Mxnet速查_CPU和GPU的mnist预测训练_模型导出_模型导入再预测_导出onnx并预测

需要做点什么

方便广大烟酒生研究生、人工智障炼丹师算法工程师快速使用mxnet，所以特写此文章，默认使用者已有基本的深度学习概念、数据集概念。

系统环境

python 3.7.4
mxnet 1.9.0
mxnet-cu112 1.9.0
onnx 1.9.0
onnxruntime-gpu 1.9.0

数据准备

MNIST数据集csv文件是一个42000x785的矩阵
42000表示有42000张图片
785中第一列是图片的类别(0,1,2,..,9),第二列到最后一列是图片数据向量 (28x28的图片张成784的向量)， 数据集长这个样子:

1 0 0 0 0 0 0 0 0 0 ..
0 0 0 0 0 0 0 0 0 0 ..
1 0 0 0 0 0 0 0 0 0 ..

1. 导入需要的包

import time
import copy
import onnx
import logging
import platform
import mxnet as mx
import numpy as np
import pandas as pd
import onnxruntime as ort
from sklearn.metrics import accuracy_score

logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

# Mxnet Chcek
if platform.system().lower() != 'windows':
    print(mx.runtime.feature_list())
print(mx.context.num_gpus())
a = mx.nd.ones((2, 3), mx.cpu())
b = a * 2 + 1
print(b)

运行输出

[✔ CUDA, ✔ CUDNN, ✔ NCCL, ✔ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✖ CPU_SSE4_1, ✖ CPU_SSE4_2, ✖ CPU_SSE4A, ✖ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✖ F16C, ✖ JEMALLOC, ✔ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✔ MKLDNN, ✔ OPENCV, ✖ CAFFE, ✖ PROFILER, ✔ DIST_KVSTORE, ✖ CXX14, ✖ INT64_TENSOR_SIZE, ✔ SIGNAL_HANDLER, ✖ DEBUG, ✖ TVM_OP]
1

[[3. 3. 3.]
 [3. 3. 3.]]
<NDArray 2x3 @cpu(0)>

2. 参数准备

N_EPOCH = 1
N_BATCH = 32
N_BATCH_NUM = 900
S_DATA_PATH = r"mnist_train.csv"
S_MODEL_PATH = r"mxnet_cnn"
S_SYM_PATH = './mxnet_cnn-symbol.json'
S_PARAMS_PATH = './mxnet_cnn-0001.params'
S_ONNX_MODEL_PATH = './mxnet_cnn.onnx'
S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cuda", 0, "cuda:0"
# S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cpu", 0, "cpu"
CTX = mx.cpu() if S_DEVICE == "cpu" else mx.gpu(N_DEVICE_ID)
B_IS_UNIX = True

3. 读取数据

df = pd.read_csv(S_DATA_PATH, header=None)
print(df.shape)
np_mat = np.array(df)
print(np_mat.shape)
X = np_mat[:, 1:]
Y = np_mat[:, 0]
X = X.astype(np.float32) / 255
X_train = X[:N_BATCH * N_BATCH_NUM]
X_test = X[N_BATCH * N_BATCH_NUM:]
Y_train = Y[:N_BATCH * N_BATCH_NUM]
Y_test = Y[N_BATCH * N_BATCH_NUM:]
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28) 
print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)
train_iter = mx.io.NDArrayIter(X_train, Y_train, batch_size=N_BATCH)
test_iter = mx.io.NDArrayIter(X_test, Y_test, batch_size=N_BATCH)
test_iter_2 = copy.copy(test_iter)

运行输出

(37800, 785)
(37800, 785)
(28800, 1, 28, 28)
(28800,)
(9000, 1, 28, 28)
(9000,)

4. 模型构建

net = mx.gluon.nn.HybridSequential()
with net.name_scope():
    net.add(mx.gluon.nn.Conv2D(channels=32, kernel_size=3, activation='relu'))  # bx28x28 ==>
    net.add(mx.gluon.nn.MaxPool2D(pool_size=2, strides=2))
    net.add(mx.gluon.nn.Flatten())
    net.add(mx.gluon.nn.Dense(128, activation="relu"))
    net.add(mx.gluon.nn.Dense(10))
net.hybridize()
print(net)
net.collect_params().initialize(mx.init.Xavier(), ctx=CTX)
softmax_cross_entropy = mx.gluon.loss.SoftmaxCrossEntropyLoss()
trainer = mx.gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .001})

