Update model.py

model.py

@@ -2,28 +2,16 @@ import torch
 import torch.nn as nn
 from transformers import PreTrainedModel, PretrainedConfig
 
-# -----------------------------
-# GroupNorm Helper
-# -----------------------------
 def GN(c, groups=16):
     return nn.GroupNorm(min(groups, c), c)
 
-# -----------------------------
-# CNN Backbone
-# -----------------------------
 class LightResNetCNN(nn.Module):
     def __init__(self, in_channels=1, adaptive_height=8):
         super().__init__()
         self.adaptive_height = adaptive_height
-        self.layer1 = nn.Sequential(
-            nn.Conv2d(in_channels, 32, 3, 1, 1), GN(32), nn.ReLU(), nn.MaxPool2d(2, 2)
-        )
-        self.layer2 = nn.Sequential(
-            nn.Conv2d(32, 64, 3, 1, 1), GN(64), nn.ReLU(), nn.MaxPool2d(2, 2)
-        )
-        self.layer3 = nn.Sequential(
-            nn.Conv2d(64, 128, 3, 1, 1), GN(128), nn.ReLU(), nn.MaxPool2d(2, 2)
-        )
+        self.layer1 = nn.Sequential(nn.Conv2d(in_channels, 32, 3, 1, 1), GN(32), nn.ReLU(), nn.MaxPool2d(2, 2))
+        self.layer2 = nn.Sequential(nn.Conv2d(32, 64, 3, 1, 1), GN(64), nn.ReLU(), nn.MaxPool2d(2, 2))
+        self.layer3 = nn.Sequential(nn.Conv2d(64, 128, 3, 1, 1), GN(128), nn.ReLU(), nn.MaxPool2d(2, 2))
         self.layer4 = nn.Sequential(nn.Conv2d(128, 256, 3, 1, 1), GN(256), nn.ReLU())
         self.layer5 = nn.Sequential(nn.Conv2d(256, 256, 3, 1, 1), GN(256), nn.ReLU())
         self.layer6 = nn.Sequential(nn.Conv2d(256, 128, 3, 1, 1), GN(128), nn.ReLU())
@@ -35,40 +23,23 @@ class LightResNetCNN(nn.Module):
         x = self.adaptive_pool(x)
         return x
 
-# -----------------------------
-# Positional Encoding
-# -----------------------------
 class PositionalEncoding(nn.Module):
     def __init__(self, d_model, max_len=2000):
         super().__init__()
         pe = torch.zeros(max_len, d_model)
         position = torch.arange(0, max_len).unsqueeze(1)
-        div_term = torch.exp(
-            torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(10000.0)) / d_model)
-        )
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-torch.log(torch.tensor(10000.0)) / d_model))
         pe[:, 0::2] = torch.sin(position * div_term)
         pe[:, 1::2] = torch.cos(position * div_term)
         self.register_buffer("pe", pe.unsqueeze(0))
 
     def forward(self, x):
-        return x + self.pe[:, : x.size(1), :]
+        return x + self.pe[:, :x.size(1), :]
 
-# -----------------------------
-# Config
-# -----------------------------
 class PersianOCRConfig(PretrainedConfig):
     model_type = "persianocr"
 
-    def __init__(
-        self,
-        num_classes=100,
-        d_model=1280,
-        nhead=16,
-        num_layers=8,
-        dropout=0.2,
-        adaptive_height=8,
-        **kwargs
-    ):
+    def __init__(self, num_classes=100, d_model=1280, nhead=16, num_layers=8, dropout=0.2, adaptive_height=8, **kwargs):
         super().__init__(**kwargs)
         self.num_classes = num_classes
         self.d_model = d_model
@@ -77,43 +48,24 @@ class PersianOCRConfig(PretrainedConfig):
         self.dropout = dropout
         self.adaptive_height = adaptive_height
 
-# -----------------------------
-# Model
-# -----------------------------
 class PersianOCRModel(PreTrainedModel):
     config_class = PersianOCRConfig
 
     def __init__(self, config):
         super().__init__(config)
-        self.cnn = LightResNetCNN(
-            in_channels=1, adaptive_height=config.adaptive_height
-        )
+        self.cnn = LightResNetCNN(in_channels=1, adaptive_height=config.adaptive_height)
         self.proj = nn.Linear(128 * config.adaptive_height, config.d_model)
         self.posenc = PositionalEncoding(config.d_model)
-        encoder_layer = nn.TransformerEncoderLayer(
-            config.d_model, config.nhead, batch_first=True, dropout=config.dropout
-        )
-        self.transformer = nn.TransformerEncoder(
-            encoder_layer, num_layers=config.num_layers
-        )
+        encoder_layer = nn.TransformerEncoderLayer(config.d_model, config.nhead, batch_first=True, dropout=config.dropout)
+        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=config.num_layers)
         self.fc = nn.Linear(config.d_model, config.num_classes)
-
-        # این خط خیلی مهمه برای HuggingFace
         self.post_init()
 
     def forward(self, x, labels=None):
-        """
-        Args:
-            x: Tensor [batch, 1, H, W] - grayscale input
-            labels: optional, برای CTC loss
-        Returns:
-            dict با logits
-        """
-        f = self.cnn(x)  # [B, C, H, W]
+        f = self.cnn(x)
         B, C, H, W = f.size()
         f = f.permute(0, 3, 1, 2).reshape(B, W, C * H)
         f = self.posenc(self.proj(f))
         out = self.transformer(f)
-        logits = self.fc(out)  # [B, W, num_classes]
-
+        logits = self.fc(out)
         return {"logits": logits}