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未验证 提交 b8cb839a 编写于 作者: L LielinJiang 提交者： GitHub
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Merge pull request #96 from LielinJiang/adapt-to-2.0-api 

Adapt some model to 2.0 api
上级 f53f360a 64c766ac
隐藏空白更改
内联 并排
Showing with 134 addition and 126 deletion
handwritten_number_recognition/mnist.py
浏览文件 @ b8cb839a
......@@ -16,38 +16,36 @@ from __future__ import division
from __future__ import print_function
import argparse
import paddle
from paddle import fluid
from paddle.fluid.optimizer import Momentum
from paddle.incubate.hapi.datasets.mnist import MNIST as MnistDataset
from paddle.vision.datasets.mnist import MNIST
from paddle.incubate.hapi.model import Input, set_device
from paddle.incubate.hapi.loss import CrossEntropy
from paddle.incubate.hapi.metrics import Accuracy
from paddle.incubate.hapi.vision.models import LeNet
from paddle.vision.models import LeNet
from paddle.static import InputSpec as Input
def main():
device = set_device(FLAGS.device)
fluid.enable_dygraph(device) if FLAGS.dynamic else None
device = paddle.set_device(FLAGS.device)
paddle.disable_static(device) if FLAGS.dynamic else None
train_dataset = MnistDataset(mode='train')
val_dataset = MnistDataset(mode='test')
train_dataset = MNIST(mode='train')
val_dataset = MNIST(mode='test')
inputs = [Input([None, 1, 28, 28], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
inputs = [Input(shape=[None, 1, 28, 28], dtype='float32', name='image')]
labels = [Input(shape=[None, 1], dtype='int64', name='label')]
net = LeNet()
model = paddle.Model(net, inputs, labels)
model = LeNet()
optim = Momentum(
learning_rate=FLAGS.lr, momentum=.9, parameter_list=model.parameters())
model.prepare(
optim,
CrossEntropy(),
Accuracy(topk=(1, 2)),
inputs,
labels,
device=FLAGS.device)
paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(topk=(1, 2)))
if FLAGS.resume is not None:
model.load(FLAGS.resume)
......
image_classification/imagenet_dataset.py
浏览文件 @ b8cb839a
......@@ -18,9 +18,8 @@ import math
import random
import numpy as np
from paddle.incubate.hapi.datasets import DatasetFolder
from paddle.incubate.hapi.vision.transforms import transforms
from paddle import fluid
from paddle.vision.datasets import DatasetFolder
from paddle.vision.transforms import transforms
class ImageNetDataset(DatasetFolder):
......
image_classification/main.py
浏览文件 @ b8cb839a
......@@ -15,25 +15,19 @@
from __future__ import division
from __future__ import print_function
import argparse
import contextlib
import os
import time
import math
import argparse
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.parallel import ParallelEnv
from paddle.io import BatchSampler, DataLoader
from paddle.incubate.hapi.model import Input, set_device
from paddle.incubate.hapi.loss import CrossEntropy
from paddle.incubate.hapi.distributed import DistributedBatchSampler
from paddle.incubate.hapi.metrics import Accuracy
import paddle.incubate.hapi.vision.models as models
import paddle.vision.models as models
from paddle.static import InputSpec as Input
from imagenet_dataset import ImageNetDataset
from paddle.distributed import ParallelEnv
from paddle.io import BatchSampler, DataLoader, DistributedBatchSampler
def make_optimizer(step_per_epoch, parameter_list=None):
......@@ -72,21 +66,23 @@ def make_optimizer(step_per_epoch, parameter_list=None):
def main():
device = set_device(FLAGS.device)
fluid.enable_dygraph(device) if FLAGS.dynamic else None
device = paddle.set_device(FLAGS.device)
paddle.disable_static(device) if FLAGS.dynamic else None
model_list = [x for x in models.__dict__["__all__"]]
assert FLAGS.arch in model_list, "Expected FLAGS.arch in {}, but received {}".format(
model_list, FLAGS.arch)
model = models.__dict__[FLAGS.arch](pretrained=FLAGS.eval_only and
not FLAGS.resume)
if FLAGS.resume is not None:
model.load(FLAGS.resume)
net = models.__dict__[FLAGS.arch](pretrained=FLAGS.eval_only and
not FLAGS.resume)
inputs = [Input([None, 3, 224, 224], 'float32', name='image')]
labels = [Input([None, 1], 'int64', name='label')]
model = paddle.Model(net, inputs, labels)
if FLAGS.resume is not None:
model.load(FLAGS.resume)
train_dataset = ImageNetDataset(
os.path.join(FLAGS.data, 'train'),
mode='train',
......@@ -106,11 +102,8 @@ def main():
model.prepare(
optim,
CrossEntropy(),
Accuracy(topk=(1, 5)),
inputs,
labels,
FLAGS.device)
paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(topk=(1, 5)))
if FLAGS.eval_only:
model.evaluate(
......
