Mercurial > repos > goeckslab > image_learner
annotate MetaFormer/metaformer_stacked_cnn.py @ 11:c5150cceab47 draft default tip
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
| author | goeckslab | 
|---|---|
| date | Sat, 18 Oct 2025 03:17:09 +0000 | 
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| rev | line source | 
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11
 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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1 import logging | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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2 import os | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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3 import sys | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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4 from typing import Dict, List, Optional | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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5 | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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 | 
6 import torch | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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7 import torch.nn as nn | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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8 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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9 sys.path.insert(0, os.path.dirname(__file__)) | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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 | 
10 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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11 logging.basicConfig( | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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12 level=logging.INFO, | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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13 format="%(asctime)s %(levelname)s %(name)s: %(message)s", | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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14 ) | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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15 logger = logging.getLogger(__name__) | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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 | 
16 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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17 SUPPORTED_PREFIXES = ( | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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18 'identityformer_', | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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19 'randformer_', | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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 | 
20 'poolformerv2_', | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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 | 
21 'convformer_', | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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22 'caformer_', | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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23 ) | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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24 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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 | 
25 try: | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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26 from metaformer_models import default_cfgs as META_DEFAULT_CFGS | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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27 META_MODELS_AVAILABLE = True | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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28 logger.info("MetaFormer models imported successfully") | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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29 except Exception as e: | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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30 META_MODELS_AVAILABLE = False | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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31 logger.warning(f"MetaFormer models not available: {e}") | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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32 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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33 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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34 def _resolve_metaformer_ctor(model_name: str): | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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35 # Prefer getattr to avoid importing every factory explicitly | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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36 try: | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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37 # Import the module itself for dynamic access | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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38 import metaformer_models | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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39 _factories = metaformer_models.__dict__ | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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40 if model_name in _factories and callable(_factories[model_name]): | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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41 return _factories[model_name] | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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42 except Exception: | 
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43 pass | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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44 return None | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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45 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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46 | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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47 class MetaFormerStackedCNN(nn.Module): | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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48 def __init__( | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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49 self, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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50 height: int = 224, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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51 width: int = 224, | 
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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52 num_channels: int = 3, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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53 output_size: int = 128, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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54 custom_model: str = "identityformer_s12", | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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55 use_pretrained: bool = True, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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56 trainable: bool = True, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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57 conv_layers: Optional[List[Dict]] = None, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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58 num_conv_layers: Optional[int] = None, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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59 conv_activation: str = "relu", | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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60 conv_dropout: float = 0.0, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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61 conv_norm: Optional[str] = None, | 
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62 conv_use_bias: bool = True, | 
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63 fc_layers: Optional[List[Dict]] = None, | 
