Mxnet基础知识（一）
2021-02-17 22:17
标签：地方 transform 自适应 fir 表示 windows strip() 第一个 lin 1. 基本数据结构 和pytorch等中的tensor类似，mxnet中的ndarray或者nd，用来操作矩阵或者张量数据。基本操作类似于Numpy， 支持计算，索引等。 创建矩阵 操作矩阵 矩阵索引 2. 创建神经网络 mxnet中gluon包中包含神经网络创建中的相关操作，和pytorch类似，可以继承block来创建神经网络，只需定义网络结构和实现前向传播函数。 方式一： 继承nn.Block 方式二：直接利用nn.Sequential 3. 神经网络训练 梯度反向传播，mxnet会自动求导，需要利用mxnet的autograd,如下： 3.1. 加载数据 自己加载数据，主要需要继承mxnet.gluon.data.Dataset，然后传递给mxnet.gluon.data.DataLoader。有几个坑： A. Dataset返回img和label， label不能为字符串格式 B. Dataloader中的num_workers设置大于0时, 对于windows系统，由于采用多进程，需要写在__main__中；若还是报错时，num_workers改为0 3.2 定义网络 见文章上面第二点 3.3 定义损失函数 gluon.loss包含了部分常用的Loss，如下： 3.4 定义优化器 优化器定义在gluon.Trainer() ，第一个参数params为网络参数，第二个参数optimizer为优化器的名字，第三个参数optimizer_params为传给优化器的参数 支持的optimizer如下： 共同支持的optimizer_params如下： （不同优化器还有其特定的参数） 常用优化器使用如下： 3.5 模型训练 4. 网络参数保存和加载 Block 只能保存网络参数，如下： HybridBlock可以向Block一样保存网络参数，也可以同时保存网络结构和网络参数， 如下： 5. 使用GPU 在进行训练和计算时网络参数和数据必须在同一环境下，同在CPU或同在GPU，采用GPU计算矩阵时能加速运算；可以在GPU上操作数据和网络，如下： 数据：可以在GPU上创建数据，也可以在CPU上创建数据，载移动到GPU 网络：可以在GPU上加载网络参数，或者在CPU上加载，随后移动到GPU https://github.com/apache/incubator-mxnet https://zhuanlan.zhihu.com/p/39420301 http://mxnet.incubator.apache.org/ Mxnet基础知识（一） 标签：地方 transform 自适应 fir 表示 windows strip() 第一个 lin 原文地址：https://www.cnblogs.com/silence-cho/p/11999817.htmlfrom mxnet import nd #或者 from mxnet import ndarray as nd
#创建矩阵
x1 = nd.array([[1, 2,], [3, 4]])
x2 = nd.random.uniform(1, 10, shape=(3, 3)) #3*3的矩阵
x3 = nd.random.randn(2,3) #2*3 的矩阵
x4 = nd.random.randint(1, 10, shape=(2, 3)) #2*3 的矩阵
x5 = nd.ones(shape=(2, 2)) #2*2 的矩阵
x6 = nd.full(shape=(2, 3), val=2) #2*3 的矩阵, 值为2
print(x1.shape, x1.size, x1.dtype) #(2, 2) 4 class ‘numpy.float32‘>
x = nd.random.randn(2, 3)
y = nd.random.randn(2, 3)
print(y.exp()) # 2*3 的矩阵
print(x*y) # 2*3 的矩阵
print(nd.dot(x, y.T)) # 2*2 的矩阵
#和numpy相互转换
a = y.asnumpy()
print(a)
a = nd.array(np.ones((2, 3)))
print(a)y = nd.random.randint(1, 10, shape=(3, 3))
print(y[1, 2]) # [5]
print(y[:, 1:3]) # 3*2
y[:,1:3] = 2 #赋值
y[1:2,0:2] = 4 #赋值
print(y)
class MyNet(nn.Block):
def __init__(self):
super(MyNet, self).__init__()
self.features = nn.Sequential()
self.features.add(
nn.Conv2D(channels=16, kernel_size=5, strides=(1, 1),
padding=(0, 0), activation="relu"), #和pytorch不同之处：不需要设置输入通道数，可以设置激活函数
nn.MaxPool2D(pool_size=(2, 2), stides=2, padding=0),
nn.Conv2D(channels=32, kernel_size=3, strides=(1, 1),
padding=(0, 0), activation="relu"),
nn.MaxPool2D(pool_size=(2, 2), stides=2, padding=0),
)
self.fc = nn.Sequential()
self.fc.add(
nn.Dense(units=120, activation="relu"), #和pytorch不同之处：不需要设置输入向量的大小，可以设置激活函数
nn.Dense(units=84, activation="relu"),
nn.Dense(units=10)
)
def forward(self, x):
x = self.features(x)
x = self.fc(x)
return x
net = MyNet()
net.initialize() # 网络内部的参数必须先进行初始化 (pytorch中需要逐层进行初始化)
x = nd.random.uniform(shape=(1, 3, 300, 300))
print(net(x))net = nn.Sequential()
net.add(
nn.Conv2D(channels=16, kernel_size=5, strides=(1, 1),
padding=(0, 0), activation="relu"), # 和pytorch不同之处：不需要设置输入通道数，可以设置激活函数