style-transfer/README.md
浏览文件 @ b8cb839a
......@@ -15,9 +15,9 @@
```python
# tensor shape is [1, c, h, w]
_, c, h, w = tensor.shape
tensor = fluid.layers.reshape(tensor, [c, h * w])
tensor = paddle.reshape(tensor, [c, h * w])
# gram matrix with shape: [c, c]
gram_matrix = fluid.layers.matmul(tensor, fluid.layers.transpose(tensor, [1, 0]))
gram_matrix = paddle.matmul(tensor, paddle.transpose(tensor, [1, 0]))
```
最终风格迁移的问题转化为优化上述的两个欧式距离的问题。这里要注意的是，我们使用一个在imagenet上预训练好的模型vgg16，并且固定参数，优化器只更新输入的生成图像的值。
......@@ -32,12 +32,11 @@ gram_matrix = fluid.layers.matmul(tensor, fluid.layers.transpose(tensor, [1, 0])
import numpy as np
import matplotlib.pyplot as plt
from paddle.incubate.hapi.model import Model, Loss
import paddle
from paddle.incubate.hapi.vision.models import vgg16
from paddle.incubate.hapi.vision.transforms import transforms
from paddle.vision.models import vgg16
from paddle.vision.transforms import transforms
from paddle import fluid
from paddle.fluid.io import Dataset
import cv2
import copy
......@@ -49,7 +48,7 @@ from .style_transfer import load_image, image_restore
```python
# 启动动态图模式
fluid.enable_dygraph()
paddle.disable_static()
```
```python
......@@ -77,22 +76,23 @@ ax2.imshow(image_restore(style))
```python
# 定义风格迁移模型，使用在imagenet上预训练好的vgg16作为基础模型
class StyleTransferModel(Model):
class StyleTransferModel(paddle.nn.Layer):
def __init__(self):
super(StyleTransferModel, self).__init__()
# pretrained设置为true，会自动下载imagenet上的预训练权重并加载
vgg = vgg16(pretrained=True)
self.base_model = vgg.features
for p in self.base_model.parameters():
p.stop_gradient=True
p.stop_gradient = True
self.layers = {
'0': 'conv1_1',
'3': 'conv2_1',
'6': 'conv3_1',
'10': 'conv4_1',
'11': 'conv4_2', ## content representation
'14': 'conv5_1'
}
'0': 'conv1_1',
'5': 'conv2_1',
'10': 'conv3_1',
'17': 'conv4_1',
'19': 'conv4_2', ## content representation
'24': 'conv5_1'
}
def forward(self, image):
outputs = []
......@@ -106,27 +106,33 @@ class StyleTransferModel(Model):
```python
# 定义风格迁移个损失函数
class StyleTransferLoss(Loss):
def __init__(self, content_loss_weight=1, style_loss_weight=1e5, style_weights=[1.0, 0.8, 0.5, 0.3, 0.1]):
class StyleTransferLoss(paddle.nn.Layer):
def __init__(self,
content_loss_weight=1,
style_loss_weight=1e5,
style_weights=[1.0, 0.8, 0.5, 0.3, 0.1]):
super(StyleTransferLoss, self).__init__()
self.content_loss_weight = content_loss_weight
self.style_loss_weight = style_loss_weight
self.style_weights = style_weights
def forward(self, outputs, labels):
def forward(self, *features):
outputs = features[:6]
labels = features[6:]
content_features = labels[-1]
style_features = labels[:-1]
# 计算图像内容相似度的loss
content_loss = fluid.layers.mean((outputs[-2] - content_features)**2)
content_loss = paddle.mean((outputs[-2] - content_features)**2)
# 计算风格相似度的loss
style_loss = 0
style_grams = [self.gram_matrix(feat) for feat in style_features ]
style_grams = [self.gram_matrix(feat) for feat in style_features]
style_weights = self.style_weights
for i, weight in enumerate(style_weights):
