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PARSeq Model (#2089)
* Base for parseq model * make it vit compatiable with diff height and width sizes * correct vit conv scripts * make class token optional in backbone by default its included * add flags to adjust vit network * add test case for without class_token * decoder file * parseq tokenizer base * add api for parseq tokenizer * Add missing arg max_label_length. * nit * add missing normalization step using tf_text * add missing config for preprocessor * add default start, pad and end tokens * nit * correct special token order * return padding mask as well * use proper keras ops * nit * add decoder for parseq * Build unbuilt layers for model validation * fix forward pass and decoder * add missing mlp forward pass * add generate prprocess and generate step * nit * add generate_step to parseq causal lm * minor fixes for jax backend and config fix * update decoder layer with caching mechanism which is used for generate step * modify generate step including cache * re structure code to make jax backend compatiable * add postprocess step into preprocessor * test only forward pass * nit * test build cache * test generate step only build cache * correct class name * correct dropout * remove slicing in forward pass * nit * use python style slicing * support jax for generate step * compute attention mask for permutation at decoder block level * correct syntax error * nit * Add method for geenrating attention masks during train & permutations method * update end token after 2 perms * minor bug fix * add save assets and load assets methods * fix conflict issue * nit * fix minor issues while loading preset * fix jax dynamic shape issues * try to fix jax backend concretization error * fix mask broadcast error * fix repeat for mismatch output length * ignore permutation based training * fix dtype and add test case for parseq * fix input format and add causal lm testing * use numpy random images * fix jax backend issue when reduction set to "mean_with_sample_weight" * remove redudant classes and use causal lm base calsses itself. * nit * fix decoder_head_dim usage * fix preprocessing issues * add checkpoint convertion script * add missing flag * validate convertion outputs * nit * fix training for permutation logic * add example usage for backbone and causal lm * nit * fix minor issues * use default params from args as we preprocessor can be None * during pre compile self variables not available * nit * nit
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‎keras_hub/api/layers/__init__.py‎

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from keras_hub.src.models.pali_gemma.pali_gemma_image_converter import (
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PaliGemmaImageConverter as PaliGemmaImageConverter,
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)
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from keras_hub.src.models.parseq.parseq_image_converter import (
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PARSeqImageConverter as PARSeqImageConverter,
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)
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from keras_hub.src.models.resnet.resnet_image_converter import (
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ResNetImageConverter as ResNetImageConverter,
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)

‎keras_hub/api/models/__init__.py‎

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from keras_hub.src.models.pali_gemma.pali_gemma_tokenizer import (
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PaliGemmaTokenizer as PaliGemmaTokenizer,
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)
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from keras_hub.src.models.parseq.parseq_backbone import (
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PARSeqBackbone as PARSeqBackbone,
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)
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from keras_hub.src.models.parseq.parseq_causal_lm import (
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PARSeqCausalLM as PARSeqCausalLM,
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)
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from keras_hub.src.models.parseq.parseq_causal_lm_preprocessor import (
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PARSeqCausalLMPreprocessor as PARSeqCausalLMPreprocessor,
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)
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from keras_hub.src.models.parseq.parseq_tokenizer import (
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PARSeqTokenizer as PARSeqTokenizer,
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)
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from keras_hub.src.models.phi3.phi3_backbone import Phi3Backbone as Phi3Backbone
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from keras_hub.src.models.phi3.phi3_causal_lm import (
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Phi3CausalLM as Phi3CausalLM,

‎keras_hub/api/tokenizers/__init__.py‎

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from keras_hub.src.models.pali_gemma.pali_gemma_tokenizer import (
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PaliGemmaTokenizer as PaliGemmaTokenizer,
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)
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from keras_hub.src.models.parseq.parseq_tokenizer import (
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PARSeqTokenizer as PARSeqTokenizer,
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)
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from keras_hub.src.models.phi3.phi3_tokenizer import (
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Phi3Tokenizer as Phi3Tokenizer,
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)

