def buildNetwork(n, k):
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
# Building Residual Network
net = tflearn.input_data(shape=[None, b.IMG_WIDTH, b.IMG_HEIGHT, b.CHANNELS], data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
# wideresnet part
net = wideresnet_block(net, n, 16, k, downsample=True)
net = wideresnet_block(net, 1, 32, k, downsample=True)
net = wideresnet_block(net, n - 1, 32, k, downsample=True)
net = wideresnet_block(net, 1, 64, 2, downsample=True)
net = wideresnet_block(net, n - 1, 64, k, downsample=True)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, b.CLASS_3_NUMBER, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=mom, loss='categorical_crossentropy')
model = tflearn.DNN(net, tensorboard_verbose=0, clip_gradients=0., tensorboard_dir=b.PATH + 'log')
return model
def get_input_layer(flags):
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
curr = input_data(shape=[None, flags.inres, flags.inres, flags.inchan],
name='input',
data_preprocessing=img_prep)
if flags.rts_aug:
w, h = curr.get_shape().as_list()[1:3]
a = -flags.rts_aug_ang + 2 * flags.rts_aug_ang * tf.random_uniform(
[flags.bs])
a *= np.pi / 180
# centralize rot/scale
y = ((w - 1) - (tf.cos(a) * (w - 1) - tf.sin(a) * (h - 1))) / 2.0
x = ((h - 1) - (tf.sin(a) * (w - 1) + tf.cos(a) * (h - 1))) / 2.0
transforms = tf.transpose(
tf.stack([
tf.cos(a),
tf.sin(a), x, -tf.sin(a),
tf.cos(a), y,
tf.zeros(flags.bs),
tf.zeros(flags.bs)
]))
return tf.cond(tflearn.get_training_mode(),
lambda: tf.contrib.image.transform(curr, transforms),
lambda: curr)
else:
return curr
def build_network(self):
"""
Build the convnet.
Input is 48x48
3072 nodes in fully connected layer
"""
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(
per_channel=True, mean=[0.53990436, 0.4405486, 0.39328504])
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([49, 49], padding=4)
# Building Residual Network
self.network = tflearn.input_data(shape=[None, 49, 49, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
self.network = tflearn.conv_2d(self.network,
16,
3,
regularizer='L2',
weight_decay=0.0001)
self.network = tflearn.resnext_block(self.network, 5, 16, 32)
self.network = tflearn.resnext_block(self.network,
1,
32,
32,
downsample=True)
self.network = tflearn.resnext_block(self.network, 4, 32, 32)
self.network = tflearn.resnext_block(self.network,
1,
64,
32,
downsample=True)
self.network = tflearn.resnext_block(self.network, 4, 64, 32)
self.network = tflearn.batch_normalization(self.network)
self.network = tflearn.activation(self.network, 'relu')
self.network = tflearn.global_avg_pool(self.network)
# Regression
self.network = tflearn.fully_connected(self.network,
11,
activation='softmax')
opt = tflearn.Momentum(0.1,
lr_decay=0.1,
decay_step=32000,
staircase=True)
self.network = tflearn.regression(self.network,
optimizer=opt,
loss='categorical_crossentropy')
# Training
self.model = tflearn.DNN(self.network,
checkpoint_path='Snapshots/model_resnext',
max_checkpoints=10,
tensorboard_verbose=0,
tensorboard_dir='Logs/',
clip_gradients=0.)
self.load_model()
def return2img():
n = 5
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
# Building Residual Network
net = tflearn.input_data(shape=[None, 32, 32, 1],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n - 1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n - 1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
# Regression
net = tflearn.fully_connected(net, 3, activation='softmax')
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net, optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net)
model.load("model_resnet_+-------cifar10-6500")
img = load_img_label('/media/bai/Elements/LiangData_Afterchoose/img1')
livermask = load_mask('/media/bai/Elements/LiangData_Afterchoose/mask1')
abc=[]
np.array(abc)
for i in range(img.shape[0]):
a = np.zeros((512,512,3,3),dtype=np.float32)#最后一项表示第几个分割方式，倒数第二项表示预测的三个结果
for j in range(0,3):
this_mask = livermask[i, :, :, j]
this_mask_num = int(np.max(this_mask))
for k in range(1,this_mask_num+1):
if os.path.exists('/media/bai/Elements/LiangData_Afterchoose/superpixel/'+str(i)+'_'+str(j)+'_'+str(k)+'.jpg'):
this_img = io.imread('/media/bai/Elements/LiangData_Afterchoose/superpixel/'+str(i)+'_'+str(j)+'_'+str(k)+'.jpg')
this_img = np.reshape(this_img,(1,32,32,1))
this_img = this_img.astype(np.float32)
result = model.predict(this_img)
# print(result)
abc.append(result)
thisPartLoc=np.where(this_mask==k)
for num in range(len(thisPartLoc[1])):
a[thisPartLoc[0][num],thisPartLoc[1][num],0,j] = result[0,0]
a[thisPartLoc[0][num],thisPartLoc[1][num], 1, j] = result[0,1]
a[thisPartLoc[0][num],thisPartLoc[1][num], 2, j] = result[0,2]
b = np.max(a,axis=3)
final = np.argmax(b,axis=2)
#添加3D CRF
misc.imsave('/media/bai/Elements/LiangData_Afterchoose/result/' + str(i) + '.jpg', final)
def build_network(self):
# Smaller 'AlexNet'
# https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
# img_aug.add_random_flip_updown()
img_aug.add_random_crop([SIZE_FACE, SIZE_FACE], padding=4)
img_aug.add_random_rotation(max_angle=16.0)
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
img_prep.add_featurewise_stdnorm(per_channel=True)
print('[+] Building CNN')
self.network = input_data(shape = [None, SIZE_FACE, SIZE_FACE, 1], data_preprocessing=img_prep, data_augmentation=img_aug)
self.network = conv_2d(self.network, 64, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = conv_2d(self.network, 64, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = local_response_normalization(self.network)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = conv_2d(self.network, 128, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = conv_2d(self.network, 128, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = conv_2d(self.network, 256, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = conv_2d(self.network, 256, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = conv_2d(self.network, 512, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = conv_2d(self.network, 512, 3, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = max_pool_2d(self.network, 2, strides=2)
self.network = dropout(self.network, 0.8)
self.network = fully_connected(self.network, 4096, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = dropout(self.network, 0.7)
self.network = fully_connected(self.network, 4096, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = dropout(self.network, 0.7)
self.network = fully_connected(self.network, 1024, activation='relu', regularizer='L2')#, weight_decay=0.0001)
self.network = dropout(self.network, 0.7)
self.network = fully_connected(self.network, len(EMOTIONS), activation='softmax')#, restore=False)
mom = tflearn.optimizers.Momentum(learning_rate=0.02, lr_decay=0.8, decay_step=500)
self.network = regression(self.network, optimizer=mom, loss='categorical_crossentropy')#, restore=False)
self.model = tflearn.DNN(
self.network,
tensorboard_dir = '../tmp/',
checkpoint_path = None,
max_checkpoints = None,
tensorboard_verbose = 0
)
def get_model(model_name):
# First we load the network
print("Setting up neural networks...")
