Mercurial > repos > goeckslab > image_learner

comparison constants.py @ 2:186424a7eca7 draft

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planemo upload for repository https://github.com/goeckslab/gleam.git commit 91fa4aba245520fc0680088a07cead66bcfd4ed2
author goeckslab
date Thu, 03 Jul 2025 20:43:24 +0000
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children 2c3a3dfaf1a9
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1:39202fe5cf97 2:186424a7eca7
1 from typing import Any, Dict
2
3 # --- Constants ---
4 SPLIT_COLUMN_NAME = "split"
5 LABEL_COLUMN_NAME = "label"
6 IMAGE_PATH_COLUMN_NAME = "image_path"
7 DEFAULT_SPLIT_PROBABILITIES = [0.7, 0.1, 0.2]
8 TEMP_CSV_FILENAME = "processed_data_for_ludwig.csv"
9 TEMP_CONFIG_FILENAME = "ludwig_config.yaml"
10 TEMP_DIR_PREFIX = "ludwig_api_work_"
11 MODEL_ENCODER_TEMPLATES: Dict[str, Any] = {
12 "stacked_cnn": "stacked_cnn",
13 "resnet18": {"type": "resnet", "model_variant": 18},
14 "resnet34": {"type": "resnet", "model_variant": 34},
15 "resnet50": {"type": "resnet", "model_variant": 50},
16 "resnet101": {"type": "resnet", "model_variant": 101},
17 "resnet152": {"type": "resnet", "model_variant": 152},
18 "resnext50_32x4d": {"type": "resnext", "model_variant": "50_32x4d"},
19 "resnext101_32x8d": {"type": "resnext", "model_variant": "101_32x8d"},
20 "resnext101_64x4d": {"type": "resnext", "model_variant": "101_64x4d"},
21 "resnext152_32x8d": {"type": "resnext", "model_variant": "152_32x8d"},
22 "wide_resnet50_2": {"type": "wide_resnet", "model_variant": "50_2"},
23 "wide_resnet101_2": {"type": "wide_resnet", "model_variant": "101_2"},
24 "wide_resnet103_2": {"type": "wide_resnet", "model_variant": "103_2"},
25 "efficientnet_b0": {"type": "efficientnet", "model_variant": "b0"},
26 "efficientnet_b1": {"type": "efficientnet", "model_variant": "b1"},
27 "efficientnet_b2": {"type": "efficientnet", "model_variant": "b2"},
28 "efficientnet_b3": {"type": "efficientnet", "model_variant": "b3"},
29 "efficientnet_b4": {"type": "efficientnet", "model_variant": "b4"},
30 "efficientnet_b5": {"type": "efficientnet", "model_variant": "b5"},
31 "efficientnet_b6": {"type": "efficientnet", "model_variant": "b6"},
32 "efficientnet_b7": {"type": "efficientnet", "model_variant": "b7"},
33 "efficientnet_v2_s": {"type": "efficientnet", "model_variant": "v2_s"},
34 "efficientnet_v2_m": {"type": "efficientnet", "model_variant": "v2_m"},
35 "efficientnet_v2_l": {"type": "efficientnet", "model_variant": "v2_l"},
36 "regnet_y_400mf": {"type": "regnet", "model_variant": "y_400mf"},
37 "regnet_y_800mf": {"type": "regnet", "model_variant": "y_800mf"},
38 "regnet_y_1_6gf": {"type": "regnet", "model_variant": "y_1_6gf"},
39 "regnet_y_3_2gf": {"type": "regnet", "model_variant": "y_3_2gf"},
40 "regnet_y_8gf": {"type": "regnet", "model_variant": "y_8gf"},
41 "regnet_y_16gf": {"type": "regnet", "model_variant": "y_16gf"},
42 "regnet_y_32gf": {"type": "regnet", "model_variant": "y_32gf"},
43 "regnet_y_128gf": {"type": "regnet", "model_variant": "y_128gf"},
44 "regnet_x_400mf": {"type": "regnet", "model_variant": "x_400mf"},
45 "regnet_x_800mf": {"type": "regnet", "model_variant": "x_800mf"},
46 "regnet_x_1_6gf": {"type": "regnet", "model_variant": "x_1_6gf"},
47 "regnet_x_3_2gf": {"type": "regnet", "model_variant": "x_3_2gf"},
48 "regnet_x_8gf": {"type": "regnet", "model_variant": "x_8gf"},
49 "regnet_x_16gf": {"type": "regnet", "model_variant": "x_16gf"},
50 "regnet_x_32gf": {"type": "regnet", "model_variant": "x_32gf"},