‎keras_hub/src/models/parseq/__init__.py‎

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‎keras_hub/src/models/parseq/parseq_backbone.py‎

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import keras
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from keras_hub.src.api_export import keras_hub_export
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from keras_hub.src.models.backbone import Backbone
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from keras_hub.src.models.parseq.parseq_decoder import PARSeqDecoder
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@keras_hub_export("keras_hub.models.PARSeqBackbone")
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class PARSeqBackbone(Backbone):
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"""Scene Text Detection with PARSeq.
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Performs OCR in natural scenes using the PARSeq model described in [Scene
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Text Recognition with Permuted Autoregressive Sequence Models](
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https://arxiv.org/abs/2207.06966). PARSeq is a ViT-based model that allows
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iterative decoding by performing an autoregressive decoding phase, followed
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by a refinement phase.
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Args:
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image_encoder: keras.Model. The image encoder model.
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vocabulary_size: int. The size of the vocabulary.
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max_label_length: int. The maximum length of the label sequence.
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decoder_hidden_dim: int. The dimension of the decoder hidden layers.
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num_decoder_layers: int. The number of decoder layers.
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num_decoder_heads: int. The number of attention heads in the decoder.
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decoder_mlp_dim: int. The dimension of the decoder MLP hidden layer.
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dropout_rate: float. The dropout rate for the decoder network.
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Defaults to `0.1`.
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attention_dropout: float. The dropout rate for the attention weights.
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Defaults to `0.1`.
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dtype: str. `None`, str, or `keras.mixed_precision.DTypePolicy`. The
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dtype to use for the computations and weights.
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**kwargs: Additional keyword arguments passed to the base
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`keras.Model` constructor.
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"""
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def __init__(
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self,
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image_encoder,
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vocabulary_size,
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max_label_length,
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decoder_hidden_dim,
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num_decoder_layers,
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num_decoder_heads,
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decoder_mlp_dim,
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dropout_rate=0.1,
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attention_dropout=0.1,
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dtype=None,
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**kwargs,
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):
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# === Layers ===
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self.image_encoder = image_encoder
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self.decoder = PARSeqDecoder(
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vocabulary_size=vocabulary_size,
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max_label_length=max_label_length,
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num_layers=num_decoder_layers,
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num_heads=num_decoder_heads,
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hidden_dim=decoder_hidden_dim,
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mlp_dim=decoder_mlp_dim,
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dropout_rate=dropout_rate,
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attention_dropout=attention_dropout,
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name="decoder",
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dtype=dtype,
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)
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self.head = keras.layers.Dense(
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vocabulary_size - 2, # We don't predict <bos> nor <pad>
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dtype=dtype,
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)
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# === Functional Model ===
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image_input = self.image_encoder.input
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token_id_input = keras.Input(
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shape=(None,), dtype="int32", name="token_ids"
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)
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padding_mask_input = keras.Input(
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shape=(None,), dtype="int32", name="padding_mask"
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)
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memory = self.image_encoder(image_input)
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target_out = self.decoder(
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token_id_input, memory, padding_mask=padding_mask_input
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)
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logits = self.head(target_out)
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# === Config ===
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self.vocabulary_size = vocabulary_size
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self.max_label_length = max_label_length
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self.decoder_hidden_dim = decoder_hidden_dim
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self.num_decoder_layers = num_decoder_layers
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self.num_decoder_heads = num_decoder_heads
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self.decoder_mlp_dim = decoder_mlp_dim
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self.dropout_rate = dropout_rate
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self.attention_dropout = attention_dropout
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super().__init__(
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inputs={
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"images": image_input,
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"token_ids": token_id_input,
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"padding_mask": padding_mask_input,
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},
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outputs=logits,
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dtype=dtype,
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**kwargs,
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)
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def get_config(self):
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config = super().get_config()
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config.update(
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{
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"image_encoder": keras.layers.serialize(self.image_encoder),
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"vocabulary_size": self.vocabulary_size,
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"max_label_length": self.max_label_length,
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"decoder_hidden_dim": self.decoder_hidden_dim,
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"num_decoder_layers": self.num_decoder_layers,
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"num_decoder_heads": self.num_decoder_heads,
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"decoder_mlp_dim": self.decoder_mlp_dim,
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"dropout_rate": self.dropout_rate,
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"attention_dropout": self.attention_dropout,
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}
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)
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return config
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@classmethod
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def from_config(cls, config):
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config.update(
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{
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"image_encoder": keras.layers.deserialize(
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config["image_encoder"]
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),
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}
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)
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return super().from_config(config)