target_gram = self.gram_matrix(outputs[i])
layer_loss = weight * fluid.layers.mean((target_gram - style_grams[i])**2)
layer_loss = weight * paddle.mean((target_gram - style_grams[
i])**2)
b, d, h, w = outputs[i].shape
style_loss += layer_loss / (d * h * w)
......@@ -135,9 +141,9 @@ class StyleTransferLoss(Loss):
def gram_matrix(self, A):
if len(A.shape) == 4:
batch_size, c, h, w = A.shape
A = fluid.layers.reshape(A, (c, h*w))
GA = fluid.layers.matmul(A, fluid.layers.transpose(A, [1, 0]))
_, c, h, w = A.shape
A = paddle.reshape(A, (c, h * w))
GA = paddle.matmul(A, paddle.transpose(A, [1, 0]))
return GA
```
......@@ -145,7 +151,8 @@ class StyleTransferLoss(Loss):
```python
# 创建模型
model = StyleTransferModel()
net = StyleTransferModel()
model = paddle.Model(net)
```
......@@ -157,7 +164,7 @@ style_loss = StyleTransferLoss()
```python
# 使用内容图像初始化要生成的图像
target = Model.create_parameter(model, shape=content.shape)
target = net.create_parameter(shape=content.shape)
target.set_value(content.numpy())
```
......
style-transfer/style-transfer.ipynb
浏览文件 @ b8cb839a
......@@ -36,12 +36,11 @@
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from hapi.model import Model, Loss\n",
"import paddle\n",
"\n",
"from hapi.vision.models import vgg16\n",
"from hapi.vision.transforms import transforms\n",
"from paddle.vision.models import vgg16\n",
"from paddle.vision.transforms import transforms\n",
"from paddle import fluid\n",
"from paddle.fluid.io import Dataset\n",
"\n",
"import cv2\n",
"import copy"
......@@ -54,7 +53,7 @@
"outputs": [],
"source": [
"# 启动动态图模式\n",
"fluid.enable_dygraph()"
"paddle.disable_static()"
]
},
{
......@@ -67,9 +66,9 @@
"```python\n",
"# tensor shape is [1, c, h, w]\n",
"_, c, h, w = tensor.shape\n",
"tensor = fluid.layers.reshape(tensor, [c, h * w])\n",
"tensor = paddle.reshape(tensor, [c, h * w])\n",
"# gram matrix with shape: [c, c]\n",
"gram_matrix = fluid.layers.matmul(tensor, fluid.layers.transpose(tensor, [1, 0]))\n",
"gram_matrix = paddle.matmul(tensor, paddle.transpose(tensor, [1, 0]))\n",
"```\n",
"\n",
"最终风格迁移的问题转化为优化上述的两个欧式距离的问题。这里要注意的是，我们使用一个在imagenet上预训练好的模型vgg16，并且固定参数，优化器只更新输入的生成图像的值。"
......@@ -176,23 +175,24 @@
"outputs": [],
"source": [
"# 定义风格迁移模型，使用在imagenet上预训练好的vgg16作为基础模型\n",
"class StyleTransferModel(Model):\n",
"class StyleTransferModel(paddle.nn.Layer):\n",
" def __init__(self):\n",
" super(StyleTransferModel, self).__init__()\n",
" # pretrained设置为true，会自动下载imagenet上的预训练权重并加载\n",
" vgg = vgg16(pretrained=True)\n",
" self.base_model = vgg.features\n",
"\n",
" for p in self.base_model.parameters():\n",
" p.stop_gradient=True\n",
" p.stop_gradient = True\n",
" self.layers = {\n",
" '0': 'conv1_1',\n",
" '3': 'conv2_1', \n",
" '6': 'conv3_1', \n",
" '10': 'conv4_1',\n",
" '11': 'conv4_2', ## content representation\n",
" '14': 'conv5_1'\n",
" }\n",
" \n",
" '0': 'conv1_1',\n",
" '5': 'conv2_1',\n",
" '10': 'conv3_1',\n",
" '17': 'conv4_1',\n",
" '19': 'conv4_2', ## content representation\n",
" '24': 'conv5_1'\n",
" }\n",
"\n",
