def main():
img = Image.open(IMAGEPATH)
transed_img = trans(img)
n_channels = 3
n_classes = 5
test_model = CNN(n_channels, n_classes)
model_dict = test_model.load_state_dict(torch.load(MODELPATH))
transed_img = transed_img.view(1, 3, DROPSIZE, DROPSIZE)
outputs = test_model(transed_img)
_, predicted = torch.max(outputs.data, 1)
# loss:
criterion = nn.CrossEntropyLoss()
loss = criterion(outputs, torch.from_numpy(np.array([2])))
# if loss >= 1:
# print("Unknown")
# return
if predicted == 0:
print("Dorm")
if predicted == 1:
print("Gym")
if predicted == 2:
print("Library")
if predicted == 3:
print("Market")
if predicted == 4:
print("Teaching Building 1")
def train(trainloader):
"""
Performs training and evaluation of CNN model.
NOTE: You should the model on the whole test set each eval_freq iterations.
"""
# YOUR TRAINING CODE GOES HERE
n_channels = 3
n_classes = 5
cnn = CNN(n_channels, n_classes)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
cnn.to(device)
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=LEARNING_RATE_DEFAULT)
losses = []
accuracies = []
for epoch in range(MAX_EPOCHS_DEFAULT):
timestart = time.time()
running_loss = 0.0
for step, (batch_x, batch_y) in enumerate(trainloader):
# zero the parameter gradients
optimizer.zero_grad()
# Forward + Backward + Optimize
batch_x, batch_y = batch_x.to(device), batch_y.to(device)
outputs = cnn(batch_x)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
running_loss += loss.item()
if step % EVAL_FREQ_DEFAULT == EVAL_FREQ_DEFAULT - 1:
print('[epoch: %d, step: %5d] loss: %.4f' %
(epoch, step, running_loss / EVAL_FREQ_DEFAULT))
losses.append(running_loss / EVAL_FREQ_DEFAULT)
running_loss = 0.0
accu = accuracy(outputs, batch_y)
accuracies.append(accu)
print('Accuracy on the %d train images: %.3f %%' %
(batch_y.size(0), accu))
break
print('epoch %d cost %3f sec' % (epoch, time.time() - timestart))
print('---Finished Training---')
return cnn, losses, accuracies
def train(config):
"""
Performs training and evaluation of MLP model.
NOTE: You should the model on the whole test set each eval_freq iterations.
"""
# YOUR TRAINING CODE GOES HERE
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_data = CIFAR10(DATA_DIR_DEFAULT,
train=True,
download=True,
transform=transform)
data_loader = DataLoader(train_data, batch_size=config.batch_size)
model = CNN(3, 10)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
for step, (batch_inputs, batch_targets) in enumerate(data_loader):
# print(batch_inputs.size())
hit = 0
n, dim, _, __ = batch_inputs.size()
# for i in range(n):
# temp_x = torch.unsqueeze(batch_inputs[i], 0)
# print(temp_x.size())
# y_pre = model.forward(temp_x)
y_pre = model.forward(batch_inputs)
for i in range(n):
y_ev, _ = max(enumerate(y_pre[i]), key=itemgetter(1))
y = batch_targets[i].item()
if y_ev == y:
hit += 1
torch.nn.utils.clip_grad_norm(model.parameters(), max_norm=10)
# Add more code here ...
loss = criterion(y_pre, batch_targets) # fixme
accuracy = hit / n * 100 # fixme
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % config.eval_freq == 0:
print("loss: ", loss.item())
print("accuracy: ", accuracy)
if step == config.max_steps:
# If you receive a PyTorch data-loader error, check this bug report:
# https://github.com/pytorch/pytorch/pull/9655
break
print('Done training.')
def main(args):
transform = transforms.ToTensor()
mode = args.mode
train_loader = torch.utils.data.DataLoader(
UrbanSound8KDataset('UrbanSound8K_train.pkl', mode),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.worker_count,
pin_memory=True
)
val_loader = torch.utils.data.DataLoader(
UrbanSound8KDataset('UrbanSound8K_test.pkl', mode),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.worker_count,
pin_memory=True
)
## Build a model based on mode
if args.mode == 'MLMC':
model = CNN(height=145, width=41, channels=1, class_count=10, dropout=args.dropout,mode = args.mode)
else:
model = CNN(height=85, width=41, channels=1, class_count=10, dropout=args.dropout,mode = args.mode)
## Redefine the criterion to be softmax cross entropy
criterion = nn.CrossEntropyLoss()
## Use adam optimizer. AdamW is Adam with L-2 regularisation.
optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
log_dir = get_summary_writer_log_dir(args)
print(f"Writing logs to {log_dir}")
summary_writer = SummaryWriter(
str(log_dir),
flush_secs=5
)
trainer = Trainer(
model, train_loader, val_loader, criterion, optimizer, summary_writer,
DEVICE, args.checkpoint_path, checkpoint_frequency = args.checkpoint_frequency
)
trainer.train(
args.epochs,
args.val_frequency,
print_frequency=args.print_frequency,
log_frequency=args.log_frequency,
)
summary_writer.close()
def __init__(self):
self.session = tf.Session()
self.model = CNN()
self.session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=self.session, save_path=SAVE_DIR_MNIST)
def buildCNN(X,
Y,
layers,
activation="relu",
loss_type="softmax",
optimizer="adam",
regularization=None,
batch_size=64,
padding='SAME',
learning_rate=1e-3,
iteration=10000,
batch_norm=False,
drop_out=False,
drop_out_rate=0):
'''
Create a CNN object given the model specifications
input: Details can be found in CNN class of cnn_model.py
return: CNN model
'''
model = CNN(X, Y, layers, activation, loss_type, optimizer, regularization,
batch_size, padding, learning_rate, iteration, batch_norm,
drop_out, drop_out_rate)
return model
def initialize(self, is_load_other_model, other_model_name=""):
self._model = CNN()
if (is_load_other_model):
serializers.load_npz(other_model_name, self.model)
self._optimizer = optimizers.Adam()
self.optimizer.setup(self.model)
def init_model(self):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.config = CNNConfig()
self.cnn = CNN(self.config)
# self.cnn.setVGG16()
print('Loading model from file:', self.model_path)
saver = tf.train.import_meta_graph(self.model_path + '.meta')
saver.restore(self.sess, self.model_path)
self.graph = tf.get_default_graph()
# 从图中读取变量
self.input_x = self.graph.get_operation_by_name("input_x").outputs[0]
self.labels = self.graph.get_operation_by_name("labels").outputs[0]
self.dropout_keep_prob = self.graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
self.score = self.graph.get_operation_by_name('score/Relu').outputs[0]
self.prediction = self.graph.get_operation_by_name(
"prediction").outputs[0]
self.training = self.graph.get_operation_by_name("training").outputs[0]
def test(model_name='model.pkl'):
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load(model_name))
print('load cnn net.')
