# 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 doctr.models.classification import magc_resnet31
from doctr.models.modules.transformer import Decoder, PositionalEncoding
from ...utils.tensorflow import _bf16_to_float32, load_pretrained_params
from .base import _MASTER, _MASTERPostProcessor
__all__ = ["MASTER", "master"]
default_cfgs: Dict[str, Dict[str, Any]] = {
"master": {
"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/master-a8232e9f.zip&src=0",
},
}
class MASTER(_MASTER, Model):
"""Implements MASTER as described in paper: <https://arxiv.org/pdf/1910.02562.pdf>`_.
Implementation based on the official TF implementation: <https://github.com/jiangxiluning/MASTER-TF>`_.
Args:
----
feature_extractor: the backbone serving as feature extractor
vocab: vocabulary, (without EOS, SOS, PAD)
d_model: d parameter for the transformer decoder
dff: depth of the pointwise feed-forward layer
num_heads: number of heads for the mutli-head attention module
num_layers: number of decoder layers to stack
max_length: maximum length of character sequence handled by the model
dropout: dropout probability of the decoder
input_shape: size of the image inputs
exportable: onnx exportable returns only logits
cfg: dictionary containing information about the model
"""
def __init__(
self,
feature_extractor: tf.keras.Model,
vocab: str,
d_model: int = 512,
dff: int = 2048,
num_heads: int = 8, # number of heads in the transformer decoder
num_layers: int = 3,
max_length: int = 50,
dropout: float = 0.2,
input_shape: Tuple[int, int, int] = (32, 128, 3), # different from the paper
exportable: bool = False,
cfg: Optional[Dict[str, Any]] = None,
) -> None:
super().__init__()
self.exportable = exportable
self.max_length = max_length
self.d_model = d_model
self.vocab = vocab
self.cfg = cfg
self.vocab_size = len(vocab)
self.feat_extractor = feature_extractor
self.positional_encoding = PositionalEncoding(self.d_model, dropout, max_len=input_shape[0] * input_shape[1])
self.decoder = Decoder(
num_layers=num_layers,
d_model=self.d_model,
num_heads=num_heads,
vocab_size=self.vocab_size + 3, # EOS, SOS, PAD
dff=dff,
dropout=dropout,
maximum_position_encoding=self.max_length,
)
self.linear = layers.Dense(self.vocab_size + 3, kernel_initializer=tf.initializers.he_uniform())
self.postprocessor = MASTERPostProcessor(vocab=self.vocab)
@tf.function
def make_source_and_target_mask(self, source: tf.Tensor, target: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
# [1, 1, 1, ..., 0, 0, 0] -> 0 is masked
# (N, 1, 1, max_length)
target_pad_mask = tf.cast(tf.math.not_equal(target, self.vocab_size + 2), dtype=tf.uint8)
target_pad_mask = target_pad_mask[:, tf.newaxis, tf.newaxis, :]
target_length = target.shape[1]
# sub mask filled diagonal with 1 = see 0 = masked (max_length, max_length)
target_sub_mask = tf.linalg.band_part(tf.ones((target_length, target_length)), -1, 0)
# source mask filled with ones (max_length, positional_encoded_seq_len)
source_mask = tf.ones((target_length, source.shape[1]))
# combine the two masks into one boolean mask where False is masked (N, 1, max_length, max_length)
target_mask = tf.math.logical_and(
tf.cast(target_sub_mask, dtype=tf.bool), tf.cast(target_pad_mask, dtype=tf.bool)
)
return source_mask, target_mask
@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:
----
gt: the encoded tensor with gt labels
model_output: predicted logits of the model
seq_len: lengths of each gt word inside the batch
Returns:
-------
The loss of the model on the batch
"""
# Input length : number of timesteps
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>. Delete last logit of the model output.
cce = tf.nn.softmax_cross_entropy_with_logits(oh_gt[:, 1:, :], model_output[:, :-1, :])
# Compute mask
mask_values = tf.zeros_like(cce)
mask_2d = tf.sequence_mask(seq_len, input_len - 1) # delete the last mask timestep as well
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]:
"""Call function for training
Args:
----
x: images
target: list of str labels
return_model_output: if True, return logits
return_preds: if True, decode logits
**kwargs: keyword arguments passed to the decoder
Returns:
-------
A dictionnary containing eventually loss, logits and predictions.
