# 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, Optional, Tuple
import tensorflow as tf
from tensorflow.keras import Sequential, layers
from doctr.datasets import VOCABS
from doctr.models.modules.transformer import EncoderBlock
from doctr.models.modules.vision_transformer.tensorflow import PatchEmbedding
from doctr.utils.repr import NestedObject
from ...utils import load_pretrained_params
__all__ = ["vit_s", "vit_b"]
default_cfgs: Dict[str, Dict[str, Any]] = {
"vit_s": {
"mean": (0.694, 0.695, 0.693),
"std": (0.299, 0.296, 0.301),
"input_shape": (3, 32, 32),
"classes": list(VOCABS["french"]),
"url": "https://doctr-static.mindee.com/models?id=v0.6.0/vit_s-6300fcc9.zip&src=0",
},
"vit_b": {
"mean": (0.694, 0.695, 0.693),
"std": (0.299, 0.296, 0.301),
"input_shape": (32, 32, 3),
"classes": list(VOCABS["french"]),
"url": "https://doctr-static.mindee.com/models?id=v0.6.0/vit_b-57158446.zip&src=0",
},
}
class ClassifierHead(layers.Layer, NestedObject):
"""Classifier head for Vision Transformer
Args:
----
num_classes: number of output classes
"""
def __init__(self, num_classes: int) -> None:
super().__init__()
self.head = layers.Dense(num_classes, kernel_initializer="he_normal", name="dense")
def call(self, x: tf.Tensor) -> tf.Tensor:
# (batch_size, num_classes) cls token
return self.head(x[:, 0])
class VisionTransformer(Sequential):
"""VisionTransformer architecture as described in
`"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
<https://arxiv.org/pdf/2010.11929.pdf>`_.
Args:
----
d_model: dimension of the transformer layers
num_layers: number of transformer layers
num_heads: number of attention heads
ffd_ratio: multiplier for the hidden dimension of the feedforward layer
patch_size: size of the patches
input_shape: size of the input image
dropout: dropout rate
num_classes: number of output classes
include_top: whether the classifier head should be instantiated
"""
def __init__(
self,
d_model: int,
num_layers: int,
num_heads: int,
ffd_ratio: int,
patch_size: Tuple[int, int] = (4, 4),
input_shape: Tuple[int, int, int] = (32, 32, 3),
dropout: float = 0.0,
num_classes: int = 1000,
include_top: bool = True,
cfg: Optional[Dict[str, Any]] = None,
) -> None:
_layers = [
PatchEmbedding(input_shape, d_model, patch_size),
EncoderBlock(
num_layers,
num_heads,
d_model,
d_model * ffd_ratio,
dropout,
activation_fct=layers.Activation("gelu"),
),
]
if include_top:
_layers.append(ClassifierHead(num_classes))
super().__init__(_layers)
self.cfg = cfg
def _vit(
arch: str,
pretrained: bool,
**kwargs: Any,
) -> VisionTransformer:
kwargs["num_classes"] = kwargs.get("num_classes", len(default_cfgs[arch]["classes"]))
kwargs["input_shape"] = kwargs.get("input_shape", default_cfgs[arch]["input_shape"])
kwargs["classes"] = kwargs.get("classes", default_cfgs[arch]["classes"])
_cfg = deepcopy(default_cfgs[arch])
_cfg["num_classes"] = kwargs["num_classes"]
_cfg["input_shape"] = kwargs["input_shape"]
_cfg["classes"] = kwargs["classes"]
kwargs.pop("classes")
# Build the model
model = VisionTransformer(cfg=_cfg, **kwargs)
# Load pretrained parameters
if pretrained:
load_pretrained_params(model, default_cfgs[arch]["url"])
return model
[docs]
def vit_s(pretrained: bool = False, **kwargs: Any) -> VisionTransformer:
"""VisionTransformer-S architecture
`"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
<https://arxiv.org/pdf/2010.11929.pdf>`_. Patches: (H, W) -> (H/8, W/8)
NOTE: unofficial config used in ViTSTR and ParSeq
>>> import tensorflow as tf
>>> from doctr.models import vit_s
>>> model = vit_s(pretrained=False)
>>> input_tensor = tf.random.uniform(shape=[1, 32, 32, 3], maxval=1, dtype=tf.float32)
>>> out = model(input_tensor)
Args:
----
pretrained: boolean, True if model is pretrained
**kwargs: keyword arguments of the VisionTransformer architecture
Returns:
-------
A feature extractor model
"""
return _vit(
"vit_s",
pretrained,
d_model=384,
num_layers=12,
num_heads=6,
ffd_ratio=4,
**kwargs,
)
[docs]
def vit_b(pretrained: bool = False, **kwargs: Any) -> VisionTransformer:
"""VisionTransformer-B architecture as described in
`"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale",
<https://arxiv.org/pdf/2010.11929.pdf>`_. Patches: (H, W) -> (H/8, W/8)
>>> import tensorflow as tf
>>> from doctr.models import vit_b
>>> model = vit_b(pretrained=False)
>>> input_tensor = tf.random.uniform(shape=[1, 32, 32, 3], maxval=1, dtype=tf.float32)
>>> out = model(input_tensor)
Args:
----
pretrained: boolean, True if model is pretrained
**kwargs: keyword arguments of the VisionTransformer architecture
Returns:
-------
A feature extractor model
"""
return _vit(
"vit_b",
pretrained,
d_model=768,
num_layers=12,
num_heads=12,
ffd_ratio=4,
**kwargs,
)