import torchvision
from torchvision import datasets, models, transforms
import time
import os
from PIL import Image
import sys
import torch.nn.functional as F
from net import simpleconv3
data_transforms = transforms.Compose([
transforms.Resize(48),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
net = simpleconv3()
net.eval()
modelpath = sys.argv[1]
net.load_state_dict(
torch.load(modelpath, map_location=lambda storage, loc: storage))
imagepath = sys.argv[2]
image = Image.open(imagepath)
imgblob = data_transforms(image).unsqueeze(0)
imgblob = Variable(imgblob)
torch.no_grad()
predict = F.softmax(net(imgblob))
print(predict)
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration use for testing only rescaling
val_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size)
val_generator = val_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size)
num_train_samples = train_generator.samples
num_val_samples = val_generator.samples
tensorboard = TensorBoard(log_dir=('./logs'))
callbacks = []
callbacks.append(tensorboard)
model = simpleconv3()
loss = binary_crossentropy
metrics = [binary_accuracy]
optimizer = SGD(lr=0.001, decay=1e-6, momentum=0.9)
model = train_model(model, loss, metrics, optimizer, num_epochs)
if not os.path.exists('models'):
os.mkdir('models')
model.save_weights('models/model.h5')
import cv2
txtfile = sys.argv[1]
batch_size = 64
num_classes = 2
image_size = (48,48)
learning_rate = 0.0001
debug=False
if __name__=="__main__":
dataset = ImageData(txtfile,batch_size,num_classes,image_size)
iterator = dataset.data.make_one_shot_iterator()
dataset_size = dataset.dataset_size
batch_images,batch_labels = iterator.get_next()
Ylogits = simpleconv3(batch_images,True)
print "Ylogits size=",Ylogits.shape
Y = tf.nn.softmax(Ylogits)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=batch_labels)
cross_entropy = tf.reduce_mean(cross_entropy)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(batch_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
saver = tf.train.Saver()
in_steps = 100
checkpoint_dir = 'checkpoints/'
if not os.path.exists(checkpoint_dir):
x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
for x in ['train', 'val']
}
dataloders = {
x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=16,
shuffle=True,
num_workers=4)
for x in ['train', 'val']
}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
use_gpu = torch.cuda.is_available()
modelclc = simpleconv3()
print(modelclc)
if use_gpu:
modelclc = modelclc.cuda()
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(modelclc.parameters(), lr=0.1, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft,
step_size=100,
gamma=0.1)
modelclc = train_model(model=modelclc,
criterion=criterion,
optimizer=optimizer_ft,
scheduler=exp_lr_scheduler,
num_epochs=500)
import lasagne
import theano
import theano.tensor as T
import sys
import numpy as np
import matplotlib.pyplot as plt
from net import simpleconv3
from dataset import Dataset
input_var = T.tensor4('X')
target_var = T.ivector('y')
network = simpleconv3(input_var)
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
#loss = loss.mean() + 1e-4 * lasagne.regularization.regularize_network_params(
# network, lasagne.regularization.l2)
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(
test_prediction, target_var)
test_loss = test_loss.mean()
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# create parameter update expressions
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
import time
import os
from PIL import Image
import sys
import torch.nn.functional as F
## 全局变量
## sys.argv[1] 权重文件
## sys.argv[2] 图像文件夹
testsize = 48 ##测试图大小
from net import simpleconv3
net = simpleconv3(2) ## 定义模型
net.eval() ## 设置推理模式，使得dropout和batchnorm等网络层在train和val模式间切换
torch.no_grad() ## 停止autograd模块的工作，以起到加速和节省显存
## 载入模型权重
modelpath = sys.argv[1]
net.load_state_dict(
torch.load(modelpath, map_location=lambda storage, loc: storage))
## 定义预处理函数
data_transforms = transforms.Compose([
transforms.Resize(48),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