test_dataloader = dataset.get_test_data_loader()
correct = 0
total = 0
for i, (images, labels) in enumerate(test_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
chars = ''
for i in range(setting.MAX_CAPTCHA):
chars += setting.ALL_CHAR_SET[np.argmax(
predict_label[0, i * setting.ALL_CHAR_SET_LEN:(i + 1) *
setting.ALL_CHAR_SET_LEN].data.numpy())]
predict_label = chars
true_label = one_hot.decode(labels.numpy()[0])
total += labels.size(0)
if (predict_label == true_label):
correct += 1
else:
print('Predict:' + predict_label)
print('Real :' + true_label)
if (total % 200 == 0):
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model/1500_model.pkl'))
print("load cnn net.")
predict_dataloader = my_dataset.get_predict_data_loader()
for i, (images, labels) in enumerate(predict_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, captcha_setting.ALL_CHAR_SET_LEN:2 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 2 * captcha_setting.ALL_CHAR_SET_LEN:3 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c3 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c = '%s%s%s%s' % (c0, c1, c2, c3)
return c
def train_cnn_model(emb_layer, x_train, y_train, x_val, y_val, opt):
model = CNN(embedding_layer=emb_layer,
num_words=opt.n_words,
embedding_dim=opt.embed_dim,
filter_sizes=opt.cnn_filter_shapes,
feature_maps=opt.filter_sizes,
max_seq_length=opt.sent_len,
dropout_rate=opt.dropout_ratio,
hidden_units=200,
nb_classes=2).build_model()
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(),
metrics=['accuracy'])
# y_train = y_train.reshape(-1, 1)
# model = build_model(emb_layer, opt)
print(model.summary())
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
history = model.fit(x_train,
y_train,
epochs=opt.cnn_epoch,
batch_size=opt.batch_size,
verbose=1,
validation_data=(x_val, y_val),
callbacks=[early_stopping])
with open("CNN_train_history.txt", "w") as f:
print(history.history, file=f)
return model
def evaluation(method, dataset, user, device_source):
log_name = 'log/cnn_%s_%s_evaluation.txt' % (dataset, method)
if os.path.exists(log_name):
os.remove(log_name)
if user == 'mlsnrs':
root_dir_prefix = '/home/mlsnrs/apks'
elif user == 'shellhand':
root_dir_prefix = '/mnt'
save_feature_path = '%s/%s/mamadroid/%s/%s/%s_save_feature_list.csv' % (
root_dir_prefix, device_source, dataset, method, method)
save_feature_dict = get_save_feature_dict(save_feature_path)
print('have read save_feature_dict: %d' % len(save_feature_dict))
x_train, y_train = get_train_data(dataset, method, save_feature_dict,
root_dir_prefix, device_source)
print('x_train shape: %s y_train shape: %s' %
(str(x_train.shape), str(y_train.shape)))
start = time.time()
print('start train')
clf = CNN(layer_num=3, kernel_size=3, gpu_id=2)
clf.fit(x_train, y_train, epoch=5, batch_size=500, lr=0.01)
end = time.time()
print('Training model time used: %f s' % (end - start))
# torch.cuda.empty_cache()
print(x_train.shape)
y_pred = clf.predict(x_train, batch_size=20)
print(y_pred.shape)
cm = confusion_matrix(y_train, np.int32(y_pred >= 0.5))
TP = cm[1][1]
FP = cm[0][1]
TN = cm[0][0]
FN = cm[1][0]
F1 = float(2 * TP) / (2 * TP + FN + FP)
print('train data TP FP TN FN F1: %d %d %d %d %.4f' % (TP, FP, TN, FN, F1))
with open(log_name, 'a') as f:
f.write('train data TP FP TN FN F1: %d %d %d %d %.4f\n' %
(TP, FP, TN, FN, F1))
x_train = []
y_train = []
for test_id in range(0, 1): #13):
x_test, y_test = get_test_data(dataset, test_id, method,
save_feature_dict, root_dir_prefix,
device_source)
print('x_test shape: %s y_test shape: %s' %
(str(x_test.shape), str(y_test.shape)))
y_pred = clf.predict(x_test, batch_size=500)
# y_pred = classify(y_pred)
cm = confusion_matrix(y_test, y_pred)
TP = cm[1][1]
FP = cm[0][1]
TN = cm[0][0]
FN = cm[1][0]
F1 = float(2 * TP) / (2 * TP + FN + FP)
print('test_id %d TP FP TN FN F1: %d %d %d %d %.4f' %
(test_id, TP, FP, TN, FN, F1))
with open(log_name, 'a') as f:
f.write('test_id %d TP FP TN FN F1: %d %d %d %d %.4f\n' %
(test_id, TP, FP, TN, FN, F1))
def main():
args = parse_args()
twitter_csv_path = args.tweet_csv_file
device_type = args.device
use_bert = False
shuffle = False
train_data, dev_data, test_data = load_twitter_data(twitter_csv_path, test_split_percent=0.1, val_split_percent=0.2, overfit=True, shuffle=shuffle, use_bert=use_bert, overfit_val=12639)
vocab_size = train_data.vocab_size
print(vocab_size)
print(train_data.length)
print(dev_data.length)
print(test_data.length)
cnn_net = CNN(vocab_size, DIM_EMB=300, NUM_CLASSES = 2)
if device_type == "gpu" and torch.cuda.is_available():
device = torch.device('cuda:0')
cnn_net = cnn_net.cuda()
epoch_losses, eval_accuracy = train_network(cnn_net,
train_data.Xwordlist,
(train_data.labels + 1.0)/2.0,
10, dev_data, lr=0.003,
batchSize=150, use_gpu=True, device=device)
cnn_net.eval()
print("Test Set")
test_accuracy = eval_network(test_data, cnn_net, use_gpu=True, device=device)
else:
device = torch.device('cpu')
epoch_losses, eval_accuracy = train_network(cnn_net,
train_data.Xwordlist,
(train_data.labels + 1.0)/2.0,
10, dev_data, lr=0.003,
batchSize=150, use_gpu=False, device=device)
cnn_net.eval()
print("Test Set")
test_accuracy = eval_network(test_data, cnn_net, use_gpu=False, batch_size=batchSize, device=device)
# plot_accuracy((min_accs, eval_accuracy, max_accs), "Sentiment CNN lr=0.001", train_data.length)
plot_accuracy(eval_accuracy, "Sentiment CNN lr=0.003", train_data.length)
plot_losses(epoch_losses, "Sentiment CNN lr=0.003", train_data.length)
torch.save(cnn_net.state_dict(), "saved_models\\cnn.pth")
np.save("cnn_train_loss_" + str(train_data.length) + ".npy", np.array(epoch_losses))
np.save("cnn_validation_accuracy_" + str(train_data.length) + ".npy", np.array(eval_accuracy))
def train(args):
"""Train model"""
data = CIFAR10(args.batch_size, TRAIN_FILES)
# create save directory if it does not already exist
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
print('Initializing model...')
images = tf.placeholder(tf.float32, [None, 32, 32, 3], 'input_images')
distorted = distort_images(images)
model = CNN(distorted, learning_rate=args.learning_rate)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
print('Starting training...')
for n in range(args.num_epochs):
for i in range(data.n_batches):
start = time.time()
x, y = data.next_batch()
loss, _ = sess.run([model.loss, model.train_step],
feed_dict={
images: x,
model.labels: y
})
end = time.time()
print('{}/{} (epoch {}), train_loss={:.3f}, time/batch={:.3f}'.
