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utils_2.py
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utils_2.py
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import numpy as np
from sklearn.metrics import confusion_matrix
import random
import torch
import torch.nn.functional as F
import torch.nn as nn
import itertools
from torchvision.utils import make_grid
from torch.autograd import Variable
from PIL import Image
from skimage import io
import os
# import imagecodes
def seed_torch(seed=42):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed) # 为了禁止hash随机化，使得实验可复现
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
seed_torch()
# Parameters
## SwinFusion
# WINDOW_SIZE = (64, 64) # Patch size
WINDOW_SIZE = (256, 256) # Patch size
STRIDE = 32 # Stride for testing
IN_CHANNELS = 3 # Number of input channels (e.g. RGB)
FOLDER = r"/home/SSRS-main/SAM_RS/"
BATCH_SIZE = 10
# BATCH_SIZE = 16
LABELS = ["roads", "buildings", "low veg.", "trees", "cars", "clutter"] # Label names
N_CLASSES = len(LABELS) # Number of classes
WEIGHTS = torch.ones(N_CLASSES) # Weights for class balancing
CACHE = True # Store the dataset in-memory
# ISPRS color palette
# Let's define the standard ISPRS color palette
palette = {0 : (255, 255, 255), # Impervious surfaces (white)
1 : (0, 0, 255), # Buildings (blue)
2 : (0, 255, 255), # Low vegetation (cyan)
3 : (0, 255, 0), # Trees (green)
4 : (255, 255, 0), # Cars (yellow)
5 : (255, 0, 0), # Clutter (red)
6 : (0, 0, 0)} # Undefined (black)
invert_palette = {v: k for k, v in palette.items()}
MODEL = 'FTUNetformer'
MODE = 'Train'
# MODE = 'Test'
# DATASET = 'Vaihingen'
DATASET = 'Loveda'
# DATASET = 'Potsdam'
LOSS = 'SEG+BDY+OBJ'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if DATASET == 'Potsdam':
#Postdam数据集
#all
# train_ids = 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#0.5
# train_ids = 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#
# #0.25
train_ids = 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#
# #0.125
# train_ids = ['2_10_36','2_10_10','2_10_102','2_10_112','2_10_119','2_10_125','2_10_97','2_10_49','2_10_55','2_10_19','2_10_81','2_10_108','2_10_101','2_10_72','2_10_94','2_10_95','2_10_127','2_10_68','2_11_71','2_11_79','2_11_84','2_11_31','2_11_19','2_11_34','2_11_139','2_11_7','2_11_130','2_11_40','2_11_67','2_11_124','2_11_126','2_11_93','2_11_105','2_11_128','2_11_88','2_11_120','2_12_111','2_12_88','2_12_81','2_12_109','2_12_58','2_12_68','2_12_40','2_12_138','2_12_74','2_12_3','2_12_53','2_12_120','2_12_124','2_12_131','2_12_80','2_12_7','2_12_0','2_12_113','3_10_44','3_10_140','3_10_123','3_10_98','3_10_143','3_10_96','3_10_47','3_10_31','3_10_18','3_10_107','3_10_29','3_10_71