运行输出

HybridSequential(
  (0): Conv2D(None -> 32, kernel_size=(3, 3), stride=(1, 1), Activation(relu))
  (1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
  (2): Flatten
  (3): Dense(None -> 128, Activation(relu))
  (4): Dense(None -> 10, linear)
)

5. 模型训练

for epoch in range(N_EPOCH):
    for batch_num, itr in enumerate(train_iter):
        data = itr.data[0].as_in_context(CTX)
        label = itr.label[0].as_in_context(CTX)
        with mx.autograd.record():
            output = net(data)  # Run the forward pass
            loss = softmax_cross_entropy(output, label)  # Compute the loss
        loss.backward()
        trainer.step(data.shape[0])
        if batch_num % 50 == 0:  # Print loss once in a while
            curr_loss = mx.nd.mean(loss)  # .asscalar()
            pred = mx.nd.argmax(output, axis=1)
            np_pred, np_lable = pred.asnumpy(), label.asnumpy()
            f_acc = accuracy_score(np_lable, np_pred)
            print(f"Epoch: {epoch}; Batch {batch_num}; ACC {f_acc}")
            print(f"loss: {curr_loss}")
            print()
            # print("Epoch: %d; Batch %d; Loss %s; ACC %f" %
            #       (epoch, batch_num, str(curr_loss), f_acc))
    print()

运行输出

Epoch: 0; Batch 0; ACC 0.09375
loss: 
[2.2868602]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 50; ACC 0.875
loss: 
[0.512461]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 100; ACC 0.90625
loss: 
[0.43415746]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 150; ACC 0.84375
loss: 
[0.3854709]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 200; ACC 1.0
loss: 
[0.04192135]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 250; ACC 0.90625
loss: 
[0.21156572]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 300; ACC 0.9375
loss: 
[0.15938525]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 350; ACC 1.0
loss: 
[0.0379494]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 400; ACC 0.96875
loss: 
[0.17104594]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 450; ACC 0.96875
loss: 
[0.12192786]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 500; ACC 0.96875
loss: 
[0.09210478]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 550; ACC 0.9375
loss: 
[0.13728428]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 600; ACC 0.96875
loss: 
[0.0762211]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 650; ACC 0.96875
loss: 
[0.12162409]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 700; ACC 1.0
loss: 
[0.04334489]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 750; ACC 1.0
loss: 
[0.06458903]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 800; ACC 0.96875
loss: 
[0.07410634]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 850; ACC 0.96875
loss: 
[0.14233188]
<NDArray 1 @gpu(0)>

6.模型预测

for batch_num, itr in enumerate(test_iter_2):
    data = itr.data[0].as_in_context(CTX)
    label = itr.label[0].as_in_context(CTX)

    output = net(data)  # Run the forward pass
    loss = softmax_cross_entropy(output, label)  # Compute the loss

    if batch_num % 50 == 0:  # Print loss once in a while
        curr_loss = mx.nd.mean(loss)  # .asscalar()
        pred = mx.nd.argmax(output, axis=1)
        np_pred, np_lable = pred.asnumpy(), label.asnumpy()
        f_acc = accuracy_score(np_lable, np_pred)
        print(f"Epoch: {epoch}; Batch {batch_num}; ACC {f_acc}")
        print(f"loss: {curr_loss}")
        print()

运行输出

Epoch: 0; Batch 0; ACC 0.96875
loss: 
[0.22968824]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 50; ACC 0.96875
loss: 
[0.05668993]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 100; ACC 0.96875
loss: 
[0.08171713]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 150; ACC 1.0
loss: 
[0.02264522]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 200; ACC 0.96875
loss: 
[0.080383]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 250; ACC 1.0
loss: 
[0.03774196]
<NDArray 1 @gpu(0)>