" def forward(self, image):\n",
" outputs = []\n",
" for name, layer in self.base_model.named_sublayers():\n",
......@@ -208,38 +208,44 @@
"metadata": {},
"outputs": [],
"source": [
"class StyleTransferLoss(Loss):\n",
" def __init__(self, content_loss_weight=1, style_loss_weight=1e5, style_weights=[1.0, 0.8, 0.5, 0.3, 0.1]):\n",
"class StyleTransferLoss(paddle.nn.Layer):\n",
" def __init__(self,\n",
" content_loss_weight=1,\n",
" style_loss_weight=1e5,\n",
" style_weights=[1.0, 0.8, 0.5, 0.3, 0.1]):\n",
" super(StyleTransferLoss, self).__init__()\n",
" self.content_loss_weight = content_loss_weight\n",
" self.style_loss_weight = style_loss_weight\n",
" self.style_weights = style_weights\n",
" \n",
" def forward(self, outputs, labels):\n",
"\n",
" def forward(self, *features):\n",
" outputs = features[:6]\n",
" labels = features[6:]\n",
" content_features = labels[-1]\n",
" style_features = labels[:-1]\n",
" \n",
"\n",
" # 计算图像内容相似度的loss\n",
" content_loss = fluid.layers.mean((outputs[-2] - content_features)**2)\n",
" \n",
" content_loss = paddle.mean((outputs[-2] - content_features)**2)\n",
"\n",
" # 计算风格相似度的loss\n",
" style_loss = 0\n",
" style_grams = [self.gram_matrix(feat) for feat in style_features ]\n",
" style_grams = [self.gram_matrix(feat) for feat in style_features]\n",
" style_weights = self.style_weights\n",
" for i, weight in enumerate(style_weights):\n",
" target_gram = self.gram_matrix(outputs[i])\n",
" layer_loss = weight * fluid.layers.mean((target_gram - style_grams[i])**2)\n",
" layer_loss = weight * paddle.mean((target_gram - style_grams[\n",
" i])**2)\n",
" b, d, h, w = outputs[i].shape\n",
" style_loss += layer_loss / (d * h * w)\n",
" \n",
"\n",
" total_loss = self.content_loss_weight * content_loss + self.style_loss_weight * style_loss\n",
" return total_loss\n",
" \n",
"\n",
" def gram_matrix(self, A):\n",
" if len(A.shape) == 4:\n",
" batch_size, c, h, w = A.shape\n",
" A = fluid.layers.reshape(A, (c, h*w))\n",
" GA = fluid.layers.matmul(A, fluid.layers.transpose(A, [1, 0]))\n",
" _, c, h, w = A.shape\n",
" A = paddle.reshape(A, (c, h * w))\n",
" GA = paddle.matmul(A, paddle.transpose(A, [1, 0]))\n",
"\n",
" return GA"
]
......@@ -260,7 +266,8 @@
],
"source": [
"# 创建模型\n",
"model = StyleTransferModel()"
"net = StyleTransferModel()\n",
"model = paddle.Model(net)"
]
},
{
......@@ -280,7 +287,7 @@
"outputs": [],
"source": [
"# 使用内容图像初始化要生成的图像\n",
"target = Model.create_parameter(model, shape=content.shape)\n",
"target = net.create_parameter(shape=content.shape)\n",
"target.set_value(content.numpy())"
]
},
......@@ -586,7 +593,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.7.5"
}
},
"nbformat": 4,
style-transfer/style_transfer.py
浏览文件 @ b8cb839a
......@@ -3,12 +3,11 @@ import argparse
import numpy as np
import matplotlib.pyplot as plt
from paddle.incubate.hapi.model import Model, Loss
import paddle
from paddle.incubate.hapi.vision.models import vgg16
from paddle.incubate.hapi.vision.transforms import transforms
from paddle.vision.models import vgg16
