def get_augs(shape):
train_augs = [
image.HorizontalFlipAug(.5),
image.RandomCropAug((shape, shape))
]
valid_augs = train_augs
tests_augs = [image.CenterCropAug((shape, shape))]
return train_augs, valid_augs, tests_augs
def augment_data(imags, label):
datas, labels = [], []
n = imags.shape[0]
for k in range(n):
imag = imags[k].reshape(shape=(1, imags[k].shape[0], imags[k].shape[1],
imags[k].shape[2]))
for i in range(4):
trans_band = transform(
imag, image.HorizontalFlipAug(.5)).astype('float32')
datas.append(img_norm(trans_band))
labels.append(label[k].asscalar())
for i in range(9):
trans_band = transform(
imag, image.RandomSizedCropAug((75, 75), .75,
(.8, 1.2))).astype('float32')
datas.append(img_norm(trans_band))
labels.append(label[k].asscalar())
# brightness augmenter
for i in range(9):
trans_band = transform(
imag, image.BrightnessJitterAug(.1)).astype('float32')
datas.append(img_norm(trans_band))
labels.append(label[k].asscalar())
# random crop augmenter
for i in range(9):
trans_band = resize(transform(imag, image.RandomCropAug((50, 50))),
75).astype('float32')
datas.append(img_norm(trans_band))
labels.append(label[k].asscalar())
# center crop augmenter
trans_band = resize(transform(imag, image.CenterCropAug((50, 50))),
75).astype('float32')
datas.append(img_norm(trans_band))
labels.append(label[k].asscalar())
ds = nd.concat(*datas, dim=0)
return ds, labels
from mxnet import nd, gluon, image
batch_size = 64
train_augs = [image.HorizontalFlipAug(.5), image.RandomCropAug((224, 224))]
test_augs = [image.CenterCropAug((224, 224))]
data_dir = '/home/zhbli/temp/dog_dataset'
train_imgs = gluon.data.vision.ImageFolderDataset(
data_dir + '/train', transform=lambda X, y: transform(X, y, train_augs))
test_imgs = gluon.data.vision.ImageFolderDataset(
data_dir + '/test', transform=lambda X, y: transform(X, y, test_augs))
def transform(data, label, augs):
data = data.astype('float32')
for aug in augs:
data = aug(data)
data = nd.transpose(data, (2, 0, 1))
return data, nd.array([label]).asscalar().astype('float32')
def getDataset():
train_data = gluon.data.DataLoader(train_imgs, batch_size, shuffle=True)
test_data = gluon.data.DataLoader(test_imgs, batch_size)
return train_data, test_data
print("classes: " + str(len(training_map)))
net = mobilenet1_0(classes=len(training_map))
from mxnet import init
net.features = pretrained_net.features
net.output.initialize(init.Xavier())
from mxnet.image import color_normalize
from mxnet import image
train_augs = [
image.HorizontalFlipAug(0.5),
image.BrightnessJitterAug(.3),
image.HueJitterAug(.1)
]
test_augs = [image.ResizeAug(224), image.CenterCropAug((224, 224))]
def transform(data, label, augs):
data = data.astype('float32')
for aug in augs:
data = aug(data)
data = nd.transpose(data, (2, 0, 1))
return data, nd.array([label]).asscalar().astype('float32')
from mxnet.gluon.data.vision import ImageRecordDataset
train_rec = root + '/data/household/train/household.rec'
validation_rec = root + '/data/household/validation/household.rec'
test_rec = root + '/data/household/test/household.rec'
continue
img_dir = os.path.join(path_crocodile, file_name)
img_arr = mx.image.imread(img_dir)
img_arr = nd.transpose(img_arr, (2, 0, 1))
img_arr = (img_arr.astype(np.float32) / 255)
img_arr = nd.array(img_arr.reshape((1, ) + img_arr.shape))
img_list.append(img_arr)
img_list_crocodile.append(img_arr)
size = 28
train_augs = [
image.HorizontalFlipAug(.5),
image.ColorJitterAug(.5, .5, .5),
image.RandomCropAug((size, size)),
]
test_augs = [image.CenterCropAug((size, size))]
def apply_aug_list(img, augs):
for f in augs:
img = f(img)
return img
def try_gpu():
try:
ctx = mx.gpu()
_ = nd.array([0], ctx=ctx)
except:
ctx = mx.cpu()
return ctx
apply(img, aug)
# 随机色调变化
aug = image.HueJitterAug(.5)
apply(img, aug)
def apply_aug_list(img, augs):
for aug in augs:
img = aug(img)
return img
train_augs = [image.HorizontalFlipAug(.5), image.RandomCropAug((28, 28))]