nn.MaxPool2D(pool_size=(2, 2), strides=2, padding=0),
nn.Conv2D(channels=32, kernel_size=3, strides=(1, 1),
padding=(0, 0), activation="relu"),
nn.MaxPool2D(pool_size=(2, 2), strides=2, padding=0),
nn.Dense(units=120, activation="relu"), # 和pytorch不同之处：不需要设置输入向量的大小，可以设置激活函数
nn.Dense(units=84, activation="relu"),
nn.Dense(units=10)
)
net.initialize() # 网络内部的参数必须先进行初始化 (pytorch中需要逐层进行初始化)
x = nd.random.uniform(shape=(1, 3, 300, 300))
print(net(x))
from mxnet import nd
from mxnet import autograd
x = nd.array([[1, 2], [3, 4]])
x.attach_grad() #1. 声明存储导数的地方
with autograd.record(): #2. 该上下文中的过程，反向传播时会自动求导
y = 2*x*x
y.backward() #3. 反向传播; 会自动求和再计算导数，相当于y.sum().backward()
print(x.grad) #4. 取导数值
#coding:utf-8
import mxnet as mx
from mxnet import gluon
from mxnet.gluon.data import Dataset, DataLoader
from mxnet.gluon.data.vision import transforms
import os
import cv2
#1.继承mxnet.gluon.data.Dataset, 实现__len__和__getitem__（返回每张图片和标注）
class MyDataset(Dataset):
def __init__(self, img_root, anno_file):
assert os.path.exists(anno_file), print("Annotation file {} not exist".format(anno_file))
self.img_root = img_root
self.anno_file = anno_file
with open(anno_file, "r", encoding="utf-8") as f:
lines = f.readlines()
self.items = [line.strip().split() for line in lines if line.strip()]
def __len__(self):
return len(self.items)
def __getitem__(self, x):
img_name, label = self.items[x]
img_path = os.path.join(self.img_root, img_name)
assert os.path.exists(img_path), print("img_file {} does not exist".format(img_path))
img = mx.image.imread(img_path)
return img, label #注意此处label为字符串会报错
if __name__ == "__main__":
#2. 将dataset传入mxnet.gluon.data.Dataloader
img_root = r"D:\data\synthtext"
anno_file = r"D:\data\synthtext\labels.txt"
dataset = MyDataset(img_root, anno_file)
transformer = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
]) # dataset.transform_first(transformer), 对图片进行增强(即对__getitem__返回的第一项进行处理)
train_data = DataLoader(dataset.transform_first(transformer), batch_size=2, shuffle=True, num_workers=0)
print(train_data)
for img, label in train_data:
print(label)
print(img.shape)
loss = gluon.loss.SoftmaxCrossEntropyLoss() #交叉熵损失函数
loss = gluon.loss.L2Loss() #均方差损失函数
loss = gluon.loss.CTCLoss() # CTC损失函数
loss = gluon.loss.L1Loss() # L1 损失函数
#位找到smoothL1,发现两个相关的 mx.nd.smooth_l1()； mx.metric.Loss("SmoothL1")
_all__ = [‘Loss‘, ‘L2Loss‘, ‘L1Loss‘,
‘SigmoidBinaryCrossEntropyLoss‘, ‘SigmoidBCELoss‘,
‘SoftmaxCrossEntropyLoss‘, ‘SoftmaxCELoss‘,
‘KLDivLoss‘, ‘CTCLoss‘, ‘HuberLoss‘, ‘HingeLoss‘,
‘SquaredHingeLoss‘, ‘LogisticLoss‘, ‘TripletLoss‘, ‘PoissonNLLLoss‘, ‘CosineEmbeddingLoss‘] __all__ = [
‘AdaDelta‘, ‘AdaGrad‘, ‘Adam‘, ‘Adamax‘, ‘DCASGD‘, ‘FTML‘, ‘Ftrl‘, ‘LBSGD‘,
‘NAG‘, ‘NDabs‘, ‘Nadam‘, ‘Optimizer‘, ‘RMSProp‘, ‘SGD‘, ‘SGLD‘, ‘Signum‘,
‘Test‘, ‘ccSGD‘,
]
Parameters
rescale_grad (float, optional, default 1.0) – Multiply the gradient with rescale_grad before updating. Often choose to be 1.0/batch_size.