| 
 
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64 num_fc_layers: int = 1, | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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65 fc_activation: str = "relu", | 
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66 fc_dropout: float = 0.0, | 
| 
 
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67 fc_norm: Optional[str] = None, | 
| 
 
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68 fc_use_bias: bool = True, | 
| 
 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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69 **kwargs, | 
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70 ): | 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
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71 super().__init__() | 
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72 logger.info("MetaFormerStackedCNN encoder instantiated") | 
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73 logger.info(f"Using MetaFormer model: {custom_model}") | 
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74 | 
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75 try: | 
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76 height = int(height) | 
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77 width = int(width) | 
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78 num_channels = int(num_channels) | 
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79 except (TypeError, ValueError) as exc: | 
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80 raise ValueError("MetaFormerStackedCNN requires integer height, width, and num_channels.") from exc | 
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81 | 
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82 if height <= 0 or width <= 0: | 
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83 raise ValueError(f"MetaFormerStackedCNN received non-positive dimensions: {height}x{width}.") | 
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84 if num_channels <= 0: | 
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85 raise ValueError(f"MetaFormerStackedCNN requires num_channels > 0, received {num_channels}.") | 
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86 | 
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87 self.height = height | 
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88 self.width = width | 
| 
 
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
 
goeckslab 
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89 self.num_channels = num_channels | 
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90 self.output_size = output_size | 
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91 self.custom_model = custom_model | 
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92 self.use_pretrained = use_pretrained | 
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93 self.trainable = trainable | 
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94 | 
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95 cfg = META_DEFAULT_CFGS.get(custom_model, {}) | 
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96 input_size = cfg.get('input_size', (3, 224, 224)) | 
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97 if isinstance(input_size, (list, tuple)) and len(input_size) == 3: | 
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98 expected_channels, expected_height, expected_width = input_size | 
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99 else: | 
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100 expected_channels, expected_height, expected_width = 3, 224, 224 | 
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101 | 
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102 self.expected_channels = expected_channels | 
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103 self.expected_height = expected_height | 
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104 self.expected_width = expected_width | 
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105 | 
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106 logger.info(f"Initializing MetaFormerStackedCNN with model: {custom_model}") | 
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107 logger.info( | 
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108 "Input: %sx%sx%s -> Output: %s (expected backbone size: %sx%s)", | 
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109 num_channels, | 
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110 height, | 
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111 width, | 
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112 output_size, | 
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113 self.expected_height, | 
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114 self.expected_width, | 
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115 ) | 
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116 | 
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117 self.channel_adapter: Optional[nn.Conv2d] = None | 
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118 if num_channels != self.expected_channels: | 
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119 self.channel_adapter = nn.Conv2d( | 
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120 num_channels, self.expected_channels, kernel_size=1, stride=1, padding=0 | 
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121 ) | 
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122 logger.info( | 
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123 "Added channel adapter: %s -> %s channels", | 
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124 num_channels, | 
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125 self.expected_channels, | 
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126 ) | 
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127 | 
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128 self.size_adapter: Optional[nn.Module] = None | 
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129 if height != self.expected_height or width != self.expected_width: | 
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130 self.size_adapter = nn.AdaptiveAvgPool2d((height, width)) | 
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131 logger.info( | 
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132 "Configured size adapter to requested input: %sx%s", | 
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133 height, | 
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134 width, | 
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135 ) | 
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136 self.backbone_adapter: Optional[nn.Module] = None | 
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137 | 
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138 self.backbone = self._load_metaformer_backbone() | 
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139 self.feature_dim = self._get_feature_dim() | 
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140 | 
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141 self.fc_layers = self._create_fc_layers( | 
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142 input_dim=self.feature_dim, | 
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143 output_dim=output_size, | 
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144 num_layers=num_fc_layers, | 
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145 activation=fc_activation, | 
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146 dropout=fc_dropout, | 
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147 norm=fc_norm, | 
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148 use_bias=fc_use_bias, | 
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149 fc_layers_config=fc_layers, | 
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150 ) | 
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151 | 
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152 if not trainable: | 
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153 for param in self.backbone.parameters(): | 
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154 param.requires_grad = False | 
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155 logger.info("MetaFormer backbone frozen (trainable=False)") | 
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156 | 
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157 logger.info("MetaFormerStackedCNN initialized successfully") | 
