text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
@property
def num_attention_heads(self) -> int:
return self.encoder_attention_heads
@property
def hidden_size(self) -> int:
return self.d_model | 4,078 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
class ConditionalDetrOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
... | 4,079 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/conditional_detr/configuration_conditional_detr.py |
class VisionTextDualEncoderProcessor(ProcessorMixin):
r"""
Constructs a VisionTextDualEncoder processor which wraps an image processor and a tokenizer into a single
processor.
[`VisionTextDualEncoderProcessor`] offers all the functionalities of [`AutoImageProcessor`] and [`AutoTokenizer`].
See the ... | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
feature_extractor = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.",
... | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.__call__`] if `text` is ... | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_sp... | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchEncoding`]: A [`BatchEncoding`] with the foll... | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
if text is not None:
encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
if images is not None:
image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)
if text is not None and images is not None:
encoding["pixel_va... | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to VisionTextDualEncoderTokenizer's [`~PreTrainedTokenizer.decode`].
Please refer to the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
... | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
@property
def feature_extractor(self):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",
FutureWarning,
)
return self.image_processor | 4,080 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/processing_vision_text_dual_encoder.py |
class TFVisionTextDualEncoderModel(TFPreTrainedModel):
config_class = VisionTextDualEncoderConfig
base_model_prefix = "vision_text_dual_encoder"
load_weight_prefix = "tf_vision_text_dual_encoder_model"
def __init__(
self,
config: Optional[VisionTextDualEncoderConfig] = None,
vis... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
if vision_model is None:
if isinstance(config.vision_config, CLIPVisionConfig):
vision_model = TFCLIPVisionModel.from_config(config.vision_config, name="vision_model")
else:
vision_model = TFAutoModel.from_config(config.vision_config, name="vision_model")
... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
self.visual_projection = keras.layers.Dense(self.projection_dim, use_bias=False, name="visual_projection")
self.text_projection = keras.layers.Dense(self.projection_dim, use_bias=False, name="text_projection")
self.logit_scale = None
self.config = config
def build(self, input_shape=None):
... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
if getattr(self, "visual_projection", None) is not None:
with tf.name_scope(self.visual_projection.name):
self.visual_projection.build([None, None, self.vision_embed_dim])
if getattr(self, "text_projection", None) is not None:
with tf.name_scope(self.text_projection.name)... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
def tf_to_pt_weight_rename(self, tf_weight):
# Matt: The TF and PT weights don't align because our TF base classes have an extra layer compared to PT models
# (the main model stem is in the MainLayer class). If we remove that layer, then weight names sync up as normal.
# However, the name of tha... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
return (re.sub(r"text_model\..*?\.", "text_model.", tf_weight),)
else:
return (tf_weight,) | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
@add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING)
def get_text_features(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
token_type_ids=None,
output_attentions=None,
output_hidden_states=None,
return_d... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
>>> inputs = tokenizer(["una foto di un gatto", "una foto di un cane"], padding=True, return_tensors="np")
>>> text_features = model.get_text_features(**inputs)
```"""
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
@add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Returns:
image_features (`tf.... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = image_processor(images=image, return_tensors="np")
>>> image_features = model.get_image_features(**inputs)
```"""
vision_outputs = self.vis... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
@unpack_inputs
@add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFCLIPOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids: tf.Tensor | None = None,
pixel_values: tf.Tensor | None = None,
atten... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import (
... TFVisionTextDualEncoderModel,
... VisionTextDualEncoderProcessor,
... AutoImageProcessor,
... AutoTokenizer,
... )
>>> tokenizer = AutoToken... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
>>> # contrastive training
>>> urls = [
... "http://images.cocodataset.org/val2017/000000039769.jpg",
... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg",
... ]
>>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
>>> inp... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
>>> # inference
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = tf.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities
```"""
return_dict = return_dict ... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=re... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
# cosine similarity as logits
logit_scale = tf.math.exp(self.logit_scale)
logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale
logits_per_image = tf.transpose(logits_per_text)
loss = None
if return_loss:
loss = clip_loss(logits_per_t... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
@classmethod
def from_vision_text_pretrained(
cls,
vision_model_name_or_path: str = None,
text_model_name_or_path: str = None,
*model_args,
**kwargs,
) -> TFPreTrainedModel:
"""
Params:
vision_model_name_or_path (`str`, *optional*, defaults to ... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
text_model_name_or_path (`str`, *optional*):
Information necessary to initiate the text model. Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved u... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the text configuration, use the prefix *text_* for each configurati... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
>>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized.