param_idx2name (dict from int to string, optional, default None) – A dictionary that maps int index to string name.
clip_gradient (float, optional, default None) – Clip the gradient by projecting onto the box [-clip_gradient, clip_gradient].
learning_rate (float) – The initial learning rate. If None, the optimization will use the learning rate from lr_scheduler. If not None, it will overwrite the learning rate in lr_scheduler. If None and lr_scheduler is also None, then it will be set to 0.01 by default.
lr_scheduler (LRScheduler, optional, default None) – The learning rate scheduler.
wd (float, optional, default 0.0) – The weight decay (or L2 regularization) coefficient. Modifies objective by adding a penalty for having large weights.
sym (Symbol, optional, default None) – The Symbol this optimizer is applying to.
begin_num_update (int, optional, default 0) – The initial number of updates.
multi_precision (bool, optional, default False) – Flag to control the internal precision of the optimizer. False: results in using the same precision as the weights (default), True: makes internal 32-bit copy of the weights and applies gradients in 32-bit precision even if actual weights used in the model have lower precision. Turning this on can improve convergence and accuracy when training with float16.
param_dict (dict of int -> gluon.Parameter, default None) – Dictionary of parameter index to gluon.Parameter, used to lookup parameter attributes such as lr_mult, wd_mult, etc. param_dict shall not be deep copied.
aggregate_num (int, optional, default None) – Number of weights to be aggregated in a list. They are passed to the optimizer for a single optimization step. In default, only one weight is aggregated. When aggregate_num is set to numpy.inf, all the weights are aggregated.
use_fused_step (bool, optional, default None) – Whether or not to use fused kernels for optimizer. When use_fused_step=False, step is called, otherwise, fused_step is called.
Properties –
---------- –
learning_rate – The current learning rate of the optimizer. Given an Optimizer object optimizer, its learning rate can be accessed as optimizer.learning_rate.
#优化器
#1.动量法
gluon.Trainer(params=net.collect_params(), optimizer="SGD",
optimizer_params={"learning_rate":0.001, "wd":0.00005, "momentum":0.9})
#2. 自适应
#AdaGrad
gluon.Trainer(params=net.collect_params(), optimizer="AdaGrad",
optimizer_params={"learning_rate":0.001, "wd":0.00005,})
#RMSProp
gluon.Trainer(params=net.collect_params(), optimizer="RMSProp",
optimizer_params={"learning_rate": 0.001, "wd": 0.00005, "momentum":0.9})
#Adam
gluon.Trainer(params=net.collect_params(), optimizer="RMSProp",
optimizer_params={"learning_rate": 0.001, "wd": 0.00005})
for epoch in range(10):
train_loss, train_acc, valid_acc = 0., 0., 0.
