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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) | |
| 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 | |