Mxnet如何实现mnist预测训练，模型导出为onnx，再导入预测？
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需要做什么？+ 方便广泛 + 烟酒生 + 研究生、+ 人工智能工程师 + 算法工程师快速使用mxnet，特写此文章。默认用户已有基本的深度学习、数据集概念。+ 系统环境 + Python 3.7.4 + mxnet 1.+
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。
python 3.7.4
mxnet 1.9.0
mxnet-cu112 1.9.0
onnx 1.9.0
onnxruntime-gpu 1.9.0
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 ..
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]
[-1.96954179e+00 -1.13817227e+00 9.40351069e-01 -1.75684047e+00
3.60373807e+00 2.01377797e+00 1.00558109e+01 -1.10547984e+00
5.17374456e-01 -3.94047165e+00]
[-5.81787634e+00 -1.20211565e+00 -3.53216052e+00 1.17569458e+00
4.21314144e+00 -2.53644252e+00 -7.64466667e+00 4.19782829e+00
4.28840429e-01 1.04579344e+01]
[-4.20310974e+00 -3.19272375e+00 -4.62792778e+00 2.71683741e+00
4.43899345e+00 3.31357956e-01 -6.24839544e+00 3.80388188e+00
-1.22620119e-02 9.65024757e+00]
[-8.26945066e-01 -5.25947523e+00 3.72772887e-02 2.30585241e+00
-4.95726252e+00 -1.19987357e+00 -1.20395079e+01 1.53253164e+01
-2.10372299e-01 1.89387524e+00]
[-5.09596729e+00 -7.76027665e-02 -9.53466833e-01 2.89041376e+00
-1.50858855e+00 2.27854323e+00 -1.95591903e+00 -3.15785193e+00
1.00103540e+01 1.08987451e+00]
[-4.01680946e-01 -4.62062168e+00 3.90530303e-02 -1.66790807e+00
5.43311167e+00 -1.78802896e+00 -2.88405848e+00 2.93439984e+00
-2.16558409e+00 8.71198368e+00]
[-1.29969406e+00 -3.92871022e+00 -3.82151055e+00 -2.93831253e+00
1.03674269e+01 8.88044477e-01 7.88922787e-01 3.86107159e+00
1.60807288e+00 -3.76913965e-01]
[-2.25020099e+00 -8.17249107e+00 -1.82360613e+00 -5.90175152e-01
4.72407389e+00 9.39436078e-01 -3.85674310e+00 3.95303702e+00
1.83473241e+00 9.13874054e+00]
[-3.26617742e+00 -2.91517663e+00 8.37770653e+00 1.61820054e-01
-2.98638320e+00 -2.47211266e+00 5.08574843e-01 4.65608168e+00
2.66001201e+00 -4.67262363e+00]
[-2.29874635e+00 7.77097034e+00 1.11359918e+00 -2.06103897e+00
-7.61267126e-01 1.00877440e+00 1.47708499e+00 -1.20483887e+00
1.99922264e+00 -3.81118345e+00]
[-6.87821198e+00 -9.18823600e-01 2.16773844e+00 1.07671242e+01
-7.48823595e+00 2.90310860e+00 -1.02075748e+01 -3.83400464e+00
4.76818371e+00 4.06564474e+00]
[-2.06487226e+00 8.76828384e+00 1.10449910e+00 -2.29669046e+00
-1.15668392e+00 -2.50351834e+00 -1.69508122e-02 -1.05916834e+00
1.91057479e+00 -2.64592767e+00]
[-2.24318981e+00 9.02024174e+00 1.38990092e+00 -2.72154903e+00
1.46101296e-01 -4.43454313e+00 -8.21092844e-01 -2.40808502e-01
3.36577922e-01 -2.63193059e+00]
[-4.35961342e+00 -7.74704576e-01 -2.74345660e+00 -3.27951574e+00
1.50971518e+01 -2.80669570e+00 -1.28740633e+00 3.94157290e+00
4.20372874e-01 8.37333024e-01]
[-2.80749345e+00 -3.33036280e+00 -1.00865018e+00 4.57633829e+00
-5.03952360e+00 2.93345642e+00 -8.54609489e+00 2.26549125e+00
4.73208952e+00 5.93849993e+00]
[-3.31042576e+00 9.97719002e+00 4.38573778e-01 -1.35296178e+00
-1.21057940e+00 -2.46178842e+00 -8.34564090e-01 2.19030753e-01
2.03147411e+00 -1.80211437e+00]
[-2.14534068e+00 -2.93023801e+00 4.41405416e-01 -2.29865336e+00
1.47422533e+01 -1.86358702e+00 4.61042017e-01 -6.20108247e-01
-1.36792552e+00 2.14018896e-01]
[-2.64241481e+00 -2.28332114e+00 2.01109338e+00 9.67352509e-01
6.09287119e+00 -2.35626236e-01 -3.02941656e+00 4.32772923e+00
-4.63955021e+00 3.73136783e+00]
[-4.55847168e+00 1.04014337e+00 9.12987328e+00 2.06433630e+00
-1.67355919e+00 -1.49593079e+00 4.09124941e-01 2.41894865e+00
-6.86871633e-02 -4.42179346e+00]
[-6.10608578e-01 -2.73860097e+00 -1.09864855e+00 -5.68899512e-01
