def __getitem__(self, idx):
sample_info = self.annotation_db[idx]
current_sample = Sample()
if self._use_features:
features = self.features_db[idx]
if hasattr(self, "transformer_bbox_processor"):
features["image_info_0"] = self.transformer_bbox_processor(
features["image_info_0"])
if self.config.get("use_image_feature_masks", False):
current_sample.update({
"image_labels":
self.masked_region_processor(features["image_feature_0"])
})
current_sample.update(features)
else:
image_path = str(sample_info["image_name"]) + ".jpg"
current_sample.image = self.image_db.from_path(
image_path)["images"][0]
current_sample = self._add_masked_question(sample_info, current_sample)
if self._add_answer:
current_sample = self.add_answer_info(sample_info, current_sample)
return current_sample
def load_item(self, idx):
sample_info = self.annotation_db[idx]
sample_info = self.preprocess_sample_info(sample_info)
current_sample = Sample()
if self._dataset_type != "test":
text_processor_argument = {"tokens": sample_info["caption_tokens"]}
processed_caption = self.text_processor(text_processor_argument)
current_sample.text = processed_caption["text"]
current_sample.caption_id = torch.tensor(sample_info["caption_id"],
dtype=torch.int)
current_sample.caption_len = torch.tensor(len(
sample_info["caption_tokens"]),
dtype=torch.int)
current_sample.image_id = object_to_byte_tensor(
sample_info["image_id"])
if self._use_features:
features = self.features_db[idx]
current_sample.update(features)
else:
image_path = str(sample_info["image_name"]) + ".jpg"
current_sample.image = self.image_db.from_path(
image_path)["images"][0]
# Add reference captions to sample
current_sample = self.add_reference_caption(sample_info,
current_sample)
return current_sample
def load_item(self, idx):
sample_info = self.annotation_db[idx]
current_sample = Sample()
if "question_tokens" in sample_info:
text_processor_argument = {
"tokens": sample_info["question_tokens"],
"text": sample_info["question_str"],
}
else:
text_processor_argument = {"text": sample_info["question"]}
processed_question = self.text_processor(text_processor_argument)
current_sample.text = processed_question["text"]
if "input_ids" in processed_question:
current_sample.update(processed_question)
current_sample.question_id = torch.tensor(sample_info["question_id"],
dtype=torch.int)
if isinstance(sample_info["image_id"], int):
current_sample.image_id = torch.tensor(sample_info["image_id"],
dtype=torch.int)
else:
current_sample.image_id = sample_info["image_id"]
if "question_tokens" in sample_info:
current_sample.text_len = torch.tensor(len(
sample_info["question_tokens"]),
dtype=torch.int)
if self._use_features:
features = self.features_db[idx]
if hasattr(self, "transformer_bbox_processor"):
features["image_info_0"] = self.transformer_bbox_processor(
features["image_info_0"])
current_sample.update(features)
else:
image_path = sample_info["image_name"] + ".jpg"
current_sample.image = self.image_db.from_path(
image_path)["images"][0]
# Add details for OCR like OCR bbox, vectors, tokens here
current_sample = self.add_ocr_details(sample_info, current_sample)
# Depending on whether we are using soft copy this can add
# dynamic answer space
current_sample = self.add_answer_info(sample_info, current_sample)
return current_sample
def classify(self, image: ImageType, text: str):
"""Classifies a given image and text in it into Hateful/Non-Hateful.
Image can be a url or a local path or you can directly pass a PIL.Image.Image
object. Text needs to be a sentence containing all text in the image.
>>> from multimodelity.models.mmbt import MMBT
>>> model = MMBT.from_pretrained("mmbt.hateful_memes.images")
>>> model.classify("some_url", "some_text")
{"label": 0, "confidence": 0.56}
Args:
image (ImageType): Image to be classified
text (str): Text in the image
Returns:
bool: Whether image is hateful (1) or non hateful (0)
"""
if isinstance(image, str):
if image.startswith("http"):
temp_file = tempfile.NamedTemporaryFile()
download(image,
*os.path.split(temp_file.name),
disable_tqdm=True)
image = tv_helpers.default_loader(temp_file.name)
temp_file.close()
else:
image = tv_helpers.default_loader(image)
text = self.processor_dict["text_processor"]({"text": text})
image = self.processor_dict["image_processor"](image)
sample = Sample()
sample.text = text["text"]
if "input_ids" in text:
sample.update(text)
sample.image = image
sample_list = SampleList([sample])
device = next(self.model.parameters()).device
sample_list = sample_list.to(device)
output = self.model(sample_list)
scores = nn.functional.softmax(output["scores"], dim=1)
confidence, label = torch.max(scores, dim=1)
return {"label": label.item(), "confidence": confidence.item()}
def __getitem__(self, idx: int) -> Type[Sample]:
sample_info = self.annotation_db[idx]
current_sample = Sample()
processed_question = self.text_processor(
{"text": sample_info["question"]})
current_sample.update(processed_question)
current_sample.id = torch.tensor(int(sample_info["question_id"]),
dtype=torch.int)
