def learning_curve(dataset_size, model_type):
""" runs cross validation to plot learning curve """
print "LEARNING CURVE", dataset_size, model_type
dset = dataset.read('contradictions', dataset_size)
data, targets = [], []
for case in dset['content']:
data.append(get_features(case['sentence'], case['hypothesis']))
targets.append(case['contradiction'])
model = ClassificationModel(model_type)
train_sizes, train_scores, test_scores = model.learning_curve(
data, targets)
with open(config.LEARNING_CURVE_PATH.format(dataset_size, model_type),
'wb') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
csv_writer.writerow(
['model', 'dataset_size', 'train_size', 'train_mse', 'test_mse'])
for (train_size, train_score,
test_score) in zip(train_sizes, train_scores, test_scores):
csv_writer.writerow([
model_type, dataset_size, train_size,
','.join(np.char.mod('%f', train_score)),
','.join(np.char.mod('%f', test_score))
])
plot = plotter.learning_curve(train_sizes, train_scores, test_scores)
plot.savefig("../res/plot/learning_{}_{}.pdf".format(
dataset_size, model_type))
def test():
c = ClassificationModel()
_, normVector, _ = c.buildModel(is_training=False)
restore_vars = []
for var in tf.global_variables():
if('temp' not in var.name):
restore_vars.append(var)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(var_list=restore_vars)
saver.restore(sess, 'step/model.ckpt-2700')
speaker_path = '/home/logview/workspace/projects/TI-SV/samples'
utterance_specs = []
files = os.listdir(speaker_path)
files.sort()
for utter_name in files:
utter_path = os.path.join(speaker_path, utter_name) # path of each utterance
utter, sr = librosa.core.load(utter_path, 16000) # load utterance audio
utter_trim, index = librosa.effects.trim(utter, top_db=20) # voice activity detection, only trim
S = librosa.feature.mfcc(y=utter_trim, sr=sr, n_mfcc=40)
inputs = S.transpose((1,0))[:160]
print(inputs.shape)
utterance_specs.append(inputs)
utterance_specs = np.array(utterance_specs)
print(utterance_specs.shape)
vectors = sess.run(normVector, feed_dict={c.melInputs:utterance_specs})
similar(vectors)
def train():
lr = 0.002
c = ClassificationModel()
cost, normVector, alfas_mean = c.buildModel()
gStep = tf.Variable(tf.constant(0))
learning_rate = tf.train.exponential_decay(float(lr), gStep, 300, 0.9, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
gradients, vars = zip(*optimizer.compute_gradients(cost)) #var_list=train_vars
gradients, _ = tf.clip_by_global_norm(gradients, 100)
train_op = optimizer.apply_gradients(zip(gradients, vars))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# saver = tf.train.Saver(var_list=restore_vars) #var_list=restore_vars
# saver.restore(sess, 'step/model.ckpt-'+ckpoint)
saver = tf.train.Saver()
for step in range(10000):
mels, ids = c.getBatch_data_label(10)
feed_dict = {c.melInputs:mels, c.labelInputs: ids, gStep: step}
_, _lr, _loss, _alfas_mean = sess.run([train_op, learning_rate, cost, alfas_mean], feed_dict=feed_dict)
print('{}: lr = {:.6f}, loss = {}, alfas_mean = {}'.format(step, _lr, _loss, _alfas_mean))
if(step%300==0 and step!=0):
saver.save(sess, 'step/model.ckpt', global_step=step)
def get_model(model_path):
label = get_label("./util/label.txt")
model = ClassificationModel(
class_list=label,
img_width=256,
img_height=256,
)
status = model.load(model_path)
if not status:
raise Exception("model load failed...")
return model
def export(output_dir, ckpt=None, model_version=1):
# Define model.
audio_meta_train = VoxCelebMeta(hp.train.data_path, hp.train.meta_path)
model = ClassificationModel(num_classes=audio_meta_train.num_speaker, **hp.model)
with TowerContext('', is_training=False):
input = PlaceholderInput()
input.setup(model.get_inputs_desc())
model.build_graph(*input.get_input_tensors())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Restore variables from training checkpoints.
