comparison MetaFormer/metaformer_stacked_cnn.py @ 16:8729f69e9207 draft default tip

planemo upload for repository https://github.com/goeckslab/gleam.git commit bb4bcdc888d73bbfd85d78ce8999a1080fe813ff

15:d17e3a1b8659

        if isinstance(input_size, (list, tuple)) and len(input_size) == 3:
            expected_channels, expected_height, expected_width = input_size
        else:
            expected_channels, expected_height, expected_width = 3, 224, 224

        self.expected_channels = expected_channels
        self.expected_height = expected_height
        self.expected_width = expected_width

        logger.info(f"Initializing MetaFormerStackedCNN with model: {custom_model}")

        ctor = _resolve_metaformer_ctor(self.custom_model)
        if ctor is None:
            raise ValueError(f"Unknown MetaFormer model: {self.custom_model}")

        cfg = META_DEFAULT_CFGS.get(self.custom_model, {})
        weights_url = cfg.get('url')
        # track loading
        self._pretrained_loaded = False
        self._loaded_weights_url: Optional[str] = None
        if self.use_pretrained and weights_url:
            print(f"LOADING MetaFormer pretrained weights from: {weights_url}")
            logger.info(f"Loading pretrained weights from: {weights_url}")
        # Ensure we log whenever the factories call torch.hub.load_state_dict_from_url
        orig_loader = getattr(torch.hub, 'load_state_dict_from_url', None)

        def _wrapped_loader(url, *args, **kwargs):
            print(f"DOWNLOADING weights from: {url}")

        def patched_stacked_cnn_init(self, *args, **kwargs):
            custom_model = kwargs.pop("custom_model", None)
            if custom_model is None:
                custom_model = getattr(patch_ludwig_direct, '_metaformer_model', None)

            try:
                if META_MODELS_AVAILABLE and _is_supported_metaformer(custom_model):
                    print(f"DETECTED MetaFormer model: {custom_model}")
                    print("MetaFormer encoder is being loaded and used.")

16:8729f69e9207

        if isinstance(input_size, (list, tuple)) and len(input_size) == 3:
            expected_channels, expected_height, expected_width = input_size
        else:
            expected_channels, expected_height, expected_width = 3, 224, 224

        # Use legacy behavior: keep requested size for adapters but align backbone to 224 for stability
        if expected_height != 224 or expected_width != 224:
            logger.info(
                "Overriding expected backbone size to 224x224 for compatibility (was %sx%s)",
                expected_height,
                expected_width,
            )
            expected_height = expected_width = 224

        self.expected_channels = expected_channels
        self.expected_height = expected_height
        self.expected_width = expected_width

        logger.info(f"Initializing MetaFormerStackedCNN with model: {custom_model}")

        ctor = _resolve_metaformer_ctor(self.custom_model)
        if ctor is None:
            raise ValueError(f"Unknown MetaFormer model: {self.custom_model}")

        logger.info("MetaFormer backbone requested: %s, use_pretrained=%s", self.custom_model, self.use_pretrained)
        cfg = META_DEFAULT_CFGS.get(self.custom_model, {})
        logger.info("MetaFormer cfg present=%s", bool(cfg))
        if not cfg:
            logger.warning("MetaFormer config missing for %s; will fall back to random initialization", self.custom_model)
        weights_url = cfg.get('url') if isinstance(cfg, dict) else None
        logger.info("MetaFormer weights_url=%s", weights_url)
        # track loading
        self._pretrained_loaded = False
        self._loaded_weights_url: Optional[str] = None
        if self.use_pretrained and weights_url:
            print(f"LOADING MetaFormer pretrained weights from: {weights_url}")
            logger.info(f"Loading pretrained weights from: {weights_url}")
        elif self.use_pretrained and not weights_url:
            logger.warning("MetaFormer: no pretrained URL found for %s; continuing with random weights", self.custom_model)
            self.use_pretrained = False
        # Ensure we log whenever the factories call torch.hub.load_state_dict_from_url
        orig_loader = getattr(torch.hub, 'load_state_dict_from_url', None)

        def _wrapped_loader(url, *args, **kwargs):
            print(f"DOWNLOADING weights from: {url}")

        def patched_stacked_cnn_init(self, *args, **kwargs):
            custom_model = kwargs.pop("custom_model", None)
            if custom_model is None:
                custom_model = getattr(patch_ludwig_direct, '_metaformer_model', None)
            if custom_model is None:
                # Fallback for multi-process contexts
                custom_model = os.environ.get("GLEAM_META_FORMER_MODEL")
                if custom_model:
                    logger.info("Recovered MetaFormer model from env: %s", custom_model)

            logger.info(
                "patched Stacked2DCNN init called; custom_model=%s kwargs_keys=%s",
                custom_model,
                list(kwargs.keys()),
            )

            try:
                if META_MODELS_AVAILABLE and _is_supported_metaformer(custom_model):
                    print(f"DETECTED MetaFormer model: {custom_model}")
                    print("MetaFormer encoder is being loaded and used.")