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未验证 提交 5400a6be 编写于 作者: saxon_zh's avatar saxon_zh 提交者： GitHub
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rerun code with 2.0-beta whl (#895) 

* 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
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paddle2.0_docs/getting_started/getting_started.ipynb
浏览文件 @ 5400a6be
......@@ -31,16 +31,16 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.0.0'"
"'2.0.0-beta0'"
]
},
"execution_count": 4,
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
......
paddle2.0_docs/high_level_api/high_level_api.ipynb
浏览文件 @ 5400a6be
{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python37464bitc4da1ac836094043840bff631bedbf7f",
"display_name": "Python 3.7.4 64-bit"
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"cell_type": "markdown",
......@@ -57,16 +36,18 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "'0.0.0'"
"text/plain": [
"'2.0.0-beta0'"
]
},
"execution_count": 1,
"metadata": {},
"execution_count": 2
"output_type": "execute_result"
}
],
"source": [
......@@ -115,9 +96,12 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "视觉相关数据集： ['DatasetFolder', 'ImageFolder', 'MNIST', 'Flowers', 'Cifar10', 'Cifar100', 'VOC2012']\n自然语言相关数据集： ['Conll05st', 'Imdb', 'Imikolov', 'Movielens', 'MovieReviews', 'UCIHousing', 'WMT14', 'WMT16']\n"
"output_type": "stream",
"text": [
"视觉相关数据集： ['DatasetFolder', 'ImageFolder', 'MNIST', 'Flowers', 'Cifar10', 'Cifar100', 'VOC2012']\n",
"自然语言相关数据集： ['Conll05st', 'Imdb', 'Imikolov', 'Movielens', 'MovieReviews', 'UCIHousing', 'WMT14', 'WMT16']\n"
]
}
],
"source": [
......@@ -138,7 +122,7 @@
"metadata": {},
"outputs": [],
"source": [
"# 测试数据集\n",
"# 训练数据集\n",
"train_dataset = vision.datasets.MNIST(mode='train')\n",
"\n",
"# 验证数据集\n",
......@@ -162,9 +146,20 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "=============train dataset=============\ntraindata1 label1\ntraindata2 label2\ntraindata3 label3\ntraindata4 label4\n=============evaluation dataset=============\ntestdata1 label1\ntestdata2 label2\ntestdata3 label3\ntestdata4 label4\n"
"output_type": "stream",
"text": [
"=============train dataset=============\n",
"traindata1 label1\n",
"traindata2 label2\n",
"traindata3 label3\n",
"traindata4 label4\n",
"=============evaluation dataset=============\n",
"testdata1 label1\n",
"testdata2 label2\n",
"testdata3 label3\n",
"testdata4 label4\n"
]
}
],
"source": [
......@@ -893,5 +888,26 @@
"有了用于推理部署的模型，就可以使用推理部署框架来完成预测服务部署，具体可以参见：[预测部署](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/index_cn.html)， 包括服务端部署、移动端部署和模型压缩。"
]
}
]
],
"metadata": {
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"display_name": "Python 3.7.4 64-bit",
"language": "python",
"name": "python37464bitc4da1ac836094043840bff631bedbf7f"
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"language_info": {
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
paddle2.0_docs/image_segmentation/pets_image_segmentation_U_Net_like.ipynb
浏览文件 @ 5400a6be
......@@ -7,7 +7,7 @@
"id": "ueGUN2EQeScw"
},
"source": [
"# 基于U型语义分割模型实现的宠物图像分割\n",
"# 基于U-Net卷积神经网络实现宠物图像分割\n",
"\n",
"本示例教程当前是基于2.0-beta版本Paddle做的案例实现，未来会随着2.0的系列版本发布进行升级。"
]
......@@ -34,16 +34,16 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'0.0.0'"