ckpt = ckpt if ckpt else tf.train.latest_checkpoint(hp.logdir)
if ckpt:
tf.train.Saver().restore(sess, ckpt)
print('Successfully loaded model: {} from {}'.format(ckpt, ckpt))
else:
print('No checkpoint file found at {}'.format(ckpt))
return
# Export inference model.
output_path = os.path.join(
tf.compat.as_bytes(output_dir),
tf.compat.as_bytes(str(model_version)))
print('Exporting trained model to', output_path)
builder = tf.saved_model.builder.SavedModelBuilder(output_path)
# Build the signature_def_map.
inputs_tensor_info = tf.saved_model.utils.build_tensor_info(model.x)
prob_output_tensor_info = tf.saved_model.utils.build_tensor_info(model.prob)
embedding_output_tensor_info = tf.saved_model.utils.build_tensor_info(model.y)
predict_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'x': inputs_tensor_info},
outputs={
'prob': prob_output_tensor_info,
'embedding': embedding_output_tensor_info,
},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
))
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict': predict_signature
})
builder.save()
print('Successfully exported model to %s' % output_dir)
def main():
# Read data
X, Y = load_cfar10_batch('datasets/cifar-10-batches-py', 1)
Xs, Ys = load_cfar10_test('datasets/cifar-10-batches-py')
for i in range(1, 5):
_X, _Y = load_cfar10_batch('datasets/cifar-10-batches-py', i + 1)
X = np.concatenate((X, _X))
Y = np.concatenate((Y, _Y))
X = np.reshape(X, [X.shape[0], -1])
Xs = np.reshape(Xs, [Xs.shape[0], -1])
Y = np.reshape(Y, [-1, 1])
Ys = np.reshape(Ys, [-1, 1])
# pre-processing
X_mean = np.mean(X, 0)
X_std = np.std(X, 0)
X = (X - X_mean) / (X_std + 1e-7)
Xs = (Xs - X_mean) / (X_std + 1e-7)
print("=== DATA SUMMARY ===")
print("X is normalized.")
print("Y is not whitened. Y variance: ", np.var(Y))
model = ClassificationModel(args.layers, args.num_inducing)
def predict_accuracy():
a, b = model.predict(Xs)
c = np.argmax(a, axis=1) - (Ys[:, 0])
L = np.abs(c)
count = 0
for i in range(len(L)):
if L[i] == 0:
count += 1
print("Test accuracy: ", count / len(L))
def train_accuracy():
a, b = model.predict(X[0:1000])
c = np.argmax(a, axis=1) - (Y[0:1000, 0])
L = np.abs(c)
count = 0
for i in range(len(L)):
if L[i] == 0:
count += 1
print("Train accuracy: ", count / len(L))
for epoch in range(1000):
print("EPOCH", epoch)
model.fit(X, Y)
train_accuracy()
predict_accuracy()
def main(unused_argv):
if len(unused_argv) != 1: # prints a message if you've entered flags incorrectly
raise Exception("Problem with flags: %s" % unused_argv)
tf.logging.set_verbosity(tf.logging.INFO) # choose what level of logging you want
tf.logging.info('Running the code in %s mode...', (FLAGS.mode))
# Change log_root to FLAGS.log_root/FLAGS.exp_name and create the dir if necessary
FLAGS.log_root = os.path.join(FLAGS.log_root, FLAGS.exp_name)
if not os.path.exists(FLAGS.log_root):
if FLAGS.mode=="train":
os.makedirs(FLAGS.log_root)
else:
raise Exception("Logdir %s doesn't exist. Run in train mode to create it." % (FLAGS.log_root))
vocab = Vocab(FLAGS.vocab_path, FLAGS.vocab_size) # create a source vocabulary
# Make a namedtuple hps, containing the values of the hyperparameters that the model needs
hparam_list = ['adam_epsilon','mode', 'loss', 'lr', 'adagrad_init_acc', 'rand_unif_init_mag', 'trunc_norm_init_std', 'max_grad_norm', 'hidden_dim', 'emb_dim', 'batch_size', 'max_enc_steps']
hps_dict = {}
for key,val in FLAGS.__flags.iteritems(): # for each flag
if key in hparam_list: # if it's in the list
hps_dict[key] = val # add it to the dict
hps = namedtuple("HParams", hps_dict.keys())(**hps_dict)
# Create a batcher object that will create minibatches of data
tf.set_random_seed(1233) # a seed value for randomness
if hps.mode == 'train':
print "creating model..."