n = 18
# Real-time data preprocessing
print("Doing preprocessing...")
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(
per_channel=True, mean=[0.573364, 0.44924123, 0.39455055])
# Real-time data augmentation
print("Building augmentation...")
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
#Build the model (for 32 x 32)
print("Shaping input data...")
net = tflearn.input_data(shape=[None, 32, 32, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
print("Carving Resnext blocks...")
net = tflearn.resnext_block(net, n, 16, 32)
net = tflearn.resnext_block(net, 1, 32, 32, downsample=True)
net = tflearn.resnext_block(net, n - 1, 32, 32)
net = tflearn.resnext_block(net, 1, 64, 32, downsample=True)
net = tflearn.resnext_block(net, n - 1, 64, 32)
print("Erroding Gradient...")
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, 8, activation='softmax')
opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
net = tflearn.regression(net,
optimizer=opt,
loss='categorical_crossentropy')
print("Structuring model...")
model = tflearn.DNN(net, tensorboard_verbose=0, clip_gradients=0.)
# Load the model from checkpoint
print("Loading the model...")
model.load(model_name)
return model
def build(IMAGE_H,
IMAGE_W,
IMAGE_C,
LABELS,
model_file,
learning_rate=0.01,
val_acc_thresh=0.99):
img_prep = tflearn.ImagePreprocessing()
# img_prep.add_featurewise_zero_center(per_channel=True) # 输入图像要减图像均值
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_rotation(max_angle=10.0) # 随机旋转角度
# img_aug.add_random_blur(sigma_max=5.0)
# Building Residual Network
net = tflearn.input_data(shape=[None, IMAGE_H, IMAGE_W, IMAGE_C],
data_preprocessing=img_prep,
data_augmentation=img_aug,
name='input')
net = tflearn.conv_2d(net,
16,
3,
regularizer='L2',
weights_init='variance_scaling',
weight_decay=0.0001,
name="conv1") # 卷积处理, 16个卷积，卷积核大小为3，L2 正则化减少过拟合
net = tflearn.residual_block(net, 1, 16, name="res1") # 1 个残差层，输出16特征
net = tflearn.residual_block(net, 1, 32, downsample=True,
name="res2") # 1 个残差层，输出32特征，降维1/2
net = tflearn.residual_block(net, 1, 64, downsample=True,
name="res3") # 1 个残差层，输出64特征，降维1/2
# Regression
net = tflearn.fully_connected(net, len(LABELS), activation='softmax')
mom = tflearn.Momentum(learning_rate,
lr_decay=0.1,
decay_step=32000,
staircase=True)
net = tflearn.regression(net,
optimizer=mom,
loss='categorical_crossentropy')
# Training
model = tflearn.DNN(net, max_checkpoints=1, tensorboard_verbose=3)
return model
def retrain(output_filename):
num_classes = 120
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_blur(sigma_max=5.)
img_aug.add_random_crop((224, 224))
img_aug.add_random_rotation(max_angle=25.)
softmax = vgg16(softmax_size=num_classes,
restore_softmax=False,
data_preprocessing=img_prep,
data_augmentation=img_aug)
regression = tflearn.regression(softmax,
optimizer='rmsprop',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(regression,
checkpoint_path=output_filename,
max_checkpoints=3,
tensorboard_verbose=3)
# Load pre-existing model, restoring all weights, except softmax layer ones
model_file = 'vgg/vgg16.tflearn'
if not os.path.exists(model_file):
maybe_download(DATA_URL, 'vgg')
model.load(model_file)
# Start fine-tuning
X, Y = grozi120.load_data()
model.fit(X,
Y,
n_epoch=10,
validation_set=0.1,
shuffle=True,
show_metric=True,
batch_size=64,
snapshot_step=200,
snapshot_epoch=False,
run_id=output_filename)
model.save(output_filename)
def _build_network(self, n, image_size):
