def Datset_Generate(self):
token_dict = yaml.load(open(self.hp.Token_Path), Loader=yaml.Loader)
train_dataset = Dataset(
token_dict=token_dict,
pattern_path=self.hp.Train.Train_Pattern.Path,
metadata_file=self.hp.Train.Train_Pattern.Metadata_File)
dev_dataset = Dataset(
token_dict=token_dict,
pattern_path=self.hp.Train.Eval_Pattern.Path,
metadata_file=self.hp.Train.Eval_Pattern.Metadata_File)
inference_dataset = Inference_Dataset(
token_dict=token_dict,
pattern_paths=self.hp.Train.Inference_Pattern_in_Train)
if self.gpu_id == 0:
logging.info('The number of train patterns = {}.'.format(
len(train_dataset)))
logging.info('The number of development patterns = {}.'.format(
len(dev_dataset)))
logging.info('The number of inference patterns = {}.'.format(
len(inference_dataset)))
collater = Collater(token_dict=token_dict)
inference_collater = Inference_Collater(token_dict=token_dict)
self.dataloader_dict = {}
self.dataloader_dict['Train'] = torch.utils.data.DataLoader(
dataset= train_dataset,
sampler= torch.utils.data.DistributedSampler(train_dataset, shuffle= True) \
if self.hp.Use_Multi_GPU else \
torch.utils.data.RandomSampler(train_dataset),
collate_fn= collater,
batch_size= self.hp.Train.Batch_Size,
num_workers= self.hp.Train.Num_Workers,
pin_memory= True
)
self.dataloader_dict['Dev'] = torch.utils.data.DataLoader(
dataset= dev_dataset,
sampler= torch.utils.data.DistributedSampler(dev_dataset, shuffle= True) \
if self.num_gpus > 1 else \
torch.utils.data.RandomSampler(dev_dataset),
collate_fn= collater,
batch_size= self.hp.Train.Batch_Size,
num_workers= self.hp.Train.Num_Workers,
pin_memory= True
)
self.dataloader_dict['Inference'] = torch.utils.data.DataLoader(
dataset=inference_dataset,
sampler=torch.utils.data.SequentialSampler(inference_dataset),
collate_fn=inference_collater,
batch_size=self.hp.Inference_Batch_Size
or self.hp.Train.Batch_Size,
num_workers=self.hp.Train.Num_Workers,
pin_memory=True)
def Dataset_Generate(self):
train_dataset = Dataset(
pattern_path=self.hp.Train.Train_Pattern.Path,
metadata_file=self.hp.Train.Train_Pattern.Metadata_File,
pattern_per_speaker=self.hp.Train.Batch.Train.Pattern_per_Speaker)
dev_dataset = Dataset(
pattern_path=self.hp.Train.Eval_Pattern.Path,
metadata_file=self.hp.Train.Eval_Pattern.Metadata_File,
pattern_per_speaker=self.hp.Train.Batch.Eval.Pattern_per_Speaker)
inference_dataset = Dataset(
pattern_path=self.hp.Train.Eval_Pattern.Path,
metadata_file=self.hp.Train.Eval_Pattern.Metadata_File,
pattern_per_speaker=self.hp.Train.Batch.Eval.Pattern_per_Speaker,
num_speakers=50, #Maximum number by tensorboard.
)
logging.info('The number of train speakers = {}.'.format(
len(train_dataset)))
logging.info('The number of development speakers = {}.'.format(
len(dev_dataset)))
collater = Collater(min_frame_length=self.hp.Train.Frame_Length.Min,
max_frame_length=self.hp.Train.Frame_Length.Max)
inference_collater = Inference_Collater(
samples=self.hp.Train.Inference.Samples,
frame_length=self.hp.Train.Inference.Frame_Length,
overlap_length=self.hp.Train.Inference.Overlap_Length)
self.dataloader_dict = {}
self.dataloader_dict['Train'] = torch.utils.data.DataLoader(
dataset= train_dataset,
sampler= torch.utils.data.DistributedSampler(train_dataset, shuffle= True) \
if self.hp.Use_Multi_GPU else \
torch.utils.data.RandomSampler(train_dataset),
collate_fn= collater,
batch_size= self.hp.Train.Batch.Train.Speaker,
num_workers= self.hp.Train.Num_Workers,
pin_memory= True
)
self.dataloader_dict['Dev'] = torch.utils.data.DataLoader(
dataset= dev_dataset,
sampler= torch.utils.data.DistributedSampler(dev_dataset, shuffle= True) \
if self.num_gpus > 1 else \
torch.utils.data.RandomSampler(dev_dataset),
collate_fn= collater,
batch_size= self.hp.Train.Batch.Eval.Speaker,
num_workers= self.hp.Train.Num_Workers,
pin_memory= True
)
self.dataloader_dict['Inference'] = torch.utils.data.DataLoader(
dataset=inference_dataset,
shuffle=True,
collate_fn=inference_collater,
batch_size=self.hp.Train.Batch.Eval.Speaker,
num_workers=self.hp.Train.Num_Workers,
pin_memory=True)
def distribution_user_view():
"""
view the distribution of distance