7.模型保存

net.export(S_MODEL_PATH, epoch=N_EPOCH)  # 保存模型结构和全部参数

8.模型加载和加载模型使用

print("load net and do test")
load_net = mx.gluon.nn.SymbolBlock.imports(S_SYM_PATH, ['data'], S_PARAMS_PATH, ctx=CTX)  # 加载模型
print("load ok")
for batch_num, itr in enumerate(test_iter):  # Test
    data = itr.data[0].as_in_context(CTX)
    label = itr.label[0].as_in_context(CTX)

    output = load_net(data)  # Run the forward pass
    loss = softmax_cross_entropy(output, label)  # Compute the loss

    if batch_num % 50 == 0:  # Print loss once in a while
        curr_loss = mx.nd.mean(loss)  # .asscalar()
        pred = mx.nd.argmax(output, axis=1)
        np_pred, np_lable = pred.asnumpy(), label.asnumpy()
        f_acc = accuracy_score(np_lable, np_pred)
        print(f"Epoch: {epoch}; Batch {batch_num}; ACC {f_acc}")
        print(f"loss: {curr_loss}")
        print()
print("finish")

运行输出

load net and do test
load ok
Epoch: 0; Batch 0; ACC 0.96875
loss: 
[0.22968824]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 50; ACC 0.96875
loss: 
[0.05668993]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 100; ACC 0.96875
loss: 
[0.08171713]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 150; ACC 1.0
loss: 
[0.02264522]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 200; ACC 0.96875
loss: 
[0.080383]
<NDArray 1 @gpu(0)>

Epoch: 0; Batch 250; ACC 1.0
loss: 
[0.03774196]
<NDArray 1 @gpu(0)>

finish

9.导出ONNX

if platform.system().lower() != 'windows':
    mx.onnx.export_model(S_SYM_PATH, S_PARAMS_PATH, [(32, 1, 28, 28)], [np.float32], S_ONNX_MODEL_PATH, verbose=True, dynamic=True)

运行输出

INFO:root:Converting json and weight file to sym and params
INFO:root:Converting idx: 0, op: null, name: data
INFO:root:Converting idx: 1, op: null, name: hybridsequential0_conv0_weight
INFO:root:Converting idx: 2, op: null, name: hybridsequential0_conv0_bias
INFO:root:Converting idx: 3, op: Convolution, name: hybridsequential0_conv0_fwd
INFO:root:Converting idx: 4, op: Activation, name: hybridsequential0_conv0_relu_fwd
INFO:root:Converting idx: 5, op: Pooling, name: hybridsequential0_pool0_fwd
INFO:root:Converting idx: 6, op: Flatten, name: hybridsequential0_flatten0_flatten0
INFO:root:Converting idx: 7, op: null, name: hybridsequential0_dense0_weight
INFO:root:Converting idx: 8, op: null, name: hybridsequential0_dense0_bias
INFO:root:Converting idx: 9, op: FullyConnected, name: hybridsequential0_dense0_fwd
INFO:root:Converting idx: 10, op: Activation, name: hybridsequential0_dense0_relu_fwd
INFO:root:Converting idx: 11, op: null, name: hybridsequential0_dense1_weight
INFO:root:Converting idx: 12, op: null, name: hybridsequential0_dense1_bias
INFO:root:Converting idx: 13, op: FullyConnected, name: hybridsequential0_dense1_fwd
INFO:root:Output node is: hybridsequential0_dense1_fwd
INFO:root:Input shape of the model [(32, 1, 28, 28)] 
INFO:root:Exported ONNX file ./mxnet_cnn.onnx saved to disk

10. 加载ONNX并运行

if platform.system().lower() != 'windows':
    model = onnx.load(S_ONNX_MODEL_PATH)
    print(onnx.checker.check_model(model))  # Check that the model is well formed
    # print(onnx.helper.printable_graph(model.graph))  # Print a human readable representation of the graph
    ls_input_name, ls_output_name = [input.name for input in model.graph.input], [output.name for output in model.graph.output]
    print("input name ", ls_input_name)
    print("output name ", ls_output_name)
    s_input_name = ls_input_name[0]