# Define the input to the network.
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
# Real-time data augmentation.
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
net = tflearn.input_data(shape=[None, image_size[0], image_size[1], 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
# Start with a normal convolutional layer.
net = tflearn.conv_2d(net,
64,
3,
regularizer='L2',
weight_decay=0.0001)
# Since this is a ResNet with <50 layers, we'll use regular residual blocks;
# otherwise, we'd use residual bottleneck blocks instead.
net = tflearn.residual_block(net, n, 64)
net = tflearn.residual_block(net, 1, 128, downsample=True)
net = tflearn.residual_block(net, n - 1, 128)
net = tflearn.residual_block(net, 1, 256, downsample=True)
net = tflearn.residual_block(net, n - 1, 256)
# Perform batch normalization.
net = tflearn.batch_normalization(net)
# Activation at the end of the network pre-FC.
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
net = tflearn.fully_connected(net, 2, activation='softmax')
mom = tflearn.Momentum(0.1,
lr_decay=0.1,
decay_step=32000,
staircase=True)
net = tflearn.regression(net,
optimizer=mom,
loss='categorical_crossentropy')
return net
def add_layer(net, lay):
if lay[0] == 'down_conv_2d':
net = tfl.conv_2d(net,
nb_filter=lay[1],
strides=[1, 2, 2, 1],
filter_size=lay[2],
activation=lay[3])
elif lay[0] == 'conv_2d':
net = tfl.conv_2d(net,
nb_filter=lay[1],
filter_size=lay[2],
activation=lay[3])
elif lay[0] == 'flatten':
s = net.get_shape()
net = tf.reshape(net, [tf.shape(net)[0], s[1] * s[2] * s[3]])
elif lay[0] == 'fully_connected':
net = tfl.fully_connected(net, n_units=lay[1], activation=lay[2])
elif lay[0] == 'expand':
net = tf.reshape(net, [
tf.shape(net)[0], lay[1], lay[2],
net.get_shape()[1] / lay[1] / lay[2]
])
elif lay[0] == 'up_conv_2d':
net = tfl.conv_2d_transpose(net,
nb_filter=lay[1],
filter_size=lay[2],
strides=[1, 2, 2, 1],
output_shape=[lay[3], lay[4]],
activation=lay[5],
padding="same")
elif lay[0] == 'input_layer':
image_prep = tfl.ImagePreprocessing()
image_prep.add_featurewise_stdnorm(per_channel=True,
std=0.24051991589344662)
image_prep.add_featurewise_zero_center(per_channel=True,
mean=0.14699117337640238)
net = tfl.layers.input_data(shape=[None, lay[1], lay[2]],
data_preprocessing=image_prep)
net = tf.expand_dims(net, axis=-1)
if lay[-1] == 'batch_norm':
net = tfl.batch_normalization(net)
return net
class ConvNeuralNetwork():
# Image preprocessing
imgprep = tflearn.ImagePreprocessing()
imgprep.add_featurewise_zero_center()
imgprep.add_featurewise_stdnorm()
# Image augmentation
imgaug = tflearn.ImageAugmentation()
imgaug.add_random_rotation()
imgaug.add_random_flip_leftright()
# Input layer
convnet = input_data(shape=[None, settings.IMG_SIZE, settings.IMG_SIZE, 1],
data_preprocessing=imgprep,
data_augmentation=imgaug,
name='input')
# Hidden layers
convnet = conv_2d(convnet, 32, 3, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 128, 3, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 256, 3, activation='relu')
convnet = max_pool_2d(convnet, 2)
# Fully connected layer
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, settings.DROPOUT_RATE)
# Output layer
convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=settings.LEARNING_RATE,
name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log')
from tflearn.helpers.regularizer import add_weights_regularizer from tensorflow.contrib.slim import dataset from tensorflow.contrib.slim import dataset tflearn.input_data() tflearn.variable() tflearn.conv_2d() tflearn.single_unit() tflearn.lstm() tflearn.embedding() tflearn.batch_normalization() tflearn.merge() tflearn.regression() tflearn.tanh() tflearn.softmax_categorical_crossentropy() tflearn.SGD() tflearn.initializations.uniform() tflearn.losses.L1() tflearn.add_weights_regularizer() tflearn.metrics.Accuracy() tflearn.summaries() tflearn.ImagePreprocessing() tflearn.ImageAugmentation() tflearn.init_graph()
def train(env, epoch_num, learning_rate=0.01, clean_start=False):
'''
a function that create input, create network, train network and report results
:param env: local/AWS; environment that our system work on
:param epoch_num: number of epochs to run
:return:
'''
#----------------------reading constants from config file---------------------------------
config = configparser.ConfigParser()
config.read('config.ini')
conf = config[env]
image_size = conf['window_size']
mean_colors = [
float(conf['mean_r']),
float(conf['mean_g']),
float(conf['mean_b'])
]
#-----------------------------------------------------------------------------------------
#----------------------------------------input layer------------------------------------------
# read input http://tflearn.org/data_utils/#build-hdf5-image-dataset
X_train, Y_train, X_test, Y_test = deep_learning.data_prep(
conf, clean_start=clean_start)
#prepare input layer http://tflearn.org/data_preprocessing/
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(mean=mean_colors, per_channel=True)
# http://tflearn.org/layers/core/#input-data
input_layer = input_data(shape=[None, image_size, image_size, 3],
name='input_layer')
#,data_preprocessing=img_prep)
#---------------------------------------------------------------------------------------------
