Delete model.py

model.py

@@ -1,62 +0,0 @@
-import torch
-import torch.nn as nn
-from transformers import PretrainedConfig, PreTrainedModel
-
-# 1. Custom configuration class required by Hugging Face auto_map
-class TransformConfig(PretrainedConfig):
-    model_type = "brawnz_style_transfer"
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-# 2. Network helper blocks matching your training script
-def conv_bn_relu(in_c, out_c, k, stride=1, pad=0):
-    return nn.Sequential(
-        nn.ReflectionPad2d(pad),
-        nn.Conv2d(in_c, out_c, k, stride),
-        nn.InstanceNorm2d(out_c),
-        nn.ReLU(inplace=True),
-    )
-
-class ResBlock(nn.Module):
-    def __init__(self, c):
-        super().__init__()
-        self.block = nn.Sequential(
-            nn.ReflectionPad2d(1),
-            nn.Conv2d(c, c, 3),
-            nn.InstanceNorm2d(c),
-            nn.ReLU(inplace=True),
-            nn.ReflectionPad2d(1),
-            nn.Conv2d(c, c, 3),
-            nn.InstanceNorm2d(c),
-        )
-
-    def forward(self, x):
-        return x + self.block(x)
-
-# 3. Main Network Class hooked into Hugging Face structures
-class TransformNet(PreTrainedModel):
-    config_class = TransformConfig
-
-    def __init__(self, config=None):
-        # Fallback to default configuration if none is passed
-        if config is None:
-            config = TransformConfig()
-        super().__init__(config)
-        
-        self.net = nn.Sequential(
-            conv_bn_relu(3,   32,  9, pad=4),
-            conv_bn_relu(32,  64,  3, stride=2, pad=1),
-            conv_bn_relu(64, 128,  3, stride=2, pad=1),
-            ResBlock(128), ResBlock(128), ResBlock(128),
-            ResBlock(128), ResBlock(128),
-            nn.Upsample(scale_factor=2, mode="nearest"),
-            conv_bn_relu(128, 64, 3, pad=1),
-            nn.Upsample(scale_factor=2, mode="nearest"),
-            conv_bn_relu(64, 32, 3, pad=1),
-            nn.ReflectionPad2d(4),
-            nn.Conv2d(32, 3, 9),
-            nn.Tanh(),
-        )
-
-    def forward(self, x):
-        return self.net(x)