# Copyright (C) 2021-2024, Mindee.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple
import tensorflow as tf
from tensorflow.keras import Model, layers
from doctr.datasets import VOCABS
from ...classification import vit_b, vit_s
from ...utils.tensorflow import _bf16_to_float32, load_pretrained_params
from .base import _ViTSTR, _ViTSTRPostProcessor
__all__ = ["ViTSTR", "vitstr_small", "vitstr_base"]
default_cfgs: Dict[str, Dict[str, Any]] = {
"vitstr_small": {
"mean": (0.694, 0.695, 0.693),
"std": (0.299, 0.296, 0.301),
"input_shape": (32, 128, 3),
"vocab": VOCABS["french"],
"url": "https://doctr-static.mindee.com/models?id=v0.6.0/vitstr_small-358fab2e.zip&src=0",
},
"vitstr_base": {
"mean": (0.694, 0.695, 0.693),
"std": (0.299, 0.296, 0.301),
"input_shape": (32, 128, 3),
"vocab": VOCABS["french"],
"url": "https://doctr-static.mindee.com/models?id=v0.6.0/vitstr_base-2889159a.zip&src=0",
},
}
class ViTSTR(_ViTSTR, Model):
"""Implements a ViTSTR architecture as described in `"Vision Transformer for Fast and
Efficient Scene Text Recognition" <https://arxiv.org/pdf/2105.08582.pdf>`_.
Args:
----
feature_extractor: the backbone serving as feature extractor
vocab: vocabulary used for encoding
embedding_units: number of embedding units
max_length: maximum word length handled by the model
dropout_prob: dropout probability for the encoder and decoder
input_shape: input shape of the image
exportable: onnx exportable returns only logits
cfg: dictionary containing information about the model
"""
_children_names: List[str] = ["feat_extractor", "postprocessor"]
def __init__(
self,
feature_extractor,
vocab: str,
embedding_units: int,
max_length: int = 32,
dropout_prob: float = 0.0,
input_shape: Tuple[int, int, int] = (32, 128, 3), # different from paper
exportable: bool = False,
cfg: Optional[Dict[str, Any]] = None,
) -> None:
super().__init__()
self.vocab = vocab
self.exportable = exportable
self.cfg = cfg
self.max_length = max_length + 2 # +2 for SOS and EOS
self.feat_extractor = feature_extractor
self.head = layers.Dense(len(self.vocab) + 1, name="head") # +1 for EOS
self.postprocessor = ViTSTRPostProcessor(vocab=self.vocab)
@staticmethod
def compute_loss(
model_output: tf.Tensor,
gt: tf.Tensor,
seq_len: List[int],
) -> tf.Tensor:
"""Compute categorical cross-entropy loss for the model.
Sequences are masked after the EOS character.
Args:
----
model_output: predicted logits of the model
gt: the encoded tensor with gt labels
seq_len: lengths of each gt word inside the batch
Returns:
-------
The loss of the model on the batch
"""
# Input length : number of steps
input_len = tf.shape(model_output)[1]
# Add one for additional <eos> token (sos disappear in shift!)
seq_len = tf.cast(seq_len, tf.int32) + 1
# One-hot gt labels
oh_gt = tf.one_hot(gt, depth=model_output.shape[2])