51 "vgg11": {"type": "vgg", "model_variant": 11},
52 "vgg11_bn": {"type": "vgg", "model_variant": "11_bn"},
53 "vgg13": {"type": "vgg", "model_variant": 13},
54 "vgg13_bn": {"type": "vgg", "model_variant": "13_bn"},
55 "vgg16": {"type": "vgg", "model_variant": 16},
56 "vgg16_bn": {"type": "vgg", "model_variant": "16_bn"},
57 "vgg19": {"type": "vgg", "model_variant": 19},
58 "vgg19_bn": {"type": "vgg", "model_variant": "19_bn"},
59 "shufflenet_v2_x0_5": {"type": "shufflenet_v2", "model_variant": "x0_5"},
60 "shufflenet_v2_x1_0": {"type": "shufflenet_v2", "model_variant": "x1_0"},
61 "shufflenet_v2_x1_5": {"type": "shufflenet_v2", "model_variant": "x1_5"},
62 "shufflenet_v2_x2_0": {"type": "shufflenet_v2", "model_variant": "x2_0"},
63 "squeezenet1_0": {"type": "squeezenet", "model_variant": "1_0"},
64 "squeezenet1_1": {"type": "squeezenet", "model_variant": "1_1"},
65 "swin_t": {"type": "swin_transformer", "model_variant": "t"},
66 "swin_s": {"type": "swin_transformer", "model_variant": "s"},
67 "swin_b": {"type": "swin_transformer", "model_variant": "b"},
68 "swin_v2_t": {"type": "swin_transformer", "model_variant": "v2_t"},
69 "swin_v2_s": {"type": "swin_transformer", "model_variant": "v2_s"},
70 "swin_v2_b": {"type": "swin_transformer", "model_variant": "v2_b"},
71 "vit_b_16": {"type": "vision_transformer", "model_variant": "b_16"},
72 "vit_b_32": {"type": "vision_transformer", "model_variant": "b_32"},
73 "vit_l_16": {"type": "vision_transformer", "model_variant": "l_16"},
74 "vit_l_32": {"type": "vision_transformer", "model_variant": "l_32"},
75 "vit_h_14": {"type": "vision_transformer", "model_variant": "h_14"},
76 "convnext_tiny": {"type": "convnext", "model_variant": "tiny"},
77 "convnext_small": {"type": "convnext", "model_variant": "small"},
78 "convnext_base": {"type": "convnext", "model_variant": "base"},
79 "convnext_large": {"type": "convnext", "model_variant": "large"},
80 "maxvit_t": {"type": "maxvit", "model_variant": "t"},
81 "alexnet": {"type": "alexnet"},
82 "googlenet": {"type": "googlenet"},
83 "inception_v3": {"type": "inception_v3"},
84 "mobilenet_v2": {"type": "mobilenet_v2"},
85 "mobilenet_v3_large": {"type": "mobilenet_v3_large"},
86 "mobilenet_v3_small": {"type": "mobilenet_v3_small"},
87 }
88 METRIC_DISPLAY_NAMES = {
89 "accuracy": "Accuracy",
90 "accuracy_micro": "Accuracy-Micro",
91 "loss": "Loss",
92 "roc_auc": "ROC-AUC",
93 "roc_auc_macro": "ROC-AUC-Macro",
94 "roc_auc_micro": "ROC-AUC-Micro",
95 "hits_at_k": "Hits at K",
96 "precision": "Precision",
97 "recall": "Recall",
98 "specificity": "Specificity",
99 "kappa_score": "Cohen's Kappa",
100 "token_accuracy": "Token Accuracy",
101 "avg_precision_macro": "Precision-Macro",
102 "avg_recall_macro": "Recall-Macro",
103 "avg_f1_score_macro": "F1-score-Macro",
104 "avg_precision_micro": "Precision-Micro",
105 "avg_recall_micro": "Recall-Micro",
106 "avg_f1_score_micro": "F1-score-Micro",
107 "avg_precision_weighted": "Precision-Weighted",
108 "avg_recall_weighted": "Recall-Weighted",
109 "avg_f1_score_weighted": "F1-score-Weighted",
110 "average_precision_macro": "Precision-Average-Macro",
111 "average_precision_micro": "Precision-Average-Micro",
112 "average_precision_samples": "Precision-Average-Samples",
113 "mean_squared_error": "Mean Squared Error",
114 "mean_absolute_error": "Mean Absolute Error",
115 "r2": "R² Score",
116 "root_mean_squared_error": "Root Mean Squared Error",
117 "mean_absolute_percentage_error": "Mean Absolute % Error",
118 "root_mean_squared_percentage_error": "Root Mean Squared % Error",
119 }