    x_input = X_train[:N_BATCH*2, :, :, :].astype(np.float32)
    ort_val = ort.OrtValue.ortvalue_from_numpy(x_input, S_DEVICE, N_DEVICE_ID)
    print("val device ", ort_val.device_name())
    print("val shape ", ort_val.shape())
    print("val data type ", ort_val.data_type())
    print("is_tensor ", ort_val.is_tensor())
    print("array_equal ", np.array_equal(ort_val.numpy(), x_input))
    providers = 'CUDAExecutionProvider' if S_DEVICE == "cuda" else 'CPUExecutionProvider'
    print("providers ", providers)
    ort_session = ort.InferenceSession(S_ONNX_MODEL_PATH, providers=[providers])  # gpu运行
    ort_session.set_providers([providers])
    outputs = ort_session.run(None, {s_input_name: ort_val})
    print("sess env ", ort_session.get_providers())
    print(type(outputs))
    print(outputs[0])

    '''
    For example ['CUDAExecutionProvider', 'CPUExecutionProvider']
        means execute a node using CUDAExecutionProvider if capable, otherwise execute using CPUExecutionProvider.
    '''

运行输出

None
input name  ['data', 'hybridsequential0_conv0_weight', 'hybridsequential0_conv0_bias', 'hybridsequential0_dense0_weight', 'hybridsequential0_dense0_bias', 'hybridsequential0_dense1_weight', 'hybridsequential0_dense1_bias']
output name  ['hybridsequential0_dense1_fwd']
val device  cuda
val shape  [64, 1, 28, 28]
val data type  tensor(float)
is_tensor  True
array_equal  True
providers  CUDAExecutionProvider
sess env  ['CUDAExecutionProvider', 'CPUExecutionProvider']
<class 'list'>
[[-2.69336128e+00  8.42524242e+00 -3.34120363e-01 -1.17912292e+00
   3.82278800e-01 -3.60794234e+00  3.58125120e-01 -2.58064723e+00
   1.55215383e+00 -2.03553891e+00]
 [ 1.02665892e+01 -6.65782404e+00 -2.04501271e-01 -2.25653172e+00
  -6.31941366e+00  1.13084137e+00 -3.83885235e-01  8.22283030e-01
  -1.21192622e+00  3.33601260e+00]
 [-3.27186418e+00  1.00050325e+01  5.39114550e-02 -1.44938648e+00
  -9.89762247e-01 -2.09957671e+00 -1.49389958e+00  6.52510405e-01
   1.73153889e+00 -1.25597775e+00]
 [ 5.72116375e-01 -3.36192799e+00 -6.68362260e-01 -2.81247520e+00
   8.36382389e+00 -3.67477946e-02  2.23792076e+00 -2.91093756e-02
  -4.56922323e-01 -6.77382052e-01]
 [ 1.18602552e+01 -5.09683752e+00  4.54203248e-01 -2.55723000e+00
  -8.68753910e+00  6.96948707e-01 -1.50591761e-01 -3.62227589e-01
   9.83437955e-01  7.46711075e-01]
 [ 7.33289337e+00 -6.65414715e+00  1.57180679e+00 -2.62657452e+00
   4.11511570e-01 -1.35336161e+00 -1.40558392e-01  3.81030589e-01
   1.73799121e+00  8.02671254e-01]
 [-3.02898431e+00  1.26861107e+00 -2.04946566e+00 -2.52499342e-01
  -2.73597687e-01 -3.01714039e+00 -7.10914516e+00  1.10452967e+01
  -5.82177579e-01  1.86712158e+00]
 [-7.78098392e+00 -6.01984358e+00  1.23355007e+00  1.18682652e+01
  -9.83472538e+00  8.27242088e+00 -1.02135544e+01  3.95661980e-01
   6.63226461e+00  3.33681512e+00]
 [-2.72245955e+00 -6.74849796e+00 -6.24665642e+00  3.11165476e+00
  -4.71174330e-01  1.22390661e+01 -1.23519528e+00 -1.24356663e+00
