def validation(args, model, testloader, epoch, mode='test'):
conf_matrix = torch.zeros(args.classes, args.classes).cuda()
model.eval()
criterion = nn.CrossEntropyLoss()
metrics = Metrics('')
metrics.reset()
batch_idx = 0
ucsd_correct_total = 0
sars_correct_total = 0
ucsd_test_total = 0
sars_test_total = 0
with torch.no_grad():
for input_tensors in tqdm(testloader):
batch_idx = batch_idx + 1
input_data, target, site = input_tensors
if args.cuda:
input_data = input_data.cuda()
target = target.cuda()
ucsd_input = torch.from_numpy(np.array([]))
new_input = torch.from_numpy(np.array([]))
ucsd_label = torch.from_numpy(np.array([]))
new_label = torch.from_numpy(np.array([]))
for i in range(len(input_data)):
if site[i] == 'ucsd':
if len(ucsd_input) == 0:
ucsd_input = input_data[i].unsqueeze(0)
ucsd_label = torch.from_numpy(np.array([target[i]]))
else:
ucsd_input = torch.cat((ucsd_input, input_data[i].unsqueeze(0)))
ucsd_label = torch.cat((ucsd_label, torch.from_numpy(np.array([target[i]]))))
else:
if len(new_input) == 0:
new_input = input_data[i].unsqueeze(0)
new_label = torch.from_numpy(np.array([target[i]]))
else:
new_input = torch.cat((new_input, input_data[i].unsqueeze(0)))
new_label = torch.cat((new_label, torch.from_numpy(np.array([target[i]]))))
if len(ucsd_input) > 1:
ucsd_output, ucsd_features = model(ucsd_input, 'ucsd')
ucsd_correct, ucsd_total, ucsd_acc = accuracy(ucsd_output, ucsd_label.cuda())
ucsd_correct_total += ucsd_correct
ucsd_test_total += ucsd_total
if len(new_input) > 1:
new_output, new_features = model(new_input, 'ucsd')
sars_correct, sars_total, sars_acc = accuracy(new_output, new_label.cuda())
sars_correct_total += sars_correct
sars_test_total += sars_total
if len(ucsd_input) > 1 and len(new_input) > 1:
output = torch.cat((ucsd_output, new_output))
labels = torch.cat((ucsd_label, new_label)).cuda()
features = torch.cat((ucsd_features, new_features))
elif len(ucsd_input) > 1 and len(new_input) < 2:
output = ucsd_output
labels = ucsd_label.cuda()
else:
output = new_output
labels = new_label.cuda()
loss = criterion(output, labels)
preds = torch.argmax(output, dim=1)
for t, p in zip(target.view(-1), preds.view(-1)):
conf_matrix[t.long(), p.long()] += 1
correct, total, acc = accuracy(output, labels)
# top k acc
top1_correct = top_k_acc(output, labels, k=1)
top3_correct = top_k_acc(output, labels, k=2)
metrics.update({'correct': correct, 'total': total, 'loss': loss.item(), 'accuracy': acc,
'top1_correct': top1_correct, 'top3_correct': top3_correct})
print_summary(args, epoch, metrics, mode="test")
return metrics, conf_matrix, ucsd_correct_total, sars_correct_total, ucsd_test_total, sars_test_total
def train(args, model, trainloader, optimizer, epoch):
model.train()
criterion = nn.CrossEntropyLoss(reduction='elementwise_mean')
metrics = Metrics('')
metrics.reset()
batch_idx = 0
for input_tensors in tqdm(trainloader):
batch_idx = batch_idx + 1
optimizer.zero_grad()
input_data, target, site = input_tensors
if args.cuda:
input_data = input_data.cuda()
target = target.cuda()
ucsd_input = torch.from_numpy(np.array([]))
new_input = torch.from_numpy(np.array([]))
ucsd_label = torch.from_numpy(np.array([]))
new_label = torch.from_numpy(np.array([]))
for i in range(len(input_data)):
if site[i] == 'ucsd':
if len(ucsd_input) == 0:
ucsd_input = input_data[i].unsqueeze(0)
ucsd_label = torch.from_numpy(np.array([target[i]]))
else:
ucsd_input = torch.cat((ucsd_input, input_data[i].unsqueeze(0)))
ucsd_label = torch.cat((ucsd_label, torch.from_numpy(np.array([target[i]]))))
else:
if len(new_input) == 0:
new_input = input_data[i].unsqueeze(0)
new_label = torch.from_numpy(np.array([target[i]]))
else:
new_input = torch.cat((new_input, input_data[i].unsqueeze(0)))
new_label = torch.cat((new_label, torch.from_numpy(np.array([target[i]]))))
if len(ucsd_input) > 1:
ucsd_output, ucsd_features = model(ucsd_input, 'ucsd')
if len(new_input) > 1:
new_output, new_features = model(new_input, 'ucsd')
if len(ucsd_input) > 1 and len(new_input) > 1:
output = torch.cat((ucsd_output, new_output))
labels = torch.cat((ucsd_label, new_label)).cuda()
features = torch.cat((ucsd_features, new_features))
elif len(ucsd_input) > 1 and len(new_input) < 2:
output = ucsd_output
labels = ucsd_label.cuda()
features = ucsd_features
else:
output = new_output
labels = new_label.cuda()
features = new_features
if len(output) != len(labels):
continue
if len(features) == 32 and args.cont:
temperature = 0.05
cont_loss_func = losses.NTXentLoss(temperature)
cont_loss = cont_loss_func(features, labels)
loss = criterion(output, labels) + cont_loss
else:
loss = criterion(output, labels)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 2.0)
optimizer.step()
correct, total, acc = accuracy(output, labels)
top1_correct = top_k_acc(output, labels, k=1)
top3_correct = top_k_acc(output, labels, k=2)
metrics.update({'correct': correct, 'total': total, 'loss': loss.item(), 'accuracy': acc,
'top1_correct': top1_correct, 'top3_correct': top3_correct})
print_summary(args, epoch, metrics, mode="train")
return metrics
print("Building model...")
model = Sequential()
model.add(Embedding(input_dim=nb_words+1, output_dim=outputDim, weights=[embedding_matrix], input_length=INPUT_LENGTH, trainable=True))
model.add(Convolution1D(FILTER_LENGTH, NB_FILTER, activation="relu", border_mode='valid'))
model.add(MaxPooling1D((21)))
model.add(Flatten())
model.add(Dense(5, activation="softmax"))
model.compile(loss="categorical_crossentropy",
optimizer="adam",
metrics=['accuracy'])
print('Train...')
#model.summary()
checkpoint = ModelCheckpoint(CALLBACK_PATH, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
model.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epoch=NB_EPOCH, callbacks=callbacks_list,
validation_data=(x_test, y_test))
score, accuracy = model.evaluate(x_test, y_test, batch_size=BATCH_SIZE)
y_pred = model.predict_classes(x_test, batch_size=BATCH_SIZE, verbose=1)
met.eval_MAE(test_data, y_pred, PRED_FILE=PRED_FILE, TRUE_FILE=TRUE_FILE)
print('Test score:', score)
print('Test accuracy:', accuracy)