>>> model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
... "google/vit-base-patch16-224", "google-bert/bert-base-uncased"
... )
>>> # saving mode... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
# remove vision, text kwargs from kwargs
for key in kwargs_vision.keys():
del kwargs["vision_" + key]
for key in kwargs_text.keys():
del kwargs["text_" + key]
# Load and initialize the vision and text model
vision_model = kwargs_vision.pop("model", None)
... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
vision_config_dict, unused_args = PretrainedConfig.get_config_dict(vision_model_name_or_path, **kwargs)
if vision_config_dict.get("model_type", None) == "clip_vision_model":
vision_config = CLIPVisionConfig.from_dict(vision_config_dict)
else:
vision_config = AutoC... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
text_model = kwargs_text.pop("model", None)
if text_model is None:
if text_model_name_or_path is None:
raise ValueError(
"If `text_model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
)
kwargs_text["name"... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
# the projection layers are always newly initialized when loading the model
# using pre-trained vision and text model.
logger.warning(
"The projection layer and logit scale weights `['visual_projection.weight', 'text_projection.weight',"
" 'logit_scale']` are newly initialized. Y... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32)
batch_size, seq_len = input_ids.shape
VISION_DUMMY_INPUTS = tf.random.uniform(
shape=(
batch_size,
self.config.vision_conf... | 4,081 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py |
class FlaxVisionTextDualEncoderModule(nn.Module):
config: VisionTextDualEncoderConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
vision_config = self.config.vision_config
text_config = self.config.text_config
self.vision_embed_dim = vision_config.hidden_size
self.text_... | 4,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
self.visual_projection = nn.Dense(
self.projection_dim,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(0.02),
use_bias=False,
)
self.text_projection = nn.Dense(
self.projection_dim,
dtype=self.dtype,
kernel_ini... | 4,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
vision_outputs = self.vision_model(
pixel_values=pixel_values,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
text_outputs = self.text_model(
... | 4,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
# normalized features
image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True)
text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True)
# cosine similarity as logits
logit_scale = jnp.exp(self.logit_scale)
logits_per_text = jnp.... | 4,082 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
class FlaxVisionTextDualEncoderModel(FlaxPreTrainedModel):
config_class = VisionTextDualEncoderConfig
module_class = FlaxVisionTextDualEncoderModule
def __init__(
self,
config: VisionTextDualEncoderConfig,
input_shape: Optional[Tuple] = None,
seed: int = 0,
dtype: jn... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensor
input_ids = jnp.zeros(input_shape[0], dtype="i4")
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0])
token_type_... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freez... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
def __call__(
self,
input_ids,
pixel_values,
attention_mask=None,
position_ids=None,
token_type_ids=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
ou... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
if token_type_ids is None:
token_type_ids = jnp.zeros_like(input_ids)
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
def get_text_features(
self,
input_ids,
attention_mask=None,
position_ids=None,
token_type_ids=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train=False,
):
r"""
Args:
input_ids (`numpy.ndarray` of shape... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
Returns:
text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
the projection layer to the pooled output of text model.
"""
if position_ids is None:
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_id... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
def _get_features(module, input_ids, attention_mask, position_ids, token_type_ids, deterministic):
text_outputs = module.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
token_type_ids=token_type_ids,
... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
def get_image_features(
self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False
):
r"""
Args:
pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
def _get_features(module, pixel_values, deterministic):
vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic)
pooled_output = vision_outputs[1] # pooled_output
image_features = module.visual_projection(pooled_output)
return image_fe... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *PyTor... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *PyTor... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the text configuration, use the prefix *text_* for each configurati... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
>>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized.
>>> model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
... "google/vit-base-patch16-224", "google-bert/bert-base-uncased"
... )
>>> # saving mo... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
# remove text, vision kwargs from kwargs
for key in kwargs_vision.keys():
del kwargs["vision_" + key]
for key in kwargs_text.keys():
del kwargs["text_" + key]
# Load and initialize the text and vision model
vision_model = kwargs_vision.pop("model", None)
... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
if vision_config.model_type == "clip":
kwargs_vision["config"] = vision_config.vision_config
vision_model = FlaxCLIPVisionModel.from_pretrained(
vision_model_name_or_path, *model_args, **kwargs_vision
)
else:
kwargs_vision["... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
text_model = FlaxAutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text)
# instantiate config with corresponding kwargs
dtype = kwargs.pop("dtype", jnp.float32)
config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs)... | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
return model | 4,083 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_flax_vision_text_dual_encoder.py |
class VisionTextDualEncoderConfig(PretrainedConfig):
r"""
[`VisionTextDualEncoderConfig`] is the configuration class to store the configuration of a
[`VisionTextDualEncoderModel`]. It is used to instantiate [`VisionTextDualEncoderModel`] model according to the
specified arguments, defining the text mode... | 4,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py |
```python
>>> from transformers import ViTConfig, BertConfig, VisionTextDualEncoderConfig, VisionTextDualEncoderModel
>>> # Initializing a BERT and ViT configuration
>>> config_vision = ViTConfig()
>>> config_text = BertConfig()
>>> config = VisionTextDualEncoderConfig.from_vision_text_configs(con... | 4,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py |
model_type = "vision-text-dual-encoder"