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158 | 
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159 def _load_metaformer_backbone(self): | 
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160 if not META_MODELS_AVAILABLE: | 
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161 raise ImportError("MetaFormer models are not available") | 
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162 | 
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163 ctor = _resolve_metaformer_ctor(self.custom_model) | 
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164 if ctor is None: | 
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165 raise ValueError(f"Unknown MetaFormer model: {self.custom_model}") | 
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166 | 
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167 cfg = META_DEFAULT_CFGS.get(self.custom_model, {}) | 
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168 weights_url = cfg.get('url') | 
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169 # track loading | 
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170 self._pretrained_loaded = False | 
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171 self._loaded_weights_url: Optional[str] = None | 
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172 if self.use_pretrained and weights_url: | 
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173 print(f"LOADING MetaFormer pretrained weights from: {weights_url}") | 
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174 logger.info(f"Loading pretrained weights from: {weights_url}") | 
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175 # Ensure we log whenever the factories call torch.hub.load_state_dict_from_url | 
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176 orig_loader = getattr(torch.hub, 'load_state_dict_from_url', None) | 
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177 | 
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178 def _wrapped_loader(url, *args, **kwargs): | 
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179 print(f"DOWNLOADING weights from: {url}") | 
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180 logger.info(f"DOWNLOADING weights from: {url}") | 
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181 self._pretrained_loaded = True | 
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182 self._loaded_weights_url = url | 
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183 result = orig_loader(url, *args, **kwargs) | 
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184 print(f"WEIGHTS DOWNLOADED successfully from: {url}") | 
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185 return result | 
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186 try: | 
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187 if self.use_pretrained and orig_loader is not None: | 
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188 torch.hub.load_state_dict_from_url = _wrapped_loader # type: ignore[attr-defined] | 
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189 print(f"CREATING MetaFormer model: {self.custom_model} (pretrained={self.use_pretrained})") | 
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190 try: | 
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191 model = ctor(pretrained=self.use_pretrained, num_classes=1000) | 
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192 print(f"MetaFormer model CREATED: {self.custom_model}") | 
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193 except Exception as model_error: | 
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194 if self.use_pretrained: | 
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195 print(f"âš Warning: Failed to load {self.custom_model} with pretrained weights: {model_error}") | 
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196 print("Attempting to load without pretrained weights as fallback...") | 
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197 logger.warning(f"Failed to load {self.custom_model} with pretrained weights: {model_error}") | 
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198 model = ctor(pretrained=False, num_classes=1000) | 
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199 print(f"✓ Successfully loaded {self.custom_model} without pretrained weights") | 
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200 self.use_pretrained = False # Update state to reflect actual loading | 
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201 else: | 
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202 raise model_error | 
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203 finally: | 
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204 if orig_loader is not None: | 
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205 torch.hub.load_state_dict_from_url = orig_loader # type: ignore[attr-defined] | 
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206 self._metaformer_weights_url = weights_url | 
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207 if self.use_pretrained: | 
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208 if self._pretrained_loaded: | 
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209 print(f"MetaFormer: pretrained weights loaded from {self._loaded_weights_url}") | 
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210 logger.info(f"MetaFormer: pretrained weights loaded from {self._loaded_weights_url}") | 
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211 else: | 
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212 # Warn but don't fail - weights may have failed to load but model creation succeeded | 
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213 print("âš Warning: MetaFormer pretrained weights were requested but not confirmed as loaded") | 
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214 logger.warning("MetaFormer: pretrained weights were requested but not confirmed as loaded") | 
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215 else: | 
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216 print(f"MetaFormer: using randomly initialized weights for {self.custom_model}") | 
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217 logger.info(f"MetaFormer: using randomly initialized weights for {self.custom_model}") | 
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218 logger.info(f"Loaded MetaFormer backbone: {self.custom_model} (pretrained={self.use_pretrained})") | 
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219 return model | 
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220 | 
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221 def _get_feature_dim(self): | 
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222 with torch.no_grad(): | 
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223 dummy_input = torch.randn(1, 3, 224, 224) | 
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224 features = self.backbone.forward_features(dummy_input) | 
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225 feature_dim = features.shape[-1] | 
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226 logger.info(f"MetaFormer feature dimension: {feature_dim}") | 
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227 return feature_dim | 
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228 | 
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229 def _create_fc_layers(self, input_dim, output_dim, num_layers, activation, dropout, norm, use_bias, fc_layers_config): | 
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230 layers = [] | 
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231 if fc_layers_config: | 
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232 current_dim = input_dim | 
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233 for i, layer_config in enumerate(fc_layers_config): | 
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234 layer_output_dim = layer_config.get('output_size', output_dim if i == len(fc_layers_config) - 1 else current_dim) | 
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235 layers.append(nn.Linear(current_dim, layer_output_dim, bias=use_bias)) | 
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236 if i < len(fc_layers_config) - 1: | 
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237 if activation == "relu": | 
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238 layers.append(nn.ReLU()) | 
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239 elif activation == "tanh": | 
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240 layers.append(nn.Tanh()) | 