','3_10_25','3_10_1','3_10_89','3_10_42','3_10_32','3_10_34','3_11_3','3_11_43','3_11_56','3_11_49','3_11_90','3_11_129','3_11_26','3_11_136','3_11_67','3_11_63','3_11_70','3_11_18','3_11_61','3_11_1','3_11_130','3_11_41','3_11_108','3_11_33','3_12_118','3_12_76','3_12_105','3_12_33','3_12_32','3_12_125','3_12_57','3_12_61','3_12_99','3_12_138','3_12_113','3_12_34','3_12_56','3_12_51','3_12_75','3_12_21','3_12_45','3_12_122','4_10_4','4_10_103','4_10_68','4_10_59','4_10_22','4_10_131','4_10_77','4_10_141','4_10_100','4_10_84','4_10_78','4_10_38','4_10_45','4_10_64','4_10_54','4_10_67','4_10_11','4_10_110','4_11_83','4_11_133','4_11_69','4_11_105','4_11_11','4_11_80','4_11_46','4_11_42','4_11_75','4_11_34','4_11_19','4_11_14','4_11_101','4_11_1','4_11_129','4_11_126','4_11_65','4_11_70','5_10_124','5_10_44','5_10_25','5_10_48','5_10_56','5_10_111','5_10_51','5_10_122','5_10_34','5_10_138','5_10_60','5_10_123','5_10_132','5_10_8','5_10_77','5_10_142','5_10_29','5_10_125','5_11_128','5_11_34','5_11_142','5_11_60','5_11_108','5_11_21','5_11_116','5_11_15','5_11_53','5_11_82','5_11_68','5_11_96','5_11_29','5_11_105','5_11_58','5_11_48','5_11_52','5_11_90','5_12_1','5_12_121','5_12_73','5_12_26','5_12_37','5_12_91','5_12_28','5_12_122','5_12_95','5_12_4','5_12_79','5_12_129','5_12_58','5_12_36','5_12_140','5_12_68','5_12_38','5_12_33','6_7_36','6_7_137','6_7_94','6_7_31','6_7_125','6_7_143','6_7_133','6_7_72','6_7_122','6_7_59','6_7_22','6_7_23','6_7_119','6_7_1','6_7_52','6_7_3','6_7_27','6_7_123','6_8_105','6_8_94','6_8_12','6_8_131','6_8_97','6_8_128','6_8_38','6_8_78','6_8_43','6_8_138','6_8_57','6_8_87','6_8_91','6_8_27','6_8_95','6_8_140','6_9_23','6_9_81','6_9_1','6_9_105','6_9_20','6_9_107','6_9_113','6_9_79','6_9_42','6_9_134','6_9_90','6_9_24','6_9_33','6_9_93','6_9_34','6_9_122','6_9_47','6_9_66','6_10_46','6_10_23','6_10_26','6_10_5','6_10_49','6_10_107','6_10_13','6_10_15','6_10_117','6_10_141','6_10_33','6_10_93','6_10_138','6_10_70','6_10_128','6_10_18','6_10_31','6_10_95','6_11_0','6_11_55','6_11_123','6_11_101','6_11_82','6_11_100','6_11_93','6_11_129','6_11_17','6_11_66','6_11_65','6_11_140','6_11_3','6_11_128','6_11_105','6_11_87','6_11_94','6_11_5','6_12_20','6_12_71','6_12_140','6_12_38','6_12_102','6_12_62','6_12_88','6_12_141','6_12_64','6_12_85','6_12_89','6_12_122','6_12_75','6_12_131','6_12_30','6_12_40','6_12_66','6_12_95','7_8_62','7_8_41','7_8_12','7_8_54','7_8_49','7_8_21','7_8_129','7_8_10','7_8_87','7_8_9','7_8_30','7_8_114','7_8_5','7_8_1','7_8_60','7_8_86','7_8_112','7_8_110','7_7_64','7_7_113','7_7_34','7_7_61','7_7_122','7_7_117','7_7_127','7_7_2','7_7_88','7_7_110','7_7_114','7_7_26','7_7_143','7_7_60','7_7_39','7_7_140','7_7_52','