def load_mobilenet1_0(ctx):
return vision.mobilenet1_0(pretrained=True, ctx=ctx, prefix="")
import os
import random
import time
import mxnet as mx
import mxnet.ndarray as nd
from mxnet.gluon.model_zoo.vision import mobilenet1_0
from mxnet import init, image, autograd, nd
from mxnet.image import color_normalize
from mxnet.gluon.data.vision import ImageRecordDataset
import mxnet.gluon as gluon
pretrained_net = mobilenet1_0(pretrained=True)
net = mobilenet1_0(classes=2)
net.features = pretrained_net.features
net.output.initialize(init.Xavier())
license_map = {'d': [], 'n': []}
license_to_idx = {'n': 0, 'd': 1}
data_dir = "C:\\Users\\Jason\\Documents\\Python Projects\\ml\\hackathon\\licenses mxnet"
fnames = os.listdir(data_dir)
counter = 0
for fn in fnames:
arr = fn.split('_')
category = arr[0]
license_map[category].append({
'idx': counter,
train_rec = os.path.join(data_dir, 'train/img.rec')
validation_rec = os.path.join(data_dir, 'validation/img.rec')
model_out_path = os.path.join(data_dir, '../models/{}.params'.format(model_name))
# load data
train_iterator = ImageRecordDataset(
filename=train_rec,
transform=lambda X, y: transform(X, y, train_augs)
)
validation_iterator = ImageRecordDataset(
filename=validation_rec,
transform=lambda X, y: transform(X, y, test_augs)
)
# instantiate source model
pretrained_net = mobilenet1_0(pretrained=True, prefix='model_')
# instantiate target model
net = mobilenet1_0(classes=2, prefix='model_')
# transfer non output layers from source model to target model
net.features = pretrained_net.features
# initializing parameters for output layers of target model
net.output.initialize(init.Xavier())
# train target model
start = time.time()
train(net, train_iterator, validation_iterator, ctx, batch_size=batch_size, epochs=epochs, learning_rate=lr)
end = time.time()
print('\nTOTAL TIME ELAPSED: {}s\n'.format(end - start))
# save target model's learned parameters
net.collect_params().save(model_out_path)
##################
# 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.mobilenet1_0(classes=10, pretrained=False, ctx=ctx)
# net = vision.mobilenet0_75(classes=10, pretrained=False, ctx=ctx)
# net = vision.mobilenet0_5(classes=10, pretrained=False, ctx=ctx)
# net = vision.mobilenet0_25(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))
data = data.astype(np.float32)