#-------------------------------create model--------------------------------------------------------
# network
softmax = deep_learning.inception(input_layer, 2)
#softmax = deep_learning.VGGNet(input_layer, 2)
# estimator layer
f_score = tflearn.metrics.F2Score()
network = tflearn.regression(
softmax,
optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=learning_rate,
metric=f_score) #if want to finetune give 'restore=False'
# model http://tflearn.org/models/dnn/
model = tflearn.DNN(network,
checkpoint_path='model_inception',
max_checkpoints=1,
tensorboard_verbose=0,
tensorboard_dir="./logs2")
if os.path.isfile('inception_ercis.model') and not clean_start:
print 'load model learning_rate: ' + str(learning_rate)
model.load('inception_ercis.model', weights_only=True)
model.fit(X_train,
Y_train,
validation_set=(X_test, Y_test),
n_epoch=epoch_num,
shuffle=True,
show_metric=True,
batch_size=128,
snapshot_step=200,
snapshot_epoch=False,
run_id='inception_ercis')
#---------------------------------------------------------------------------------------------
model.save('inception_ercis.model')
import tflearn
def train(env, epoch_num, learning_rate = 0.01, clean_start = True, model_name = None):
'''
a function that create input, create network, train network and report results
:param env: local/AWS; environment that our system work on
:param epoch_num: number of epochs to run
:return:
'''
print 'model_name: ', model_name
#----------------------reading constants from config file---------------------------------
config = configparser.ConfigParser()
config.read('config.ini')
conf = config[env]
image_size = conf['window_size']
mean_colors = [float(conf['mean_r']), float(conf['mean_g']), float(conf['mean_b'])]
data_splitting_method = conf['data_splitting_method']
train_size = conf['train_size']
#-----------------------------------------------------------------------------------------
#----------------------------------------input layer------------------------------------------
# read input http://tflearn.org/data_utils/#build-hdf5-image-dataset
X_train, Y_train, X_test, Y_test = data_prep(conf, clean_start = clean_start, method=data_splitting_method, training_size=train_size)
#prepare input layer http://tflearn.org/data_preprocessing/
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(mean=mean_colors,per_channel=True)
# http://tflearn.org/layers/core/#input-data
input_layer = input_data(shape=[None, image_size, image_size, 3], name = 'input_layer')
#,data_preprocessing=img_prep)
#---------------------------------------------------------------------------------------------
#-------------------------------create model--------------------------------------------------------
# network
#softmax = deep_learning.inception(input_layer, 2)
#softmax = deep_learning.VGGNet(input_layer, 2)
if model_name.split('.')[0].split('-')[0] == 'inception':
softmax = deep_learning.inception(input_layer, 2)
elif model_name.split('.')[0].split('-')[0] == 'ResNet':
softmax = deep_learning.ResNet(input_layer, 2)
elif model_name.split('.')[0].split('-')[0] == 'VGGNet':
softmax = deep_learning.VGGNet(input_layer, 2)
elif model_name.split('.')[0].split('-')[0] == 'NiN':
softmax = deep_learning.NiN(input_layer, 2)
else:
return
# estimator layer
f_score = tflearn.metrics.F2Score()
momentum = Momentum(learning_rate=0.1, lr_decay=0.9, decay_step=250)
network = tflearn.regression(softmax, optimizer='adam',
loss='categorical_crossentropy',metric=f_score) #if want to finetune give 'restore=False'
# model http://tflearn.org/models/dnn/
model = tflearn.DNN(network, checkpoint_path='./models', best_checkpoint_path='./models/best',
max_checkpoints=1, tensorboard_verbose=0, tensorboard_dir="./logs", best_val_accuracy=0.0)
if model_name != None:
if os.path.isfile(model_name) and not clean_start:
print 'load model learning_rate: ' + str(learning_rate)
model.load(model_name,weights_only=True)
model.fit(X_train, Y_train, validation_set = (X_test, Y_test),n_epoch=epoch_num, shuffle=True,
show_metric=True, batch_size=128, snapshot_epoch=True, run_id=model_name)
#---------------------------------------------------------------------------------------------
print 'saving model.'
model.save( model_name)
print 'model: ', model_name, 'saved.'
return model_name
def return2img():
n = 5
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True)
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
inp = tflearn.input_data(shape=[None, 32, 32, 1],
data_preprocessing=img_prep,
data_augmentation=img_aug,
name='input')
conv1_1 = tflearn.conv_2d(inp, 64, 3, activation='relu', name="conv1_1")
conv1_2 = tflearn.conv_2d(conv1_1,
64,
3,
activation='relu',
name="conv1_2")
pool1 = tflearn.max_pool_2d(conv1_2, 2, strides=2)
conv2_1 = tflearn.conv_2d(pool1, 128, 3, activation='relu', name="conv2_1")
conv2_2 = tflearn.conv_2d(conv2_1,
128,
3,
activation='relu',
name="conv2_2")
pool2 = tflearn.max_pool_2d(conv2_2, 2, strides=2)
conv3_1 = tflearn.conv_2d(pool2, 256, 3, activation='relu', name="conv3_1")
conv3_2 = tflearn.conv_2d(conv3_1,
256,
3,
activation='relu',
name="conv3_2")
conv3_3 = tflearn.conv_2d(conv3_2,
256,
3,
activation='relu',
name="conv3_3")
pool3 = tflearn.max_pool_2d(conv3_3, 2, strides=2)
conv4_1 = tflearn.conv_2d(pool3, 512, 3, activation='relu', name="conv4_1")
