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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 functools import partial | |
| from typing import Sequence, Any, Optional | |
| import torch | |
| import torch.nn.functional as F | |
| from pytorch_lightning.utilities.types import STEP_OUTPUT | |
| from timm.models.helpers import named_apply | |
| from torch import Tensor | |
| from strhub.models.base import CrossEntropySystem, CTCSystem | |
| from strhub.models.utils import init_weights | |
| from .model import TRBA as Model | |
| class TRBA(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], num_fiducial: int, output_channel: int, hidden_size: 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 | |
| img_h, img_w = img_size | |
| self.model = Model(img_h, img_w, len(self.tokenizer), num_fiducial, | |
| output_channel=output_channel, hidden_size=hidden_size, use_ctc=False) | |
| named_apply(partial(init_weights, exclude=['Transformation.LocalizationNetwork.localization_fc2']), self.model) | |
| def no_weight_decay(self): | |
| return {'model.Prediction.char_embeddings.weight'} | |
| 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) | |
| text = images.new_full([1], self.bos_id, dtype=torch.long) | |
| return self.model.forward(images, max_length, text) | |
| def training_step(self, batch, batch_idx) -> STEP_OUTPUT: | |
| images, labels = batch | |
| encoded = self.tokenizer.encode(labels, self.device) | |
| inputs = encoded[:, :-1] # remove <eos> | |
| targets = encoded[:, 1:] # remove <bos> | |
| max_length = encoded.shape[1] - 2 # exclude <bos> and <eos> from count | |
| logits = self.model.forward(images, max_length, inputs) | |
| loss = F.cross_entropy(logits.flatten(end_dim=1), targets.flatten(), ignore_index=self.pad_id) | |
| self.log('loss', loss) | |
| return loss | |
| class TRBC(CTCSystem): | |
| 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], num_fiducial: int, output_channel: int, hidden_size: 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 | |
| img_h, img_w = img_size | |
| self.model = Model(img_h, img_w, len(self.tokenizer), num_fiducial, | |
| output_channel=output_channel, hidden_size=hidden_size, use_ctc=True) | |
| named_apply(partial(init_weights, exclude=['Transformation.LocalizationNetwork.localization_fc2']), self.model) | |
| def forward(self, images: Tensor, max_length: Optional[int] = None) -> Tensor: | |
| # max_label_length is unused in CTC prediction | |
| return self.model.forward(images, None) | |
| 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 | |