batcher = Batcher(FLAGS.data_path, vocab, hps, single_pass=False)
model = ClassificationModel(hps, vocab)
# batcher = None
setup_training(model, batcher, vocab, hps)
elif hps.mode == 'eval':
model = ClassificationModel(hps, vocab)
run_eval(model, vocab, hps)
elif hps.mode == 'decode':
model = ClassificationModel(hps, vocab)
run_decode(model, vocab, hps)
# elif hps.mode == 'decode':
# decode_model_hps = hps # This will be the hyperparameters for the decoder model
# decode_model_hps = hps._replace(max_dec_steps=1) # The model is configured with max_dec_steps=1 because we only ever run one step of the decoder at a time (to do beam search). Note that the batcher is initialized with max_dec_steps equal to e.g. 100 because the batches need to contain the full summaries
else:
raise ValueError("The 'mode' flag must be one of train/eval/decode")
def main():
# Image Augmentations
transform = transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.RandomHorizontalFlip()
])
valid_data_loader = None
train_data_loader = None
if valid_dataset_path:
val_dataset = ImageFolder(valid_dataset_path, transform=transform)
train_dataset = ImageFolder(train_dataset_path, transform=transform)
train_data_loader, valid_data_loader = get_data_loaders_from_dataset(
train_dataset, val_dataset)
else:
image_ds = ImageFolder(train_dataset_path, transform=transform)
print(image_ds.classes)
val_size = int(val_pct * len(image_ds))
train_size = len(image_ds) - val_size
train_dataset, val_dataset = random_split(image_ds,
[train_size, val_size])
train_data_loader, valid_data_loader = get_data_loaders_from_dataset(
train_dataset, val_dataset)
flowers_model = ClassificationModel()
trainer = pl.Trainer(gpus=1, max_epochs=max_epochs)
trainer.fit(flowers_model,
train_dataloader=train_data_loader,
val_dataloaders=valid_data_loader)
def inference_random():
# 加载验证集验证
model = ClassificationModel(len(cfg.char2idx))
model = load_custom_model(model, cfg.save_model_path).to(cfg.device)
tokenizer = Tokenizer(cfg.char2idx)
error = 0
with open(cfg.test_data_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
for line in lines:
pairs = line.split('\t')
label, text = pairs[0], pairs[1]
input_index, _ = tokenizer.encode(text, max_length=cfg.max_seq_len)
inputs = torch.tensor(input_index).unsqueeze(0)
inputs_mask = (inputs > 0).to(torch.float32)
with torch.no_grad():
scores = model(inputs, inputs_mask)
prediction = scores.argmax(-1).item()
if prediction != int(label):
print(scores[:, int(label)].item())
print(label)
print(text)
print('-' * 50)
error += 1
print(error)
def new_client(client, server, state=state):
state["num_clients"] += 1
if not state["model_loaded"]:
logger.info("Loading model")
state["ftModel"] = ClassificationModel(path=model_path)
state["model_loaded"] = True
logger.info("New client connected and was given id %d" % client['id'])
server.send_message(client, time_str)
def analyze_campus_policies():
""" runs tests with the trained Random Forest model, with each pair of intents in the campi dataset """
print "MODEL TEST USING CAMPI"
dset = dataset.read('contradictions', 'campi')
intents = []
for case in dset['intents']:
# if case['university'] not in intents:
# intents[case['university']] = []
intents.append((case['university'], case['text'], case['nile']))
model = ClassificationModel('forest')
results = []
if model.load_model(10000):
# for (uni, intents) in intents.items():
for i in range(len(intents)):
(uni_stn, text_stn, sentence) = intents[i]
for j in range(i + 1, len(intents)):
(uni_hyp, text_hyp, hypothesis) = intents[j]
if sentence != hypothesis:
results.append(
(uni_stn, uni_hyp, text_stn, text_hyp, sentence,
hypothesis,
model.predict([get_features(sentence, hypothesis)])))
with open(
config.CONTRADICTIONS_RESULTS_PATH.format('summary', 'campi'),
'wb') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
csv_writer.writerow([
'university stn', 'university hyp', 'text stn', 'text hyp',
'sentence', 'hypothesis', 'prediction'
])
for (uni_stn, uni_hyp, text_stn, text_hyp, sentence, hypothesis,
prediction) in results:
csv_writer.writerow([
uni_stn, uni_hyp, text_stn, text_hyp, sentence, hypothesis,
prediction[0]
])
else:
print "Problem loading model"
def output_prob(text, end_to_end=e2e, state=state):
if not state["model_loaded"]:
logger.info("Loading model")
state["ftModel"] = ClassificationModel(path=model_path)
state["model_loaded"] = True
report_text = "IMPRESSION: " + text + "\nEND OF IMPRESSION"
processed_report_text, ground_truth = e2e.transform([report_text])[0]
logger.info(processed_report_text)
processed_report_text = " ".join(processed_report_text)
prediction = state["ftModel"].predict(processed_report_text)
logger.info(prediction)
return (processed_report_text, ground_truth, prediction)
def validate(dataset_size, model_type):
""" runs cross validation in classification model """
print "MODEL VALIDATION", dataset_size, model_type
dset = dataset.read('contradictions', dataset_size)
data, targets = [], []
for case in dset['content']:
data.append(get_features(case['sentence'], case['hypothesis']))
targets.append(case['contradiction'])
model = ClassificationModel(model_type)
scores = model.cross_validate(data, targets)
print "scores", scores
print "FIT TIME", scores['fit_time']
print "VALIDATION TIME", scores['score_time']
print "PRECISION", scores['test_precision_macro']
print "RECALL", scores['test_recall_macro']
print "F1 SCORE", scores['test_f1_macro']
return scores['fit_time'], scores['score_time'], scores[
'test_precision_macro'], scores['test_recall_macro'], scores[
'test_f1_macro']
def test(dataset_size, model_type):
""" opens fit dataset and trains SVM/LogReg/Forest model with it, then tests it"""
print "MODEL TEST", dataset_size, model_type
dset = dataset.read('contradictions', dataset_size)
data, targets = [], []
for case in dset['content']:
data.append(case)
targets.append(case['contradiction'])
fit_data, test_data = [], []
fit_cases, test_cases, fit_target, test_target = train_test_split(
data, targets, test_size=0.25, shuffle=True, random_state=0)
for fit_case in fit_cases:
fit_data.append(
get_features(fit_case['sentence'], fit_case['hypothesis']))
for test_case in test_cases:
test_data.append(
get_features(test_case['sentence'], test_case['hypothesis']))
model = ClassificationModel(model_type)
start_time = time.time()
model.train(fit_data, fit_target, dataset_size)
elapsed_time = time.time() - start_time
test_results = model.test(test_data)
with open(
config.CONTRADICTIONS_RESULTS_PATH.format(dataset_size,
model_type),
'wb') as csvfile:
csv_writer = csv.writer(csvfile, delimiter=',')
csv_writer.writerow([
'hypothesis', 'sentence', 'type', 'contradiction', 'prediction',
'features'
])
for (test_case, result, features) in zip(test_cases, test_results,
test_data):
csv_writer.writerow([
test_case['hypothesis'], test_case['sentence'],
test_case['type'], test_case['contradiction'], result, features
])
precision = metrics.precision_score(test_target, test_results)
recall = metrics.recall_score(test_target, test_results)
f1_score = metrics.f1_score(test_target, test_results)
print "FIT TIME", elapsed_time
print "PRECISION", precision
print "RECALL", recall
print "F1 SCORE", f1_score
model.save(dataset_size)
def roc_curve(dataset_size):
""" runs cross validation to plot precision recall curve """
print "ROC CURVE", dataset_size
dset = dataset.read('contradictions', dataset_size)
data, targets = [], []
for case in dset['content']:
data.append(get_features(case['sentence'], case['hypothesis']))
targets.append(case['contradiction'])
for mtype in ['svm', 'log', 'forest']:
model = ClassificationModel(mtype)
plot = plotter.plot_roc_curve(dataset_size, mtype, model, data,
targets)
plot.savefig("../res/plot/roc_{}_{}.pdf".format(dataset_size, mtype),
bbox_inches='tight')
def train():
# 加载数据
tokenizer = Tokenizer(cfg.char2idx)
train_dataset = CustomDataset(cfg.train_data_path, tokenizer, cfg)
train_dataloader = DataLoader(train_dataset,
batch_size=cfg.batch_size,
collate_fn=padding,
shuffle=True,
num_workers=4,
pin_memory=True)
model = ClassificationModel(len(cfg.char2idx))