tic = time.time()
for data, label in train_data:
# forward + backward
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
# update parameters
trainer.step(batch_size)
# calculate training metrics
train_loss += loss.mean().asscalar()
train_acc += acc(output, label)
# calculate validation accuracy
for data, label in valid_data:
valid_acc += acc(net(data), label)
print("Epoch %d: loss %.3f, train acc %.3f, test acc %.3f, in %.1f sec" % (
epoch, train_loss / len(train_data), train_acc / len(train_data),
valid_acc / len(valid_data), time.time() - tic))
net = nn.Sequential()
net.add(
nn.Conv2D(channels=16, kernel_size=5, strides=(1, 1),
padding=(0, 0), activation="relu"), # 和pytorch不同之处：不需要设置输入通道数，可以设置激活函数
nn.MaxPool2D(pool_size=(2, 2), strides=2, padding=0),
nn.Conv2D(channels=32, kernel_size=3, strides=(1, 1),
padding=(0, 0), activation="relu"),
nn.MaxPool2D(pool_size=(2, 2), strides=2, padding=0),
nn.Dense(units=120, activation="relu"), # 和pytorch不同之处：不需要设置输入向量的大小，可以设置激活函数
nn.Dense(units=84, activation="relu"),
nn.Dense(units=10)
) #1 保存网络权重参数
net.save_parameters("checkpoint.params")
#2 加载权重参数
net.load_parameters("checkpoint.params", ctx=None, allow_missing=False,
ignore_extra=False, cast_dtype=False, dtype_source=‘current‘)
ctx: 默认为Cpu
allow_missing: True时表示：网络结构中存在, 参数文件中不存在参数，不加载
ignore_extra: True时表示: 参数文件中存在，网络结构中不存在的参数，不加载
net = nn.HybridSequential()
net.add(
nn.Conv2D(channels=16, kernel_size=5, strides=(1, 1),
padding=(0, 0), activation="relu"), # 和pytorch不同之处：不需要设置输入通道数，可以设置激活函数
nn.MaxPool2D(pool_size=(2, 2), strides=2, padding=0),
nn.Conv2D(channels=32, kernel_size=3, strides=(1, 1),
padding=(0, 0), activation="relu"),
nn.MaxPool2D(pool_size=(2, 2), strides=2, padding=0),
nn.Dense(units=120, activation="relu"), # 和pytorch不同之处：不需要设置输入向量的大小，可以设置激活函数
nn.Dense(units=84, activation="relu"),
nn.Dense(units=10)
)
# 1.对于HybridBlock, 可以同时保存网络结构和权重参数
#首先要进行hybridize()和一次前向传播，才能进行export
net.initialize()
net.hybridize()
x = mx.nd.zeros((1, 3, 100, 100))
print(net(x))
net.export(path="./checkpoint", epoch=1) #同时生成checkpoint-0001.params 和 checkpoint-symbol.json
# # net.save_parameters("./checkpoint.params")
#2. 加载export的网络结构(json)和权重参数(params)
#或者mx.SymbolBlock.imports()
net = gluon.SymbolBlock.imports(symbol_file="./checkpoint-symbol.json",
input_names=["data"],
param_file="./checkpoint-0100.params",
ctx=mx.cpu())
net.hybridize()
x = mx.nd.zeros((1, 3, 100, 100))
print(net(x))
# net = mx.mod.Module.load(prefix="./checkpoint", epoch=100)#1. 在GPU上创建数据，或者将数据从cpu移动到GPU
#GPU创建
x = mx.nd.zeros((1, 3, 100, 100), ctx=mx.gpu(0))
print(x)
#cpu创建，复制一份到GPU
x = mx.nd.zeros((1, 3, 100, 100))
x = x.copyto(mx.gpu(0))
print(x)
# cpu创建，复制一份移动到GPU
x = mx.nd.zeros((1, 3, 100, 100))
x = x.as_in_context(mx.gpu(0))
print(x)
#2.在GPU上加载网络参数，或者将网络参数移动到GPU
net = nn.Sequential()
net.add(
nn.Conv2D(channels=16, kernel_size=3, strides=1, padding=1),
nn.Dense(18)
)
#GPU上初始化参数
net.initialize(init=mx.init.Xavier(), ctx=mx.gpu(0))
net.load_parameters("./checkpoint.params", ctx=mx.gpu(0))
# #CPU上初始化参数，移动到GPU
net.initialize(init=mx.init.Xavier())
net.collect_params().reset_ctx(mx.gpu())
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