"""
dataset = Dataset()
data = dataset.data
label = dataset.label
data = data.reshape((len(data), -1))
data_nagitive = []
data_positive = []
for i in range(len(label)):
item = data[i]
if label[i] == 0.0:
for j in range(len(item)):
if item[j] != -1.0:
data_nagitive.append(float(item[j]))
else:
for j in range(len(item)):
if item[j] != -1.0:
data_positive.append(float(item[j]))
print(type(data_positive[0]))
sns.kdeplot(
data_positive,
color='r',
)
sns.kdeplot(
data_nagitive,
color='b',
)
plt.show()
def load_ds(query_params):
if query_params.query_name in os.listdir('../outputs/pickles/'):
print 'loading saved ds'
data = pickle.load(open('../outputs/pickles/' + query_params.query_name, 'rb'))
else:
print 'generating new ds'
data = Dataset(execute_query(query_params.queries))
pickle.dump(data, open('../outputs/pickles/' + query_params.query_name, 'wb'))
return data
def data_init(self):
print("\nData init")
#self.dataset = TCGA_Dataset(self.config)
self.dataset = Dataset(self.config)
generator = Generator(self.config, self.dataset)
self.train_generator = generator.generate()
self.X_val, self.y_val = self.dataset.convert_to_arrays(
self.dataset._partition[0]['val'],
self.dataset._partition[1]['val'],
phase='val',
size=self.config.sampling_size_val)
self.X_test, self.y_test = self.dataset.convert_to_arrays(
self.dataset._partition[0]['test'],
self.dataset._partition[1]['test'],
phase='test',
size=self.config.sampling_size_test)
self.y_test = self.patch_to_image(self.y_test, proba=False)
def Datset_Generate(self):
train_Dataset = Dataset(
pattern_path=hp.Train.Train_Pattern.Path,
metadata_file=hp.Train.Train_Pattern.Metadata_File,
accumulated_dataset_epoch=hp.Train.Train_Pattern.
Accumulated_Dataset_Epoch,
mel_length_min=hp.Train.Train_Pattern.Mel_Length.Min,
mel_length_max=hp.Train.Train_Pattern.Mel_Length.Max,
text_length_min=hp.Train.Train_Pattern.Text_Length.Min,
text_length_max=hp.Train.Train_Pattern.Text_Length.Max,
use_cache=hp.Train.Use_Pattern_Cache)
dev_Dataset = Dataset(
pattern_path=hp.Train.Eval_Pattern.Path,
metadata_file=hp.Train.Eval_Pattern.Metadata_File,
mel_length_min=hp.Train.Eval_Pattern.Mel_Length.Min,
mel_length_max=hp.Train.Eval_Pattern.Mel_Length.Max,
text_length_min=hp.Train.Eval_Pattern.Text_Length.Min,
text_length_max=hp.Train.Eval_Pattern.Text_Length.Max,
use_cache=hp.Train.Use_Pattern_Cache)
inference_Dataset = Inference_Dataset(
pattern_path=hp.Train.Inference_Pattern_File_in_Train)
logging.info('The number of train patterns = {}.'.format(
len(train_Dataset) //
hp.Train.Train_Pattern.Accumulated_Dataset_Epoch))
logging.info('The number of development patterns = {}.'.format(
len(dev_Dataset)))
logging.info('The number of inference patterns = {}.'.format(
len(inference_Dataset)))
collater = Collater()
inference_Collater = Inference_Collater()
self.dataLoader_Dict = {}
self.dataLoader_Dict['Train'] = torch.utils.data.DataLoader(
dataset=train_Dataset,
shuffle=True,
collate_fn=collater,
batch_size=hp.Train.Batch_Size,
num_workers=hp.Train.Num_Workers,
pin_memory=True)
self.dataLoader_Dict['Dev'] = torch.utils.data.DataLoader(
dataset=dev_Dataset,
shuffle=True,
collate_fn=collater,
batch_size=hp.Train.Batch_Size,
num_workers=hp.Train.Num_Workers,
pin_memory=True)
self.dataLoader_Dict['Inference'] = torch.utils.data.DataLoader(
dataset=inference_Dataset,
shuffle=False,
collate_fn=inference_Collater,
batch_size=hp.Inference_Batch_Size or hp.Train.Batch_Size,
num_workers=hp.Train.Num_Workers,
pin_memory=True)
if hp.Mode in ['PE', 'GR']:
self.dataLoader_Dict[
'Prosody_Check'] = torch.utils.data.DataLoader(
dataset=Prosody_Check_Dataset(
pattern_path=hp.Train.Train_Pattern.Path,
metadata_file=hp.Train.Train_Pattern.Metadata_File,
mel_length_min=hp.Train.Train_Pattern.Mel_Length.Min,
mel_length_max=hp.Train.Train_Pattern.Mel_Length.Max,
use_cache=hp.Train.Use_Pattern_Cache),
shuffle=False,
collate_fn=Prosody_Check_Collater(),
batch_size=hp.Train.Batch_Size,
num_workers=hp.Train.Num_Workers,
pin_memory=True)
print(device)
kwargs = {'num_workers': 8, 'pin_memory': True} if args.cuda else {}
print('Loading the dataset...')
