Upload processor

preprocessor_config.json

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+{
+  "auto_map": {
+    "AutoProcessor": "processing_veld.VELDProcessor"
+  },
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "VELDProcessor",
+  "resample": 2,
+  "size": 384
+}

processing_veld.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team and san kim.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for VELD
+"""
+
+from transformers.processing_utils import ProcessorMixin
+from transformers.tokenization_utils_base import BatchEncoding
+
+
+class VELDProcessor(ProcessorMixin):
+    r"""
+    Constructs a VELD processor which wraps a vision feature extractor and a tokenizer into a single
+    processor.
+    [`VELDProcessor`] offers all the functionalities of [`AutoFeatureExtractor`] and
+    [`AutoTokenizer`]. See the [`~VELDProcessor.__call__`] and
+    [`~VELDProcessor.decode`] for more information.
+    Args:
+        feature_extractor ([`AutoFeatureExtractor`]):
+            The feature extractor is a required input.
+        tokenizer ([`PreTrainedTokenizer`]):
+            The tokenizer is a required input.
+    """
+    feature_extractor_class = "AutoFeatureExtractor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, feature_extractor, tokenizer):
+        super().__init__(feature_extractor, tokenizer)
+        self.current_processor = self.feature_extractor
+
+    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 not
+        `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
+        AutoFeatureExtractor's [`~AutoFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
+        doctsring of the above two methods for more information.
+        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_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
+                number of channels, H and W are image height and width.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'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 following fields:
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+
+        if text is None and images is None:
+            raise ValueError("You have to specify either text or images. Both cannot be none.")
+
+        if text is not None:
+            encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs)
+
+        if images is not None:
+            image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
+
+        if text is not None and images is not None:
+            encoding["pixel_values"] = image_features.pixel_values
+            return encoding
+        elif text is not None:
+            return encoding
+        else:
+            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to VELDProcessor's
+        [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to VELDProcessor's [`~PreTrainedTokenizer.decode`].
+        Please refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)

special_tokens_map.json

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+{
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+    "<extra_id_1>",
+    "<extra_id_2>",
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+  ],
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

spiece.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:edc595fcd800672d6ef0e2aea5ba0f1dc8826471f6f40a87b70619dcc60ddd4a
+size 1466734

tokenizer_config.json

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+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
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+  },
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+  "model_max_length": 512,
+  "name_or_path": "checkpoints/veld_e1_linear",
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+  "tokenizer_class": "T5Tokenizer",
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+}