conv4_2 = tflearn.conv_2d(conv4_1,
512,
3,
activation='relu',
name="conv4_2")
conv4_3 = tflearn.conv_2d(conv4_2,
512,
3,
activation='relu',
name="conv4_3")
pool4 = tflearn.max_pool_2d(conv4_3, 2, strides=2)
conv5_1 = tflearn.conv_2d(pool4, 512, 3, activation='relu', name="conv5_1")
conv5_2 = tflearn.conv_2d(conv5_1,
512,
3,
activation='relu',
name="conv5_2")
conv5_3 = tflearn.conv_2d(conv5_2,
512,
3,
activation='relu',
name="conv5_3")
pool5 = tflearn.max_pool_2d(conv5_3, 2, strides=2)
fc6 = tflearn.fully_connected(pool5, 4096, activation='relu', name="fc6")
fc6_dropout = tflearn.dropout(fc6, 0.5)
fc7 = tflearn.fully_connected(fc6_dropout,
4096,
activation='relu',
name="fc7")
fc7_droptout = tflearn.dropout(fc7, 0.5)
fc8 = tflearn.fully_connected(fc7_droptout,
3,
activation='softmax',
name="fc8")
mm = tflearn.Momentum(learning_rate=0.01,
momentum=0.9,
lr_decay=0.1,
decay_step=1000)
network = tflearn.regression(fc8,
optimizer=mm,
loss='categorical_crossentropy',
restore=False)
# Training
model = tflearn.DNN(network)
model.load("model_resnet_+-------cifar10-14000")
img = load_img_label('/home/bai/最新数据/验证数据/经过预处理的原始图像')
livermask = load_mask('/home/bai/最新数据/验证数据/mask')
abc = []
np.array(abc)
for i in range(img.shape[0]):
a = np.zeros((512, 512, 3, 3),
dtype=np.float32) #最后一项表示第几个分割方式，倒数第二项表示预测的三个结果
for j in range(0, 3):
this_mask = livermask[i, :, :, j]
this_mask_num = int(np.max(this_mask))
for k in range(1, this_mask_num + 1):
if os.path.exists('/home/bai/最新数据/验证数据/超像素块/' + str(i) + '_' +
str(j) + '_' + str(k) + '.jpg'):
this_img = io.imread('/home/bai/最新数据/验证数据/超像素块/' + str(i) +
'_' + str(j) + '_' + str(k) + '.jpg')
this_img = np.reshape(this_img, (1, 32, 32, 1))
this_img = this_img.astype(np.float32)
result = model.predict(this_img)
# print(result)
abc.append(result)
thisPartLoc = np.where(this_mask == k)
for num in range(len(thisPartLoc[1])):
a[thisPartLoc[0][num], thisPartLoc[1][num], 0,
j] = result[0, 0]
a[thisPartLoc[0][num], thisPartLoc[1][num], 1,
j] = result[0, 1]
a[thisPartLoc[0][num], thisPartLoc[1][num], 2,
j] = result[0, 2]
b = np.max(a, axis=3)
final = np.argmax(b, axis=2)
misc.imsave(
'/home/bai/PycharmProjects/DeepRESIDUALNETWORKS/result/' + str(i) +
'.jpg', final)
def train_nn_tflearn(data_handler, num_epochs=50):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
batch_size = data_handler.mini_batch_size
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25)
img_aug.add_random_crop([32, 32], padding=4)
x = tflearn.input_data(shape=[None, 32, 32, 3],
dtype='float',
data_preprocessing=img_prep,
data_augmentation=img_aug)
# x = tf.placeholder('float', [None, 32, 32, 3])
#y = tf.placeholder('float', [None, 10])
# test_data, test_labels = data_handler.get_test_data()
# test_data = test_data.reshape([-1,32,32,3])
ntrain = data_handler.train_size
ntest = data_handler.meta['num_cases_per_batch']
# from tflearn.datasets import cifar10
# (X, Y), (X_test, Y_test) = cifar10.load_data(dirname="/home/hamza/meh/bk_fedora24/Documents/tflearn_example/cifar-10-batches-py")
# X, Y = tflearn.data_utils.shuffle(X, Y)
# Y = tflearn.data_utils.to_categorical(Y, 10)
# Y_test = tflearn.data_utils.to_categorical(Y_test, 10)
X, Y = data_handler.get_all_train_data()
X, Y = tflearn.data_utils.shuffle(X, Y)
X = np.dstack((X[:, :1024], X[:, 1024:2048], X[:, 2048:]))
X = X / 255.0
X = X.reshape([-1, 32, 32, 3])
Y = tflearn.data_utils.to_categorical(Y, 10)
X_test, Y_test = data_handler.get_test_data()
X_test = np.dstack((X_test[:, :1024], X_test[:, 1024:2048], X_test[:,
2048:]))
X_test = X_test / 255.0
X_test = X_test.reshape([-1, 32, 32, 3])
#network = tflearn.regression(net3(x),optimizer='adam',loss='categorical_crossentropy',learning_rate=0.001)
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
network = tflearn.regression(resnet1(x),
optimizer=mom,
loss='categorical_crossentropy')
print np.shape(X)
print np.shape(Y)
print network
model = tflearn.DNN(network, tensorboard_verbose=0)
model.fit(X,
Y,
n_epoch=50,
shuffle=True,
validation_set=(X_test, Y_test),
show_metric=True,
batch_size=data_handler.mini_batch_size,
run_id='cifar10_cnn')
def test(env, model_name, clean_start=False):
'''
a function that create input, create network, train network and report results
:param env: local/AWS; environment that our system work on
:param epoch_num: number of epochs to run
:return:
'''
#----------------------reading constants from config file---------------------------------
config = configparser.ConfigParser()
config.read('config.ini')
conf = config[env]
image_size = conf['window_size']
mean_colors = [
float(conf['mean_r']),
float(conf['mean_g']),
float(conf['mean_b'])
]
#-----------------------------------------------------------------------------------------
#----------------------------------------input layer------------------------------------------
# read input http://tflearn.org/data_utils/#build-hdf5-image-dataset
X_train, Y_train, X_test, Y_test = data_prep(conf)
#prepare input layer http://tflearn.org/data_preprocessing/
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(mean=mean_colors, per_channel=True)
# http://tflearn.org/layers/core/#input-data
input_layer = input_data(shape=[None, image_size, image_size, 3],
name='input_layer')
#,data_preprocessing=img_prep)