path = '/home/john/Desktop/Dissertation/data/labels_PCA'
with open(path, 'rb') as f:
labels_dict = pickle.load(f)
labels_ID = list(labels_dict.keys())
path = '/home/john/Desktop/Dissertation/data/Dataset_1.npy'
df_train = np.load(path)
labels = np.array(list(labels_dict.values()))
print('Creating DataLoader...')
train_dataset = Dataset(data=df_train, labels=labels)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
train_loss = []
model = RICA(df_train.shape[1], n_clusters=256, penalty=1.2).to(device)
optimizer = torch.optim.RMSprop(model.parameters(), lr=1e-3, momentum=0.9)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.5)
for epoch in range(1, args.epochs + 1):
print('Executing Epoch...', epoch)
train_loss.append(train(epoch, model, optimizer, scheduler))
path = '/home/john/Desktop/Dissertation/TrainingError/RICA_ADAM_loss'
with open(path, 'wb') as f:
import pandas as pd
import numpy as np
from Datasets import Dataset
from vectorization import Vectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from cotraining import CotClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report
#Data paths
train_dir = "train/"
test_dir = "test/"
directory ="/home/goksenin/Desktop/GRADUATION PROJECT/Programming/"
dataset = Dataset(directory)
n_train = 3375
n_test = 1125
X_train, Y_train = dataset.get_set(train_dir)
X_test, Y_test = dataset.get_set(test_dir)
# -1: unlabeled 0:non-relative 1:relative
y_train = np.asarray(Y_train)
y_train[n_train//4: ] = -1
#######FEATURE EXTRACTION
#getting related documents for feature_extraction
relative_index = [i for i, y_i in enumerate(Y_train) if y_i == 1]
related_data = []
for index in relative_index:
related_data.append(X_train[index])
def data_init(self):
print("\nData init")
self.dataset = Dataset(self.config)
self.generator = Generator(self.config, self.dataset)
class DRAM(object):
def __init__(self, config):
self.config = config
self.data_init()
self.model_init()
def data_init(self):
print("\nData init")
self.dataset = Dataset(self.config)
self.generator = Generator(self.config, self.dataset)
def model_init(self):
self.rnn_cell = tf.contrib.rnn
self.config = config
self.regularizer = tf.contrib.layers.l2_regularizer(
scale=self.config.regularizer)
self.initializer = tf.contrib.layers.xavier_initializer()
self.images_ph = tf.placeholder(
tf.float32,
[None, self.config.input_shape, self.config.input_shape, 3])
self.labels_ph = tf.placeholder(tf.int64, [None])
self.N = tf.shape(self.images_ph)[0]
# ------- GlimpseNet / LocNet -------
with tf.variable_scope('glimpse_net'):
self.gl = ConvGlimpseNetwork(self.config, self.images_ph)
with tf.variable_scope('loc_net'):
self.loc_net = LocNet(self.config)
self.init_loc = tf.zeros(shape=[self.N, 2], dtype=tf.float32)
with tf.variable_scope("rnn_decoder/loop_function",
reuse=tf.AUTO_REUSE):
self.init_glimpse = self.gl(self.init_loc)
self.inputs = [self.init_glimpse]
self.inputs.extend([0] * (self.config.num_glimpses - 1))
# ------- Recurrent network -------
def get_next_input(output, i):
loc, loc_mean = self.loc_net(output)
gl_next = self.gl(loc)
self.loc_mean_arr.append(loc_mean)
self.sampled_loc_arr.append(loc)
self.glimpses.append(self.gl.glimpse)
return gl_next
def rnn_decoder(decoder_inputs,
initial_state,
cell,
loop_function=None):
with tf.variable_scope("rnn_decoder"):
state = initial_state
outputs = []
prev = None
for i, inp in enumerate(decoder_inputs):
if loop_function is not None and prev is not None:
with tf.variable_scope("loop_function",
reuse=tf.AUTO_REUSE):
inp = loop_function(prev, i)
if i > 0:
tf.get_variable_scope().reuse_variables()
output, state = cell(inp, state)
outputs.append(output)
if loop_function is not None:
prev = output
return outputs, state
self.loc_mean_arr = [self.init_loc]
self.sampled_loc_arr = [self.init_loc]
self.glimpses = [self.gl.glimpse]
self.lstm_cell = self.rnn_cell.LSTMCell(self.config.cell_size,
state_is_tuple=True,
activation=tf.nn.tanh,
forget_bias=1.)