format(n * data.n_batches + i,
args.num_epochs * data.n_batches, n, loss,
end - start))
checkpoint_path = os.path.join(args.save_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=n * data.n_batches)
print("model saved to {}".format(checkpoint_path))
def main():
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load('model/1500_model.pkl'))
print("load cnn net.")
test_dataloader = my_dataset.get_test_data_loader()
correct = 0
total = 0
error = []
true = []
for i, (images, labels) in enumerate(test_dataloader):
image = images
vimage = Variable(image)
predict_label = cnn(vimage)
c0 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 0:captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c1 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, captcha_setting.ALL_CHAR_SET_LEN:2 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c2 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 2 * captcha_setting.ALL_CHAR_SET_LEN:3 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
c3 = captcha_setting.ALL_CHAR_SET[np.argmax(
predict_label[0, 3 * captcha_setting.ALL_CHAR_SET_LEN:4 *
captcha_setting.ALL_CHAR_SET_LEN].data.numpy())]
predict_label = '%s%s%s%s' % (c0, c1, c2, c3)
true_label = one_hot_encoding.decode(labels.numpy()[0])
print("true_label: ", true_label)
print("predict_lable: ", predict_label, "\n")
total += labels.size(0)
if (predict_label == true_label):
correct += 1
else:
error.append(predict_label)
true.append(true_label)
if (total % 200 == 0):
print('测试集数量：%d， 准确率 : %f %%' % (total, 100 * correct / total))
print('测试集数量：%d， 准确率 : %f %%' % (total, 100 * correct / total))
print('预测错误例子：\n')
print('正确字符：', true)
print('错误字符：', error)
def train_baseline_cnn(emb_layer, x_train, y_train, x_val, y_val, opt):
model = CNN(embedding_layer=emb_layer,
num_words=opt.transfer_n_words,
embedding_dim=opt.baseline_embed_dim,
filter_sizes=opt.cnn_filter_shapes,
feature_maps=opt.filter_sizes,
max_seq_length=opt.baseline_sent_len,
dropout_rate=opt.baseline_drop_out_ratio,
hidden_units=200,
nb_classes=2).build_model()
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(),
metrics=['accuracy'])
# y_train = y_train.reshape(-1, 1)
# model = build_model(emb_layer, opt)
print(model.summary())
tb_call_back = TensorBoard(log_dir=f'{opt.tbpath}/baseline_cnn_{time()}',
histogram_freq=1,
write_graph=True,
write_images=True)
checkpoint = ModelCheckpoint("baseline_cnn.h5",
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto',
period=1)
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
history = model.fit(x_train,
y_train,
epochs=opt.baseline_epochs,
batch_size=opt.baseline_batchsize,
verbose=1,
validation_data=(x_val, y_val),
callbacks=[early_stopping, tb_call_back, checkpoint])
with open("CNN_train_baseline_history.txt", "w") as f:
print(history.history, file=f)
return model
def train(model_name='model.pkl'):
cnn = CNN()
cnn.train()
print('init net')
criterion = nn.MultiLabelSoftMarginLoss()
optimizer = torch.optim.Adam(cnn.parameters(),
lr=setting.TRAIN_LEARNING_RATE)
# Train the Model
train_dataloader = dataset.get_train_data_loader()
for epoch in range(setting.TRAIN_NUM_EPOCHS):
for i, (images, labels) in enumerate(train_dataloader):
images = Variable(images)
labels = Variable(labels.float())
predict_labels = cnn(images)
loss = criterion(predict_labels, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch: % -3s loss: %s' % (epoch, loss.item()))
torch.save(cnn.state_dict(), model_name) # current is model.pkl
print('save last model')
def recognize(model_name='model.pk'):
cnn = CNN()
cnn.eval()
cnn.load_state_dict(torch.load(model_name))
# print(load cnn net.)
# NUM_LEN = len(setting.NUMBER)
captcha_dataloader = dataset.get_captcha_data_loader()
code = ''
images = {}
for image, label in captcha_dataloader:
images[label] = image
images = [images[key] for key in sorted(images)]
for image in images:
vimage = Variable(image)
predict_label = cnn(vimage)
for i in range(setting.MAX_CAPTCHA):
code += setting.ALL_CHAR_SET[np.argmax(
predict_label[0, i * setting.ALL_CHAR_SET_LEN:(i + 1) *
setting.ALL_CHAR_SET_LEN].data.numpy())]
return code
def get_model(conf, data_helper, model_name):
# retrieve model configurations
max_epoch = conf.max_epoch
num_negatives = conf.num_negatives
batch_size_p = conf.batch_size_p
eval_topk = conf.eval_topk
optimizer = conf.optimizer
loss = conf.loss
user_dim = conf.user_dim
item_dim = conf.item_dim
data_spec = data_helper.data_spec
user_count = data_spec.user_count
item_count = data_spec.item_count
word_count = data_spec.word_count
emb_normalization = conf.emb_normalization
max_content_len = data_spec.max_content_len
support_groupping_for_all = True # provide general speed-up
# standard input & output of the model
input_dtype = 'int32'
row_cidx_prefx = tf.Variable(np.arange(batch_size_p, \
dtype=input_dtype).reshape((batch_size_p, 1)))
uid = Input(shape=(1,), dtype=input_dtype)
cid = Input(shape=(1,), dtype=input_dtype)
U_emb_given = Input(shape=(user_dim,), dtype='float32')
C_emb_given = Input(shape=(item_dim,), dtype='float32')
# unique cid, in two Lambda thanks to Keras, dummy
cid_u = Lambda(lambda x: tf.reshape(tf.unique(x)[0], (-1, 1)),
output_shape=(1,))(Reshape(())(cid))
cid_x = Lambda(lambda x: tf.reshape(tf.unique(x)[1], (-1, 1)),
output_shape=(1,))(Reshape(())(cid))
# retrieve content
with tf.device('/cpu:0'):
C = tf.Variable(data_helper.data['C'])
get_content = lambda x: tf.reshape(tf.gather(C, x),
(-1, max_content_len))
content = Lambda(get_content, output_shape=(max_content_len, ))(cid)
content_u = Lambda(get_content, output_shape=(max_content_len, ))(cid_u)
# user embedding: U_emb, U_emb_front (first batch_size_p)
Emb_U = Embedding(user_count, user_dim, name='user_embedding',
activity_regularizer=activity_l2(conf.u_reg))
U_emb = Reshape((user_dim, ))(Emb_U(uid))
if emb_normalization:
U_emb = Lambda(lambda x: tf.nn.l2_normalize(x, dim=-1))(U_emb)
uid_front = Lambda(lambda x: x[:batch_size_p])(uid) # thanks keras, dummy
U_emb_front = Reshape((user_dim, ))(Emb_U(uid_front))
# item embedding: C_emb_compact (no duplication), C_emb
get_item_emb_combined_pretrain = ItemCombination().get_model()
if model_name == 'pretrained':
if conf.evaluation_mode:
Emb_U = Embedding(user_count, user_dim, trainable=False,
weights=[conf.pretrain['user_emb']])
U_emb = Reshape((user_dim, ))(Emb_U(uid))
Emb_C = Embedding(item_count, item_dim, trainable=False,
weights=[conf.pretrain['item_emb']])
C_emb = Reshape((item_dim, ))(Emb_C(cid))
else:
if conf.pretrain['transform']:
C_emb = get_item_emb_combined_pretrain(None, cid, conf, data_spec)
else:
Emb_C = Embedding(item_count, item_dim, trainable=False,
weights=[data_spec.C_pretrain])
C_emb = Reshape((item_dim, ))(Emb_C(cid))
C_emb_compact = C_emb
elif model_name == 'mf':
Emb_C = Embedding(item_count, item_dim, name='item_embedding')
C_emb = Reshape((item_dim, ))(Emb_C(cid))
C_emb_compact = C_emb
else:
if model_name == 'basic_embedding':
Content_model = MeanPool(data_spec, conf).get_model()
elif model_name == 'cnn_embedding':
Content_model = CNN(data_spec, conf).get_model()
elif model_name == 'rnn_embedding':
Content_model = RNN(data_spec, conf).get_model()
else:
assert False, '[ERROR] Model name {} unknown'.format(model_name)
C_emb_compact = Content_model([content_u, cid_u]) # (None, item_dim)
C_emb_compact = get_item_emb_combined_pretrain(C_emb_compact, cid_u, \
conf, data_spec) # (None, item_dim)
# C_emb_u only computes unique set of items, no duplication
C_emb_u = Lambda( \
lambda x: tf.reshape(tf.gather(x[0], x[1]), (-1, item_dim)), \
output_shape=(item_dim, ))([C_emb_compact, cid_x])
if support_groupping_for_all:
C_emb = C_emb_u
else: # otherwise only support groupping for group_neg_shared
C_emb = Content_model([content, cid]) # (None, item_dim)
if emb_normalization:
C_emb_compact = Lambda(lambda x: tf.nn.l2_normalize(x, dim=-1))(C_emb_compact)
C_emb = Lambda(lambda x: tf.nn.l2_normalize(x, dim=-1))(C_emb)
# item embedding more: C_emb_front, C_emb_back
cid_front = Lambda(lambda x: x[:batch_size_p])(cid)
cid_back = Lambda(lambda x: x[batch_size_p:])(cid)
C_emb_front = Lambda(lambda x: x[:batch_size_p])(C_emb)
C_emb_back = Lambda(lambda x: x[batch_size_p:])(C_emb)
# interact (with or without bias)
Interact = InteractionDot(bias=conf.interaction_bias,
user_count=user_count, item_count=item_count)
pred_score = Interact.set_form('mul')([U_emb, C_emb, uid, cid])
pred_score_with_given = Interact.set_form('mul')([U_emb_given, C_emb_given,
uid, cid])
pred_score_neg_shared = Interact.set_form('matmul')([U_emb, C_emb,
uid, cid])
pred_score_neg_shared_comp = Interact.set_form('matmul')([ \
U_emb, C_emb_compact, uid, cid_u])
pos_idxs = tf.concat([row_cidx_prefx, \
tf.reshape(cid_x, (-1, 1))], 1) # (batch_size_p, 2)
loss_neg_shared_comp = get_group_neg_shared_loss( \
pred_score_neg_shared_comp, pos_idxs, loss, batch_size_p, conf)
pred_pos_sampled_neg_shared = Interact.set_form('mul')([ \
U_emb_front, C_emb_front, uid_front, cid_front]) # (batch_size_p, 1)
pred_neg_sampled_neg_shared = Interact.set_form('matmul')([ \
U_emb_front, C_emb_back, uid_front, cid_back]) # (batch_size_p, num_negatives)
pred_score_sampled_neg_shared = Lambda(lambda x: tf.concat([x[0], x[1]], 1))( \
[pred_pos_sampled_neg_shared, pred_neg_sampled_neg_shared])
# uid-cid element-wise interaction
# during training, first batch_size_p assumed positive
model = Model(input=[uid, cid], output=[pred_score])
model.compile(optimizer=optimizer, \
loss=get_original_loss(loss, batch_size_p, num_negatives, conf))
# uid-cid complete pairwise interaction (produce prediction matrix)
# during training, diag is assumed positive
model_neg_shared = Model(input=[uid, cid], output=[pred_score_neg_shared])
model_neg_shared.compile(optimizer=optimizer, \
loss=get_neg_shared_loss(loss, batch_size_p, conf))
# uid and compacted cid complete pairwise interactions
model_group_neg_shared = Model(input=[uid, cid], \
output=[pred_score_neg_shared_comp])
model_group_neg_shared.compile(optimizer=optimizer, \
loss=lambda y_true, y_pred: loss_neg_shared_comp) # dummy
# sampled negatives are shared
# first batch_size_p pairs are positive ones,
# uid[:batch_size_p] and cid[batch_size_p:] are negative links
model_sampled_neg_shared = Model(input=[uid, cid], \
output=[pred_score_sampled_neg_shared])
model_sampled_neg_shared.compile(optimizer=optimizer, \
loss=get_sampled_neg_shared_loss(loss, batch_size_p,
num_negatives, conf))
# test efficient methods with given (uid, cid) pairs
model_user_emb = Model(input=[uid], output=[U_emb])
model_item_emb = Model(input=[cid], output=[C_emb])
model_pred_pairs = Model(input=[U_emb_given, C_emb_given, uid, cid], \
output=[pred_score_with_given])
# construct models for monitoring all types of losses during training
def get_all_losses(input, output, loss):
model_all_loss = {'skip-gram': None, 'mse': None,
'log-loss': None, 'max-margin': None}
for lname in model_all_loss:
from keras.optimizers import SGD
m = Model(input=input, output=output)
m.compile(optimizer=SGD(0.), loss=loss)
model_all_loss[lname] = m
return model_all_loss
model_all_loss = get_all_losses([uid, cid], [pred_score], \
get_original_loss(loss, batch_size_p, num_negatives, conf))
model_neg_shared_all_loss = get_all_losses([uid, cid], \
[pred_score_neg_shared], \
get_neg_shared_loss(loss, batch_size_p, conf))
model_dict = {'model': model,
'model_neg_shared': model_neg_shared,
'model_group_neg_shared': model_group_neg_shared,
'model_sampled_neg_shared': model_sampled_neg_shared,
'model_user_emb': model_user_emb,
'model_item_emb': model_item_emb,
'model_pred_pairs': model_pred_pairs,
'model_all_loss': model_all_loss,
'model_neg_shared_all_loss': model_neg_shared_all_loss
}
return model_dict
if __name__ == '__main__':
train_data_dir = 'data_scaled/'
validation_data_dir = 'data_scaled_validation/'
nb_train_samples = 1763
nb_validation_samples = 194
epochs = 100
batch_size = 16
mimg = MoleImages()
X_test, y_test = mimg.load_test_images('data_scaled_test/benign',
'data_scaled_test/malign')
mycnn = CNN()
train_datagen = ImageDataGenerator(
vertical_flip=True,
horizontal_flip=True)
test_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(128, 128),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(128, 128),
batch_size=batch_size,
def train_model(embedding_size, hidden_size, filter_width, max_or_mean,
max_num_epochs, batch_size, learning_rate, loss_margin,
training_checkpoint, dropout_prob, eval_batch_size):
global load_model_path, train_data, source_questions
global dev_data, dev_label_dict, test_data, test_label_dict
global dev_pos_data, dev_neg_data, test_pos_data, test_neg_data, target_questions
# Generate model
cnn = CNN(embedding_size, hidden_size, filter_width, max_or_mean,
dropout_prob)
optimizer = optim.Adam(cnn.parameters(), lr=learning_rate)
criterion = nn.MultiMarginLoss(margin=loss_margin)
init_epoch = 1
# Load model
if load_model_path is not None:
print("Loading model from \"" + load_model_path + "\"...")