2.45831108e+00 5.50326490e+00 1.22601585e+01 -5.23877192e+00
2.11066246e+00 -2.98584485e+00]
[-5.96745872e+00 -1.91458237e+00 -3.10774088e-01 1.00216856e+01
-3.81997776e+00 7.14399862e+00 -4.61386251e+00 -5.18248987e+00
4.25162363e+00 1.18878789e-01]
[-4.24126434e+00 -9.63249326e-01 1.06391013e+00 4.45316315e+00
-4.47125340e+00 -1.21906054e+00 -9.75789547e+00 1.10335569e+01
-1.17632782e+00 8.78942788e-01]
[ 3.76867175e-01 -4.75102758e+00 -2.59345794e+00 -3.96257102e-01
-7.50329159e-03 1.81642962e+00 -6.01041269e+00 9.97849655e+00
-2.57468176e+00 5.00644207e+00]
[-2.21995306e+00 -4.23465443e+00 -2.19536662e+00 -3.71420813e+00
1.49460211e+01 -2.73240638e+00 3.03538777e-02 4.29108334e+00
1.21963143e+00 6.85284913e-01]
[-1.93794692e+00 -3.39166284e+00 -3.41372967e-01 -2.16144085e-01
1.32588074e-01 -3.83050554e-02 -7.32452822e+00 9.68561840e+00
-1.16044319e+00 3.63913298e+00]
[-3.31972694e+00 -4.84879112e+00 -3.41381001e+00 1.93332338e+00
-5.16150045e+00 1.05730495e+01 1.52961123e+00 -4.64916992e+00
4.11477995e+00 4.80105543e+00]
[-3.22445488e+00 1.01509037e+01 6.03635088e-02 -1.48001885e+00
-3.28380197e-01 -2.36782789e+00 -8.66441727e-01 6.68077886e-01
1.45576179e+00 -1.95271623e+00]
[-5.64618587e+00 -3.71156931e+00 -2.31174397e+00 1.76912701e+00
2.95111752e+00 2.09562635e+00 -5.34609461e+00 -2.59399921e-01
1.10373080e+00 1.10444403e+01]
[ 1.32743053e+01 -5.11389780e+00 1.10446036e+00 -5.01595545e+00
-4.87907743e+00 4.46035750e-02 4.57046556e+00 -1.76434004e+00
-5.40824793e-02 -1.01547205e+00]
[-4.27589798e+00 1.26832044e+00 6.49948978e+00 -8.06193352e-02
1.34645328e-01 6.92090929e-01 -8.92272711e-01 2.15252757e+00
-9.95365143e-01 -2.46636438e+00]
[-5.08349514e+00 -1.79646879e-01 9.57399654e+00 3.35643005e+00
-3.42183971e+00 -2.33653522e+00 -1.98645079e+00 1.50538552e+00
-1.43313253e+00 -9.75638926e-01]
[-8.48450565e+00 -4.32870531e+00 -1.54253757e+00 1.61029205e+01
-8.41084957e+00 5.45092726e+00 -1.00705996e+01 2.31331086e+00
1.05400957e-01 5.19563723e+00]
[-4.89484310e+00 -4.33120441e+00 -3.71932483e+00 -1.18670315e-01
4.70187807e+00 2.67010808e-01 -5.52650118e+00 5.62736416e+00
5.05499423e-01 8.69004250e+00]
[-1.94048929e+00 7.98250961e+00 -3.75457823e-01 -2.09433126e+00
-4.63896915e-02 -2.64650702e+00 -5.73348761e-01 -1.34597674e-01
1.26143038e+00 -1.32178509e+00]
[-6.36190224e+00 5.44552183e+00 1.89667892e+00 3.91665459e+00
-1.99860716e+00 2.76620483e+00 3.57649279e+00 -3.53898597e+00
-1.21411644e-01 -2.60307193e+00]
[-3.60333633e+00 4.92228556e+00 2.87770915e+00 1.31902504e+00
-4.28756446e-01 -3.29862523e+00 -2.29294825e+00 -1.10349190e+00
3.81862259e+00 -1.23572731e+00]
[ 1.06328213e+00 -6.86575174e+00 -3.61938500e+00 1.15000987e+00
-2.32747698e+00 1.32029047e+01 -3.19671059e+00 8.91836137e-02
4.70500660e+00 2.51928687e+00]
[ 1.00363417e+01 -8.02793884e+00 9.38569680e-02 -2.78522468e+00
-2.84671545e+00 2.95867848e+00 4.23545599e+00 2.52083421e+00
-3.35666537e+00 1.45630157e+00]]
你甚至不愿意Start的Github
ai_fast_handbook
本文共计3935个文字，预计阅读时间需要16分钟。
需要做什么？+ 方便广泛 + 烟酒生 + 研究生、+ 人工智能工程师 + 算法工程师快速使用mxnet，特写此文章。默认用户已有基本的深度学习、数据集概念。+ 系统环境 + Python 3.7.4 + mxnet 1.+
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。
python 3.7.4
mxnet 1.9.0
mxnet-cu112 1.9.0
onnx 1.9.0
onnxruntime-gpu 1.9.0
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 ..
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'>
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1.55215383e+00 -2.03553891e+00]
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