self.init_state = self.lstm_cell.zero_state(self.N, tf.float32)
self.outputs, self.rnn_state = rnn_decoder(
self.inputs,
self.init_state,
self.lstm_cell,
loop_function=get_next_input)
# ------- Classification -------
baselines = []
for t, output in enumerate(self.outputs):
with tf.variable_scope('baseline', reuse=tf.AUTO_REUSE):
baseline_t = tf.layers.dense(
inputs=output,
units=2,
kernel_initializer=self.initializer)
baseline_t = tf.squeeze(baseline_t)
baselines.append(baseline_t)
baselines = tf.stack(baselines)
self.baselines = tf.transpose(baselines)
with tf.variable_scope('classification', reuse=tf.AUTO_REUSE):
self.class_prob_arr = []
for t, op in enumerate(self.outputs):
self.glimpse_logit = tf.layers.dense(
inputs=op,
units=self.config.num_classes,
kernel_initializer=self.initializer,
name='FCCN',
reuse=tf.AUTO_REUSE)
self.glimpse_logit = tf.stop_gradient(self.glimpse_logit)
self.glimpse_logit = tf.nn.softmax(self.glimpse_logit)
self.class_prob_arr.append(self.glimpse_logit)
self.class_prob_arr = tf.stack(self.class_prob_arr, axis=1)
self.output = self.outputs[-1]
with tf.variable_scope('classification', reuse=tf.AUTO_REUSE):
self.logits = tf.layers.dense(inputs=self.output,
units=self.config.num_classes,
kernel_initializer=self.initializer,
name='FCCN',
reuse=tf.AUTO_REUSE)
self.softmax = tf.nn.softmax(self.logits)
self.sampled_locations = tf.concat(self.sampled_loc_arr, axis=0)
self.mean_locations = tf.concat(self.loc_mean_arr, axis=0)
self.sampled_locations = tf.reshape(
self.sampled_locations, (self.config.num_glimpses, self.N, 2))
self.sampled_locations = tf.transpose(self.sampled_locations,
[1, 0, 2])
self.mean_locations = tf.reshape(self.mean_locations,
(self.config.num_glimpses, self.N, 2))
self.mean_locations = tf.transpose(self.mean_locations, [1, 0, 2])
prefix = tf.expand_dims(self.init_loc, 1)
self.sampled_locations = tf.concat([prefix, self.sampled_locations],
axis=1)
self.mean_locations = tf.concat([prefix, self.mean_locations], axis=1)
self.glimpses = tf.stack(self.glimpses, axis=1)
# Losses/reward
def loglikelihood(mean_arr, sampled_arr, sigma):
mu = tf.stack(mean_arr)
sampled = tf.stack(sampled_arr)
gaussian = tf.contrib.distributions.Normal(mu, sigma)
logll = gaussian.log_prob(sampled)
logll = tf.reduce_sum(logll, 2)
logll = tf.transpose(logll)
return logll
self.xent = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.logits, labels=self.labels_ph)
self.xent = tf.reduce_mean(self.xent)
self.pred_labels = tf.argmax(self.logits, 1)
self.reward = tf.cast(tf.equal(self.pred_labels, self.labels_ph),
tf.float32)
self.rewards = tf.expand_dims(self.reward, 1)
self.rewards = tf.tile(self.rewards, [1, self.config.num_glimpses])
self.logll = loglikelihood(self.loc_mean_arr, self.sampled_loc_arr,
self.config.loc_std)
self.advs = self.rewards - tf.stop_gradient(self.baselines)
self.logllratio = tf.reduce_mean(self.logll * self.advs)
self.reward = tf.reduce_mean(self.reward)
self.baselines_mse = tf.reduce_mean(
tf.square((self.rewards - self.baselines)))
self.var_list = tf.trainable_variables()
self.loss = -self.logllratio + self.xent + self.baselines_mse
self.grads = tf.gradients(self.loss, self.var_list)
self.grads, _ = tf.clip_by_global_norm(self.grads,
self.config.max_grad_norm)
self.setup_optimization()
# session
self.session_config = tf.ConfigProto()
self.session_config.gpu_options.visible_device_list = self.config.gpu
self.session_config.gpu_options.allow_growth = True
self.session = tf.Session(config=self.session_config)
self.session.run(tf.global_variables_initializer())
def setup_optimization(self):
# learning rate
self.global_step = tf.get_variable(
'global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
self.training_steps_per_epoch = int(
len(self.generator.training_ids) // self.config.batch_size)
print('Training Step Per Epoch:', self.training_steps_per_epoch)
self.starter_learning_rate = self.config.lr_start
self.learning_rate = tf.train.exponential_decay(
self.starter_learning_rate,
self.global_step,
self.training_steps_per_epoch,
0.70,
staircase=False)
self.learning_rate = tf.maximum(self.learning_rate, self.config.lr_min)
self.optimizer = tf.train.MomentumOptimizer(self.learning_rate,
momentum=0.90,
use_nesterov=True)
#self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
self.train_op = self.optimizer.apply_gradients(
zip(self.grads, self.var_list), global_step=self.global_step)
def setup_logger(self):
"""Creates log directory and initializes logger."""