init_epoch = load_model(load_model_path, cnn, optimizer)
# Training
print("***************************************")
print("Starting run with following parameters:")
print(" --embedding size: %d" % (cnn.input_size))
print(" --hidden size: %d" % (cnn.hidden_size))
print(" --filter width: %d" % (cnn.n))
print(" --dropout: %f" % (cnn.dropout_prob))
print(" --pooling: %s" % (cnn.max_or_mean))
print(" --initial epoch: %d" % (init_epoch))
print(" --number of epochs: %d" % (max_num_epochs))
print(" --batch size: %d" % (batch_size))
print(" --learning rate: %f" % (learning_rate))
print(" --loss margin: %f" % (loss_margin))
start = time.time()
current_loss = 0
for iter in range(init_epoch, max_num_epochs + 1):
current_loss += train(cnn, criterion, optimizer, train_data,
source_questions, batch_size, 21)
if iter % training_checkpoint == 0:
print("Epoch %d: Average Train Loss: %.5f, Time: %s" %
(iter,
(current_loss / training_checkpoint), timeSince(start)))
d_auc = evaluate_auc(cnn, dev_pos_data, dev_neg_data,
target_questions, eval_batch_size)
t_auc = evaluate_auc(cnn, test_pos_data, test_neg_data,
target_questions, eval_batch_size)
print("Dev AUC(0.05): %.2f" % (d_auc))
print("Test AUC(0.05): %.2f" % (t_auc))
current_loss = 0
if SAVE_MODEL:
state = {}
state["model"] = cnn.state_dict()
state["optimizer"] = optimizer.state_dict()
state["epoch"] = iter
save_model(save_model_path, "cnn_dt", state,
iter == max_num_epochs)
# Compute final results
print("-------")
print("FINAL RESULTS:")
d_auc = evaluate_auc(cnn, dev_pos_data, dev_neg_data, target_questions,
eval_batch_size)
t_auc = evaluate_auc(cnn, test_pos_data, test_neg_data, target_questions,
eval_batch_size)
print("Training time: %s" % (timeSince(start)))
print("Dev AUC(0.05): %.2f" % (d_auc))
print("Test AUC(0.05): %.2f" % (t_auc))
if SAVE_MODEL:
state = {}
state["model"] = cnn.state_dict()
state["optimizer"] = optimizer.state_dict()
state[
"epoch"] = max_num_epochs if init_epoch < max_num_epochs else init_epoch
save_model(save_model_path, "cnn", state, True)
return (d_auc, t_auc)
def main(arg):
# build vocab
CSV_PATH = './MSR_Video_Description_Corpus.csv'
data = MSVD_Caption(CSV_PATH)
captions = data['Description'].values
del data
vocabs = Vocabs(list(replace_map(captions)))
# build model
cnn = CNN(arg.net) if arg.net else CNN()
cnn.load_weights(arg.cnn_weight_path)
rnn = CaptionGenerator(n_words=vocabs.n_words,
batch_size=1,
dim_feature=1280,
dim_hidden=500,
n_video_lstm=80,
n_caption_lstm=20,
bias_init_vector=vocabs.bias_init_vector)
rnn.load_weights(arg.rnn_weight_path, by_name=True)
# extract video features
print('extract %s video features' % (arg.video_path))
this_features = []
if arg.video_path.endswith('.avi'):
cap = cv2.VideoCapture(arg.video_path)
flame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT) # 视频总帧数
if flame_count > rnn.n_video_lstm: # 高于要求的帧数均匀采样
select_flames = np.linspace(0,
flame_count,
num=rnn.n_video_lstm,
dtype=np.int32)
else:
select_flames = np.arange(0, flame_count, dtype=np.int32)
print('flame count:', flame_count, 'select: ', select_flames[:10])
flames = []
i, flame_index, selected_num = 0, 0, 0
while True:
ret, flame = cap.read()
if ret is False:
break
if i == select_flames[flame_index]:
flame_index += 1
selected_num += 1
flame = preprocess_image(flame)
flames.append(flame)
if selected_num == 64:
selected_num = 0
this_features.append(cnn.get_features(np.array(flames)))
flames = []
i += 1
if i == flame_count and flames:
this_features.append(cnn.get_features(np.array(flames)))
else:
raise ValueError("only support .avi video")
# generator caption
this_features = np.concatenate(this_features, axis=0)
this_feature_nums, dims_feature = this_features.shape
if this_feature_nums < rnn.n_video_lstm: # 小于需要的帧数则填充0
this_features = np.vstack([
this_features,
np.zeros(shape=(rnn.n_video_lstm - this_feature_nums,
dims_feature))
])
if this_feature_nums > rnn.n_video_lstm: # 大于指定帧数则使用均匀采样
selected_idxs = np.linspace(0, this_feature_nums, num=rnn.n_video_lstm)
this_features = this_features[selected_idxs, :]
generator = rnn.predict(this_features.reshape(1, *this_features.shape))
captions = []
for i, gen_caption in enumerate(generator):
sent = []
for ii in gen_caption:
if ii == vocabs.word2idx['<eos>']:
break
if ii == vocabs.word2idx['<pad>']:
continue
if ii != vocabs.word2idx['<bos>']:
sent.append(vocabs.idx2word[ii])
caption = ' '.join(sent)
captions.append(caption)
return captions
# change this according to your path
path_to_train_set = "/home/tassos/Desktop/DATA_ASR/ASVspoof2017_V2_train_fbank"
path_to_valid_set = "/home/tassos/Desktop/DATA_ASR/ASVspoof2017_V2_train_dev"
model_id = get_model_id()
# model_id = read_model_id
n_tfiles=200 # how many train files will read per step
n_vfiles=round(0.25*n_tfiles) # number of validation files to be read per iter
# print("a= \n")
# print(n_vfiles)
# cheat count files number
total_inp_files = len(os.listdir(path_to_train_set))
# Create the network
network = CNN(model_id)
iter=0
# mf.input(network,n_tfiles,n_vfiles)
# print(network.Xtrain_in)
for i in range(1,total_inp_files,n_tfiles):
# loop until all data are read
mf.input(network,n_tfiles,n_vfiles)
with tf.device('/gpu:0'):
# restore()
if(iter==0):
# Define the train computation graph
network.define_train_operations()
# Train the network
random_state=random_state)
X_train = [X_train, X_train_c]
X_val = [X_val, X_val_c]
emb_layer = None
if USE_GLOVE:
emb_layer = create_glove_embeddings()
model = CNN(
embedding_layer=emb_layer,
num_words=MAX_NUM_WORDS,
embedding_dim=EMBEDDING_DIM,
kernel_sizes=KERNEL_SIZES,
feature_maps=FEATURE_MAPS,
max_seq_length=MAX_SEQ_LENGTH,
use_char=USE_CHAR,
char_max_length=CHAR_MAX_LENGTH,
alphabet_size=ALPHABET_SIZE,
char_kernel_sizes=CHAR_KERNEL_SIZES,
char_feature_maps=CHAR_FEATURE_MAPS,
dropout_rate=DROPOUT_RATE,
hidden_units=HIDDEN_UNITS,
nb_classes=NB_CLASSES
).build_model()
model.compile(
loss='categorical_crossentropy',
optimizer=keras.optimizers.Adam(),
metrics=['accuracy']
)
# model.summary()
def main(args):
load, save, train, evaluate = args.load, args.save, not args.no_train, not args.no_evaluate
del args.load
del args.save
del args.no_train
del args.no_evaluate
print "Initializing Training Datasets..."
