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) #####################################
return transformer(data.astype(np.float32)), label
trainset = data.vision.datasets.ImageFolderDataset("./dataset/valid_train",
1, train_transform)
validset = data.vision.datasets.ImageFolderDataset("./dataset/valid_test",
1, valid_transform)
trainloader = data.DataLoader(trainset,
batch_size,
True,
num_workers=8,
pin_memory=True)
validloader = data.DataLoader(validset, batch_size, False)
# model
mobilenet = vision.mobilenet0_5(pretrained=True, ctx=ctx).features
clf = nn.Sequential()
with clf.name_scope():
clf.add(nn.Dense(4096, activation='relu'),
nn.Dense(4096, activation='relu'),
nn.Dense(1024, activation='relu'), nn.Dense(6))
clf.collect_params().initialize(init=init.Xavier(), ctx=ctx)
# scheduler + trainer
if args.mask:
steps_epochs = [150, 175]
else:
steps_epochs = [150]
it_per_epoch = math.ceil(1243 / batch_size)
steps_iterations = [s * it_per_epoch for s in steps_epochs]
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).