##################
# Hyperparameter #
#----------------#
ctx = mx.cpu()
lr = 0.05
epochs = 10
momentum = 0.9
batch_size = 64
#----------------#
# Hyperparameter #
##################
################## model
from mxnet.gluon.model_zoo import vision
net = vision.densenet121(classes=10, pretrained=False, ctx=ctx)
# net = vision.densenet161(classes=10, pretrained=False, ctx=ctx)
# net = vision.densenet169(classes=10, pretrained=False, ctx=ctx)
# net = vision.densenet201(classes=10, pretrained=False, ctx=ctx)
################## 그래프
import gluoncv
inputShape = (1, 3, 224, 224)
gluoncv.utils.viz.plot_network(net, shape=inputShape)
##### 전처리 ##############################################
def transformer(data, label):
data = mx.image.imresize(data, 224, 224)
data = mx.nd.transpose(data, (2, 0, 1))
import mxnet as mx import time import gluoncv from mxnet import nd, autograd from mxnet import gluon from mxnet.gluon import nn inputShape = (1, 3, 224, 224) from mxnet.gluon.model_zoo import vision alexnet = vision.alexnet() inception = vision.inception_v3() resnet18v1 = vision.resnet18_v1() resnet18v2 = vision.resnet18_v2() squeezenet = vision.squeezenet1_0() densenet = vision.densenet121() mobilenet = vision.mobilenet0_5() ############### 그래프 ############### import gluoncv gluoncv.utils.viz.plot_network(resnet18v1, shape=inputShape) #####################################
out = out.as_in_context(mx.cpu())
out = out.asnumpy()
res = ''
for item in out:
res += str(item) +' '
#print(res)
fw1.write(res+'\n')
label = root.strip().split('/')[-1]
if(train == True):
fw2.write(label+'\n')
print(label)
else:
fw2.write(file+'\n')
print(file)
fw1.close()
fw2.close()
print('net done!!!')
pretrained_net = models.densenet121(pretrained=True)
net = nn.HybridSequential()
for layer in pretrained_net.features:
net.add(layer)
print(net)
ctx = mx.gpu()
net.collect_params().reset_ctx(ctx)
net.hybridize()
dir = 'data/test_b/'
suffix=['jpg']
batch_net(dir,suffix,net,train=False)
import json
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import gluon, nd
from mxnet.gluon.model_zoo import vision
import numpy as np
ctx = mx.cpu()
densenet121 = vision.densenet121(pretrained=True, ctx=ctx)
mobileNet = vision.mobilenet0_5(pretrained=True, ctx=ctx)
resnet18 = vision.resnet18_v1(pretrained=True, ctx=ctx)
mx.test_utils.download(
'https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/onnx/image_net_labels.json'
)
categories = np.array(json.load(open('image_net_labels.json', 'r')))
# filename = mx.test_utils.download('https://github.com/dmlc/web-data/blob/master/mxnet/doc/tutorials/onnx/images/dog.jpg?raw=true', fname='dog.jpg')
filename = 'scorp.jpg'
image = mx.image.imread(filename)
plt.imshow(image.asnumpy())
plt.show()
# Read the image: this will return a NDArray shaped as (image height, image width, 3), with the three channels in RGB order.
# Resize the shorter edge of the image 224.
# Crop, using a size of 224x224 from the center of the image.
# Shift the mean and standard deviation of our color channels to match the ones of the dataset the network has been trained on.
# Transpose the array from (Height, Width, 3) to (3, Height, Width).
def densenet121mxnetload():
net = vision.densenet121(pretrained=True)
net.hybridize()
return net