sub_configs = {"vision_config": AutoConfig, "text_config": AutoConfig}
is_composition = True
def __init__(self, projection_dim=512, logit_scale_init_value=2.6592, **kwargs):
super().__init__(**kwargs)
if "vision_config" not in kwargs:
rai... | 4,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py |
vision_config_class = VISION_MODEL_CONFIGS.get(vision_model_type)
if vision_config_class is not None:
self.vision_config = vision_config_class(**vision_config)
else:
self.vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
if hasattr(self.vision_c... | 4,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py |
return cls(vision_config=vision_config.to_dict(), text_config=text_config.to_dict(), **kwargs) | 4,084 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/configuration_vision_text_dual_encoder.py |
class VisionTextDualEncoderModel(PreTrainedModel):
config_class = VisionTextDualEncoderConfig
base_model_prefix = "vision_text_dual_encoder"
_supports_flash_attn_2 = True
_supports_sdpa = True
def __init__(
self,
config: Optional[VisionTextDualEncoderConfig] = None,
vision_m... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
if vision_model is None:
if isinstance(config.vision_config, CLIPVisionConfig):
vision_model = CLIPVisionModel(config.vision_config)
else:
vision_model = AutoModel.from_config(config.vision_config)
if text_model is None:
text_model = AutoModel... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
self.vision_embed_dim = config.vision_config.hidden_size
self.text_embed_dim = config.text_config.hidden_size
self.projection_dim = config.projection_dim
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
self.text_projection = nn.Linear(self.text... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
@add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_TEXT_INPUTS_DOCSTRING)
def get_text_features(
self,
input_ids=None,
attention_mask=None,
position_ids=None,
token_type_ids=None,
output_attentions=None,
output_hidden_states=None,
return_d... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
>>> inputs = tokenizer(["una foto di un gatto", "una foto di un cane"], padding=True, return_tensors="pt")
>>> text_features = model.get_text_features(**inputs)
```"""
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
@add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Returns:
image_features (`tor... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> image_features = model.get_image_features(**inputs)
```"""
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidd... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
@add_start_docstrings_to_model_forward(VISION_TEXT_DUAL_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CLIPOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
at... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import (
... VisionTextDualEncoderModel,
... VisionTextDualEncoderProcessor,
... AutoImageProcessor,
... AutoTokenizer,
... )
>>> tokenizer = AutoTokeniz... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
>>> # contrastive training
>>> urls = [
... "http://images.cocodataset.org/val2017/000000039769.jpg",
... "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg",
... ]
>>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
>>> inp... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
>>> # inference
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```"""
return_dict = return_dict if retur... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
image_embeds = vision_outputs[1] # pooler_output
image_embeds = self.visual_projection(image_embeds)
text_embeds = text_outputs[1] # pooler_output
text_embeds = self.text_projection(text_embeds)
# normalized features
image_embeds = image_embeds / image_embeds.norm(dim=-1, kee... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
return CLIPOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
@cl... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *PyTorch c... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *PyTorch c... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`).
- To update the text configuration, use the prefix *text_* for each configurati... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
>>> # initialize a model from pretrained ViT and BERT models. Note that the projection layers will be randomly initialized.
>>> model = VisionTextDualEncoderModel.from_vision_text_pretrained(
... "google/vit-base-patch16-224", "google-bert/bert-base-uncased"
... )
>>> # saving model ... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
# remove vision, text kwargs from kwargs
for key in kwargs_vision.keys():
del kwargs["vision_" + key]
for key in kwargs_text.keys():
del kwargs["text_" + key]
# Load and initialize the vision and text model
vision_model = kwargs_vision.pop("model", None)
... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
if vision_config.model_type == "clip":
kwargs_vision["config"] = vision_config.vision_config
vision_model = CLIPVisionModel.from_pretrained(vision_model_name_or_path, *model_args, **kwargs_vision)
# TODO: Should we use the pre-trained projection as well ?
else... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
text_model = AutoModel.from_pretrained(text_model_name_or_path, *model_args, **kwargs_text)
# instantiate config with corresponding kwargs
config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_model.config, text_model.config, **kwargs)
# init model
model = cls(config=con... | 4,085 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py |
class MvpLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# MVP is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjus... | 4,086 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
sel... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (d... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if attn_prompt is not None:
key_states = torch.cat([attn_prompt[0].expand(bsz, -1, -1, -1), key_states], dim=2)
value_states = torch.cat([attn_prompt[1].expand(bsz, -1, -1, -1), value_states], dim=2)
if attention_mask is not None:
prompt_mask = torch.zeros(bsz, 1, tgt... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if atten... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = la... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
... | 4,087 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpEncoderLayer(nn.Module):
def __init__(self, config: MvpConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MvpAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,... | 4,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
self_attn_prompt: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
... | 4,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=atte... | 4,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self... | 4,088 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
class MvpDecoderLayer(nn.Module):
def __init__(self, config: MvpConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = MvpAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = MvpAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_d... | 4,089 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mvp/modeling_mvp.py |
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