def create_and_check_model(self, config, pixel_values, labels):
model = DeiTModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model(self, config, pixel_values, labels):
model = DeiTModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# expected sequence length = num_patches + 2 (we add 2 for the [CLS] and distillation tokens)
image_size = to_2tuple(self.image_size)
patch_size = to_2tuple(self.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] //
patch_size[0])
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, num_patches + 2, self.hidden_size))
def test_model_from_pretrained(self):
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DeiTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def get_vision_text_model(self, vision_config, text_config):
vision_model = DeiTModel(vision_config).eval()
text_model = RobertaModel(text_config).eval()
return vision_model, text_model
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = DeiTModel(config).eval()
decoder_model = BertLMHeadModel(decoder_config).eval()
return encoder_model, decoder_model
