| import torch
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| import torch.nn as nn
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| import torch.nn.functional as F
|
| from transformers import PreTrainedModel, PretrainedConfig
|
|
|
|
|
| class CaptchaConfig(PretrainedConfig):
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| model_type = "captcha"
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|
|
| def __init__(self, input_dim=(40, 110), output_ndigits=5, output_vocab_size=10, vocab=None, **kwargs):
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| super().__init__(**kwargs)
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| self.input_dim = input_dim
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| self.output_ndigits = output_ndigits
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| self.output_vocab_size = output_vocab_size
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| self.vocab = vocab if vocab else [str(i) for i in range(10)]
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|
|
|
|
| class CaptchaModel(PreTrainedModel):
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| config_class = CaptchaConfig
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| model_type = "captcha"
|
|
|
| def __init__(self, config):
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| super().__init__(config)
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| self.vocab = config.vocab
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| self.output_ndigits = config.output_ndigits
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| self.output_vocab_size = config.output_vocab_size
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|
|
| self.batchnorm0 = nn.BatchNorm2d(3)
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| self.conv1 = nn.Conv2d(3, 32, kernel_size=3)
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| self.batchnorm1 = nn.BatchNorm2d(32)
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| self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
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| self.batchnorm2 = nn.BatchNorm2d(64)
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| self.conv3 = nn.Conv2d(64, 64, kernel_size=3)
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| self.batchnorm3 = nn.BatchNorm2d(64)
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| self.dropout1 = nn.Dropout(0.25)
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| self.dropout2 = nn.Dropout(0.5)
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|
|
| def calc_dim(x):
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| for _ in range(3):
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| x = (x - 2) // 2
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| return x
|
|
|
| conv_h = calc_dim(config.input_dim[0])
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| conv_w = calc_dim(config.input_dim[1])
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| fc1_in_features = conv_h * conv_w * 64
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|
|
| self.fc1 = nn.Linear(fc1_in_features, 200)
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| self.batchnorm_dense = nn.BatchNorm1d(200)
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| self.fc2 = nn.Linear(200, self.output_vocab_size * self.output_ndigits)
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|
|
| def forward(self, pixel_values):
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| x = self.batchnorm0(pixel_values)
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| x = F.relu(self.batchnorm1(F.max_pool2d(self.conv1(x), 2)))
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| x = F.relu(self.batchnorm2(F.max_pool2d(self.conv2(x), 2)))
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| x = F.relu(self.batchnorm3(F.max_pool2d(self.conv3(x), 2)))
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| x = torch.flatten(x, start_dim=1)
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| x = self.dropout1(x)
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| x = F.relu(self.batchnorm_dense(self.fc1(x)))
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| x = self.dropout2(x)
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| logits = self.fc2(x)
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| logits = logits.view(-1, self.output_ndigits, self.output_vocab_size)
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| return logits
|
|
|
