trainable params: 407,050\n",
"Non-trainable params: 0\n",
"--------------------------------------------------------------------------------\n",
"Input size (MB): 0.00\n",
"Forward/backward pass size (MB): 0.02\n",
"Params size (MB): 1.55\n",
"Estimated Total Size (MB): 1.57\n",
"--------------------------------------------------------------------------------\n",
"\n"
]
},
{
"data": {
"text/plain": [
"{'total_params': 407050, 'trainable_params': 407050}"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.summary((1, 28, 28))"
]
......@@ -428,9 +465,45 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 34,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------------------------------------------------------------------\n",
" Layer (type) Input Shape Output Shape Param #\n",
"================================================================================\n",
" Flatten-57508 [-1, 1, 28, 28] [-1, 784] 0\n",
" Linear-5 [-1, 784] [-1, 512] 401,920\n",
" ReLU-3 [-1, 512] [-1, 512] 0\n",
" Dropout-3 [-1, 512] [-1, 512] 0\n",
" Linear-6 [-1, 512] [-1, 10] 5,130\n",
"================================================================================\n",
"Total params: 407,050\n",
"Trainable params: 407,050\n",
"Non-trainable params: 0\n",
"--------------------------------------------------------------------------------\n",
"Input size (MB): 0.00\n",
"Forward/backward pass size (MB): 0.02\n",
"Params size (MB): 1.55\n",
"Estimated Total Size (MB): 1.57\n",
"--------------------------------------------------------------------------------\n",
"\n"
]
},
{
"data": {
"text/plain": [
"{'total_params': 407050, 'trainable_params': 407050}"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"paddle.summary(mnist, (1, 28, 28))"
]
......@@ -456,7 +529,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
......@@ -475,9 +548,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 36,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"step 1875/1875 [==============================] - loss: 0.1600 - acc: 0.9022 - 10ms/step \n",
"Epoch 2/10\n",
"step 1875/1875 [==============================] - loss: 0.0455 - acc: 0.9461 - 12ms/step \n",
"Epoch 3/10\n",
"step 1875/1875 [==============================] - loss: 0.1429 - acc: 0.9544 - 19ms/step \n",
"Epoch 4/10\n",
"step 1875/1875 [==============================] - loss: 0.0197 - acc: 0.9601 - 22ms/step \n",
"Epoch 5/10\n",
"step 1875/1875 [==============================] - loss: 0.1762 - acc: 0.9644 - 25ms/step \n",
"Epoch 6/10\n",
"step 1875/1875 [==============================] - loss: 0.1304 - acc: 0.9667 - 22ms/step \n",
"Epoch 7/10\n",
"step 1875/1875 [==============================] - loss: 0.0133 - acc: 0.9682 - 22ms/step \n",
"Epoch 8/10\n",
"step 1875/1875 [==============================] - loss: 0.0097 - acc: 0.9705 - 19ms/step \n",
"Epoch 9/10\n",
"step 1875/1875 [==============================] - loss: 3.1264e-04 - acc: 0.9716 - 23ms/step \n",
"Epoch 10/10\n",
"step 1875/1875 [==============================] - loss: 0.0767 - acc: 0.9729 - 13ms/step \n"
]
}
],
"source": [
"# 启动模型训练，指定训练数据集，设置训练轮次，设置每次数据集计算的批次大小，设置日志格式\n",
"model.fit(train_dataset, \n",
......@@ -497,14 +597,39 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 20,
"metadata": {
"tags": []
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"step 1875/1875 [==============================] - loss: 0.0490 - acc: 0.9741 - 6ms/step \n",
"Epoch 2/10\n",
"step 1875/1875 [==============================] - loss: 0.1384 - acc: 0.9760 - 7ms/step \n",
"Epoch 3/10\n",
"step 1875/1875 [==============================] - loss: 0.0929 - acc: 0.9767 - 7ms/step \n",
"Epoch 4/10\n",
"step 1875/1875 [==============================] - loss: 0.0190 - acc: 0.9772 - 6ms/step \n",
"Epoch 5/10\n",
"step 1875/1875 [==============================] - loss: 0.0862 - acc: 0.9774 - 7ms/step \n",
"Epoch 6/10\n",
"step 1875/1875 [==============================] - loss: 0.0748 - acc: 0.9785 - 8ms/step \n",
"Epoch 7/10\n",
"step 1875/1875 [==============================] - loss: 0.0039 - acc: 0.9798 - 17ms/step \n",
"Epoch 8/10\n",
"step 1875/1875 [==============================] - loss: 0.0037 - acc: 0.9808 - 11ms/step \n",
"Epoch 9/10\n",