if train:
ubuntu_train_loader = DataLoader(
UbuntuDataset(name='ubuntu', partition='train'),
batch_size=args.batch_size, # 20*n -> n questions.
shuffle=False,
num_workers=8,
collate_fn=batchify,
)
# Note, Android train data isn't labeled.
android_train_loader = DataLoader(
UbuntuDataset(name='android', partition='train'),
batch_size=args.batch_size, # 20*n -> n questions.
shuffle=False,
num_workers=8,
collate_fn=batchify,
)
print "Initializing Validation Datasets..."
if evaluate:
ubuntu_val_loader = DataLoader(
UbuntuDataset(name='ubuntu', partition='dev'),
batch_size=args.batch_size, # 20*n -> n questions.
shuffle=False,
num_workers=8,
collate_fn=batchify,
)
android_val_loader = DataLoader(
UbuntuDataset(name='android', partition='dev'),
batch_size=args.batch_size, # 20*n -> n questions.
shuffle=False,
num_workers=8,
collate_fn=batchify,
)
# MODELS
dc_model = DomainClassifier(args.hidden_size)
if args.model_type == 'lstm':
print "----LSTM----"
qr_model = LSTMRetrieval(args.input_size,
args.hidden_size,
args.num_layers,
args.pool,
batch_size=args.batch_size)
elif args.model_type == 'cnn':
print "----CNN----"
qr_model = CNN(args.input_size,
args.hidden_size,
args.pool,
batch_size=args.batch_size)
else:
raise RuntimeError('Unknown --model_type')
if load != '':
print "Loading Model state from 'saved_models/{}'".format(load)
qr_model.load_state_dict(torch.load(
'saved_models/gen_{}'.format(load)))
dc_model.load_state_dict(
torch.load('saved_models/discrim_{}'.format(load)))
# CUDA
if torch.cuda.is_available():
print "Using CUDA"
for model in [dc_model, qr_model]:
model = model.cuda()
model.share_memory()
# Loss functions and Optimizers
# dc_criterion = nn.L1Loss() # TODO: Replace with actual.
dc_criterion = nn.BCELoss()
dc_optimizer = torch.optim.SGD(dc_model.parameters(), lr=args.dc_lr)
qr_criterion = MaxMarginCosineSimilarityLoss() # TODO...
qr_optimizer = torch.optim.SGD(qr_model.parameters(), lr=args.qr_lr)
if load != '' and train:
print "Loading Optimizer state from 'saved_optimizers/{}'".format(load)
qr_optimizer.load_state_dict(
torch.load(
'saved_optimizers/DA_Gen_Optimizer({}).pth'.format(load)))
qr_optimizer.state = defaultdict(
dict, qr_optimizer.state
) # https://discuss.pytorch.org/t/saving-and-loading-sgd-optimizer/2536/5
dc_optimizer.load_state_dict(
torch.load(
'saved_optimizers/DA_Discrim_Optimizer({}).pth'.format(load)))
dc_optimizer.state = defaultdict(dict, dc_optimizer.state)
for epoch in xrange(args.epochs):
if train:
run_epoch(args,
ubuntu_train_loader,
android_train_loader,
qr_model,
qr_criterion,
qr_optimizer,
dc_model,
dc_criterion,
dc_optimizer,
epoch,
mode='train')
if evaluate:
if epoch % args.val_epoch == 0:
run_epoch(args,
ubuntu_val_loader,
android_val_loader,
qr_model,
qr_criterion,
qr_optimizer,
dc_model,
dc_criterion,
dc_optimizer,
epoch,
mode='val')
if save:
print "Saving Gen Model state to 'saved_models/DA_Gen_Model({}).pth'".format(
args)
torch.save(qr_model.state_dict(),
'saved_models/DA_Gen_Model({}).pth'.format(args))
print "Saving Gen Optimizer state to 'saved_optimizers/DA_Gen_Optimizer({}).pth'".format(
args)
torch.save(qr_optimizer.state_dict(),
'saved_optimizers/DA_Gen_Optimizer({}).pth'.format(args))
print "Saving Discrim Model state to 'saved_models/DA_Discrim_Model({}).pth'".format(
args)
torch.save(dc_model.state_dict(),
'saved_models/DA_Discrim_Model({}).pth'.format(args))
print "Saving Discrim Optimizer state to 'saved_optimizers/DA_Discrim_Optimizer({}).pth'".format(
args)
torch.save(
dc_optimizer.state_dict(),
'saved_optimizers/DA_Discrim_Optimizer({}).pth'.format(args))
})
a.append(test_acc)
print("test acc:{}".format(sum(a) / 350))
logger.info("test acc:{}".format(sum(a) / 350))
# saver.save(sess, save_path=SAVE_DIR)
end_time = time.time()
print("train takes %d Seconds" % (int(end_time) - int(start_time)))
logger.info("train takes %d Seconds" % (int(end_time) - int(start_time)))
def evel(sess, data):
print("evel acc of cnn>>>>")
a = []
b = []
for i in range(350):
timg, tlab = data.test.next_batch(50)
loss, acc = sess.run([model.loss, model.acc],
feed_dict={
model.input_x: timg,
model.input_y: tlab,
model.keep_prod: 1
})
a.append(loss)
b.append(acc)
return sum(a) / 350, sum(b) / 350
if __name__ == "__main__":
model = CNN()
train()
class Predictor(object):
def __init__(self, model_path):
self.model_path = model_path
self.class_name = [
'Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'
]
def init_model(self):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.config = CNNConfig()
self.cnn = CNN(self.config)
# self.cnn.setVGG16()
print('Loading model from file:', self.model_path)
saver = tf.train.import_meta_graph(self.model_path + '.meta')
saver.restore(self.sess, self.model_path)
self.graph = tf.get_default_graph()
# 从图中读取变量
self.input_x = self.graph.get_operation_by_name("input_x").outputs[0]
self.labels = self.graph.get_operation_by_name("labels").outputs[0]
self.dropout_keep_prob = self.graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
self.score = self.graph.get_operation_by_name('score/Relu').outputs[0]
self.prediction = self.graph.get_operation_by_name(
"prediction").outputs[0]
self.training = self.graph.get_operation_by_name("training").outputs[0]
def predict(self, batch_x):
feed_dict = {
self.input_x: batch_x,
self.dropout_keep_prob: 1.0,
self.training: False
}
score, pre = self.sess.run([self.score, self.prediction], feed_dict)
return score, pre
def draw_confusion_matrix(self):
# train_init_op, test_init_op, next_train_element, next_test_element = self.cnn.prepare_data()
test_dataset = TextLineDataset(
os.path.join('data', preprocess.FILTERED_TEST_PATH)).skip(1).batch(
self.cnn.test_batch_size)
# Create a reinitializable iterator
test_iterator = test_dataset.make_one_shot_iterator()
next_test_element = test_iterator.get_next()
y_true = []
y_pred = []
test_loss = 0.0
test_accuracy = 0.0
test_precision = 0.0
test_recall = 0.0
test_f1_score = 0.0
i = 0
while True:
try:
lines = self.sess.run(next_test_element)
