2021-04-25
2021-04-25
学习报告
1、PyQt5图形和特效改进
font = QtGui.QFont()
font.setFamily("Arial") #括号里可以设置成自己想要的其它字体
font.setPointSize(18)
label_4.setFont(font)
button.setFont(font)
window.setWindowIcon(QIcon('12.png'))#图标
window.setStyleSheet("#MainWindow{border-image:url(09.jpg);}") #背景图
window.setWindowOpacity(0.9) #背景透明度
window.setStyleSheet("#MainWindow{background-color: yellow}")#背景颜色
详细参考
https://blog.csdn.net/xqlily/article/details/97467606?utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-5.control&dist_request_id=&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-5.control
https://blog.csdn.net/jia666666/article/details/81874045
https://blog.csdn.net/weixin_43283397/article/details/105995328?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522161892346616780261983965%2522%252C%2522scm%2522%253A%252220140713.130102334…%2522%257D&request_id=161892346616780261983965&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2allfirst_rank_v2~rank_v29-16-105995328.pc_search_result_hbase_insert&utm_term=pyqt%E8%AE%BE%E7%BD%AE%E5%AD%97%E4%BD%93%E5%A4%A7%E5%B0%8F
2、手写数字识别实战（层）
导入数据集，构造数据集（训练集和测试集），迭代器， Sequential，数据集的迭代 梯度计算·，测试
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
def preprocess(x, y):
#转换为tensor
# x = tf.convert_to_tensor(x, dtype = tf.float32) /255.
# y = tf.convert_to_tensor(y, dtype = tf.int32)
x = tf.cast(x, dtype = tf.float32) /255. #tf.cast()数据类型转换
y = tf.cast(y, dtype = tf.int32)
return x,y
(x, y), (x_test, y_test) = datasets.fashion_mnist.load_data()#导入数据集 train和test
print(x.shape, y.shape)
# print(x_test.shape)
batchsz = 128
db = tf.data.Dataset.from_tensor_slices((x,y))#构造数据集 使数据集能被我们用于生成迭代数据进行训练
db = db.map(preprocess).shuffle(10000).batch(batchsz)#预处理
db_test = tf.data.Dataset.from_tensor_slices((x_test,y_test))#构造数据集
db_test = db_test.map(preprocess).batch(batchsz)#预处理 一次测试完 不用shuffle
db_iter = iter(db)
sample = next(db_iter)
print('batch:',sample[0].shape, sample[1].shape)
model = Sequential([
layers.Dense(256, activation=tf.nn.relu), #[b, 784] =>[b, 256]
layers.Dense(128, activation=tf.nn.relu), # [b, 256] =>[b, 126]
layers.Dense(64, activation=tf.nn.relu), # [b, 128] =>[b, 64]
layers.Dense(32, activation=tf.nn.relu), # [b, 64] =>[b, 32]
layers.Dense(10) # [b, 32] =>[b, 10] 330 = 32*10 + 10
])
model.build(input_shape=[None, 28*28])
model.summary()#调试打印出网络
optimizers = optimizers.Adam(lr=1e-3) #优化器 w = w - lr*grad
def main():
for epoch in range(30):
for step, (x,y) in enumerate(db): #每次epoch完成一次数据集的迭代
# x: [b, 28, 28] => [b, 784]
# y: [b]
x = tf.reshape(x, [-1, 28*28])
with tf.GradientTape() as tape:#求梯度信息
# [b, 784] = > [b, 10]
logits = model(x) #完成前向传播
y_onehot = tf.one_hot(y, depth=10)
loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot, logits)) # 均方差计算
loss_ce = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)
loss_ce = tf.reduce_mean(loss_ce)
grads = tape.gradient(loss_ce, model.trainable_variables) #完成梯度计算 w1 w2 w3...
optimizers.apply_gradients(zip(grads, model.trainable_variables))#完成更新
#打印
if step % 100 == 0:
print(epoch, step, 'loss:', float(loss_ce), float(loss_mse))
# test
total_correct = 0
total_num = 0
for x,y in db_test:
# x: [b, 28, 28] => [b, 784]
# y: [b]
x = tf.reshape(x, [-1, 28 * 28])
#测试时不需要求梯度
# [b, 784] = > [b, 10]#
logits = model(x) # 完成前向传播
# logits => prob
prob = tf.nn.softmax(logits, axis=1)#转换为0——1范围
# [b, 10] => [10]
pred = tf.argmax(prob, axis=1)#概率所在值得位置
pred = tf.cast(pred, dtype=tf.int32)
# pred:[b]
# y: [b]
# correct = [b], True: equal, False: not equal
correct = tf.equal(pred, y) #equal=1, not equal=0
correct = tf.reduce_sum(tf.cast(correct, dtype=tf.int32))
total_correct += int(correct) #tensor转换为numpy
total_num += x.shape[0]
acc = total_correct / total_num
print(epoch, 'test acc:', acc)
if __name__== '__main__':
main()
3、CIFAR与VGG实战（卷积神经网络）
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
from tensorflow.keras import layers, optimizers, datasets, Sequential
tf.random.set_seed(2345)
conv_layers = [ # 5 units of conv + max pooling
# unit 1
layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(64, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
# unit 2
layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(128, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
# unit 3
layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(256, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
# unit 4
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
# unit 5
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.Conv2D(512, kernel_size=[3, 3], padding="same", activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same')
]
def preprocess(x, y):
# [0~1]
x = tf.cast(x, dtype=tf.float32) / 255.