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241 elif activation == "sigmoid": | 
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242 layers.append(nn.Sigmoid()) | 
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243 elif activation == "leaky_relu": | 
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244 layers.append(nn.LeakyReLU()) | 
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245 if dropout > 0: | 
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246 layers.append(nn.Dropout(dropout)) | 
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247 if norm == "batch": | 
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248 layers.append(nn.BatchNorm1d(layer_output_dim)) | 
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249 elif norm == "layer": | 
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250 layers.append(nn.LayerNorm(layer_output_dim)) | 
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251 current_dim = layer_output_dim | 
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252 else: | 
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253 if num_layers == 1: | 
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254 layers.append(nn.Linear(input_dim, output_dim, bias=use_bias)) | 
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255 else: | 
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256 intermediate_dims = [input_dim] | 
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257 for i in range(num_layers - 1): | 
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258 intermediate_dim = int(input_dim * (0.5 ** (i + 1))) | 
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259 intermediate_dim = max(intermediate_dim, output_dim) | 
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260 intermediate_dims.append(intermediate_dim) | 
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261 intermediate_dims.append(output_dim) | 
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262 for i in range(num_layers): | 
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263 layers.append(nn.Linear(intermediate_dims[i], intermediate_dims[i + 1], bias=use_bias)) | 
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264 if i < num_layers - 1: | 
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265 if activation == "relu": | 
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266 layers.append(nn.ReLU()) | 
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267 elif activation == "tanh": | 
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268 layers.append(nn.Tanh()) | 
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269 elif activation == "sigmoid": | 
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270 layers.append(nn.Sigmoid()) | 
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271 elif activation == "leaky_relu": | 
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272 layers.append(nn.LeakyReLU()) | 
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273 if dropout > 0: | 
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274 layers.append(nn.Dropout(dropout)) | 
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275 if norm == "batch": | 
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276 layers.append(nn.BatchNorm1d(intermediate_dims[i + 1])) | 
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277 elif norm == "layer": | 
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278 layers.append(nn.LayerNorm(intermediate_dims[i + 1])) | 
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279 return nn.Sequential(*layers) | 
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280 | 
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281 def forward(self, x): | 
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282 if x.shape[1] != self.expected_channels: | 
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283 if ( | 
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284 self.channel_adapter is None | 
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285 or self.channel_adapter.in_channels != x.shape[1] | 
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286 or self.channel_adapter.out_channels != self.expected_channels | 
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287 ): | 
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288 self.channel_adapter = nn.Conv2d( | 
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289 x.shape[1], | 
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290 self.expected_channels, | 
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291 kernel_size=1, | 
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292 stride=1, | 
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293 padding=0, | 
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294 ).to(x.device) | 
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295 logger.info( | 
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296 "Created dynamic channel adapter: %s -> %s channels", | 
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297 x.shape[1], | 
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298 self.expected_channels, | 
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299 ) | 
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300 x = self.channel_adapter(x) | 
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301 | 
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302 target_height, target_width = self.height, self.width | 
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303 if x.shape[2] != target_height or x.shape[3] != target_width: | 
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304 if ( | 
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305 self.size_adapter is None | 
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306 or getattr(self.size_adapter, "output_size", None) | 
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307 != (target_height, target_width) | 
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308 ): | 
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309 self.size_adapter = nn.AdaptiveAvgPool2d( | 
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310 (target_height, target_width) | 
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311 ).to(x.device) | 
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312 logger.info( | 
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313 "Created size adapter: %sx%s -> %sx%s", | 
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314 x.shape[2], | 
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315 x.shape[3], | 
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316 target_height, | 
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317 target_width, | 
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318 ) | 
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319 x = self.size_adapter(x) | 
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320 | 
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321 if target_height != self.expected_height or target_width != self.expected_width: | 
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322 if ( | 
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323 self.backbone_adapter is None | 
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324 or getattr(self.backbone_adapter, "output_size", None) | 
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325 != (self.expected_height, self.expected_width) | 
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326 ): | 
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327 self.backbone_adapter = nn.AdaptiveAvgPool2d( | 
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328 (self.expected_height, self.expected_width) | 
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329 ).to(x.device) | 
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330 logger.info( | 
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331 "Aligning to MetaFormer backbone size: %sx%s", | 
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332 self.expected_height, | 
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333 self.expected_width, | 
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334 ) | 
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335 x = self.backbone_adapter(x) | 
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336 | 
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337 features = self.backbone.forward_features(x) | 
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338 output = self.fc_layers(features) | 