"""
# Encode
feature = self.feat_extractor(x, **kwargs)
b, h, w, c = feature.get_shape()
# (N, H, W, C) --> (N, H * W, C)
feature = tf.reshape(feature, shape=(b, h * w, c))
# add positional encoding to features
encoded = self.positional_encoding(feature, **kwargs)
out: Dict[str, tf.Tensor] = {}
if kwargs.get("training", False) and target is None:
raise ValueError("Need to provide labels during training")
if target is not None:
# Compute target: tensor of gts and sequence lengths
gt, seq_len = self.build_target(target)
# Compute decoder masks
source_mask, target_mask = self.make_source_and_target_mask(encoded, gt)
# Compute logits
output = self.decoder(gt, encoded, source_mask, target_mask, **kwargs)
logits = self.linear(output, **kwargs)
else:
logits = self.decode(encoded, **kwargs)
logits = _bf16_to_float32(logits)
if self.exportable:
out["logits"] = logits
return out
if target is not None:
out["loss"] = self.compute_loss(logits, gt, seq_len)
if return_model_output:
out["out_map"] = logits
if return_preds:
out["preds"] = self.postprocessor(logits)
return out
@tf.function
def decode(self, encoded: tf.Tensor, **kwargs: Any) -> tf.Tensor:
"""Decode function for prediction
Args:
----
encoded: encoded features
**kwargs: keyword arguments passed to the decoder
Returns:
-------
A Tuple of tf.Tensor: predictions, logits
"""
b = encoded.shape[0]
start_symbol = tf.constant(self.vocab_size + 1, dtype=tf.int32) # SOS
padding_symbol = tf.constant(self.vocab_size + 2, dtype=tf.int32) # PAD
ys = tf.fill(dims=(b, self.max_length - 1), value=padding_symbol)
start_vector = tf.fill(dims=(b, 1), value=start_symbol)
ys = tf.concat([start_vector, ys], axis=-1)
# Final dimension include EOS/SOS/PAD
for i in range(self.max_length - 1):
source_mask, target_mask = self.make_source_and_target_mask(encoded, ys)
output = self.decoder(ys, encoded, source_mask, target_mask, **kwargs)
logits = self.linear(output, **kwargs)
prob = tf.nn.softmax(logits, axis=-1)
next_token = tf.argmax(prob, axis=-1, output_type=ys.dtype)
# update ys with the next token and ignore the first token (SOS)
i_mesh, j_mesh = tf.meshgrid(tf.range(b), tf.range(self.max_length), indexing="ij")
indices = tf.stack([i_mesh[:, i + 1], j_mesh[:, i + 1]], axis=1)
ys = tf.tensor_scatter_nd_update(ys, indices, next_token[:, i])
# Shape (N, max_length, vocab_size + 1)
return logits
class MASTERPostProcessor(_MASTERPostProcessor):
"""Post processor for MASTER architectures
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)
# N x L
probs = tf.gather(tf.nn.softmax(logits, axis=-1), out_idxs, axis=-1, batch_dims=2)
# Take the minimum confidence of the sequence
probs = tf.math.reduce_min(probs, 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()]
return list(zip(word_values, probs.numpy().clip(0, 1).tolist()))
def _master(arch: str, pretrained: bool, backbone_fn, pretrained_backbone: bool = True, **kwargs: Any) -> MASTER:
pretrained_backbone = pretrained_backbone and not pretrained
# Patch the config
_cfg = deepcopy(default_cfgs[arch])
_cfg["input_shape"] = kwargs.get("input_shape", _cfg["input_shape"])
_cfg["vocab"] = kwargs.get("vocab", _cfg["vocab"])
kwargs["vocab"] = _cfg["vocab"]
kwargs["input_shape"] = _cfg["input_shape"]
# Build the model
model = MASTER(
backbone_fn(pretrained=pretrained_backbone, input_shape=_cfg["input_shape"], include_top=False),
cfg=_cfg,
**kwargs,
)
# Load pretrained parameters
if pretrained:
load_pretrained_params(model, default_cfgs[arch]["url"])
return model
[docs]
def master(pretrained: bool = False, **kwargs: Any) -> MASTER:
"""MASTER as described in paper: <https://arxiv.org/pdf/1910.02562.pdf>`_.
>>> import tensorflow as tf
>>> from doctr.models import master
>>> model = master(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: keywoard arguments passed to the MASTER architecture
Returns:
-------
text recognition architecture
"""
return _master("master", pretrained, magc_resnet31, **kwargs)