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| 1 | +# Copyright 2024 The KerasHub Authors |
| 2 | +# |
| 3 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +# you may not use this file except in compliance with the License. |
| 5 | +# You may obtain a copy of the License at |
| 6 | +# |
| 7 | +# https://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +# See the License for the specific language governing permissions and |
| 13 | +# limitations under the License. |
| 14 | +import keras |
| 15 | + |
| 16 | +from keras_hub.src.api_export import keras_hub_export |
| 17 | +from keras_hub.src.layers.preprocessing.start_end_packer import StartEndPacker |
| 18 | +from keras_hub.src.models.clip.clip_tokenizer import CLIPTokenizer |
| 19 | +from keras_hub.src.models.preprocessor import Preprocessor |
| 20 | +from keras_hub.src.utils.tensor_utils import preprocessing_function |
| 21 | + |
| 22 | +try: |
| 23 | + import tensorflow as tf |
| 24 | +except ImportError: |
| 25 | + tf = None |
| 26 | + |
| 27 | + |
| 28 | +@keras_hub_export("keras_hub.models.CLIPPreprocessor") |
| 29 | +class CLIPPreprocessor(Preprocessor): |
| 30 | + """CLIP preprocessing layer which tokenizes and packs inputs. |
| 31 | +
|
| 32 | + This preprocessing layer will do 2 things: |
| 33 | +
|
| 34 | + - Tokenize the inputs using the `tokenizer`. |
| 35 | + - Construct a dictionary with keys `"token_ids"`, `"padding_mask"`. |
| 36 | +
|
| 37 | + This layer can be used directly with `tf.data.Dataset.map` to preprocess |
| 38 | + string data in the `(x, y, sample_weight)` format used by |
| 39 | + `keras.Model.fit`. |
| 40 | +
|
| 41 | + The call method of this layer accepts three arguments, `x`, `y`, and |
| 42 | + `sample_weight`. `x` can be a python string or tensor representing a single |
| 43 | + segment, a list of python strings representing a batch of single segments, |
| 44 | + or a list of tensors representing multiple segments to be packed together. |
| 45 | + `y` and `sample_weight` are both optional, can have any format, and will be |
| 46 | + passed through unaltered. |
| 47 | +
|
| 48 | + `CLIPPreprocessor` forces the input to have only one segment, as CLIP is |
| 49 | + mainly used for generation tasks. For tasks having multi-segment inputs |
| 50 | + like "glue/mnli", please use a model designed for classification purposes |
| 51 | + such as BERT or RoBERTa. |
| 52 | +
|
| 53 | + Args: |
| 54 | + tokenizer: A `keras_hub.models.CLIPTokenizer` instance. |
| 55 | + sequence_length: The length of the packed inputs. |
| 56 | + add_start_token: If `True`, the preprocessor will prepend the tokenizer |
| 57 | + start token to each input sequence. |
| 58 | + add_end_token: If `True`, the preprocessor will append the tokenizer |
| 59 | + end token to each input sequence. |
| 60 | + to_lower: bool. Whether to lower the inputs. |
| 61 | +
|
| 62 | + Call arguments: |
| 63 | + x: A string, `tf.Tensor` or list of python strings. |
| 64 | + y: Any label data. Will be passed through unaltered. |
| 65 | + sample_weight: Any label weight data. Will be passed through unaltered. |
| 66 | + sequence_length: Pass to override the configured `sequence_length` of |
| 67 | + the layer. |
| 68 | + """ |
| 69 | + |
| 70 | + # TODO: Add example once we have a CLIP model. |
| 71 | + |
| 72 | + tokenizer_cls = CLIPTokenizer |
| 73 | + |
| 74 | + def __init__( |
| 75 | + self, |
| 76 | + tokenizer, |
| 77 | + sequence_length=77, |
| 78 | + add_start_token=True, |
| 79 | + add_end_token=True, |
| 80 | + to_lower=True, |
| 81 | + **kwargs, |
| 82 | + ): |
| 83 | + super().__init__(**kwargs) |
| 84 | + self.tokenizer = tokenizer |
| 85 | + self.packer = None |
| 86 | + self.sequence_length = sequence_length |
| 87 | + self.add_start_token = add_start_token |
| 88 | + self.add_end_token = add_end_token |
| 89 | + self.to_lower = to_lower |
| 90 | + |
| 91 | + def build(self, input_shape): |
| 92 | + # Defer packer creation to `build()` so that we can be sure tokenizer |
| 93 | + # assets have loaded when restoring a saved model. |
| 94 | + self.packer = StartEndPacker( |
| 95 | + start_value=self.tokenizer.start_token_id, |
| 96 | + end_value=self.tokenizer.end_token_id, |
| 97 | + pad_value=self.tokenizer.end_token_id, |
| 98 | + sequence_length=self.sequence_length, |
| 99 | + return_padding_mask=True, |
| 100 | + ) |
| 101 | + self.built = True |
| 102 | + |
| 103 | + @preprocessing_function |
| 104 | + def call( |
| 105 | + self, |
| 106 | + x, |
| 107 | + y=None, |
| 108 | + sample_weight=None, |
| 109 | + sequence_length=None, |
| 110 | + ): |
| 111 | + sequence_length = sequence_length or self.sequence_length |
| 112 | + if self.to_lower: |
| 113 | + x = tf.strings.lower(x) |
| 114 | + token_ids, padding_mask = self.packer( |
| 115 | + self.tokenizer(x), |
| 116 | + sequence_length=sequence_length, |
| 117 | + add_start_value=self.add_start_token, |
| 118 | + add_end_value=self.add_end_token, |
| 119 | + ) |
| 120 | + x = { |
| 121 | + "token_ids": token_ids, |
| 122 | + "padding_mask": padding_mask, |
| 123 | + } |
| 124 | + return keras.utils.pack_x_y_sample_weight(x, y, sample_weight) |
| 125 | + |
| 126 | + def get_config(self): |
| 127 | + config = super().get_config() |
| 128 | + config.update( |
| 129 | + { |
| 130 | + "sequence_length": self.sequence_length, |
| 131 | + "add_start_token": self.add_start_token, |
| 132 | + "add_end_token": self.add_end_token, |
| 133 | + "to_lower": self.to_lower, |
| 134 | + } |
| 135 | + ) |
| 136 | + return config |
| 137 | + |
| 138 | + @property |
| 139 | + def sequence_length(self): |
| 140 | + """The padded length of model input sequences.""" |
| 141 | + return self._sequence_length |
| 142 | + |
| 143 | + @sequence_length.setter |
| 144 | + def sequence_length(self, value): |
| 145 | + self._sequence_length = value |
| 146 | + if self.packer is not None: |
| 147 | + self.packer.sequence_length = value |
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