7_7_79','7_9_85','7_9_52','7_9_94','7_9_29','7_9_37','7_9_141','7_9_80','7_9_31','7_9_127','7_9_1','7_9_119','7_9_21','7_9_100','7_9_115','7_9_14','7_9_41','7_9_19','7_11_143','7_11_120','7_11_71','7_11_139','7_11_45','7_11_64','7_11_91','7_11_94','7_11_79','7_11_53','7_11_34','7_11_21','7_11_88','7_11_100','7_11_50','7_11_12','7_11_22','7_11_137','7_12_119','7_12_113','7_12_82','7_12_95','7_12_47','7_12_121','7_12_116','7_12_34','7_12_9','7_12_92','7_12_80','7_12_133','7_12_102','7_12_86','7_12_5','7_12_70','7_12_2','7_12_56']
#
# #0.0625
# train_ids = ['2_10_15','2_10_134','2_10_121','2_10_35','2_10_6','2_10_131','2_10_107','2_10_136','2_10_126','2_11_141','2_11_126','2_11_82','2_11_79','2_11_130','2_11_10','2_11_48','2_11_86','2_11_8','2_12_16','2_12_10','2_12_132','2_12_72','2_12_49','2_12_108','2_12_47','2_12_115','2_12_94','3_10_15','3_10_28','3_10_18','3_10_122','3_10_69','3_10_102','3_10_34','3_10_47','3_10_123','3_11_93','3_11_128','3_11_87','3_11_97','3_11_125','3_11_108','3_11_110','3_11_7','3_11_41','3_12_124','3_12_68','3_12_111','3_12_106','3_12_36','3_12_39','3_12_42','3_12_143','3_12_79','4_10_12','4_10_119','4_10_68','4_10_126','4_10_1','4_10_113','4_10_9','4_10_124','4_10_10','4_11_110','4_11_73','4_11_22','4_11_11','4_11_96','4_11_80','4_11_1','4_11_19','4_11_81','4_12_122','4_12_35','4_12_30','4_12_135','4_12_134','4_12_136','4_12_48','4_12_32','4_12_132','5_10_49','5_10_140','5_10_55','5_10_41','5_10_59','5_10_1','5_10_93','5_10_47','5_10_85','5_11_111','5_11_45','5_11_140','5_11_12','5_11_81','5_11_71','5_11_136','5_11_86','5_11_98','5_12_52','5_12_134','5_12_68','5_12_123','5_12_141','5_12_35','5_12_21','5_12_34','5_12_29','6_7_143','6_7_79','6_7_119','6_7_95','6_7_99','6_7_86','6_7_21','6_7_140','6_7_24','6_8_7','6_8_31','6_8_123','6_8_130','6_8_142','6_8_71','6_8_109','6_8_62','6_8_81','6_9_94','6_9_1','6_9_67','6_9_128','6_9_109','6_9_68','6_9_135','6_9_92','6_9_28','6_10_60','6_10_80','6_10_131','6_10_5','6_10_94','6_10_7','6_10_42','6_10_43','6_10_56','6_11_116','6_11_20','6_11_7','6_11_115','6_11_131','6_11_113','6_11_78','6_11_63','6_11_46','6_12_73','6_12_72','6_12_15','6_12_136','6_12_139','6_12_135','6_12_55','6_12_121','6_12_99','7_8_140','7_8_117','7_8_112','7_8_115','7_8_76','7_8_96','7_8_133','7_8_68','7_8_78','7_7_18','7_7_9','7_7_26','7_7_25','7_7_21','7_7_28','7_7_115','7_7_41','7_7_27','7_9_8','7_9_61','7_9_131','7_9_27','7_9_6','7_9_98','7_9_127','7_9_85','7_9_83','7_11_62','7_11_114','7_11_106','7_11_12','7_11_5','7_11_109','7_11_98','7_11_66','7_11_140','7_12_26','7_12_44','7_12_77','7_12_118','7_12_21','7_12_54','7_12_134','7_12_10','7_12_48']
test_ids = 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#0.25