from paddle.vision.transforms import transforms
from paddle import fluid
from paddle.fluid.io import Dataset
import cv2
import copy
......@@ -25,7 +24,7 @@ def load_image(image_path, max_size=400, shape=None):
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image = transform(image)[np.newaxis, :3, :, :]
image = fluid.dygraph.to_variable(image)
image = paddle.to_tensor(image)
return image
......@@ -39,21 +38,22 @@ def image_restore(image):
return image
class StyleTransferModel(Model):
class StyleTransferModel(paddle.nn.Layer):
def __init__(self):
super(StyleTransferModel, self).__init__()
# pretrained设置为true，会自动下载imagenet上的预训练权重并加载
vgg = vgg16(pretrained=True)
self.base_model = vgg.features
for p in self.base_model.parameters():
p.stop_gradient = True
self.layers = {
'0': 'conv1_1',
'3': 'conv2_1',
'6': 'conv3_1',
'10': 'conv4_1',
'11': 'conv4_2', ## content representation
'14': 'conv5_1'
'5': 'conv2_1',
'10': 'conv3_1',
'17': 'conv4_1',
'19': 'conv4_2', ## content representation
'24': 'conv5_1'
}
def forward(self, image):
......@@ -65,7 +65,7 @@ class StyleTransferModel(Model):
return outputs
class StyleTransferLoss(Loss):
class StyleTransferLoss(paddle.nn.Layer):
def __init__(self,
content_loss_weight=1,
style_loss_weight=1e5,
......@@ -75,12 +75,14 @@ class StyleTransferLoss(Loss):
self.style_loss_weight = style_loss_weight
self.style_weights = style_weights
def forward(self, outputs, labels):
def forward(self, *features):
outputs = features[:6]
labels = features[6:]
content_features = labels[-1]
style_features = labels[:-1]
# 计算图像内容相似度的loss
content_loss = fluid.layers.mean((outputs[-2] - content_features)**2)
content_loss = paddle.mean((outputs[-2] - content_features)**2)
# 计算风格相似度的loss
style_loss = 0
......@@ -88,8 +90,8 @@ class StyleTransferLoss(Loss):
style_weights = self.style_weights
for i, weight in enumerate(style_weights):
target_gram = self.gram_matrix(outputs[i])
layer_loss = weight * fluid.layers.mean((target_gram - style_grams[
i])**2)
layer_loss = weight * paddle.mean((target_gram - style_grams[i])**
2)
b, d, h, w = outputs[i].shape
style_loss += layer_loss / (d * h * w)
......@@ -99,24 +101,26 @@ class StyleTransferLoss(Loss):
def gram_matrix(self, A):
if len(A.shape) == 4:
_, c, h, w = A.shape
A = fluid.layers.reshape(A, (c, h * w))
GA = fluid.layers.matmul(A, fluid.layers.transpose(A, [1, 0]))
A = paddle.reshape(A, (c, h * w))
GA = paddle.matmul(A, paddle.transpose(A, [1, 0]))
return GA
def main():
# 启动动态图模式
fluid.enable_dygraph()
paddle.disable_static()
content = load_image(FLAGS.content_image)
style = load_image(FLAGS.style_image, shape=tuple(content.shape[-2:]))
model = StyleTransferModel()
net = StyleTransferModel()
model = paddle.Model(net)
style_loss = StyleTransferLoss()
# 使用内容图像初始化要生成的图像
target = Model.create_parameter(model, shape=content.shape)
target = net.create_parameter(shape=content.shape)
target.set_value(content.numpy())
optimizer = fluid.optimizer.Adam(
......
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