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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)
@torch.jit.ignore
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
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