y = tf.cast(y, dtype=tf.int32)
return x,y
(x,y), (x_test, y_test) = datasets.cifar100.load_data()
y = tf.squeeze(y, axis=1)
y_test = tf.squeeze(y_test, axis=1)
print(x.shape, y.shape, x_test.shape, y_test.shape)
train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.shuffle(1000).map(preprocess).batch(128)
test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_db = test_db.map(preprocess).batch(64)
sample = next(iter(train_db))
print('sample:', sample[0].shape, sample[1].shape,
tf.reduce_min(sample[0]), tf.reduce_max(sample[0]))
def main():
#新建网络
# [b, 32, 32, 3] => [b, 1, 1, 512]
conv_net = Sequential(conv_layers)
#conv_net.build(input_shape=[None,32, 32, 3])
#x = tf.random.normall([4, 32, 32, 3])
#out = conv_net(x)
#print(out.shape)
#全连接层网络
fc_net = Sequential([
layers.Dense(256, activation=tf.nn.relu),
layers.Dense(128, activation=tf.nn.relu),
layers.Dense(100, activation=None),
])
conv_net.build(input_shape=[None, 32, 32, 3])
fc_net.build(input_shape=[None, 512])
optimizer = optimizers.Adam(lr=1e-4)
# [1, 2] + [3, 4] => [1, 2, 3, 4]
variables = conv_net.trainable_variables + fc_net.trainable_variables
for epoch in range(50):
for step, (x,y) in enumerate(train_db):
with tf.GradientTape() as tape:
# [b, 32, 32, 3] => [b, 1, 1, 512]
out = conv_net(x)
# flatten, => [b, 512]
out = tf.reshape(out, [-1, 512])
# [b, 512] => [b, 100]
logits = fc_net(out)
# [b] => [b, 100]
y_onehot = tf.one_hot(y, depth=100)
# compute loss
loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits=True)
loss = tf.reduce_mean(loss)
grads = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(grads, variables))
if step %100 == 0:
print(epoch, step, 'loss:', float(loss))
total_num = 0
total_correct = 0
for x,y in test_db:
out = conv_net(x)
out = tf.reshape(out, [-1, 512])
logits = fc_net(out)
prob = tf.nn.softmax(logits, axis=1)
pred = tf.argmax(prob, axis=1)
pred = tf.cast(pred, dtype=tf.int32)
correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
correct = tf.reduce_sum(correct)
total_num += x.shape[0]
total_correct += int(correct)
acc = total_correct / total_num
print(epoch, 'acc:', acc)
if __name__ == '__main__':
main()
其他：
OpenCV之处理图像前的基本准备工作
https://blog.csdn.net/hfut_why/article/details/84673348?ops_request_misc=&request_id=&biz_id=102&utm_term=%E5%9B%BE%E5%83%8F%E5%89%8D%E5%A4%84%E7%90%86&utm_medium=distribute.pc_search_result.none-task-blog-2allsobaiduweb~default-4-84673348.pc_search_result_hbase_insert
图像处理：
1、cv2.imread()接口读图像，读进来直接是BGR 格式数据格式在 0~255
需要特别注意的是图片读出来的格式是BGR，不是我们最常见的RGB格式，颜色肯定有区别。
2、cv2.cvtColor(p1,p2) 是颜色空间转换函数，p1是需要转换的图片，p2是转换成何种格式。
cv2.COLOR_BGR2RGB 将BGR格式转换成RGB格式
cv2.COLOR_BGR2GRAY 将BGR格式转换成灰度图片
详细参考：
https://blog.csdn.net/zhang_cherry/article/details/88951259?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522161892910116780261986261%2522%252C%2522scm%2522%253A%252220140713.130102334…%2522%257D&request_id=161892910116780261986261&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2allfirst_rank_v2~rank_v29-1-88951259.pc_search_result_hbase_insert&utm_term=cv2.cvtColor
图像预处理实例：
def preprocess(image_dir):
image = cv2.imread(image_dir)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
print("pri_image shape: ", image.shape)
image = cv2.resize(image, (320, 320))
# 归一化处理
image = image / np.max(image)
image[:, :, 0] = (image[:, :, 0] - np.mean(image[:, :, 0])) / np.mean(image[:, :, 0])
image[:, :, 1] = (image[:, :, 1] - np.mean(image[:, :, 1])) / np.mean(image[:, :, 1])
image[:, :, 2] = (image[:, :, 2] - np.mean(image[:, :, 2])) / np.mean(image[:, :, 2])
image = np.transpose(image, (2, 0, 1))
image = np.expand_dims(image, axis=0) # 在零维度加1
print("image_shape has been resized to : ", image.shape)
image = torch.from_numpy(image) # 转成tensor
return image
OpenVino推理框架
ie = IECore()
xml_dir = os.path.join(os.getcwd(), 'saved_models', model_name + '.xml')
bin_dir = os.path.join(os.getcwd(), 'saved_models', model_name + '.bin')
net = ie.read_network(xml_dir, bin_dir)
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))