def __init__(self, ctx, nameparams=None):
#inception = vision.inception_v3(pretrained=True, ctx=ctx).features #提取预训练模型inception_v3的features
resnet = vision.resnet152_v1(
pretrained=True, ctx=ctx).features # 提取预训练模型resnet152_v1的features
self.features = resnet
self.output = self.__get_output(ctx, nameparams) #只有输出层的网络
self.net = OneNet(self.features,
self.output) #将含有features的网络和输出层的网络构造为新的网络
def __init__(self, ctx, num_class, nameparams=None):
inception = vision.inception_v3(pretrained=True,
ctx=ctx).features #加载预训练模型
resnet = vision.resnet152_v1(pretrained=True,
ctx=ctx).features #加载预训练模型
self.features = ConcatNet(resnet, inception) #融合2个网络并得到网络特征
self.output = self.__get_output(ctx, num_class, nameparams) #得到网络输出层
self.net = OneNet(self.features, self.output) #将特征层和输出层合并为一个网络
def test_models():
ctx = mx.cpu()
data_dir = 'data'
models_dir = 'models'
target_size = (128, 128, 1)
dataset = pd.read_csv(os.path.join(data_dir, 'labels.csv'))
dic = dict(zip(np.unique(dataset.breed), range(0, np.unique(dataset.breed).__len__() + 1)))
net = build_model_mxnet(ctx)
net.load_parameters(os.path.join(models_dir, 'model.params'))
model = load_model(os.path.join(models_dir, 'dog-recognition.h5'))
test_set = dataset.sample(20).reset_index()
result = []
res151 = models.resnet152_v1(pretrained=True, ctx=ctx).features
with res151.name_scope():
res151.add(gluon.nn.GlobalAvgPool2D())
res151.collect_params().reset_ctx(ctx)
res151.hybridize()
inception = models.inception_v3(pretrained=True, ctx=ctx)
inception_net = inception.features
inception_net.collect_params().reset_ctx(ctx)
inception_net.hybridize()
for i in tqdm(range(20)):
# -- Tensorflow
img = tf_image.load_img(os.path.join(data_dir, 'train', test_set['id'][i]) + '.jpg', target_size=target_size,
grayscale=False)
img = img_to_array(img)
img = img / 255
predict_tensorflow = model.predict_classes(np.array([img]))
# -- MXNet
img = mx.nd.array(cv2.imread(os.path.join(data_dir, 'train', test_set['id'][i]) + '.jpg'))
img = transform_test(img)
img_res151, img_inception = get_features_test(res151, inception_net, img, ctx)
img_res151 = img_res151.reshape(img_res151.shape[:2])
img_inception = img_inception.reshape(img_inception.shape[:2])
img = nd.concat(mx.nd.array(img_inception), mx.nd.array(img_res151))
predict_mx = nd.softmax(net(nd.array(img).as_in_context(ctx)))
result.append({
'id': test_set['id'][i],
'expected': test_set['breed'][i],
'tensor': list(dic.keys())[list(dic.values()).index(predict_tensorflow)],
'mx': list(dic.keys())[list(dic.values()).index(predict_mx.topk(k=1).asnumpy()[0][0])],
'mx_percentage': predict_mx[0, 0].asscalar()
})
print(tabulate(result))
input("Press Enter to continue...")
def get_features1(ctx):
resnet = vision.resnet152_v1(pretrained=True,ctx=ctx)
return resnet.features
def __init__(self,ctx,nameparams=None):
inception = vision.inception_v3(pretrained=True,ctx=ctx).features
resnet = vision.resnet152_v1(pretrained=True,ctx=ctx).features
self.features = ConcatNet(resnet,inception)
self.output = get_output(ctx,nameparams)
self.net = OneNet(self.features,self.output)
feature = net.features(X.as_in_context(ctx))
features.append(feature.asnumpy())
labels.append(y.asnumpy())
features = np.concatenate(features, axis=0)
labels = np.concatenate(labels, axis=0)
return features, labels
preprocessing[0] = image.ForceResizeAug((224, 224))
imgs = vision.ImageFolderDataset('for_train_stanford', transform=transform)
data = gluon.data.DataLoader(imgs, 64)
features_vgg, labels = get_features(models.vgg16_bn(pretrained=True, ctx=ctx),
data)
features_resnet, _ = get_features(
models.resnet152_v1(pretrained=True, ctx=ctx), data)
features_densenet, _ = get_features(
models.densenet161(pretrained=True, ctx=ctx), data)
preprocessing[0] = image.ForceResizeAug((299, 299))
imgs_299 = vision.ImageFolderDataset('for_train_stanford', transform=transform)
data_299 = gluon.data.DataLoader(imgs_299, 64)
features_inception, _ = get_features(
models.inception_v3(pretrained=True, ctx=ctx), data)
with h5py.File('features_train_stanford.h5', 'w') as f:
f['vgg'] = features_vgg
f['resnet'] = features_resnet
f['densenet'] = features_densenet
f['inception'] = features_inception
f['labels'] = labels
import json
import os
import mxnet as mx
import numpy as np
from mxnet import gluon, nd, image
from mxnet.gluon import nn
from mxnet.gluon.model_zoo import vision as models
ctx = mx.cpu()
res151 = models.resnet152_v1(pretrained=True, ctx=ctx).features
with res151.name_scope():
res151.add(gluon.nn.GlobalAvgPool2D())
res151.collect_params().reset_ctx(ctx)
res151.hybridize()
inception = models.inception_v3(pretrained=True, ctx=ctx)
inception_net = inception.features
inception_net.collect_params().reset_ctx(ctx)
inception_net.hybridize()
with open(os.path.join('functions', 'labels.json'), 'r') as fp:
dic = json.load(fp)
def build_model_mxnet(ctx):
net = nn.Sequential()
with net.name_scope():
net.add(nn.BatchNorm())
net.add(nn.Dense(1024))