‎keras_hub/src/models/parseq/parseq_backbone_test.py‎

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import keras
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import pytest
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from keras import ops
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from keras_hub.src.models.parseq.parseq_backbone import PARSeqBackbone
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from keras_hub.src.models.vit.vit_backbone import ViTBackbone
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from keras_hub.src.tests.test_case import TestCase
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class PARSeqBackboneTest(TestCase):
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def setUp(self):
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self.batch_size = 2
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self.image_height = 32
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self.image_width = 128
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self.num_channels = 3
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# Image Encoder parameters (as per your example)
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self.vit_patch_size = (4, 8)
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self.vit_num_layers = 2
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self.vit_num_heads = 2
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self.vit_hidden_dim = 64
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self.vit_mlp_dim = self.vit_hidden_dim * 4
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# PARSeq Backbone parameters
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self.vocabulary_size = 97
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self.max_label_length = 25
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self.decoder_hidden_dim = self.vit_hidden_dim
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self.num_decoder_layers = 1
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self.num_decoder_heads = 2
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self.decoder_mlp_dim = self.decoder_hidden_dim * 4
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# Instantiate the actual ViTBackbone to be used as the image_encoder
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self.image_encoder = ViTBackbone(
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image_shape=(
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self.image_height,
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self.image_width,
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self.num_channels,
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),
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patch_size=self.vit_patch_size,
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num_layers=self.vit_num_layers,
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num_heads=self.vit_num_heads,
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hidden_dim=self.vit_hidden_dim,
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mlp_dim=self.vit_mlp_dim,
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use_class_token=False,
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name="image_encoder",
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)
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self.init_kwargs = {
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"image_encoder": self.image_encoder,
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"vocabulary_size": self.vocabulary_size,
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"max_label_length": self.max_label_length,
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"decoder_hidden_dim": self.decoder_hidden_dim,
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"num_decoder_layers": self.num_decoder_layers,
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"num_decoder_heads": self.num_decoder_heads,
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"decoder_mlp_dim": self.decoder_mlp_dim,
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"dropout_rate": 0.0,
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"attention_dropout": 0.0,
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}
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# Dummy input data
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dummy_images = keras.random.normal(
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shape=(
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self.batch_size,
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self.image_height,
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self.image_width,
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self.num_channels,
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),
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)
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dummy_token_ids = keras.random.randint(
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minval=0,
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maxval=self.vocabulary_size,
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shape=(self.batch_size, self.max_label_length),
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)
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dummy_padding_mask = ops.ones(
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shape=(self.batch_size, self.max_label_length), dtype="int32"
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)
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self.input_data = {
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"images": dummy_images,
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"token_ids": dummy_token_ids,
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"padding_mask": dummy_padding_mask,
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}
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def test_backbone_basics(self):
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expected_shape_full = (
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self.batch_size,
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self.max_label_length,
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self.vocabulary_size - 2,
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)
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self.run_backbone_test(
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cls=PARSeqBackbone,
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init_kwargs=self.init_kwargs,
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input_data=self.input_data,
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expected_output_shape=expected_shape_full,
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# we have image_encoder as init_kwargs which is also a backbone
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run_quantization_check=False,
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)
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@pytest.mark.large
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def test_saved_model(self):
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self.run_model_saving_test(
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cls=PARSeqBackbone,
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init_kwargs=self.init_kwargs,
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input_data=self.input_data,
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)

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