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gunicorn 和 uvicorn部署fastapi 或者flask

小云晓云 2024-11-18 阅读 3

目标

具体实现

(一)环境

语言环境:Python 3.10
编 译 器: PyCharm
框 架:

(二)具体步骤
1. 使用GPU
--------------------------utils.py-------------------
import tensorflow as tf
import PIL
import matplotlib.pyplot as plt


def GPU_ON():
# 查询tensorflow版本
print("Tensorflow Version:", tf.__version__)

# 设置使用GPU
gpus = tf.config.list_physical_devices("GPU")
print(gpus)

if gpus:
gpu0 = gpus[0] # 如果有多个GPU,仅使用第0个GPU
tf.config.experimental.set_memory_growth(gpu0, True) # 设置GPU显存按需使用
tf.config.set_visible_devices([gpu0], "GPU")

使用GPU并查看数据

import tensorflow as tf  
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import os, PIL, pathlib
from utils import GPU_ON

GPU_ON()

data_dir = "./datasets/coffee/"
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob("*/*.png")))
print("图片总数量为:", image_count)
------------------
图片总数量为: 1200
2. 加载数据
# 加载数据  
batch_size = 32
img_height, img_width = 224, 224

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size,
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size,
)
--------------------
Found 1200 files belonging to 4 classes.
Using 960 files for training.
Found 1200 files belonging to 4 classes.
Using 240 files for validation.

获取标签:

# 获取标签  
class_names = train_ds.class_names
print(class_names)
------------------
['Dark', 'Green', 'Light', 'Medium']

可视化数据:

# 可视化数据  
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(2):
for i in range(30):
ax = plt.subplot(5, 6, i+1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])

plt.axis("off")
plt.show()


检查一下数据:

# 检查一下数据  
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
----------------------------
(32, 224, 224, 3)
(32,)
**3.**配置数据集
# 配置数据集  
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y))

image_batch, labels_batch = next(iter(train_ds))
first_image = image_batch[0]

# 查看归一化后的数据
print(np.min(first_image), np.max(first_image))
--------------------
0.0 1.0
4.搭建VGG-16网络

本次准备直接调用官方模型

# 搭建VGG-16网络模型  
model = tf.keras.applications.VGG16(weights="imagenet")
print(model.summary())
-------------------------------

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5
553467096/553467096 [==============================] - 14s 0us/step
Model: "vgg16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_
1 (InputLayer) [(None, 224, 224, 3)] 0

block1_conv1 (Conv2D) (None, 224, 224, 64) 1792

block1_conv2 (Conv2D) (None, 224, 224, 64) 36928

block1_pool (MaxPooling2D) (None, 112, 112, 64) 0

block2_conv1 (Conv2D) (None, 112, 112, 128) 73856

block2_conv2 (Conv2D) (None, 112, 112, 128) 147584

block2_pool (MaxPooling2D) (None, 56, 56, 128) 0

block3_conv1 (Conv2D) (None, 56, 56, 256) 295168

block3_conv2 (Conv2D) (None, 56, 56, 256) 590080

block3_conv3 (Conv2D) (None, 56, 56, 256) 590080

block3_pool (MaxPooling2D) (None, 28, 28, 256) 0

block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160

block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808

block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808

block4_pool (MaxPooling2D) (None, 14, 14, 512) 0

block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808

block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808

block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808

block5_pool (MaxPooling2D) (None, 7, 7, 512) 0

flatten (Flatten) (None, 25088) 0

fc1 (Dense) (None, 4096) 102764544

fc2 (Dense) (None, 4096) 16781312

predictions (Dense) (None, 1000) 4097000

=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________

简简单单1亿的参数的模型。哈哈。

编译一下:

# 编译模型  
# 设置初始学习率
initial_learning_rate = 1e-4
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=initial_learning_rate,
decay_steps=30,
decay_rate=0.92,
staircase=True
)

# 设置优化器
opt = tf.keras.optimizers.Adam(learning_rate=initial_learning_rate)

model.compile(
optimizer=opt,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['accuracy']
)

训练模型:

# 训练模型  
epochs = 20
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs,
)

image.png
训练效果不错,可视化看看:

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss = history.history['loss']
val_loss = history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

image.png
果然超赞。
改成动态学习率的结果:

opt = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

image.png

5. 手动搭建VGG-16模型

image.png
image.png
image.png
VGG-16的网络 有13个卷积层(被5个max-pooling层分割)和3个全连接层(FC),所有卷积层过滤器的大小都是3X3,步长为1,进行padding。5个max-pooling层分别在第2、4、7、10,13卷积层后面。每次进行池化(max-pooling)后,特征图的长宽都缩小一半,但是channel都翻倍了,一直到512。最后三个全连接层大小分别是4096,4096, 1000,我们使用的是咖啡豆识别,根据数据集的类别数量修改最后的分类数量(即从1000改成len(class_names))


-----------------------------
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_
1 (InputLayer) [(None, 224, 224, 3)] 0

block1_conv1 (Conv2D) (None, 224, 224, 64) 1792

block1_conv2 (Conv2D) (None, 224, 224, 64) 36928

block1_pool (MaxPooling2D) (None, 112, 112, 64) 0

block2_conv1 (Conv2D) (None, 112, 112, 128) 73856

block2_conv2 (Conv2D) (None, 112, 112, 128) 147584

block2_pool (MaxPooling2D) (None, 56, 56, 128) 0

block3_conv1 (Conv2D) (None, 56, 56, 256) 295168

block3_conv2 (Conv2D) (None, 56, 56, 256) 590080

block3_conv3 (Conv2D) (None, 56, 56, 256) 590080

block3_pool (MaxPooling2D) (None, 28, 28, 256) 0

block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160

block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808

block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808

block4_pool (MaxPooling2D) (None, 14, 14, 512) 0

block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808

block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808

block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808

block5_pool (MaxPooling2D) (None, 7, 7, 512) 0

flatten (Flatten) (None, 25088) 0

fc1 (Dense) (None, 4096) 102764544

fc2 (Dense) (None, 4096) 16781312

predictions (Dense) (None, 4) 16388

=================================================================
Total params: 134,276,932
Trainable params: 134,276,932
Non-trainable params: 0
_________________________________________________________________

image.png

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