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'3_12_113', '3_12_114', '3_12_115', '3_12_116', '3_12_118', '3_12_119', '3_12_120', '3_12_121', '3_12_123', '3_12_124', '3_12_125', '3_12_126', '3_12_127', '3_12_128', '3_12_129', '3_12_130', '3_12_131', '3_12_132', '3_12_135', '3_12_137', '3_12_138', '3_12_139', '3_12_140', '3_12_141', '3_12_143', '4_10_0', '4_10_1', '4_10_4', '4_10_5', '4_10_6', '4_10_7', '4_10_8', '4_10_9', '4_10_10', '4_10_11', '4_10_12', '4_10_16', '4_10_18', '4_10_19', '4_10_20', '4_10_22', '4_10_23', '4_10_24', '4_10_25', '4_10_26', '4_10_28', '4_10_29', '4_10_30', '4_10_32', '4_10_33', '4_10_34', '4_10_35', '4_10_36', '4_10_37', '4_10_38', '4_10_39', '4_10_41', '4_10_42', '4_10_43', '4_10_44', '4_10_45', '4_10_46', '4_10_47', '4_10_48', '4_10_50', '4_10_51', '4_10_53', '4_10_54', '4_10_55', '4_10_57', '4_10_59', '4_10_61', '4_10_63', '4_10_64', '4_10_65', '4_10_66', '4_10_67', '4_10_69', '4_10_70', '4_10_71', '4_10_73', '4_10_74', '4_10_75', '4_10_76', '4_10_78', '4_10_79', '4_10_80', '4_10_81', '4_10_82', 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'4_11_63', '4_11_65', '4_11_66', '4_11_67', '4_11_69', '4_11_71', '4_11_72', '4_11_73', '4_11_74', '4_11_75', '4_11_76', '4_11_77', '4_11_79', '4_11_80', '4_11_81', '4_11_82', '4_11_83', '4_11_84', '4_11_85', '4_11_86', '4_11_88', '4_11_89', '4_11_90', '4_11_92', '4_11_93', '4_11_95', '4_11_99', '4_11_100', '4_11_101', '4_11_105', '4_11_106', '4_11_107', '4_11_108', '4_11_109', '4_11_110', '4_11_111', '4_11_112', '4_11_113', '4_11_114', '4_11_115', '4_11_117', '4_11_118', '4_11_119', '4_11_122', '4_11_123', '4_11_124', '4_11_125', '4_11_126', '4_11_127', '4_11_128', '4_11_129', '4_11_130', '4_11_131', '4_11_132', '4_11_134', '4_11_135', '4_11_136', '4_11_137', '4_11_138', '4_11_139', '4_11_140', '4_11_142', '4_11_143', '5_10_0', '5_10_1', '5_10_4', '5_10_6', '5_10_7', '5_10_10', '5_10_12', '5_10_13', '5_10_14', '5_10_15', '5_10_16', '5_10_17', '5_10_18', '5_10_19', '5_10_20', '5_10_21', '5_10_22', '5_10_23', '5_10_25', '5_10_26', '5_10_27', '5_10_28', '5_10_29', '5_10_30', '5_10_31', 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'5_11_6', '5_11_7', '5_11_8', '5_11_10', '5_11_11', '5_11_12', '5_11_13', '5_11_14', '5_11_16', '5_11_17', '5_11_18', '5_11_19', '5_11_20', '5_11_21', '5_11_22', '5_11_23', '5_11_24', '5_11_25', '5_11_27', '5_11_28', '5_11_29', '5_11_30', '5_11_31', '5_11_33', '5_11_34', '5_11_36', '5_11_37', '5_11_38', '5_11_39', '5_11_40', '5_11_41', '5_11_42', '5_11_43', '5_11_44', '5_11_45', '5_11_46', '5_11_48', '5_11_49', '5_11_50', '5_11_52', '5_11_53', '5_11_54', '5_11_55', '5_11_56', '5_11_57', '5_11_58', '5_11_59', '5_11_61', '5_11_62', '5_11_63', '5_11_64', '5_