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cba2240 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import torch
import torch.nn as nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import SequenceClassifierOutput
from .configuration_captcha import CaptchaConfig
class CaptchaCRNN(PreTrainedModel):
config_class = CaptchaConfig
def __init__(self, config):
super().__init__(config)
self.conv_layer = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, padding=1),
nn.BatchNorm2d(32),
nn.SiLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.SiLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.SiLU(),
nn.MaxPool2d(kernel_size=(2, 1)),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.SiLU()
)
self.lstm = nn.LSTM(input_size=1280, hidden_size=256, bidirectional=True, batch_first=True)
self.classifier = nn.Linear(512, config.num_chars)
self.post_init()
def forward(self, x, labels=None):
x = self.conv_layer(x)
x = x.permute(0, 3, 1, 2)
batch, width, channels, height = x.size()
x = x.view(batch, width, -1)
x, _ = self.lstm(x)
logits = self.classifier(x)
return SequenceClassifierOutput(logits=logits) |