self.summary_ops = {
'reward': tf.summary.scalar('reward', self.reward),
'hybrid_loss': tf.summary.scalar('hybrid_loss', self.loss),
'cross_entropy': tf.summary.scalar('cross_entropy', self.xent),
'baseline_mse': tf.summary.scalar('baseline_mse',
self.baselines_mse),
'logllratio': tf.summary.scalar('logllratio', self.logllratio),
'lr': tf.summary.scalar('lr', self.learning_rate)
}
# 'glimpses': tf.summary.image('glimpses',tf.reshape(self.glimpses,[-1,self.config.glimpse_size,
# self.config.glimpse_size,
# 3]),max_outputs=8)}
self.eval_ops = {
'labels': self.labels_ph,
'pred_labels': self.pred_labels,
'reward': self.reward,
'hybrid_loss': self.loss,
'cross_entropy': self.xent,
'baseline_mse': self.baselines_mse,
'logllratio': self.logllratio,
'lr': self.learning_rate
}
self.logger = Logger(self.config.logdir,
sess=self.session,
summary_ops=self.summary_ops,
global_step=self.global_step,
eval_ops=self.eval_ops,
n_verbose=self.config.n_verbose,
var_list=self.var_list)
def train(self):
print('\n\n\n------------ Starting training ------------ \nT -- %s x %s \n' \
'Model: %s glimpses, glimpse size %s x %s \n\n\n' % (
self.config.input_shape, self.config.input_shape, self.config.num_glimpses, self.config.glimpse_size,
self.config.glimpse_size))
self.setup_logger()
for i in range(self.config.steps + 1):
loc_dir_name = self.config.logdir + '/image/locations'
traj_dir_name = self.config.logdir + '/image/trajectories'
ROCs_dir_name = self.config.logdir + '/metrics/ROCs_AUCs/'
PRs_dir_name = self.config.logdir + '/metrics/PRs/'
if i == 0:
if os.path.exists(loc_dir_name):
shutil.rmtree(loc_dir_name)
os.makedirs(loc_dir_name)
else:
os.makedirs(loc_dir_name)
if os.path.exists(traj_dir_name):
shutil.rmtree(traj_dir_name)
os.makedirs(traj_dir_name)
else:
os.makedirs(traj_dir_name)
if os.path.exists(ROCs_dir_name):
shutil.rmtree(ROCs_dir_name)
os.makedirs(ROCs_dir_name)
else:
os.makedirs(ROCs_dir_name)
if os.path.exists(PRs_dir_name):
shutil.rmtree(PRs_dir_name)
os.makedirs(PRs_dir_name)
else:
os.makedirs(PRs_dir_name)
self.logger.step = i
images, labels = self.generator.generate()
images = images.reshape(
(-1, self.config.input_shape, self.config.input_shape, 3))
labels = labels[0]
feed_dict = {self.images_ph: images, self.labels_ph: labels}
fetches = [
self.output, self.rewards, self.reward, self.labels_ph,
self.pred_labels, self.logits, self.train_op, self.loss,
self.xent, self.baselines_mse, self.logllratio,
self.learning_rate, self.loc_mean_arr
]
output, rewards, reward, real_labels, pred_labels, logits, _, hybrid_loss, cross_entropy, baselines_mse, logllratio, lr, locations = self.session.run(
fetches, feed_dict)
if i % 1 == 0:
print('\n------ Step %s ------' % (i))
print('reward', reward)
print('labels', real_labels)
print('pred_labels', pred_labels)
print('hybrid_loss', hybrid_loss)
print('cross_entropy', cross_entropy)
print('baseline_mse', baselines_mse)
print('logllratio', logllratio)
print('lr', lr)
print('locations', locations[-1])
print('logits', logits)
self.logger.log('train', feed_dict=feed_dict)
#if i > 0 and i % 100 == 0:
# self.eval(i)
# self.logger.log('val', feed_dict=feed_dict)
if i == self.config.steps:
self.test(i)
#if i == self.config.steps:
# if i > 0 and i % 100 == 0:
# glimpse_images = self.session.run(self.glimpses, feed_dict)
# mean_locations = self.session.run(self.mean_locations, feed_dict)
# probs = self.session.run(self.class_prob_arr, feed_dict)
# plot_glimpses(config=self.config, glimpse_images=glimpse_images, pred_labels=pred_labels, probs=probs,
# sampled_loc=mean_locations, X=images, labels=real_labels, file_name=loc_dir_name, step=i)
#plot_trajectories(config=self.config, locations=mean_locations, X=images, labels=real_labels,
# pred_labels=pred_labels, file_name=traj_dir_name, step=i)
#self.logger.save()
def eval(self, step):
return self.evaluate(self.session, self.images_ph, self.labels_ph,
self.softmax, step)
def evaluate(self, sess, images_ph, labels_ph, softmax, step):