batch_x, batch_y = self.cnn.convert_input(lines)
feed_dict = {
self.input_x: batch_x,
self.labels: batch_y,
self.dropout_keep_prob: 1.0,
self.training: False
}
# loss, pred, true = sess.run([self.cnn.loss, self.cnn.prediction, self.cnn.labels], feed_dict)
# 多次验证，取loss和score均值
mean_score = 0
for i in range(self.config.multi_test_num):
score = self.sess.run(self.score, feed_dict)
mean_score += score
mean_score /= self.config.multi_test_num
pred = self.sess.run(tf.argmax(mean_score, 1))
y_pred.extend(pred)
y_true.extend(batch_y)
i += 1
except tf.errors.OutOfRangeError:
# 遍历完验证集，计算评估
test_loss /= i
test_accuracy = metrics.accuracy_score(y_true=y_true,
y_pred=y_pred)
test_precision = metrics.precision_score(y_true=y_true,
y_pred=y_pred,
average='weighted')
test_recall = metrics.recall_score(y_true=y_true,
y_pred=y_pred,
average='weighted')
test_f1_score = metrics.f1_score(y_true=y_true,
y_pred=y_pred,
average='weighted')
log = ('precision: %0.6f, recall: %0.6f, f1_score: %0.6f' %
(test_precision, test_recall, test_f1_score))
print(log)
cm = confusion_matrix(y_true, y_pred)
print('Total samples:', np.sum(cm))
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] # 归一化
print('Confusion matrix:\n', cm)
# 绘制混淆矩阵
# ==============================================================
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(
xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=self.class_name,
yticklabels=self.class_name,
title="Normalized confusion matrix",
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(
j,
i,
format(cm[i, j], fmt),
ha="center",
va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
plt.savefig('./data/confusion_matrix.jpg')
plt.show()
# =====================================================================
break
def _detect_sentiment(self, detector, img):
# 转为灰度图片
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = detector.detectMultiScale(image=gray,
scaleFactor=1.1,
minNeighbors=2,
minSize=(30, 30),
flags=0)
if len(faces) != 0:
batch_x = []
for face in faces:
x, y, w, h = face
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 1)
# opencv的图像是[y, x]储存的
# 裁剪并显示人脸部分
img_cropped = cv2.resize(
gray[y:y + h,
x:x + w], (self.cnn.img_size, self.cnn.img_size))
cv2.imshow('cropped', img_cropped)
img_input = img_cropped.reshape(
[self.cnn.img_size, self.cnn.img_size, 1])
batch_x.append(img_input)
batch_x = np.stack(batch_x)
mean_score = 0
for i in range(self.config.multi_test_num):
score, _ = self.predict(batch_x)
mean_score += score
mean_score /= self.config.multi_test_num
pred = self.sess.run(tf.argmax(mean_score, 1))
for i in range(len(faces)):
# 给score显示条形图
# =======================================================
plt.bar(range(self.cnn.class_num),
mean_score[i],
align='center',
color='steelblue',
alpha=0.8)
plt.ylabel('Score')
plt.xticks(range(self.cnn.class_num), self.class_name)
plt.show()
# ========================================================
cv2.putText(img=img,
text=self.class_name[pred[i]],
org=(faces[i][0], faces[i][1] + faces[i][3] + 20),
fontFace=cv2.FONT_HERSHEY_COMPLEX,
fontScale=0.6,
color=(0, 0, 255))
return img
else:
return None
def camera_detect(self):
# 调用笔记本内置摄像头，所以参数为0，如果有其他的摄像头可以调整参数为1，2
cam = cv2.VideoCapture(0)
detector = cv2.CascadeClassifier(
'./data/haarcascade_frontalface_alt.xml')
while True:
# 从摄像头读取图片
sucess, img = cam.read()
if not sucess:
continue
img = self._detect_sentiment(detector, img)
# 显示摄像头，背景是灰度。
if img is not None:
cv2.imshow("Sentiment Detection", img)
# 保持画面的持续。
k = cv2.waitKey(1)
if k == 27:
# 通过esc键退出摄像
cv2.destroyAllWindows()
break
elif k == ord("s"):
# 通过s键保存图片，并退出。
cv2.imwrite("capture.jpg", img)
cv2.destroyAllWindows()
break
# 关闭摄像头
cam.release()
def image_detect(self, img_path):
img = cv2.imread(img_path)
detector = cv2.CascadeClassifier(
'./data/haarcascade_frontalface_alt.xml')
img = self._detect_sentiment(detector, img)
cv2.imshow('Sentiment Detection Result', img)
k = cv2.waitKey()
if k == ord("s"):
# 通过s键保存图片，并退出。
cv2.imwrite("result.jpg", img)
cv2.destroyAllWindows()
def main():
args = parse_args()
# twitter_csv_path = args.tweet_csv_file
labeled_twitter_csv_path = args.labeled_tweet_csv_file
unlabeled_twitter_csv_path = args.unlabeled_tweet_csv_file
device_type = args.device
acquistion_function_type = args.acquisition_func
human_label = args.human_label
use_model_acq = True #flag for using model to generate inputs for acquisition funciton
if acquistion_function_type == "least_confidence":
acquisition_func = least_confidence
elif acquistion_function_type == "random":
acquisition_func = random_score
elif acquistion_function_type == "entropy":
acquisition_func = entropy_score
elif acquistion_function_type == "tweet_count":
acquisition_func = tweet_count_norm
use_model_acq = False
else:
acquisition_func = least_confidence
seed_data_size = args.seed_data_size
use_bert = False
shuffle = False
train_data, dev_data, test_data = load_twitter_data(
labeled_twitter_csv_path,
test_split_percent=0.1,
val_split_percent=0.2,
shuffle=shuffle,
overfit=True,
use_bert=use_bert,
overfit_val=40000)
unlabeled_tweets, ground_truth_labels = load_unlabeled_tweet_csv(
unlabeled_twitter_csv_path, num_tweets=45000)
#convert "unlabeled" tweets to token ids
X_unlabeled = train_data.convert_text_to_ids(unlabeled_tweets)
# ground_truth_labels = ground_truth_labels[0:70000]
ground_truth_labels = (ground_truth_labels + 1.0) / 2.0
X_seed = train_data.Xwordlist[0:seed_data_size]
Y_seed = train_data.labels[0:seed_data_size]
Y_seed = (Y_seed + 1.0) / 2.0
print(train_data.vocab_size)
print(len(X_seed))
print(dev_data.length)
print(test_data.length)
num_samples = args.sample_size
cnn_net = CNN(train_data.vocab_size, DIM_EMB=300, NUM_CLASSES=2)
if device_type == "gpu" and torch.cuda.is_available():
device = torch.device('cuda:0')
cnn_net = cnn_net.cuda()
epoch_losses, eval_accuracy, hand_labeled_data = train_active_learning(
cnn_net,
train_data,
X_seed,
Y_seed,
X_unlabeled,
ground_truth_labels,
dev_data,
use_model=use_model_acq,
