comparison ludwig_backend.py @ 16:8729f69e9207 draft default tip

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

15:d17e3a1b8659

import json
import logging
from pathlib import Path
from typing import Any, Dict, Optional, Protocol, Tuple

import pandas as pd
import pandas.api.types as ptypes

                custom_model = raw_encoder["custom_model"]
            else:
                custom_model = model_name

            logger.info(f"DETECTED MetaFormer model: {custom_model}")
            cfg_channels, cfg_height, cfg_width = 3, 224, 224
            if META_DEFAULT_CFGS:
                model_cfg = META_DEFAULT_CFGS.get(custom_model, {})
                input_size = model_cfg.get("input_size")
                if isinstance(input_size, (list, tuple)) and len(input_size) == 3:
                    cfg_channels, cfg_height, cfg_width = (
                        int(input_size[0]),
                        int(input_size[1]),
                        int(input_size[2]),
                    )

            target_height, target_width = cfg_height, cfg_width
            resize_value = config_params.get("image_resize")
            if resize_value and resize_value != "original":
                try:
                    dimensions = resize_value.split("x")
                    if len(dimensions) == 2:

                    )
                    target_height, target_width = cfg_height, cfg_width
            else:
                image_zip_path = config_params.get("image_zip", "")
                detected_height, detected_width = self._detect_image_dimensions(image_zip_path)
                if use_pretrained:
                    if (detected_height, detected_width) != (cfg_height, cfg_width):
                        logger.info(
                            "MetaFormer pretrained weights expect %sx%s; resizing from detected %sx%s",
                            cfg_height,
                            cfg_width,
                            detected_height,
                            detected_width,
                        )
                else:
                    target_height, target_width = detected_height, detected_width
                if target_height <= 0 or target_width <= 0:
                    raise ValueError(
                        f"Invalid detected image dimensions for MetaFormer: {target_height}x{target_width}."
                    )

16:8729f69e9207

import json
import logging
import os
from pathlib import Path
from typing import Any, Dict, Optional, Protocol, Tuple

import pandas as pd
import pandas.api.types as ptypes

                custom_model = raw_encoder["custom_model"]
            else:
                custom_model = model_name

            logger.info(f"DETECTED MetaFormer model: {custom_model}")
            # Stash the model name for patched Stacked2DCNN in case Ludwig drops custom_model from kwargs
            try:
                from MetaFormer.metaformer_stacked_cnn import set_current_metaformer_model

                set_current_metaformer_model(custom_model)
            except Exception:
                logger.debug("Could not set current MetaFormer model hint; proceeding without global override")
            # Also pass via environment to survive process boundaries (e.g., Ray workers)
            os.environ["GLEAM_META_FORMER_MODEL"] = custom_model
            cfg_channels, cfg_height, cfg_width = 3, 224, 224
            model_cfg = {}
            if META_DEFAULT_CFGS:
                model_cfg = META_DEFAULT_CFGS.get(custom_model, {})
                input_size = model_cfg.get("input_size")
                if isinstance(input_size, (list, tuple)) and len(input_size) == 3:
                    cfg_channels, cfg_height, cfg_width = (
                        int(input_size[0]),
                        int(input_size[1]),
                        int(input_size[2]),
                    )

            weights_url = None
            if isinstance(model_cfg, dict):
                weights_url = model_cfg.get("url")
            logger.info(
                "MetaFormer cfg lookup: model=%s has_cfg=%s url=%s use_pretrained=%s",
                custom_model,
                bool(model_cfg),
                weights_url,
                use_pretrained,
            )
            if use_pretrained and not weights_url:
                logger.warning(
                    "MetaFormer pretrained requested for %s but no URL found in default cfgs; model will be randomly initialized",
                    custom_model,
                )

            resize_value = config_params.get("image_resize")
            if resize_value and resize_value != "original":
                try:
                    dimensions = resize_value.split("x")
                    if len(dimensions) == 2:

                    )
                    target_height, target_width = cfg_height, cfg_width
            else:
                image_zip_path = config_params.get("image_zip", "")
                detected_height, detected_width = self._detect_image_dimensions(image_zip_path)
                target_height, target_width = detected_height, detected_width
                if use_pretrained and (detected_height, detected_width) != (cfg_height, cfg_width):
                    logger.info(
                        "MetaFormer pretrained weights expect %sx%s; proceeding with detected %sx%s",
                        cfg_height,
                        cfg_width,
                        detected_height,
                        detected_width,
                    )
                if target_height <= 0 or target_width <= 0:
                    raise ValueError(
                        f"Invalid detected image dimensions for MetaFormer: {target_height}x{target_width}."
                    )