#---------------------------------------------------------------------------------------------
#-------------------------------create model--------------------------------------------------------
# network
if model_name.split('.')[0].split('-')[0] == 'inception':
softmax = deep_learning.inception(input_layer, 2)
elif model_name.split('.')[0].split('-')[0] == 'ResNet':
softmax = deep_learning.ResNet(input_layer, 2)
elif model_name.split('.')[0].split('-')[0] == 'VGGNet':
softmax = deep_learning.VGGNet(input_layer, 2)
elif model_name.split('.')[0].split('-')[0] == 'NiN':
softmax = deep_learning.NiN(input_layer, 2)
else:
softmax = deep_learning.inception(input_layer, 2)
#softmax = deep_learning.inception(input_layer, 2)
#softmax = deep_learning.VGGNet(input_layer, 2)
# estimator layer
f_score = tflearn.metrics.F2Score()
momentum = Momentum(learning_rate=0.1, lr_decay=0.9, decay_step=250)
network = tflearn.regression(
softmax,
optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.0001,
metric=f_score) #if want to finetune give 'restore=False'
# model http://tflearn.org/models/dnn/
model = tflearn.DNN(network,
checkpoint_path='model_' + model_name,
max_checkpoints=1,
tensorboard_verbose=0,
tensorboard_dir="./logs_test")
if os.path.isfile(model_name) and not clean_start:
model.load(model_name)
#model.fit(X_train, Y_train, validation_set = (X_test, Y_test),n_epoch=epoch_num, shuffle=True,
# show_metric=True, batch_size=100, snapshot_step=100, snapshot_epoch=False, run_id='inception_ercis')
#---------------------------------------------------------------------------------------------
#model.save('inception.model')
temp = []
counter = 0
for x in X_test:
temp.append(model.predict(x.reshape(1, 40, 40, 3))[0])
counter += 1
print '{0}\r'.format(counter),
#print temp
predict = temp
with open('results/test_pred_{0}.pik'.format(gettime()), 'w') as f:
pickle.dump(predict, f)
target = np.argmax(Y_test[()], axis=1)
pred = np.argmax(predict, axis=1)
#print Y_test.shape
#for x,y in zip(target, pred):
# print x, y
print type(target)
print target.shape
print type(pred)
print pred.shape
#for y in Y_test:
# print y
#try reporting
#try:
print 'accuracy_score: ', accuracy_score(target, pred)
print 'recall_score: ', recall_score(target, pred)
print 'precision_score: ', precision_score(target, pred)
print 'f2_score: ', fbeta_score(target, pred, 2)
print 'confusion_matrix: '
print confusion_matrix(target, pred)
with open('results/results.txt', 'a+') as f:
f.write('# ' + model_name.split('_')[0] + '_' + gettime() + '\n')
f.write('accuracy_score: {0}\n'.format(accuracy_score(target, pred)))
f.write('recall_score: {0}\n'.format(recall_score(target, pred)))
f.write('precision_score: {0}\n'.format(precision_score(target, pred)))
f.write('f2_score: {0}\n'.format(fbeta_score(target, pred, 2)))
f.write('confusion_matrix: \n')
f.write(str(confusion_matrix(target, pred)))
f.write('\n\n\n')
import tensorflow as tf
# import data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar100.load_data(label_mode='fine')
# data setting
y_train = y_train.reshape(50000,)
y_test = y_test.reshape(10000,)
train_data = np.asarray(x_train, dtype=np.float32)
train_labels =np.asarray(y_train, dtype=np.float32)
eval_data = np.asarray(x_test, dtype=np.float32)
eval_labels = np.asarray(y_test, dtype=np.float32)
# one hot encoding
y_train = tflearn.data_utils.to_categorical(y_train, 100)
y_test = tflearn.data_utils.to_categorical(y_test, 100)
# image argumentation
image_preprocessing = tflearn.ImagePreprocessing()
image_preprocessing.add_featurewise_zero_center(per_channel=True)
image_argumentation = tflearn.ImageAugmentation()
image_argumentation.add_random_flip_leftright()
image_argumentation.add_random_crop([32, 32], padding=4)
# build 56 layers residual layers
n = 9
net = tflearn.input_data(shape=[None, 32, 32, 3],
data_preprocessing=image_preprocessing,
data_augmentation=image_argumentation)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n-1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n-1, 64)
def image_prep(self, stdn = True, mean = True): image_prep = tfl.ImagePreprocessing() if stdn: image_prep.add_featurewise_stdnorm(per_channel=True) if mean: image_prep.add_featurewise_zero_center(per_channel=True) return image_prep
def cnn_model_ic(features, labels, mode):
"""Model function for CNN."""
# Real-time data preprocessing
print("Doing preprocessing...")
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True, mean=[0.573364,0.44924123,0.39455055])
# Real-time data augmentation
print("Building augmentation...")
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([128, 128], padding=4)
# Input Layer
# Reshape X to 4-D tensor: [batch_size, width, height, channels]
# 61326 images are 4160x3120 pixels, i resize to 128*128 and have three color channel
input_layer = tflearn.input_data(shape=[-1, 128, 128, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
input_layer = tf.reshape(features["x"], [-1, 128, 128, 3])
# Convolutional Layer #1
# Computes 32 features using a 5x5 filter with ReLU activation.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 128, 128, 1]
# Output Tensor Shape: [batch_size, 128, 128, 32]
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
print "Conv1 is completed"