"'2.0.0-beta0'"
]
},
"execution_count": 2,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
......@@ -94,7 +94,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
......@@ -105,7 +105,20 @@
"outputId": "3985783f-7166-4afa-f511-16427b3e2a71",
"tags": []
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 755M 100 755M 0 0 1707k 0 0:07:32 0:07:32 --:--:-- 2865k0 0:12:48 524k 0 0:13:34 0:02:41 0:10:53 668k 0 0:12:45 0:03:06 0:09:39 1702k 0 1221k 0 0:10:33 0:03:25 0:07:08 3108k37 282M 0 0 1243k 0 0:10:21 0:03:52 0:06:29 719k0:05:53 566k0 1237k 0 0:10:25 0:04:43 0:05:42 1593k 0 0:09:46 0:05:28 0:04:18 2952k 1467k 0 0:08:47 0:06:43 0:02:04 1711k\n",
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 18.2M 100 18.2M 0 0 1602k 0 0:00:11 0:00:11 --:--:-- 3226k\n"
]
}
],
"source": [
"!curl -O http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
"!curl -O http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
......@@ -160,7 +173,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
......@@ -222,7 +235,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
......@@ -375,7 +388,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
......@@ -451,7 +464,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 25,
"metadata": {
"colab": {},
"colab_type": "code",
......@@ -514,7 +527,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 26,
"metadata": {
"colab": {},
"colab_type": "code",
......@@ -574,7 +587,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 27,
"metadata": {
"colab": {},
"colab_type": "code",
......@@ -596,7 +609,7 @@
" kernel_size=3, \n",
" padding='same')\n",
" self.bn = paddle.nn.BatchNorm2d(out_channels)\n",
" self.upsample = paddle.nn.UpSample(scale_factor=2.0)\n",
" self.upsample = paddle.nn.Upsample(scale_factor=2.0)\n",
" self.residual_conv = paddle.nn.Conv2d(in_channels, \n",
" out_channels, \n",
" kernel_size=1, \n",
......@@ -635,7 +648,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 30,
"metadata": {
"colab": {},
"colab_type": "code",
......@@ -643,9 +656,9 @@
},
"outputs": [],
"source": [
"class PetModel(paddle.nn.Layer):\n",
"class PetNet(paddle.nn.Layer):\n",
" def __init__(self, num_classes):\n",
" super(PetModel, self).__init__()\n",
" super(PetNet, self).__init__()\n",
"\n",
" self.conv_1 = paddle.nn.Conv2d(3, 32, \n",
" kernel_size=3,\n",
......@@ -711,7 +724,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
......@@ -730,32 +743,32 @@
"--------------------------------------------------------------------------------\n",
" Layer (type) Input Shape Output Shape Param #\n",
"================================================================================\n",
" Conv2d-1 [-1, 3, 160, 160] [-1, 32, 80, 80] 896\n",
" BatchNorm2d-1 [-1, 32, 80, 80] [-1, 32, 80, 80] 64\n",
" ReLU-1 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n",
" ReLU-4 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n",
" Conv2d-12 [-1, 128, 20, 20] [-1, 128, 20, 20] 1,152\n",
" Conv2d-13 [-1, 128, 20, 20] [-1, 256, 20, 20] 33,024\n",
"SeparableConv2d-5 [-1, 128, 20, 20] [-1, 256, 20, 20] 0\n",
" BatchNorm2d-4 [-1, 256, 20, 20] [-1, 256, 20, 20] 512\n",
" Conv2d-14 [-1, 256, 20, 20] [-1, 256, 20, 20] 2,304\n",
" Conv2d-15 [-1, 256, 20, 20] [-1, 256, 20, 20] 65,792\n",