print('Evaluating (%s x %s) using %s glimpses' %
(self.config.input_shape, self.config.input_shape,
self.config.num_glimpses))
self.X_val, self.y_val = self.dataset.convert_to_arrays(
self.dataset._partition[0]['val'],
size=self.config.sampling_size_val)
print('Validation set has %s patients' % len(self.y_val))
X_val, y_val = self.X_val, self.y_val
_num_examples = X_val.shape[0]
steps_per_epoch = _num_examples // self.config.eval_batch_size
y_scores = []
y_trues = []
for i in tqdm(iter(range(steps_per_epoch))):
images, labels_val = self.dataset.next_batch(
X_val, y_val[0], self.config.eval_batch_size, i)
#images = images.reshape((-1, self.config.input_shape, self.config.input_shape, 3))
softmax_val = sess.run(softmax,
feed_dict={
images_ph: images,
labels_ph: labels_val
})
y_trues.extend(labels_val)
y_scores.extend(softmax_val)
y_preds = np.argmax(y_scores, 1)
y_scores = np.array(y_scores)
self.metrics_ROCs(y_trues, y_preds, y_scores, step)
self.metrics(y_trues, y_preds, step)
return
def count_params(self):
return self.count_parameters(self.session)
def count_parameters(self, sess):
variables_names = [v.name for v in tf.trainable_variables()]
values = sess.run(variables_names)
n_params = 0
for k, v in zip(variables_names, values):
print('-'.center(140, '-'))
print('%s \t Shape: %s \t %s parameters' % (k, v.shape, v.size))
n_params += v.size
print('-'.center(140, '-'))
print('Total # parameters:\t\t %s \n\n' % (n_params))
return n_params
def metrics_ROCs(self, y_trues, y_preds, y_scores, step, stage=None):
y_trues_binary = label_binarize(
y_trues, classes=list(self.dataset.le_name_mapping.values()))
y_preds_binary = label_binarize(
y_preds, classes=list(self.dataset.le_name_mapping.values()))
n_classes = y_preds_binary.shape[1]
if stage == 'test':
fpr, tpr, _ = roc_curve(y_trues, y_scores)
else:
fpr, tpr, _ = roc_curve(y_trues, y_scores[:, 1])
roc_auc = auc(fpr, tpr)
plt.figure()
plt.plot(fpr,
tpr,
label='ROC curve (AUC = {0:0.2f})'
''.format(roc_auc),
color='navy',
linestyle=':',
linewidth=4)
plt.plot([0, 1], [0, 1], 'k--', lw=2)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiving Operating Characteristic Curves')
plt.legend(loc="lower right")
plt.savefig(self.config.logdir + '/metrics/ROCs_AUCs/%i' % step)
return
def metrics(self, y_trues, y_preds, step):
# y_trues_binary= label_binarize(y_trues, classes=list(self.dataset.le_name_mapping.values()))
# y_preds_binary= label_binarize(y_preds, classes=list(self.dataset.le_name_mapping.values()))
accuracy = accuracy_score(y_trues, y_preds)
f1score = f1_score(y_trues, y_preds)
recall = recall_score(y_trues, y_preds)
precision = precision_score(y_trues, y_preds)
names = ['accuracy', 'f1_score', 'recall', 'precision']
pd.DataFrame(data=np.array([accuracy, f1score, recall, precision]),
index=names).to_csv(self.config.logdir +
'/metrics/metrics_%i.csv' % step)
return
def load(self, checkpoint_dir):
folder = os.path.join(checkpoint_dir, 'checkpoints')
print('\nLoading model from <<{}>>.\n'.format(folder))
self.saver = tf.train.Saver(self.var_list)
ckpt = tf.train.get_checkpoint_state(folder)
if ckpt and ckpt.model_checkpoint_path:
print(ckpt)
self.saver.restore(self.session, ckpt.model_checkpoint_path)
def patch_to_image(self, y_patches, proba=True):
if proba == True:
y_image = np.array([
np.mean(y_patches[i * self.config.sampling_size_test:(i + 1) *
self.config.sampling_size_test],
axis=0)
for i in range(
int(len(y_patches) / self.config.sampling_size_test))
])
else:
y_image = np.array([
np.mean(y_patches[i * self.config.sampling_size_test:(i + 1) *
self.config.sampling_size_test]) > 0.5
for i in range(
int(len(y_patches) / self.config.sampling_size_test))
]).reshape((-1, 1)).astype(int)
y_image = np.asarray(y_image.flatten())
return y_image
def test(self, step):
return self.testing(self.session, self.images_ph, self.labels_ph,
self.softmax, step)
def testing(self, sess, images_ph, labels_ph, softmax, step):