# Compute loss: don't forget to shift gt! Otherwise the model learns to output the gt[t-1]!
# The "masked" first gt char is <sos>.
cce = tf.nn.softmax_cross_entropy_with_logits(oh_gt[:, 1:, :], model_output)
# Compute mask
mask_values = tf.zeros_like(cce)
mask_2d = tf.sequence_mask(seq_len, input_len)
masked_loss = tf.where(mask_2d, cce, mask_values)
ce_loss = tf.math.divide(tf.reduce_sum(masked_loss, axis=1), tf.cast(seq_len, model_output.dtype))
return tf.expand_dims(ce_loss, axis=1)
def call(
self,
x: tf.Tensor,
target: Optional[List[str]] = None,
return_model_output: bool = False,
return_preds: bool = False,
**kwargs: Any,
) -> Dict[str, Any]:
features = self.feat_extractor(x, **kwargs) # (batch_size, patches_seqlen, d_model)
if target is not None:
gt, seq_len = self.build_target(target)
seq_len = tf.cast(seq_len, tf.int32)
if kwargs.get("training", False) and target is None:
raise ValueError("Need to provide labels during training")
features = features[:, : self.max_length] # (batch_size, max_length, d_model)
B, N, E = features.shape
features = tf.reshape(features, (B * N, E))
logits = tf.reshape(
self.head(features, **kwargs), (B, N, len(self.vocab) + 1)
) # (batch_size, max_length, vocab + 1)
decoded_features = _bf16_to_float32(logits[:, 1:]) # remove cls_token
out: Dict[str, tf.Tensor] = {}
if self.exportable:
out["logits"] = decoded_features
return out
if return_model_output:
out["out_map"] = decoded_features
if target is None or return_preds:
# Post-process boxes
out["preds"] = self.postprocessor(decoded_features)
if target is not None:
out["loss"] = self.compute_loss(decoded_features, gt, seq_len)
return out
class ViTSTRPostProcessor(_ViTSTRPostProcessor):
"""Post processor for ViTSTR architecture
Args:
----
vocab: string containing the ordered sequence of supported characters
"""
def __call__(
self,
logits: tf.Tensor,
) -> List[Tuple[str, float]]:
# compute pred with argmax for attention models
out_idxs = tf.math.argmax(logits, axis=2)
preds_prob = tf.math.reduce_max(tf.nn.softmax(logits, axis=-1), axis=-1)
# decode raw output of the model with tf_label_to_idx
out_idxs = tf.cast(out_idxs, dtype="int32")
embedding = tf.constant(self._embedding, dtype=tf.string)
decoded_strings_pred = tf.strings.reduce_join(inputs=tf.nn.embedding_lookup(embedding, out_idxs), axis=-1)
decoded_strings_pred = tf.strings.split(decoded_strings_pred, "<eos>")
decoded_strings_pred = tf.sparse.to_dense(decoded_strings_pred.to_sparse(), default_value="not valid")[:, 0]
word_values = [word.decode() for word in decoded_strings_pred.numpy().tolist()]
# compute probabilties for each word up to the EOS token
probs = [
preds_prob[i, : len(word)].numpy().clip(0, 1).mean().item() if word else 0.0
for i, word in enumerate(word_values)
]
return list(zip(word_values, probs))
def _vitstr(
arch: str,
pretrained: bool,
backbone_fn,
input_shape: Optional[Tuple[int, int, int]] = None,
**kwargs: Any,
) -> ViTSTR:
# Patch the config
_cfg = deepcopy(default_cfgs[arch])
_cfg["input_shape"] = input_shape or _cfg["input_shape"]
_cfg["vocab"] = kwargs.get("vocab", _cfg["vocab"])
patch_size = kwargs.get("patch_size", (4, 8))
kwargs["vocab"] = _cfg["vocab"]
# Feature extractor
feat_extractor = backbone_fn(
# NOTE: we don't use a pretrained backbone for non-rectangular patches to avoid the pos embed mismatch
pretrained=False,
input_shape=_cfg["input_shape"],
patch_size=patch_size,
include_top=False,
)
kwargs.pop("patch_size", None)
kwargs.pop("pretrained_backbone", None)
# Build the model
model = ViTSTR(feat_extractor, cfg=_cfg, **kwargs)
# Load pretrained parameters
if pretrained:
load_pretrained_params(model, default_cfgs[arch]["url"])
return model
[docs]
def vitstr_small(pretrained: bool = False, **kwargs: Any) -> ViTSTR:
"""ViTSTR-Small as described in `"Vision Transformer for Fast and Efficient Scene Text Recognition"
<https://arxiv.org/pdf/2105.08582.pdf>`_.
>>> import tensorflow as tf
>>> from doctr.models import vitstr_small
>>> model = vitstr_small(pretrained=False)
>>> input_tensor = tf.random.uniform(shape=[1, 32, 128, 3], maxval=1, dtype=tf.float32)
>>> out = model(input_tensor)
Args:
----
pretrained (bool): If True, returns a model pre-trained on our text recognition dataset
**kwargs: keyword arguments of the ViTSTR architecture
Returns:
-------
text recognition architecture
"""
return _vitstr(
"vitstr_small",
pretrained,
vit_s,
embedding_units=384,
patch_size=(4, 8),
**kwargs,
)
[docs]
def vitstr_base(pretrained: bool = False, **kwargs: Any) -> ViTSTR:
"""ViTSTR-Base as described in `"Vision Transformer for Fast and Efficient Scene Text Recognition"
<https://arxiv.org/pdf/2105.08582.pdf>`_.
>>> import tensorflow as tf
>>> from doctr.models import vitstr_base
>>> model = vitstr_base(pretrained=False)
>>> input_tensor = tf.random.uniform(shape=[1, 32, 128, 3], maxval=1, dtype=tf.float32)
>>> out = model(input_tensor)
Args:
----
pretrained (bool): If True, returns a model pre-trained on our text recognition dataset
**kwargs: keyword arguments of the ViTSTR architecture
Returns:
-------
text recognition architecture
"""
return _vitstr(
"vitstr_base",
pretrained,
vit_b,
embedding_units=768,
patch_size=(4, 8),
**kwargs,
)