"SeparableConv2d-6 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n",
" MaxPool2d-3 [-1, 256, 20, 20] [-1, 256, 10, 10] 0\n",
" Conv2d-16 [-1, 128, 20, 20] [-1, 256, 10, 10] 33,024\n",
" Encoder-3 [-1, 128, 20, 20] [-1, 256, 10, 10] 0\n",
" ReLU-8 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n",
"ConvTranspose2d-7 [-1, 64, 80, 80] [-1, 32, 80, 80] 18,464\n",
" BatchNorm2d-8 [-1, 32, 80, 80] [-1, 32, 80, 80] 64\n",
"ConvTranspose2d-8 [-1, 32, 80, 80] [-1, 32, 80, 80] 9,248\n",
" UpSample-4 [-1, 64, 80, 80] [-1, 64, 160, 160] 0\n",
" Conv2d-20 [-1, 64, 160, 160] [-1, 32, 160, 160] 2,080\n",
" Decoder-4 [-1, 64, 80, 80] [-1, 32, 160, 160] 0\n",
" Conv2d-21 [-1, 32, 160, 160] [-1, 4, 160, 160] 1,156\n",
" Conv2d-38 [-1, 3, 160, 160] [-1, 32, 80, 80] 896\n",
" BatchNorm2d-14 [-1, 32, 80, 80] [-1, 32, 80, 80] 128\n",
" ReLU-14 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n",
" ReLU-17 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n",
" Conv2d-49 [-1, 128, 20, 20] [-1, 128, 20, 20] 1,152\n",
" Conv2d-50 [-1, 128, 20, 20] [-1, 256, 20, 20] 33,024\n",
"SeparableConv2d-17 [-1, 128, 20, 20] [-1, 256, 20, 20] 0\n",
" BatchNorm2d-17 [-1, 256, 20, 20] [-1, 256, 20, 20] 1,024\n",
" Conv2d-51 [-1, 256, 20, 20] [-1, 256, 20, 20] 2,304\n",
" Conv2d-52 [-1, 256, 20, 20] [-1, 256, 20, 20] 65,792\n",
"SeparableConv2d-18 [-1, 256, 20, 20] [-1, 256, 20, 20] 0\n",
" MaxPool2d-9 [-1, 256, 20, 20] [-1, 256, 10, 10] 0\n",
" Conv2d-53 [-1, 128, 20, 20] [-1, 256, 10, 10] 33,024\n",
" Encoder-9 [-1, 128, 20, 20] [-1, 256, 10, 10] 0\n",
" ReLU-21 [-1, 32, 80, 80] [-1, 32, 80, 80] 0\n",
"ConvTranspose2d-17 [-1, 64, 80, 80] [-1, 32, 80, 80] 18,464\n",
" BatchNorm2d-21 [-1, 32, 80, 80] [-1, 32, 80, 80] 128\n",
"ConvTranspose2d-18 [-1, 32, 80, 80] [-1, 32, 80, 80] 9,248\n",
" Upsample-8 [-1, 64, 80, 80] [-1, 64, 160, 160] 0\n",
" Conv2d-57 [-1, 64, 160, 160] [-1, 32, 160, 160] 2,080\n",
" Decoder-9 [-1, 64, 80, 80] [-1, 32, 160, 160] 0\n",
" Conv2d-58 [-1, 32, 160, 160] [-1, 4, 160, 160] 1,156\n",
"================================================================================\n",
"Total params: 167,780\n",
"Trainable params: 167,780\n",
"Non-trainable params: 0\n",
"Total params: 168,420\n",
"Trainable params: 167,140\n",
"Non-trainable params: 1,280\n",
"--------------------------------------------------------------------------------\n",
"Input size (MB): 0.29\n",
"Forward/backward pass size (MB): 43.16\n",
......@@ -768,10 +781,10 @@
{
"data": {
"text/plain": [
"{'total_params': 167780, 'trainable_params': 167780}"
"{'total_params': 168420, 'trainable_params': 167140}"
]
},
"execution_count": 10,
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
......@@ -781,7 +794,7 @@
"\n",
"paddle.disable_static()\n",
"num_classes = 4\n",
"model = paddle.Model(PetModel(num_classes))\n",
"model = paddle.Model(PetNet(num_classes))\n",
"model.summary((3, 160, 160))"
]
},
......@@ -809,7 +822,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {
"colab": {},
"colab_type": "code",
......@@ -837,7 +850,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 19,
"metadata": {
"colab": {},
"colab_type": "code",
......@@ -1022,5 +1035,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}
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