# Pooling Layer #1
# First max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 128, 128, 32]
# Output Tensor Shape: [batch_size, 64, 64, 32]
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2
# Computes 64 features using a 5x5 filter.
# Padding is added to preserve width and height.
# Input Tensor Shape: [batch_size, 64, 64, 32]
# Output Tensor Shape: [batch_size, 64, 64, 64]
print "Conv2 is in progress"
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #2
# Second max pooling layer with a 2x2 filter and stride of 2
# Input Tensor Shape: [batch_size, 64, 64, 64]
# Output Tensor Shape: [batch_size, 32, 32, 64]
print "Conv2 is completed"
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Flatten tensor into a batch of vectors
# Input Tensor Shape: [batch_size, 32, 32, 64]
# Output Tensor Shape: [batch_size, 32 * 32 * 64]
print "pool2 is in processing"
pool2_flat = tf.reshape(pool2, [-1, 32 * 32 * 64])
print "pool2 is completed"
# Dense Layer
# Densely connected layer with 1024 neurons
# Input Tensor Shape: [batch_size, 32 * 32 * 64]
# Output Tensor Shape: [batch_size, 1024]
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
# Add dropout operation; 0.6 probability that element will be kept
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits layer
# Input Tensor Shape: [batch_size, 1024]
# Output Tensor Shape: [batch_size, 6]
logits = tf.layers.dense(inputs=dropout, units=7)
predictions = {
# Generate predictions (for PREDICT and EVAL mode)
"classes": tf.argmax(input=logits, axis=1),
# Add `softmax_tensor` to the graph. It is used for PREDICT and by the
# `logging_hook`.
"probabilities": tf.nn.softmax(logits, name="softmax_tensor")
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(
loss=loss,
global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# Add evaluation metrics (for EVAL mode)
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(
labels=labels, predictions=predictions["classes"])}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def AudioPredict(soundfile, class_test,threshold):
import argparse
import tensorflow as tf
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import os
import numpy as np
import cv2
from skimage.util.shape import view_as_blocks
from skimage.util.shape import view_as_windows
np.random.seed(1001)
import librosa
from pytictoc import TicToc
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.logging.set_verbosity(tf.logging.ERROR)
patchscore = float(threshold.decode('base64', 'strict'))
timet = TicToc()
timet.tic()
goobeps=0.82
islandps=0.82
mlworldps=0.82
bootps=0.82
tpcount=2e9
tt=0
classtestlist=['boot','goobe','island', 'mlworld']
for p in range(len(classtestlist)):
if class_test==classtestlist[p]:
tt+=1
if tt==0:
print('class_test ', class_test, ' entered is not valid')
print('acceptable class entries: ', classtestlist)
print('loading audio file')
#load sound file, get melspectrogram, crop overlapping tiles and save tensor
X=np.random.random((1,128,128,1))
wav1, sr = librosa.core.load(soundfile)
stepper=int(np.ceil(len(wav1)/sr/6))
Sxx=librosa.feature.melspectrogram(wav1)
Sxx2 = np.log10(1+10*abs(Sxx))
Sxx2norm=255*(Sxx2-Sxx2.min())/(Sxx2.max()-Sxx2.min());
Sxx2norm=np.round_(Sxx2norm, decimals=0, out=None).astype(int)
Sxx2norm=Sxx2norm.astype(np.uint8)
if Sxx2norm.shape[1]>=128:
s1=int(np.floor(Sxx2norm.shape[1]/128)*128)
B = view_as_windows(Sxx2norm, window_shape=(128, 128),step=stepper)
for index in range(B.shape[1]):
x=B[0][index]
x = np.expand_dims(x, axis=0)
x = np.expand_dims(x, axis=3)
X = np.concatenate((X,x),axis=0)
# remove random start
X = X[1:,:,:,:]
tf.reset_default_graph()
# number of classes
y=2
# image preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center()# Zero Center (With mean computed over the whole dataset)
img_prep.add_featurewise_stdnorm() #STD Normalization (With std computed over the whole dataset)
# Building 'AlexNet'
network = input_data(shape=[None, 128, 128, 1],data_preprocessing=img_prep)
network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, y, activation='softmax')
network = regression(network, optimizer='momentum',
loss='categorical_crossentropy',
learning_rate=0.0001)
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
max_checkpoints=1, tensorboard_verbose=2)
# set up paramaters for model
if class_test=='boot':
classmodel='AudioModels/model_Alexnet_bootnet1/Alexnet_bootnet1'
patchscore=bootps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.75*len(X))
print('loading boot test')
elif class_test=='goobe':
print('loading goobe test')
classmodel='AudioModels/model_Alexnet_goobenet1/Alexnet_goobenet1'
patchscore=goobeps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.25*len(X))
elif class_test=='island':
print('loading island test')
classmodel='AudioModels/model_Alexnet_islandnet1/Alexnet_islandnet1'
patchscore=islandps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.25*len(X))
elif class_test=='mlworld':
print('loading mlworld test')
classmodel='AudioModels/model_Alexnet_mlworldnet1/Alexnet_mlworldnet1'
patchscore=mlworldps
tilepasscount=np.floor(tpcount/(len(wav1)*len(X)))
if tilepasscount>len(X):
tilepasscount=np.floor(0.25*len(X))
model.load(classmodel)
print("totaltilecount", len(X))
print("tilepasscount", tilepasscount)
lastPF=[]