print('Testing (%s x %s) using %s glimpses' %
(self.config.input_shape, self.config.input_shape,
self.config.num_glimpses))
print(self.dataset._partition[0]['test'])
self.X_test, self.y_test = self.dataset.convert_to_arrays(
self.dataset._partition[0]['test'],
size=self.config.sampling_size_test)
X_test, y_test = self.X_test, self.y_test
print('y_test', y_test)
_num_examples = X_test.shape[0]
steps_per_epoch = _num_examples // self.config.test_batch_size
y_scores = []
y_trues = []
for i in tqdm(iter(range(steps_per_epoch))):
images, labels_test = self.dataset.next_batch(
X_test, y_test[0], self.config.test_batch_size, i)
print(labels_test)
#images = images.reshape((-1, self.config.input_shape, self.config.input_shape, 3))
softmax_test = sess.run(softmax,
feed_dict={
images_ph: images,
labels_ph: labels_test
})
y_trues.extend(labels_test)
y_scores.extend(softmax_test)
y_trues = self.patch_to_image(y_trues, proba=False)
y_scores = self.patch_to_image(y_scores, proba=True)
y_preds = np.argmax(y_scores, 1)
print('Test Set', self.dataset._partition[0]['test'])
print(y_trues)
print(y_preds)
self.metrics_ROCs(y_trues, y_preds, y_scores, step)
self.metrics(y_trues, y_preds, step)
return
class Model(object):
def __init__(self, config):
self.config = config
self.data_init()
self.model_init()
def data_init(self):
print("\nData init")
#self.dataset = TCGA_Dataset(self.config)
self.dataset = Dataset(self.config)
generator = Generator(self.config, self.dataset)
self.train_generator = generator.generate()
self.X_val, self.y_val = self.dataset.convert_to_arrays(
self.dataset._partition[0]['val'],
self.dataset._partition[1]['val'],
phase='val',
size=self.config.sampling_size_val)
self.X_test, self.y_test = self.dataset.convert_to_arrays(
self.dataset._partition[0]['test'],
self.dataset._partition[1]['test'],
phase='test',
size=self.config.sampling_size_test)
self.y_test = self.patch_to_image(self.y_test, proba=False)
def plot_ROCs(self, y_scores):
fig = plt.figure(figsize=(10, 10))
y_true = self.y_test
y_score = y_scores
fpr, tpr, _ = roc_curve(y_true, y_score)
auc = roc_auc_score(y_true, y_score)
plt.plot(fpr,
tpr,
lw=2,
c='r',
alpha=0.8,
label=r'%s (AUC = %0.2f)' % auc)
plt.plot([0, 1], [0, 1],
linestyle='--',
lw=2,
color='black',
label='Luck',
alpha=.8)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title("ROC curve")
plt.legend(loc="lower right")
fig.savefig("output/ROC_curve")
plt.close()
def plot_PRs(self, y_scores):
fig = plt.figure(figsize=(10, 10))
y_true = self.y_test
y_score = y_scores
precision, recall, _ = precision_recall_curve(y_true, y_score)
plt.plot(recall,
precision,
lw=2,
c='b',
alpha=0.8,
label=r'PR curve (AP = %0.2f)' % (precision))
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title("PR curve")
plt.legend(loc="lower right")
fig.savefig("output/PR_curve")
plt.close()
def model_init(self):
print("\nModel init")
self.base_model = DenseNet169(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3),
pooling=None)
x = self.base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(2048, activation='relu', kernel_regularizer=l2(0.1))(x)
x = Dropout(0.30)(x, training=True)
x = Dense(100, activation='relu', kernel_regularizer=l2(0.1))(x)
x = Dropout(0.30)(x)
output = Dense(1, activation='sigmoid')(x)
self.model = keras.models.Model(inputs=self.base_model.input,
outputs=output)
def set_trainable(self, from_idx=0):
print("\nTraining")
#for layer in self.base_model.layers:
# layer.trainable = False
for layer in self.model.layers[0:]:
layer.trainable = True
def train(self, lr=1e-4, epochs=10, from_idx=0):
self.set_trainable()
optimizer = Adam(lr=lr,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=self.config.lr_decay)
self.model.compile(optimizer=optimizer,
loss='binary_crossentropy',
metrics=['accuracy'])
train_steps = len(
self.dataset._partition[0]['train']) / self.config.batch_size
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=5,
verbose=0,