# Get the first image from the set of images returned from the image_db
image_path = self.get_image_path(sample_info["image_id"])
current_sample.image = self.image_db.from_path(image_path)["images"][0]
if "answers" in sample_info:
answers = self.answer_processor(
{"answers": sample_info["answers"]})
current_sample.targets = answers["answers_scores"]
return current_sample
def __getitem__(self, idx):
sample_info = self.annotation_db[idx]
current_sample = Sample()
processed_text = self.text_processor({"text": sample_info["text"]})
current_sample.text = processed_text["text"]
if "input_ids" in processed_text:
current_sample.update(processed_text)
current_sample.id = torch.tensor(int(sample_info["id"]),
dtype=torch.int)
# Get the first image from the set of images returned from the image_db
current_sample.image = self.image_db[idx]["images"][0]
if "label" in sample_info:
current_sample.targets = torch.tensor(sample_info["label"],
dtype=torch.long)
return current_sample
def __getitem__(self, idx):
sample_info = self.annotation_db[idx]
current_sample = Sample()
plot = sample_info["plot"]
if isinstance(plot, list):
plot = plot[0]
processed_sentence = self.text_processor({"text": plot})
current_sample.text = processed_sentence["text"]
if "input_ids" in processed_sentence:
current_sample.update(processed_sentence)
if self._use_images is True:
current_sample.image = self.image_db[idx]["images"][0]
processed = self.answer_processor({"answers": sample_info["genres"]})
current_sample.answers = processed["answers"]
current_sample.targets = processed["answers_scores"]
return current_sample
def test_finetune_model(self):
self.finetune_model.eval()
test_sample = Sample()
test_sample.input_ids = torch.randint(low=0, high=30255,
size=(128, )).long()
test_sample.input_mask = torch.ones(128).long()
test_sample.segment_ids = torch.zeros(128).long()
test_sample.image = torch.rand((3, 300, 300)).float()
test_sample_list = SampleList([test_sample.copy()])
with torch.no_grad():
model_output = self.finetune_model.model(test_sample_list)
test_sample_list = SampleList([test_sample])
script_model = torch.jit.script(self.finetune_model.model)
with torch.no_grad():
script_output = script_model(test_sample_list)
self.assertTrue(
torch.equal(model_output["scores"], script_output["scores"]))
def __getitem__(self, idx):
data = self.questions[idx]
# Each call to __getitem__ from dataloader returns a Sample class object which
# collated by our special batch collator to a SampleList which is basically
# a attribute based batch in layman terms
current_sample = Sample()
question = data["question"]
tokens = tokenize(question, keep=[";", ","], remove=["?", "."])
processed = self.text_processor({"tokens": tokens})
current_sample.text = processed["text"]
processed = self.answer_processor({"answers": [data["answer"]]})
current_sample.answers = processed["answers"]
current_sample.targets = processed["answers_scores"]
image_path = os.path.join(self.image_path, data["image_filename"])
image = np.true_divide(Image.open(image_path).convert("RGB"), 255)
image = image.astype(np.float32)
current_sample.image = torch.from_numpy(image.transpose(2, 0, 1))
return current_sample
def test_modal_end_token(self):
self.finetune_model.eval()
# Suppose 0 for <cls>, 1 for <pad> 2 for <sep>
CLS = 0
PAD = 1
SEP = 2
size = 128
input_ids = torch.randint(low=0, high=30255, size=(size, )).long()
input_mask = torch.ones(size).long()
input_ids[0] = CLS
length = torch.randint(low=2, high=size - 1, size=(1, ))
input_ids[length] = SEP
input_ids[length + 1:] = PAD
input_mask[length + 1:] = 0
test_sample = Sample()
test_sample.input_ids = input_ids.clone()
test_sample.input_mask = input_mask.clone()
test_sample.segment_ids = torch.zeros(size).long()
test_sample.image = torch.rand((3, 300, 300)).float()
test_sample_list = SampleList([test_sample])
mmbt_base = self.finetune_model.model.bert
with torch.no_grad():
actual_modal_end_token = mmbt_base.extract_modal_end_token(
test_sample_list)
expected_modal_end_token = torch.zeros([1]).fill_(SEP).long()
self.assertTrue(
torch.equal(actual_modal_end_token, expected_modal_end_token))
self.assertTrue(
torch.equal(test_sample_list.input_ids[0, :-1], input_ids[1:]))
self.assertTrue(
torch.equal(test_sample_list.input_mask[0, :-1], input_mask[1:]))
def test_forward(self):
model_config = self.config.model_config.cnn_lstm
cnn_lstm = CNNLSTM(model_config)
cnn_lstm.build()
cnn_lstm.init_losses()
self.assertTrue(isinstance(cnn_lstm, torch.nn.Module))
test_sample = Sample()
test_sample.text = torch.randint(1, 79, (10, ), dtype=torch.long)
test_sample.image = torch.randn(3, 320, 480)
test_sample.targets = torch.randn(32)
test_sample_list = SampleList([test_sample])
test_sample_list.dataset_type = "train"
test_sample_list.dataset_name = "clevr"
output = cnn_lstm(test_sample_list)
scores = output["scores"]
loss = output["losses"]["train/clevr/logit_bce"]
np.testing.assert_almost_equal(loss.item(), 19.2635, decimal=4)
self.assertEqual(scores.size(), torch.Size((1, 32)))