fix(v1-api): pass required config to DensePoseHead — green main CI
The "Continuous Integration" workflow (Performance Tests + API Documentation jobs) has failed on every main commit since the API start path was exercised: pose_service._initialize_models() called `DensePoseHead()` with no args, but DensePoseHead.__init__ requires a config dict → "TypeError: DensePoseHead.__init__() missing 1 required positional argument: 'config'" → uvicorn "Application startup failed". Pass a config: input_channels=256 (matches the modality translator's output), num_body_parts=24 (DensePose standard), num_uv_coordinates=2. Both call sites (with/without pose_model_path) fixed. Verified locally: DensePoseHead(config) + ModalityTranslationNetwork(config) both construct + eval, clearing the startup TypeError.
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@ -107,16 +107,25 @@ class PoseService:
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async def _initialize_models(self):
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"""Initialize neural network models."""
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try:
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# Initialize DensePose model
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# Initialize DensePose model. DensePoseHead requires a config
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# dict — input_channels matches the modality translator's output
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# (256), with the standard DensePose 24 body parts and 2 (U,V)
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# coordinates. (Previously called with no args → TypeError at
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# startup, which broke the API service.)
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densepose_config = {
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'input_channels': 256,
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'num_body_parts': 24,
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'num_uv_coordinates': 2,
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}
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if self.settings.pose_model_path:
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self.densepose_model = DensePoseHead()
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self.densepose_model = DensePoseHead(densepose_config)
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# Load model weights if path is provided
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# model_state = torch.load(self.settings.pose_model_path)
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# self.densepose_model.load_state_dict(model_state)
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self.logger.info("DensePose model loaded")
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else:
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self.logger.warning("No pose model path provided, using default model")
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self.densepose_model = DensePoseHead()
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self.densepose_model = DensePoseHead(densepose_config)
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# Initialize modality translation
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config = {
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