num_epochs=8,
human_label=human_label,
acquisition_func=acquisition_func,
lr=0.0035,
batchSize=150,
num_samples=num_samples,
use_gpu=True,
device=device)
cnn_net.eval()
print("Test Set")
test_accuracy = eval_network(test_data,
cnn_net,
use_gpu=True,
device=device)
else:
device = torch.device('cpu')
# cnn_net = cnn_net.cuda()
epoch_losses, eval_accuracy, hand_labeled_data = train_active_learning(
cnn_net,
train_data,
X_seed,
Y_seed,
X_unlabeled,
ground_truth_labels,
dev_data,
use_model=use_model_acq,
num_epochs=8,
human_label=human_label,
acquisition_func=acquisition_func,
lr=0.0035,
batchSize=150,
num_samples=num_samples,
use_gpu=False,
device=device)
cnn_net.eval()
print("Test Set")
test_accuracy = eval_network(test_data,
cnn_net,
use_gpu=False,
device=device)
# plot_accuracy((min_accs, eval_accuracy, max_accs), "Sentiment CNN lr=0.001", train_data.length)
plot_accuracy(
eval_accuracy, "Sentiment CNN (Active Learning) lr=0.0035 " +
acquistion_function_type, seed_data_size)
# plot_losses(epoch_losses, "Sentiment CNN (Active Learning) lr=0.0030" + acquistion_function_type, train_data.length)
torch.save(cnn_net.state_dict(), "saved_models\\cnn_active_learn.pth")
# np.save("cnn_active_learning_train_loss" + acquistion_function_type + "_" + str(seed_data_size) + ".npy", np.array(epoch_losses))
np.save(
"human_labelling_results/cnn_active_learning_validation_accuracy_" +
acquistion_function_type + "_" + str(seed_data_size) + "_" +
str(num_samples) + ".npy", np.array(eval_accuracy))
human_labels = []
ground_truth_labels = []
tweets = []
save_labels = True
if save_labels:
for tweet, label, ground_truth_label in hand_labeled_data:
# tweet, score = sample
tweet = train_data.convert_to_words(tweet)
tweets.append(tweet)
human_labels.append(label)
ground_truth_labels.append(ground_truth_label)
new_labeled_tweets = pd.DataFrame({
'label': human_labels,
'ground truth': ground_truth_labels,
'text': tweets
})
new_labeled_tweets.to_csv("human_labeled_tweets_lc_rk.csv",
header=True,
index=False)
def main():
# Load net
cnn = CNN()
loss_func = nn.MultiLabelSoftMarginLoss()
optimizer = optim.Adam(cnn.parameters(), lr=learning_rate)
if torch.cuda.is_available():
cnn.cuda()
loss_func.cuda()
# Load data
train_dataloader = dataset.get_train_data_loader()
test_dataloader = dataset.get_test_data_loader()
# Train model
for epoch in range(num_epochs):
cnn.train()
for i, (images, labels) in enumerate(train_dataloader):
images = Variable(images)
labels = Variable(labels.long())
if torch.cuda.is_available():
images = images.cuda()
labels = labels.cuda()
predict_labels = cnn(images)
loss = loss_func(predict_labels, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0:
print("epoch:", epoch, "step:", i, "loss:", loss.item())
# Save and test model
if (epoch + 1) % 10 == 0:
filename = "model" + str(epoch + 1) + ".pkl"
torch.save(cnn.state_dict(), filename)
cnn.eval()
correct = 0
total = 0
for (image, label) in test_dataloader:
vimage = Variable(image)
if torch.cuda.is_available():
vimage = vimage.cuda()
output = cnn(vimage)
predict_label = ""
for k in range(4):
predict_label += config.CHAR_SET[np.argmax(
output[0, k * config.CHAR_SET_LEN:(k + 1) *
config.CHAR_SET_LEN].data.cpu().numpy())]
true_label = one_hot.vec2text(label.numpy()[0])
total += label.size(0)
if predict_label == true_label:
correct += 1
if total % 200 == 0:
print(
'Test Accuracy of the model on the %d test images: %f %%'
% (total, 100 * correct / total))
print('Test Accuracy of the model on the %d test images: %f %%' %
(total, 100 * correct / total))
print("save and test model...")
torch.save(cnn.state_dict(), "./model.pkl") # current is model.pkl
print("save last model")
def main(args):
load, save, train, evaluate = args.load, args.save, not args.no_train, not args.no_evaluate
del args.load
del args.save
del args.no_train
del args.no_evaluate
# MODEL
if args.model_type == 'lstm':
print "----LSTM----"
model = LSTMRetrieval(args.input_size,
args.hidden_size,
args.num_layers,
args.pool,
batch_size=args.batch_size)
elif args.model_type == 'cnn':
print "----CNN----"
model = CNN(args.input_size,
args.hidden_size,
args.pool,
batch_size=args.batch_size)
else:
raise RuntimeError('Unknown --model_type')
if load != '':
print "Loading Model state from 'saved_models/{}'".format(load)
model.load_state_dict(torch.load('saved_models/{}'.format(load)))
# CUDA
if torch.cuda.is_available():
print "Using CUDA"
model = model.cuda()
model.share_memory()
# Loss function and Optimizer
loss_function = MaxMarginCosineSimilarityLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
if load != '' and train:
print "Loading Optimizer state from 'saved_optimizers/{}'".format(load)
optimizer.load_state_dict(
torch.load('saved_optimizers/{}'.format(load)))
optimizer.state = defaultdict(
dict, optimizer.state
) # https://discuss.pytorch.org/t/saving-and-loading-sgd-optimizer/2536/5
# Data
# training_data = Ubuntu.load_training_data()
for epoch in xrange(args.epochs):
print "Initializing Ubuntu Dataset..."
if train:
train_dataset = UbuntuDataset(name=args.dataset, partition='train')
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size, # 100*n -> n questions.
shuffle=False,
num_workers=8,
collate_fn=batchify)
if evaluate:
val_dataset = UbuntuDataset(name=args.dataset, partition='dev')
val_dataloader = DataLoader(
val_dataset,
batch_size=args.batch_size, # 100*n -> n questions.
shuffle=False,
num_workers=8,
collate_fn=batchify)
if train:
run_epoch(args,
train_dataloader,
model,
loss_function,
optimizer,
epoch,
mode='train')
if evaluate:
if epoch % args.val_epoch == 0:
run_epoch(args,
val_dataloader,
model,
loss_function,
optimizer,
epoch,
mode='val')
if save:
print "Saving Model state to 'saved_models/Model({}).pth'".format(args)
torch.save(model.state_dict(),
'saved_models/Model({}).pth'.format(args))
print "Saving Optimizer state to 'saved_optimizers/Optimizer({}).pth'".format(
args)
torch.save(optimizer.state_dict(),
'saved_optimizers/Optimizer({}).pth'.format(args))