"step 1875/1875 [==============================] - loss: 0.0013 - acc: 0.9800 - 8ms/step \n",
"Epoch 10/10\n",
"step 1875/1875 [==============================] - loss: 0.0376 - acc: 0.9810 - 8ms/step \n"
]
}
],
"source": [
"# 启动动态图训练模式\n",
"paddle.disable_static()\n",
"\n",
"# 使用GPU训练\n",
"paddle.set_device('gpu')\n",
......@@ -530,17 +655,13 @@
"source": [
"### 5.2 单机多卡\n",
"\n",
"对于高层API来实现单机多卡非常简单，整个训练代码和单机单卡没有差异。直接使用`paddle.distributed.launch`启动单机单卡的程序即可。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# train.py里面包含的就是单机单卡代码\n",
"python -m paddle.distributed.launch train.py"
"对于高层API来实现单机多卡非常简单，整个训练代码和单机单卡没有差异。直接使用`paddle.distributed.launch`启动单机单卡的程序即可。\n",
"\n",
"```bash\n",
"$ python -m paddle.distributed.launch train.py\n",
"```\n",
"\n",
"train.py里面包含的就是单机单卡代码"
]
},
{
......@@ -810,9 +931,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 21,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Eval begin...\n",
"step 10000/10000 [==============================] - loss: 0.0000e+00 - acc: 0.9801 - 2ms/step \n",
"Eval samples: 10000\n"
]
}
],
"source": [
"result = model.evaluate(val_dataset, verbose=1)"
]
......@@ -834,9 +965,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 22,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predict begin...\n",
"step 10000/10000 [==============================] - 4ms/step \n",
"Predict samples: 10000\n"
]
}
],
"source": [
"pred_result = model.predict(val_dataset)"
]
......@@ -866,17 +1007,40 @@
"\n",
"### 8.1 模型存储\n",
"\n",
"模型训练和验证达到我们的预期后，可以使用`save`接口来将我们的模型保存下来，用于后续模型的Fine-tuning（接口参数training=True）或推理部署（接口参数training=False）。"
"模型训练和验证达到我们的预期后，可以使用`save`接口来将我们的模型保存下来，用于后续模型的Fine-tuning（接口参数training=True）或推理部署（接口参数training=False）。\n",
"\n",
"需要注意的是，在动态图模式训练时保存推理模型的参数文件和模型文件，需要在forward成员函数上添加@paddle.jit.to_static装饰器，参考下面的例子：\n",
"\n",
"```python\n",
"class Mnist(paddle.nn.Layer):\n",
" def __init__(self):\n",
" super(Mnist, self).__init__()\n",
"\n",
" self.flatten = paddle.nn.Flatten()\n",
" self.linear_1 = paddle.nn.Linear(784, 512)\n",
" self.linear_2 = paddle.nn.Linear(512, 10)\n",
" self.relu = paddle.nn.ReLU()\n",
" self.dropout = paddle.nn.Dropout(0.2)\n",
"\n",
" @paddle.jit.to_static\n",
" def forward(self, inputs):\n",
" y = self.flatten(inputs)\n",
" y = self.linear_1(y)\n",
" y = self.relu(y)\n",
" y = self.dropout(y)\n",
" y = self.linear_2(y)\n",
"\n",
" return y\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# 保存用于推理部署的模型（training=False）\n",
"model.save('~/model/mnist', training=False)"
"model.save('~/model/mnist')"
]
},
{
......
paddle2.0_docs/image_segmentation/pets_image_segmentation_U_Net_like.ipynb
浏览文件 @ 8c1aa4e6
......@@ -58,6 +58,9 @@
"import paddle\n",
"from paddle.nn import functional as F\n",
"\n",
"device = paddle.set_device('gpu')\n",
"paddle.disable_static(device)\n",
"\n",
"paddle.__version__"
]
},
......@@ -808,34 +811,6 @@
"## 5.模型训练"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8Sskbyz58X4J"
},
"source": [
"### 5.1 配置信息\n",
"\n",
"定义训练BATCH_SIZE、训练轮次和计算设备等信息。"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4fSkTiRB8OpP"
},
"outputs": [],
"source": [
"BATCH_SIZE = 32\n",
"EPOCHS = 15\n",
"device = paddle.set_device('gpu')\n",
"paddle.disable_static(device)"
]
},
{
"cell_type": "markdown",
"metadata": {
......@@ -843,14 +818,14 @@
"id": "x_vaedRa8eoy"
},
"source": [
"### 5.3 自定义Loss\n",
"### 5.1 自定义Loss\n",
"\n",
"在这个任务中我们使用SoftmaxWithCrossEntropy损失函数来做计算，飞桨中有functional形式的API，这里我们做一个自定义操作，实现一个Class形式API放到模型训练中使用。没有直接使用CrossEntropyLoss的原因主要是对计算维度的自定义需求，本次需要进行softmax计算的维度是1，不是默认的最后一维，所以我们采用上面提到的损失函数，通过axis参数来指定softmax计算维度。"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 10,
"metadata": {
"colab": {},
"colab_type": "code",
......@@ -877,7 +852,7 @@
"id": "rj6MPPMkJIdZ"
},
"source": [
"### 5.4 启动模型训练\n",
"### 5.2 启动模型训练\n",
"\n",
"使用模型代码进行Model实例生成，使用prepare接口定义优化器、损失函数和评价指标等信息，用于后续训练使用。在所有初步配置完成后，调用fit接口开启训练执行过程，调用fit时只需要将前面定义好的训练数据集、测试数据集、训练轮次（Epoch）和批次大小（batch_size）配置好即可。"
]
......@@ -907,8 +882,8 @@
"model.prepare(optim, SoftmaxWithCrossEntropy())\n",
"model.fit(train_dataset, \n",
" val_dataset, \n",
" epochs=EPOCHS, \n",
" batch_size=BATCH_SIZE)"
" epochs=15, \n",
" batch_size=32)"
]
},
{
......
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