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# Scene Text Recognition Model Hub
# Copyright 2022 Darwin Bautista
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Optional, Sequence
import torch
from torch import Tensor
from pytorch_lightning.utilities.types import STEP_OUTPUT
from strhub.models.base import CrossEntropySystem
from strhub.models.utils import init_weights
from .model import ViTSTR as Model
class ViTSTR(CrossEntropySystem):
def __init__(
self,
charset_train: str,
charset_test: str,
max_label_length: int,
batch_size: int,
lr: float,
warmup_pct: float,
weight_decay: float,
img_size: Sequence[int],
patch_size: Sequence[int],
embed_dim: int,
num_heads: int,
**kwargs: Any,
) -> None:
super().__init__(charset_train, charset_test, batch_size, lr, warmup_pct, weight_decay)
self.save_hyperparameters()
self.max_label_length = max_label_length
# We don't predict <bos> nor <pad>
self.model = Model(
img_size=img_size,
patch_size=patch_size,
depth=12,
mlp_ratio=4,
qkv_bias=True,
embed_dim=embed_dim,
num_heads=num_heads,
num_classes=len(self.tokenizer) - 2,
)
# Non-zero weight init for the head
self.model.head.apply(init_weights)
@torch.jit.ignore
def no_weight_decay(self):
return {'model.' + n for n in self.model.no_weight_decay()}
def forward(self, images: Tensor, max_length: Optional[int] = None) -> Tensor:
max_length = self.max_label_length if max_length is None else min(max_length, self.max_label_length)
logits = self.model.forward(images, max_length + 2) # +2 tokens for [GO] and [s]
# Truncate to conform to other models. [GO] in ViTSTR is actually used as the padding (therefore, ignored).
# First position corresponds to the class token, which is unused and ignored in the original work.
logits = logits[:, 1:]
return logits
def training_step(self, batch, batch_idx) -> STEP_OUTPUT:
images, labels = batch
loss = self.forward_logits_loss(images, labels)[1]
self.log('loss', loss)
return loss
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