Add: model conversion script

submissions/colorization/model_conversion.py

@@ -0,0 +1,48 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from colorization.colorizers import eccv16
+
+class ExactCaffeMatch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.core = eccv16(pretrained=True).eval()
+
+        # Load the color palette kernel
+        pts_in_hull = np.load('opencv_extra/testdata/dnn/colorization_pts_in_hull.npy')
+
+        weight_tensor = torch.tensor(pts_in_hull.flatten()).float().view(2, 313, 1, 1)
+        self.register_buffer('decode_weight', weight_tensor)
+
+    def forward(self, x):
+        x = x / 100.0
+        x = self.core.model1(x)
+        x = self.core.model2(x)
+        x = self.core.model3(x)
+        x = self.core.model4(x)
+        x = self.core.model5(x)
+        x = self.core.model6(x)
+        x = self.core.model7(x)
+        x = self.core.model8(x)
+
+        # 1. Apply Caffe temperature scaling
+        x = x * 2.606# 2. Softmax
+        x = torch.softmax(x, dim=1)
+
+        x = torch.nn.functional.conv2d(x, self.decode_weight)
+
+        return x
+
+model = ExactCaffeMatch()
+dummy_input = torch.randn(1, 1, 224, 224)
+
+torch.onnx.export(
+    model, dummy_input,
+    "colorization/colorization_deploy_v2_2026april.onnx",
+    export_params=True,
+    opset_version=11,
+    do_constant_folding=True,
+    input_names=['data_l'],
+    output_names=['class8_ab'],
+    operator_export_type=torch.onnx.OperatorExportTypes.ONNX_FALLTHROUGH
+)