Update model.py

model.py

@@ -1,54 +1,80 @@
-import torch.nn as nn
-import torch
-
-# -----------------------------
-# 3️⃣ Model definition
-# -----------------------------
-def GN(c, groups=16): return nn.GroupNorm(min(groups, c), c)
-
-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.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())
-        self.adaptive_pool = nn.AdaptiveAvgPool2d((self.adaptive_height, None))
-    def forward(self, x):
-        for i in range(1, 7):
-            x = getattr(self, f"layer{i}")(x)
-        x = self.adaptive_pool(x)
-        return x
-
-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))
-        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), :]
-
-class CNN_Transformer_OCR(nn.Module):
-    def __init__(self, num_classes, d_model=1280, nhead=16, num_layers=8, dropout=0.2):
-        super().__init__()
-        self.cnn = LightResNetCNN(in_channels=1, adaptive_height=8)
-        self.proj = nn.Linear(128 * 8, d_model)
-        self.posenc = PositionalEncoding(d_model)
-        encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, batch_first=True, dropout=dropout)
-        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
-        self.fc = nn.Linear(d_model, num_classes)
-    def forward(self, x):
-        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)
-        out = self.fc(out)
-        return out.log_softmax(2)
+import torch.nn as nn
+import torch
+
+# -----------------------------
+# 3️⃣ Model definition
+# -----------------------------
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+
+def GN(c, groups=16): 
+    return nn.GroupNorm(min(groups, c), c)
+
+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.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())
+        self.adaptive_pool = nn.AdaptiveAvgPool2d((adaptive_height, None))
+
+    def forward(self, x):
+        for i in range(1, 7):
+            x = getattr(self, f"layer{i}")(x)
+        x = self.adaptive_pool(x)
+        return x
+
+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))
+        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), :]
+
+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):
+        super().__init__(**kwargs)
+        self.num_classes = num_classes
+        self.d_model = d_model
+        self.nhead = nhead
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.adaptive_height = adaptive_height
+
+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.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)
+        self.fc = nn.Linear(config.d_model, config.num_classes)
+
+        self.post_init()  # مهم: برای HuggingFace
+
+    def forward(self, x, labels=None):
+        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)
+        return {"logits": logits}
+