Adiciona o modelo CaptchaModel para uso com transformers

tjmg/tjmg.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+
+
+class CaptchaConfig(PretrainedConfig):
+    model_type = "captcha"  # Importante para o Hugging Face reconhecer
+
+    def __init__(self, input_dim=(40, 110), output_ndigits=5, output_vocab_size=10, vocab=None, **kwargs):
+        super().__init__(**kwargs)
+        self.input_dim = input_dim
+        self.output_ndigits = output_ndigits
+        self.output_vocab_size = output_vocab_size
+        self.vocab = vocab if vocab else [str(i) for i in range(10)]
+
+
+class CaptchaModel(PreTrainedModel):
+    config_class = CaptchaConfig
+    model_type = "captcha"  # Importante para o Hugging Face reconhecer
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.vocab = config.vocab
+        self.output_ndigits = config.output_ndigits
+        self.output_vocab_size = config.output_vocab_size
+
+        self.batchnorm0 = nn.BatchNorm2d(3)
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3)
+        self.batchnorm1 = nn.BatchNorm2d(32)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
+        self.batchnorm2 = nn.BatchNorm2d(64)
+        self.conv3 = nn.Conv2d(64, 64, kernel_size=3)
+        self.batchnorm3 = nn.BatchNorm2d(64)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+
+        def calc_dim(x):
+            for _ in range(3):
+                x = (x - 2) // 2
+            return x
+
+        conv_h = calc_dim(config.input_dim[0])
+        conv_w = calc_dim(config.input_dim[1])
+        fc1_in_features = conv_h * conv_w * 64
+
+        self.fc1 = nn.Linear(fc1_in_features, 200)
+        self.batchnorm_dense = nn.BatchNorm1d(200)
+        self.fc2 = nn.Linear(200, self.output_vocab_size * self.output_ndigits)
+
+    def forward(self, pixel_values):
+        x = self.batchnorm0(pixel_values)
+        x = F.relu(self.batchnorm1(F.max_pool2d(self.conv1(x), 2)))
+        x = F.relu(self.batchnorm2(F.max_pool2d(self.conv2(x), 2)))
+        x = F.relu(self.batchnorm3(F.max_pool2d(self.conv3(x), 2)))
+        x = torch.flatten(x, start_dim=1)
+        x = self.dropout1(x)
+        x = F.relu(self.batchnorm_dense(self.fc1(x)))
+        x = self.dropout2(x)
+        logits = self.fc2(x)
+        logits = logits.view(-1, self.output_ndigits, self.output_vocab_size)
+        return logits