# model = load_pretrained_bert(model, cfg.pretrained_model_path, keep_tokens=cfg.keep_tokens).to(cfg.device)
model = load_custom_model(model, cfg.save_model_path).to(cfg.device)
loss_function = nn.CrossEntropyLoss().to(cfg.device)
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.learn_rate)
# 迭代训练
iteration, train_loss = 0, 0
model.train()
for inputs, mask, targets in tqdm(train_dataloader, position=0,
leave=True):
inputs, mask, targets = inputs.to(cfg.device), mask.to(
cfg.device), targets.to(cfg.device)
prediction = model(inputs, mask)
loss = loss_function(prediction, targets.reshape(-1))
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
iteration += 1
if iteration % cfg.print_loss_steps == 0:
eval_loss = evaluate(model, tokenizer, loss_function)
print('')
print('train_loss:{}'.format(train_loss / cfg.print_loss_steps))
print('evalu_loss:{}'.format(eval_loss))
accuracy(model, tokenizer, cfg.valid_data_path)
accuracy(model, tokenizer, cfg.test_data_path)
model.train()
train_loss = 0
if iteration % cfg.save_model_steps == 0:
torch.save(model.state_dict(), cfg.save_model_path)
# set hyper-parameters from yaml file
hp.set_hparam_yaml(case=args.case)
# dataflow
audio_meta = AudioMeta(hp.train.data_path)
if args.remote:
df = get_remote_dataflow(args.port, hp.train.batch_size)
else:
df = DataLoader(audio_meta, hp.train.batch_size).dataflow(nr_prefetch=5000, nr_thread=int(multiprocessing.cpu_count() // 1.5))
# set logger for event and model saver
logger.set_logger_dir(hp.logdir)
if True:
train_conf = TrainConfig(
model=ClassificationModel(num_classes=audio_meta.num_speaker, **hp.model),
data=FlexibleQueueInput(df, capacity=500),
callbacks=[
ModelSaver(checkpoint_dir=hp.logdir),
EvalCallback()
],
steps_per_epoch=hp.train.steps_per_epoch,
# session_config=session_config
)
ckpt = args.ckpt if args.ckpt else tf.train.latest_checkpoint(hp.logdir)
if ckpt and not args.r:
train_conf.session_init = SaverRestore(ckpt)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
import torch
from model import ClassificationModel
from torchvision import transforms
from config import classes
from PIL import Image
modelpath = 'epoch=9-step=1769.ckpt'
model = ClassificationModel()
model.load_state_dict(torch.load(modelpath)['state_dict'], strict=False)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
model.eval()
transform = transforms.Compose([
transforms.ToTensor(),
])
def check_for_gloves(image):
with torch.no_grad():
image = transform(image)
print(image.shape)
image = image.unsqueeze(0)
print(image.shape)
image = image.to(device)
print(image.shape)
output = model(image)
op, predicted = torch.max(output.data, 1)
print(predicted.item(), classes[predicted.item()], op.item())
return predicted.item(), classes[predicted.item()], op.item()
from konlpy.tag import Komoran
from tensorflow.keras import Model
from model import ClassificationModel, input_shape
if __name__ == "__main__":
komoran = Komoran()
model_parent = ClassificationModel()
model = model_parent.build_model()
embedding = model_parent.embedding
model.load_weights("curse_detection/weights-short.h5")
att_model = Model(inputs=[model.input], outputs=model.layers[10].output)
while True:
inp = input(':')
inp, mask = embedding([komoran.morphs(inp)])
out = model.predict((inp, mask)).squeeze(1)
att = att_model.predict((inp, mask))[1].squeeze(2)
print(att)
print(out)
import tensorflow as tf
from data_loader import DataLoader
from model import ClassificationModel
from config import *
# Create tensorflow session
sess = tf.Session()
# Build model graph
model = ClassificationModel(sess, "DBC")
# Initialize the model graph
sess.run(tf.global_variables_initializer())
# Build dataset pipeline graph
train_dataset = DataLoader(BATCH_SIZE)
# Get end of dataset pipeline
img, labels = train_dataset.get_train_data()
img_val, label_val = train_dataset.get_val_data()
epoch = 0
iter = 0
while True:
try:
# Fetch the dataset (tf.Tensor -> numpy array)
_img, _label = sess.run([img, labels])
# print(_img.shape)
# print(_img[0].shape)
# print(_label)
# import cv2
# cv2.imshow("img", _img[0])
# cv2.waitKey(0)
