def eval_categorical(task):
model_path = './model/{}.model'.format(task)
D_m_text, D_m_audio, D_m_video, D_m_context = 300, 384, 35, 300
D_g, D_p, D_e, D_h, D_a = 150, 150, 100, 100, 100
cuda = torch.cuda.is_available()
print('Loading model...')
model = CategoricalModel(D_m_text,
D_m_audio,
D_m_video,
D_m_context,
D_g,
D_p,
D_e,
D_h,
n_classes=2,
dropout_rec=0.1,
dropout=0.5)
if cuda:
model.cuda()
model.load_state_dict(torch.load(model_path))
loss_weights = torch.FloatTensor([1 / 0.3097, 1 / 0.6903])
loss_function = MaskedNLLLoss(
loss_weights.cuda() if cuda else loss_weights)
print('Evaluating model...')
_, _, test_loader = train_categorical.get_MOSEI_loaders(
'./data/categorical.pkl', valid=0.0, batch_size=128, num_workers=0)
avg_loss, avg_accuracy, labels, preds, masks, _, _ = train_categorical.train_or_eval_model(
model, loss_function, test_loader, None, cuda)
print('loss =', avg_loss)
print('accuracy =', avg_accuracy)
listener_state=args.active_listener,
context_attention=args.attention,
dropout_rec=args.rec_dropout,
dropout=args.dropout)
if cuda:
model.cuda()
loss_weights = torch.FloatTensor([
1/0.086747,
1/0.144406,
1/0.227883,
1/0.160585,
1/0.127711,
1/0.252668,
])
if args.class_weight:
loss_function = MaskedNLLLoss(loss_weights.cuda() if cuda else loss_weights)
else:
loss_function = MaskedNLLLoss()
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.l2)
train_loader, valid_loader, test_loader =\
get_IEMOCAP_loaders('./IEMOCAP_features/IEMOCAP_features_raw.pkl',
valid=0.0,
batch_size=batch_size,
num_workers=2)
best_loss, best_label, best_pred, best_mask = None, None, None, None
for e in range(n_epochs):
pickle.dump(tokenizer, open('datasets/dialogue_level_minibatch/' + dataset + '/' + dataset + mode + '.tokenizer', 'wb'))
pickle.dump(embedding_matrix, open('datasets/dialogue_level_minibatch/' + dataset + '/' + dataset + mode + '_embedding.matrix', 'wb'))
print ('Done.')
vocab_size, embedding_dim = embedding_matrix.shape
model = End2EndModel(dataset, vocab_size, embedding_dim, tokenizer, classification_model,
cnn_output_size, cnn_filters, cnn_kernel_sizes, cnn_dropout,
D_e, D_h, n_classes, dropout, attention, context_attention, rec_dropout, residual)
model.init_pretrained_embeddings(embedding_matrix)
model.cuda()
if args.class_weight:
loss_function = MaskedNLLLoss(loss_weights.cuda())
else:
loss_function = MaskedNLLLoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
lf = open('logs/' + dataset + '_glove_' + classification_model + '_' + classify + '.txt', 'a')
rf = open('results/' + dataset + '_glove_' + classification_model + '_' + classify + '.txt', 'a')
valid_losses, valid_fscores = [], []
test_fscores = []
best_loss, best_label, best_pred, best_mask = None, None, None, None
for e in range(n_epochs):
start_time = time.time()
n_classes = 3
D_m = 600
D_e = 100
D_h = 100
model = LSTMModel(D_m,
D_e,
D_h,
n_classes=n_classes,
dropout=args.dropout,
attention=args.attention)
if cuda:
model.cuda()
loss_function = MaskedNLLLoss()
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.l2)
train_loader, valid_loader, test_loader = get_MELD_loaders(
'MELD_features/MELD_features_raw.pkl',
batch_size=batch_size,
valid=0.0,
classify=args.classify)
best_fscore, best_loss, best_label, best_pred, best_mask = None, None, None, None, None
for e in range(n_epochs):
start_time = time.time()
train_loss, train_acc, _, _, _, train_fscore, _ = train_or_eval_model(