mode='auto')
self.history = custom_fit_generator(model=self.model,
generator=self.train_generator,
steps_per_epoch=train_steps,
epochs=epochs,
verbose=1,
validation_data=(self.X_val,
self.y_val),
shuffle=True,
max_queue_size=30,
workers=30,
use_multiprocessing=True,
callbacks=[early_stopping])
def predict(self):
df = self.dataset.get_binarized_data()
ids = df.index
labels = df.values
print("\nPredicting")
intermediate_layer_model = keras.models.Model(
inputs=self.base_model.input, outputs=self.model.layers[-1].output)
self.X_feat, self.y_feat = self.dataset.convert_to_arrays(
list(ids),
labels,
phase='train',
size=self.config.sample_size_feat)
ids = np.asarray(ids)
ids = np.repeat(ids, self.config.sample_size_feat)
for i in range(10):
intermediate_output = intermediate_layer_model.predict(self.X_feat)
features = pd.DataFrame(data=intermediate_output, index=ids)
features["ids"] = ids
features = features.groupby(["ids"]).mean()
features.to_csv("pathology_scores_%s.csv" % i)
# intermediate_output = intermediate_layer_model.predict(self.X_test, batch_size= self.config.batch_size)
# print(len(intermediate_output))
# print(len(intermediate_output[1]))
# print('intermediate_output',intermediate_output.shape)
# y_scores = self.model.predict(self.X_test, batch_size= self.config.batch_size)
# y_scores = self.patch_to_image(y_scores, proba=True)
# print('y_scores', y_scores)
# y_preds = np.array([(y_score>0.5).astype(int) for y_score in y_scores]).flatten()
# pd.DataFrame(data = y_preds, index =self.dataset._partition[0]['test'] ).to_csv('Results.csv')
# print(self.dataset._partition[0]['test'], y_preds)
# return y_scores, y_preds
def train_predict(self):
self.train(self.config.lr, self.config.epochs, self.config.from_idx)
# self.plot_loss()
# y_scores, y_preds = self.predict()
self.predict()
# np.save("output/y_scores", y_scores)
# np.save("output/y_preds", y_preds)
# return y_scores, y_preds
def patch_to_image(self, y_patches, proba=True):
if proba == True:
y_image = np.array([
np.mean(y_patches[i * self.config.sampling_size_test:(i + 1) *
self.config.sampling_size_test])
for i in range(
int(len(y_patches) / self.config.sampling_size_test))
]).reshape((-1, 1))
else:
y_image = np.array([
np.mean(y_patches[i * self.config.sampling_size_test:(i + 1) *
self.config.sampling_size_test]) > 0.5
for i in range(
int(len(y_patches) / self.config.sampling_size_test))
]).reshape((-1, 1)).astype(int)
y_image = np.asarray(y_image.flatten())
return y_image
def plot_loss(self):
keys = list(self.history.history.keys())
val_acc_keys = [
key for key in keys if key[0:3] == "val" and key[-3:] == "acc"
]
acc_keys = [
key for key in keys if key[0:3] != "val" and key[-3:] == "acc"
]
val_acc = np.mean([self.history.history[key] for key in val_acc_keys],
axis=0)
acc = np.mean([self.history.history[key] for key in acc_keys], axis=0)
loss = self.history.history["loss"]
val_loss = self.history.history["val_loss"]
fig = plt.figure(figsize=(10, 10))
ax1 = plt.subplot(121)
ax1.tick_params(labelsize=10)
plt.plot(acc)
plt.plot(val_acc)
plt.title('Mean accuracy', size=14)
plt.ylabel('accuracy', size=12)
plt.xlabel('epoch', size=12)
plt.legend(['train', 'test'], loc='upper left', fontsize=12)
ax2 = plt.subplot(122)
ax2.tick_params(labelsize=10)
plt.plot(loss)
plt.plot(val_loss)
plt.title('Mean loss', size=14)
plt.ylabel('loss', size=12)
plt.xlabel('epoch', size=12)
plt.legend(['train', 'test'], loc='upper left', fontsize=12)
plt.show()
fig.savefig("output/learning_curve")
plt.close()
def plot(self):
print("\nPlotting model")
plot_model(self.model, to_file='output/model.png')
def get_metrics(self, y_scores, y_preds):
list_of_metrics = [
"accuracy", "precision", "recall", "f1score", "AUC", "AP"
]
self.metrics = pd.DataFrame(data=np.zeros((1, len(list_of_metrics))),
columns=list_of_metrics)
# y_true = self.y_test
y_pred = y_preds
y_score = y_scores
# print('y_true',y_true)
print('y_score', y_score)
print('y_pred', y_pred)