   1.26693976e+00  5.81862879e+00]
 [-5.65229607e+00 -1.25138938e+00  3.68380380e+00  1.24947300e+01
  -8.21508980e+00  1.61641145e+00 -8.01925087e+00  8.37018967e-01
  -2.64613247e+00  7.92313635e-01]
 [-3.73405719e+00 -3.41621947e+00 -7.94842839e-01  4.55352879e+00
  -2.28238964e+00  1.88887548e+00 -5.84129477e+00  6.03430390e-01
   1.05920439e+01  2.25430655e+00]
 [-5.44103146e+00 -5.48421431e+00 -3.62234282e+00  1.20194650e+00
   3.48899674e+00  1.50794566e+00 -6.30612850e+00  4.01568127e+00
   1.61318648e+00  9.87832165e+00]
 [-3.34073186e+00  8.10987663e+00 -6.43497527e-01 -1.64372277e+00
  -4.42907363e-01 -1.46176386e+00 -8.56327295e-01  5.20323329e-02
   1.73289025e+00 -8.17061365e-01]
 [-6.88457203e+00  1.38391244e+00  1.33096969e+00  1.28132534e+01
  -6.20939922e+00  1.48244214e+00 -6.59804583e+00 -1.38118923e+00
   4.26289368e+00 -1.22962976e+00]
 [-6.09051991e+00 -3.15275192e+00  1.79273260e+00  9.92699528e+00
  -5.97349882e+00  3.68225765e+00 -6.47421646e+00 -1.99264419e+00
   2.15714622e+00  2.32836318e+00]
 [-3.25946307e+00  8.14360428e+00 -1.00535810e+00 -2.37552500e+00
   2.38139248e+00 -2.92597318e+00 -1.54173911e+00  2.25682306e+00
  -2.83430189e-01 -1.33554244e+00]
 [-2.99147058e+00  3.86941671e+00  8.82810593e+00  2.20121431e+00
  -8.40485859e+00 -8.66728902e-01 -5.97998762e+00 -5.21699572e+00
   5.80638123e+00 -2.57314467e+00]
 [ 8.64277363e+00 -4.99241495e+00  2.84688592e+00 -4.15350378e-01
  -1.87728360e-01 -2.40291572e+00  4.42544132e-01 -4.54446167e-01
  -1.88113344e+00 -1.23334014e+00]
 [-2.00169897e+00 -2.65497804e+00  1.18750989e+00  9.70900059e-01
  -4.53840446e+00 -2.65584946e+00 -8.23472023e+00  9.93836498e+00
  -5.57100773e-01  3.42955470e+00]
 [-3.57249069e+00 -5.03176594e+00 -1.79369414e+00 -5.03321826e-01
  -1.97100627e+00  9.01608944e+00  6.62497377e+00 -5.48222637e+00
   6.09256268e+00 -4.71334040e-01]
 [-5.27715540e+00 -7.84428477e-01 -6.26944721e-01  3.87298250e+00
  -1.88836837e+00  1.15252662e+00 -2.98473048e+00 -3.10233998e+00
   9.71112537e+00  3.10839200e+00]
 [-9.50223565e-01 -6.47654009e+00  2.26750326e+00  1.95419586e+00
   1.68217969e+00  1.66003108e+00  9.82697105e+00 -9.94868219e-01
  -2.03924966e+00 -1.88321277e-01]
 [-3.11575246e+00  3.43664408e+00  1.19877796e+01  4.36916590e+00
  -1.17812777e+01 -1.69431508e+00 -5.82668829e+00 -5.09948444e+00
   4.15738583e+00 -4.30461359e+00]
 [ 9.72177792e+00 -5.31352401e-01 -1.21784186e+00 -1.07392669e+00
  -7.11223555e+00  1.67838800e+00  1.01826215e+00 -8.88240516e-01
   6.95959151e-01  2.38748863e-01]
 [-2.06619406e+00  1.86608231e+00  1.12100420e+01  4.22539425e+00
  -1.21493711e+01 -4.57662535e+00 -6.88935089e+00 -9.81215835e-01
   3.87611055e+00 -3.28470826e+00]
 [-6.73031902e+00 -2.54390073e+00 -1.10151446e+00  1.51524162e+01
  -1.10052080e+01  6.60323954e+00 -7.94388771e+00  3.31939721e+00
  -1.40840662e+00  2.65730071e+00]
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ai_fast_handbook