# Get prediction of tile list
res=model.predict(X)
print(res)
# method 1: PASS if any one tile gets 99% or better
topmatch=0
for k in range(len(res[:,0])):
if res[k,0]>=0.99:
topmatch+=1
# tally consecutive tiles that meet the threshold
z = np.where((res[:,0]-patchscore)>0)
z=z[0]
tally=[0]
for j in range(len(z)-1):
if z[j+1]-z[j]==1:
tally.append(1)
if(sum(tally)!=0):
tally.append(1)
timet.toc()
if(sum(tally)>=tilepasscount or topmatch>=1):
lastPF.append(1)
print(sum(tally),'/',len(X))
else:
lastPF.append(0)
print(sum(tally),'/',len(X))
if np.sum(lastPF)>=1:
print('PASS')
ret ='PASS'
return ret
else:
print('FAIL')
ret = 'FAIL'
return ret
import csv
from os import listdir
from tifffile import imread
from math import ceil, log
import pickle
class_map = {0:'building', 1:'barren_land',2:'trees',3:'grassland',4:'road',5:'water'}
tflearn.config.init_graph (num_cores=1, gpu_memory_fraction=0.3)
# Residual blocks
# 256 layers: n=5, 256 layers: n=9, 110 layers: n=18
n = 5
# Real-time data preprocessing
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center([112.3256420256,1256.6802564592,1256.1983022562], per_channel=True)
# Building Residual Network
net = tflearn.input_data(shape=[None, 28, 28, 3], data_preprocessing=img_prep)
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
net = tflearn.residual_block(net, n, 16)
net = tflearn.residual_block(net, 1, 32, downsample=True)
net = tflearn.residual_block(net, n-1, 32)
net = tflearn.residual_block(net, 1, 64, downsample=True)
net = tflearn.residual_block(net, n-1, 64)
net = tflearn.batch_normalization(net)
net = tflearn.activation(net, 'relu')
net = tflearn.global_avg_pool(net)
def main(args):
# Data loading
fid = args.fid + "train"
data = unpickle(fid)
n_class = args.nclass
train_feats, train_labs = load_data(args, "train")
test_feats, test_labs = load_data(args, "test")
# Real-time data preprocessing
mean = [129.30416561, 124.0699627, 112.43405006]
std = [51.20360335, 50.57829831, 51.56057865]
img_prep = tflearn.ImagePreprocessing()
img_prep.add_featurewise_zero_center(per_channel=True, mean=mean)
img_prep.add_featurewise_stdnorm(per_channel=True, std=std)
# Real-time data augmentation
img_aug = tflearn.ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_crop([32, 32], padding=4)
# DenseNet
depth = args.depth
filters = args.growth_rate
nb_blocks = 3
#nb_layers_list = [6,12,48,32]
nb_layers_list = [(depth - (nb_blocks + 1)) // (2 * nb_blocks)
for i in range(nb_blocks)]
print(nb_layers_list)
net = tflearn.input_data(shape=[None, 32, 32, 3],
data_preprocessing=img_prep,
data_augmentation=img_aug)
print("input", net)
net = tflearn.conv_2d(net,
nb_filter=2 * filters,
filter_size=3,
strides=1,
padding='same',
activation='linear',
bias=False,
name='conv0',
regularizer='L2',
weight_decay=1e-4)
# net = tflearn.max_pool_2d(net, kernel_size=3, strides=2, padding='valid')
print("init_layer", net)
for i in range(nb_blocks - 1):
net = dense_block(net,
filters,
nb_layers=nb_layers_list[i],
layer_name='dense_' + str(i + 1))
net = transition_layer(net, scope='trans_' + str(i + 1))
net = dense_block(net,
filters,
nb_layers=nb_layers_list[-1],
layer_name='dense_final')
# Global Avg + FC
net = tflearn.batch_normalization(net, scope='linear_batch')
net = tf.nn.relu(net)
net = tflearn.global_avg_pool(net)
if args.finetune == 1:
net = tflearn.fully_connected(net,
n_class,
activation='softmax',
regularizer='L2',
weight_decay=1e-4,
restore=False)
else:
net = tflearn.fully_connected(net,
n_class,
activation='softmax',
regularizer='L2',
weight_decay=1e-4)
# Optimizer
opt = tf.train.MomentumOptimizer(learning_rate=args.lr,
momentum=0.9,
use_nesterov=True)
epsilon = 1e-4
learning_rate = 1e-4
# opt = tf.train.AdamOptimizer(learning_rate=learning_rate, epsilon=epsilon)
# Regression
net = tflearn.regression(net,
optimizer=opt,
loss='categorical_crossentropy',
restore=False)
# Training
config = tf.ConfigProto()
config.allow_soft_placement = True
config.gpu_options.allow_growth = True
tf.add_to_collection(tf.GraphKeys.GRAPH_CONFIG, config)
model = tflearn.DNN(net,
checkpoint_path='/data/srd/models/image/model_' +
args.model_name + '/model',
tensorboard_dir='/data/srd/logs/image/log_' +
args.model_name,
max_checkpoints=3,
tensorboard_verbose=0,
clip_gradients=0.0)
if args.onlyevalue == 1:
model.load("/data/srd/models/image/" + args.pre_train + "/model.tfl")
n_test = len(test_feats)
n_batch = 10
batch_size = n_test / 10
labsp = model.predict(test_feats[0:batch_size])
for i in range(1, 10):
labsp = np.vstack([
labsp,
model.predict(test_feats[i * batch_size:(i + 1) * batch_size])
])
print(metrics.classification_report(test_labs, np.argmax(labsp, 1)))
print("acc:", metrics.accuracy_score(test_labs, np.argmax(labsp, 1)))
np.argmax(labsp,
1).tofile("/data/srd/data/cifar/" + args.pre_train + ".bin")
return
# pre-train model
if args.pre_train:
model.load("/data/srd/models/image/" + args.pre_train + "/model.tfl",
weights_only=True)
try:
model.fit(train_feats,
train_labs,
n_epoch=args.epoch,
validation_set=(test_feats, test_labs),
snapshot_epoch=False,
snapshot_step=500,
show_metric=True,
batch_size=64,
shuffle=True,
run_id=args.model_name)
except KeyboardInterrupt:
print("Keyboard Interrupt")
model.save("/data/srd/models/image/" + args.model_name + "/model.tfl")