def test_model(self):
image_feat_dim = 40
txt_embedding_dim = 300
lstm_dim = 512
hidden_size = 30
num_of_loc = 5
batch_size = 16
num_vocab = 60
num_ans_candidates = 35
joint_embedding_dim = 500
question_len = 13
batch_first = True
image_embedding_model = image_embedding(image_feat_dim, lstm_dim, hidden_size)
question_embedding_model = QuestionEmbeding(
num_vocab,
txt_embedding_dim,
lstm_dim,
lstm_layer=2,
dropout=0.1,
batch_first=batch_first,
)
my_classifier = logit_classifier(
joint_embedding_dim, num_ans_candidates, image_feat_dim, txt_embedding_dim
)
loss = torch.nn.CrossEntropyLoss()
my_model = top_down_bottom_up_model(
image_embedding_model, question_embedding_model, my_classifier, loss
)
image_feat = np.random.rand(batch_size, num_of_loc, image_feat_dim)
input_txt = Variable(
torch.rand(batch_size, question_len).type(torch.LongTensor) % num_vocab
)
res = my_model(image_feat, input_txt, batch_first)
self.assertEqual((batch_size, num_ans_candidates), res.shape)
def test_classifier_batch_size_1(self):
batch_size = 1
joint_embedding_dim = 10
num_ans_candidates = 20
text_embeding_dim = 64
image_embedding_dim = 32
my_classifier = logit_classifier(joint_embedding_dim,
num_ans_candidates,
image_embedding_dim,
text_embeding_dim)
joint_embedding = Variable(torch.randn(batch_size,
joint_embedding_dim))
res = my_classifier(joint_embedding)
self.assertEqual((1, 20), res.shape)
def test_classifier_batch_size_1(self):
batch_size = 1
joint_embedding_dim = 10
num_ans_candidates = 20
text_embeding_dim = 64
image_embedding_dim = 32
my_classifier = logit_classifier(joint_embedding_dim,
num_ans_candidates,
image_embedding_dim,
text_embeding_dim)
joint_embedding = Variable(torch.randn(batch_size,
joint_embedding_dim))
res = my_classifier(joint_embedding)
self.assertEqual((1, 20), res.shape)
def test_model(self):
image_feat_dim = 40
txt_embedding_dim = 300
lstm_dim = 512
hidden_size = 30
num_of_loc = 5
batch_size = 16
num_vocab = 60
num_ans_candidates = 35
joint_embedding_dim = 500
question_len = 13
batch_first = True
image_embedding_model = image_embedding(image_feat_dim,
lstm_dim,
hidden_size)
question_embedding_model = QuestionEmbeding(num_vocab,
txt_embedding_dim,
lstm_dim,
lstm_layer=2,
dropout=0.1,
batch_first=batch_first)
my_classifier = logit_classifier(joint_embedding_dim,
num_ans_candidates,
image_feat_dim,
txt_embedding_dim)
loss = torch.nn.CrossEntropyLoss()
my_model = top_down_bottom_up_model(image_embedding_model,
question_embedding_model,
my_classifier, loss)
image_feat = np.random.rand(batch_size,
num_of_loc,
image_feat_dim)
input_txt = Variable(torch.rand(batch_size,
question_len).type(torch.LongTensor) % num_vocab)
res = my_model(image_feat, input_txt, batch_first)
self.assertEqual((batch_size, num_ans_candidates), res.shape)