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339 return {'encoder_output': output} | 
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340 | 
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341 @property | 
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342 def output_shape(self): | 
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343 return [self.output_size] | 
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344 | 
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345 | 
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346 def create_metaformer_stacked_cnn(model_name: str, **kwargs) -> MetaFormerStackedCNN: | 
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347 encoder = MetaFormerStackedCNN(custom_model=model_name, **kwargs) | 
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348 return encoder | 
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349 | 
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350 | 
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351 def patch_ludwig_stacked_cnn(): | 
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352 # Only patch Ludwig if MetaFormer models are available in this runtime | 
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353 if not META_MODELS_AVAILABLE: | 
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354 logger.warning("MetaFormer models unavailable; skipping Ludwig patch for stacked_cnn.") | 
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355 return False | 
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356 return patch_ludwig_direct() | 
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357 | 
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358 | 
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359 def _is_supported_metaformer(custom_model: Optional[str]) -> bool: | 
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360 return bool(custom_model) and custom_model.startswith(SUPPORTED_PREFIXES) | 
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361 | 
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362 | 
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363 def patch_ludwig_direct(): | 
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364 try: | 
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365 from ludwig.encoders.image.base import Stacked2DCNN | 
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366 original_stacked_cnn_init = Stacked2DCNN.__init__ | 
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367 | 
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368 def patched_stacked_cnn_init(self, *args, **kwargs): | 
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369 custom_model = kwargs.pop("custom_model", None) | 
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370 if custom_model is None: | 
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371 custom_model = getattr(patch_ludwig_direct, '_metaformer_model', None) | 
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372 | 
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373 try: | 
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374 if META_MODELS_AVAILABLE and _is_supported_metaformer(custom_model): | 
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375 print(f"DETECTED MetaFormer model: {custom_model}") | 
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376 print("MetaFormer encoder is being loaded and used.") | 
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377 # Initialize base class to keep Ludwig internals intact | 
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378 original_stacked_cnn_init(self, *args, **kwargs) | 
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379 # Create our MetaFormer encoder and graft behavior | 
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380 mf_encoder = create_metaformer_stacked_cnn(custom_model, **kwargs) | 
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381 # ensure base attributes won't be used accidentally | 
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382 for attr in ("conv_layers", "fc_layers", "combiner", "output_shape", "reduce_output"): | 
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383 if hasattr(self, attr): | 
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384 try: | 
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385 setattr(self, attr, getattr(mf_encoder, attr, None)) | 
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386 except Exception: | 
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387 pass | 
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388 self.forward = mf_encoder.forward | 
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389 if hasattr(mf_encoder, 'backbone'): | 
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390 self.backbone = mf_encoder.backbone | 
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391 if hasattr(mf_encoder, 'fc_layers'): | 
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392 self.fc_layers = mf_encoder.fc_layers | 
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393 if hasattr(mf_encoder, 'custom_model'): | 
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394 self.custom_model = mf_encoder.custom_model | 
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395 # explicit confirmation logs | 
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396 try: | 
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397 url_info = getattr(mf_encoder, '_loaded_weights_url', None) | 
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398 loaded_flag = getattr(mf_encoder, '_pretrained_loaded', False) | 
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399 if loaded_flag and url_info: | 
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400 print(f"CONFIRMED: MetaFormer '{custom_model}' using pretrained weights from: {url_info}") | 
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401 logger.info(f"CONFIRMED: MetaFormer '{custom_model}' using pretrained weights from: {url_info}") | 
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402 else: | 
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403 print(f"CONFIRMED: MetaFormer '{custom_model}' using randomly initialized weights (no pretrained)") | 
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404 logger.info(f"CONFIRMED: MetaFormer '{custom_model}' using randomly initialized weights") | 
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405 except Exception: | 
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406 pass | 
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407 else: | 
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408 original_stacked_cnn_init(self, *args, **kwargs) | 
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409 finally: | 
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410 if hasattr(patch_ludwig_direct, '_metaformer_model'): | 
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411 patch_ludwig_direct._metaformer_model = None | 
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412 | 
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413 Stacked2DCNN.__init__ = patched_stacked_cnn_init | 
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414 return True | 
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415 except Exception as e: | 
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416 logger.error(f"Failed to apply MetaFormer direct patch: {e}") | 
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417 return False | 
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418 | 
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419 | 
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420 def set_current_metaformer_model(model_name: str): | 
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421 """Store the current MetaFormer model name for the patch to use.""" | 
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422 setattr(patch_ludwig_direct, '_metaformer_model', model_name) | 
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423 | 
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424 | 
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425 def clear_current_metaformer_model(): | 
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426 """Remove any cached MetaFormer model hint.""" | 
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427 if hasattr(patch_ludwig_direct, '_metaformer_model'): | 
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428 delattr(patch_ludwig_direct, '_metaformer_model') | 