test_augs = [image.CenterCropAug((28, 28))]
from mxnet import gluon
def get_transform(augs):
def transform(data, label):
data = data.astype('float32')
if augs is not None:
data = nd.stack(*[apply_aug_list(d, augs) for d in data])
data = nd.transpose(data, (0, 3, 1, 2))
return data, label.astype('float32')
return transform
def apply_aug_list(img, augs):
for f in augs:
img = f(img)
return img
def train(train_augs, test_augs, learning_rate=.1):
batch_size = 128
num_epochs = 10
ctx = utils.try_all_gpus()
loss = gluon.loss.SoftmaxCrossEntropyLoss()
train_data, test_data = get_data(
batch_size, train_augs, test_augs)
net = utils.resnet18(10)
net.initialize(ctx=ctx, init=init.Xavier())
net.hybridize()
trainer = gluon.Trainer(net.collect_params(),
'sgd', {'learning_rate': learning_rate})
utils.train(
train_data, test_data, net, loss, trainer, ctx, num_epochs)
train_augs = [
image.HorizontalFlipAug(.5),
image.RandomCropAug((32,32))
]
test_augs = [
image.CenterCropAug((32,32))
]
train(train_augs, test_augs)
from src import utils
import mxnet as mx
import base64
from mxnet import image
from mxnet import nd
from collections import namedtuple
train_augs = [
image.HorizontalFlipAug(1.),
image.RandomCropAug((224, 224))
]
test_augs = [
image.CenterCropAug((224, 224))
]
def transform(data, label, augs):
data = data.astype('float32')
for aug in augs:
data = aug(data)
data = nd.transpose(data, (2, 0, 1))
return data, nd.array([label]).asscalar().astype('float32')
net = mx.mod.Module.load('../model/food', 0, True)
net.bind(data_shapes=[('data', (64, 3, 244, 244))])
batch = namedtuple('batch', ['data'])
def score_image(image_base64_string):
with open('target_image.jpg', 'wb') as f:
f.write(base64.b64decode(image_base64_string))
f.close()
return (x - x_min) / (x_max - x_min)
def rescale_per_image(x):
assert x.ndim == 4
x = x.copy()
for i in range(x.shape[0]):
min_val = x[i].min().asscalar()
max_val = x[i].max().asscalar()
x[i] = rescale(x[i], min_val, max_val)
return x
train_augs = image.HorizontalFlipAug(1)
test_augs = [image.CenterCropAug((32, 32))]
batch_size = 64
learning_rate = .1
num_epochs = 500
randseed = 10
test_n = 'test15'
ctx = utils.try_all_gpus()
loss = gluon.loss.SoftmaxCrossEntropyLoss()
train_data, test_data, data_num = get_data(
'C:/lagBear/S***N/finally_data/cnn_data.mat',
randseed,
batch_size=batch_size,
train_augs=train_augs)
### resnet network ###
import mxnet as mx
import numpy as np
import os
import Inception3_modify
from mxnet import gluon, image, nd, ndarray, init
from mxnet.gluon.model_zoo import vision as models
import sys
sys.path.append('..')
import utils
train_augs = [image.HorizontalFlipAug(.5), image.RandomCropAug((299, 299))]
test_augs = [image.CenterCropAug((299, 299))]
def transform(data, label, augs):
data = data.astype('float32')
for aug in augs:
data = aug(data)
data = nd.transpose(data, (2, 0, 1))
return data, nd.array([label]).asscalar().astype('float32')
ctx = mx.gpu()
data_dir = './data'
batch_size = 16
epochs = 1
###################################### true train data
train_imgs = gluon.data.vision.ImageFolderDataset(
data_dir + '/train_valid_test/Images',
from mxnet import image
import mxnet as mx
import model
import os
def transform(data, label, augs):
data = data.astype('float32')
for aug in augs:
data = aug(data)
data = nd.transpose(data, (2, 0, 1))
return data, nd.array([label]).asscalar().astype('float32')
augs = [image.HorizontalFlipAug(.5), image.CenterCropAug((227, 227))]
test_augs = [image.CenterCropAug((227, 227))]
train_set = gluon.data.vision.ImageFolderDataset(
root='~/.mxnet/datasets/oxford102/train_datas',
transform=lambda X, y: transform(X, y, augs))
test_set = gluon.data.vision.ImageFolderDataset(
root='~/.mxnet/datasets/oxford102/test_datas',
transform=lambda X, y: transform(X, y, augs))
lossFunc = gluon.loss.SoftmaxCrossEntropyLoss()
net = model.AlexNet()
net.initialize(init=mx.init.Xavier(), ctx=mx.gpu())