11_65', '5_11_69', '5_11_70', '5_11_71', '5_11_72', '5_11_73', '5_11_74', '5_11_75', '5_11_76', '5_11_77', '5_11_78', '5_11_79', '5_11_82', '5_11_83', '5_11_87', '5_11_90', '5_11_91', '5_11_92', '5_11_93', '5_11_95', '5_11_97', '5_11_98', '5_11_99', '5_11_104', '5_11_107', '5_11_109', '5_11_111', '5_11_112', '5_11_114', '5_11_115', '5_11_117', '5_11_118', '5_11_119', '5_11_120', '5_11_121', '5_11_122', '5_11_123', '5_11_124', 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'7_7_71', '7_7_72', '7_7_73', '7_7_75', '7_7_76', '7_7_77', '7_7_78', '7_7_80', '7_7_82', '7_7_83', '7_7_84', '7_7_85', '7_7_87', '7_7_88', '7_7_90', '7_7_91', '7_7_95', '7_7_96', '7_7_97', '7_7_98', '7_7_99', '7_7_102', '7_7_104', '7_7_105', '7_7_106', '7_7_107', '7_7_108', '7_7_110', '7_7_111', '7_7_112', '7_7_113', '7_7_114', '7_7_116', '7_7_118', '7_7_120', '7_7_121', '7_7_123', '7_7_124', '7_7_125', '7_7_126', '7_7_127', '7_7_128', '7_7_129', '7_7_130', '7_7_131', '7_7_132', '7_7_133', '7_7_135', '7_7_136', '7_7_137', '7_7_138', '7_7_140', '7_7_141', '7_7_142', '7_7_143', '7_9_1', '7_9_2', '7_9_3', '7_9_4', '7_9_5', '7_9_6', '7_9_7', '7_9_8', '7_9_9', '7_9_11', '7_9_13', '7_9_16', '7_9_17', '7_9_19', '7_9_20', '7_9_21', '7_9_22', '7_9_23', '7_9_25', '7_9_26', '7_9_27', '7_9_28', '7_9_29', '7_9_30', '7_9_31', '7_9_33', '7_9_34', '7_9_35', '7_9_36', '7_9_37', '7_9_38', '7_9_39', '7_9_40', '7_9_41', '7_9_43', '7_9_44', '7_9_46', '7_9_47', '7_9_48', '7_9_49', '7_9_51', '7_9_53', '7_9_54', '7_9_57', '7_9_58', '7_9_60', '7_9_61', '7_9_62', '7_9_63', '7_9_65', '7_9_66', '7_9_68', '7_9_69', '7_9_70', '7_9_71', '7_9_72', '7_9_73', '7_9_74', '7_9_76', '7_9_77', '7_9_78', '7_9_79', '7_9_80', '7_9_81', '7_9_82', '7_9_83', '7_9_85', '7_9_86', '7_9_89', '7_9_90', '7_9_91', '7_9_92', '7_9_93', '7_9_94', '7_9_95', '7_9_97', '7_9_98', '7_9_99', '7_9_101', '7_9_102', '7_9_103', '7_9_104', '7_9_105', '7_9_107', '7_9_111', '7_9_112', '7_9_115', '7_9_116', '7_9_117', '7_9_118', '7_9_120', '7_9_121', '7_9_122', '7_9_123', '7_9_124', '7_9_126', '7_9_127', '7_9_128', '7_9_129', '7_9_132', '7_9_135', '7_9_136', '7_9_138', '7_9_139', '7_9_140', '7_9_141', '7_9_142', '7_9_143', '7_11_0', '7_11_1', '7_11_2', '7_11_3', '7_11_4', '7_11_7', '7_11_8', '7_11_9', '7_11_10', '7_11_11', '7_11_13', '7_11_14', '7_11_15', '7_11_18', '7_11_21', '7_11_22', '7_11_23', '7_11_24', '7_11_25', '7_11_26', '7_11_28', '7_11_29', '7_11_30', '7_11_31', '7_11_32', '7_11_33', '7_11_34', '7_11_35', '7_11_36', '7_11_38', '7_11_39', '7_11_41', '7_11_43', '7_11_46', '7_11_47', '7_11_48', '7_11_49', '7_11_51', '7_11_52', '7_11_54', '7_11_55', '7_11_57', '7_11_59', '7_11_60', '7_11_61', '7_11_63', '7_11_64', '7_11_65', '7_11_67', '7_11_68', '7_11_70', '7_11_71', '7_11_73', '7_11_74', '7_11_75', '7_11_77', '7_11_79', '7_11_80', '7_11_81', '7_11_82', '7_11_84', '7_11_87', '7_11_89', '7_11_90', '7_11_93', '7_11_94', '7_11_95', '7_11_96', '7_11_97', '7_11_98', '7_11_99', '7_11_101', '7_11_102', '7_11_104', '7_11_105', '7_11_106', '7_11_107', '7_11_108', '7_11_109', '7_11_110', '7_11_111', '7_11_112', '7_11_113', '7_11_114', '7_11_115', '7_11_116', '7_11_117', '7_11_119', '7_11_120', '7_11_121', '7_11_122', '7_11_123', '7_11_124', '7_11_125', '7_11_126', '7_11_127', '7_11_128', '7_11_131', '7_11_133', '7_11_134', '7_11_135', '7_11_136', '7_11_137', '7_11_138', '7_11_140', '7_11_141', '7_11_142', '7_11_143', '7_12_0', '7_12_1', '7_12_2', '7_12_3', '7_12_4', '7_12_5', '7_12_6', '7_12_8', '7_12_9', '7_12_10', '7_12_12', '7_12_13', '7_12_14', '7_12_16', '7_12_17', '7_12_19', '7_12_21', '7_12_23', '7_12_24', '7_12_25', '7_12_26', '7_12_29', '7_12_30', '7_12_31', '7_12_33', '7_12_34', '7_12_36', '7_12_38', '7_12_39', '7_12_40', '7_12_41', '7_12_44', '7_12_45', '7_12_47', '7_12_48', '7_12_49', '7_12_50', '7_12_52', '7_12_53', '7_12_55', '7_12_56', '7_12_59', '7_12_61', '7_12_62', '7_12_63', '7_12_64', '7_12_65', '7_12_67', '7_12_68', '7_12_69', '7_12_71', '7_12_72', '7_12_73', '7_12_74', '7_12_75', '7_12_76', '7_12_77', '7_12_78', '7_12_80', '7_12_81', '7_12_82', '7_12_83', '7_12_84', '7_12_86', '7_12_87', '7_12_88', '7_12_89', '7_12_91', '7_12_92', '7_12_93', '7_12_95', '7_12_96', '7_12_97', '7_12_98', '7_12_99', '7_12_100', '7_12_102', '7_12_103', '7_12_104', '7_12_106', '7_12_108', '7_12_109', '7_12_110', '7_12_111', '7_12_114', '7_12_115', '7_12_116', '7_12_117', '7_12_119', '7_12_120', '7_12_122', '7_12_124', '7_12_125', '7_12_126', '7_12_127', '7_12_129', '7_12_130', '7_12_131', '7_12_132', '7_12_134', '7_12_135', '7_12_136', '7_12_137', '7_12_138', '7_12_139', '7_12_140', '7_12_142', '7_12_143']
#0.125
# PL_ids = ['2_10_0', '2_10_1', '2_10_2', '2_10_3', '2_10_4', '2_10_5', '2_10_6', '2_10_7', '2_10_8', '2_10_9', '2_10_11', '2_10_12', '2_10_13', '2_10_14', '2_10_15', '2_10_16', '2_10_17', '2_10_18', '2_10_20', '2_10_21', '2_10_22', '2_10_23', '2_10_24', '2_10_25', '2_10_26', '2_10_27', '2_10_28', '2_10_29', '2_10_30', '2_10_31', '2_10_32', '2_10_33', '2_10_34', '2_10_35', '2_10_37', '2_10_38', '2_10_39', '2_10_40', '2_10_41', '2_10_42', '2_10_43', '2_10_44', '2_10_45', '2_10_46', '2_10_47', '2_10_48', '2_10_50', '2_10_51', '2_10_52', '2_10_53', '2_10_54', '2_10_56', '2_10_57', '2_10_58', '2_10_59', '2_10_60', '2_10_61', '2_10_62', '2_10_63', '2_10_64', '2_10_65', '2_10_66', '2_10_67', '2_10_69', '2_10_70', '2_10_71', '2_10_73', '2_10_74', '2_10_75', '2_10_76', '2_10_77', '2_10_78', '2_10_79', '2_10_80', '2_10_82', '2_10_83', '2_10_84', '2_10_85', '2_10_86', '2_10_87', '2_10_88', '2_10_89', '2_10_90', '2_10_91', '2_10_92', '2_10_93', '2_10_96', '2_10_98', '2_10_99', '2_10_100', '2_10_103', '2_10_104', '2_10_105', '2_10_106', '2_10_107', '2_10_109', '2_10_110', '2_10_111', '2_10_113', '2_10_114', '2_10_115', '2_10_116', '2_10_117', '2_10_118', '2_10_120', '2_10_121', '2_10_122', '2_10_123', '2_10_124', '2_10_126', '2_10_128', '2_10_129', '2_10_130', '2_10_131', '2_10_132', '2_10_133', '2_10_134', '2_10_135', '2_10_136', '2_10_137', '2_10_138', '2_10_139', '2_10_140', '2_10_141', '2_10_142', '2_10_143', '2_11_0', '2_11_1', '2_11_2', '2_11_3', '2_11_4', '2_11_5', '2_11_6', '2_11_8', '2_11_9', '2_11_10', '2_11_11', '2_11_12', '2_11_13', '2_11_14', '2_11_15', '2_11_16', '2_11_17', '2_11_18', '2_11_20', '2_11_21', '2_11_22', '2_11_23', '2_11_24', '2_11_25', '2_11_26', '2_11_27', '2_11_28', '2_11_29', '2_11_30', '2_11_32', '2_11_33', '2_11_35', '2_11_36', '2_11_37', '2_11_38', '2_11_39', '2_11_41', '2_11_42', '2_11_43', '2_11_44', '2_11_45', '2_11_46', '2_11_47', '2_11_48', '2_11_49', '2_11_50', '2_11_51', '2_11_52', '2_11_53', '2_11_54', '2_11_55', '2_11_56', '2_11_57', '2_11_58', '2_11_59', '2_11_60', '2_11_61', '2_11_62', '2_11_63', '2_11_64', '2_11_65', '2_11_66', '2_11_68', '2_11_69', '2_11_70', '2_11_72', '2_11_73', '2_11_74', '2_11_75', '2_11_76', '2_11_77', '2_11_78', '2_11_80', '2_11_81', '2_11_82', '2_11_83', '2_11_85', '2_11_86', '2_11_89', '2_11_90', '2_11_91', '2_11_92', '2_11_94', '2_11_95', '2_11_96', '2_11_97', '2_11_98', '2_11_99', '2_11_100', '2_11_101', '2_11_102', '2_11_103', '2_11_104', '2_11_106', '2_11_107', '2_11_108', '2_11_109', '2_11_110', '2_11_111', '2_11_112', '2_11_113', '2_11_114', '2_11_115', '2_11_116', '2_11_117', '2_11_118', '2_11_119', '2_11_121', '2_11_122', '2_11_123', '2_11_125', '2_11_127', '2_11_129', '2_11_131', '2_11_132', '2_11_133', '2_11_134', '2_11_135', '2_11_136', '2_11_137', '2_11_138', '2_11_140', '2_11_141', '2_11_142', '2_11_143', '2_12_1', '2_12_2', '2_12_4', '2_12_5', '2_12_6', '2_12_8', '2_12_9', '2_12_10', '2_12_11', '2_12_12', '2_12_13', '2_12_14', '2_12_15', '2_12_16', '2_12_17', '2_12_18', '2_12_19', '2_12_20', '2_12_21', '2_12_22', '2_12_23', '2_12_24', '2_12_25', '2_12_26', '2_12_27', '2_12_28', '2_12_29', '2_12_30', '2_12_31', '2_12_32', '2_12_33', '2_12_34', '2_12_35', '2_12_36', '2_12_37', '2_12_38', '2_12_39', '2_12_41', '2_12_42', '2_12_43', '2_12_44', '2_12_45', '2_12_46', '2_12_47', '2_12_48', '2_12_49', '2_12_50', '2_12_51', '2_12_52', '2_12_54', '2_12_55', '2_12_56', '2_12_57', '2_12_59', '2_12_60', '2_12_61', '2_12_62', '2_12_63', '2_12_64', '2_12_65', '2_12_66', '2_12_67', '2_12_69', '2_12_70', '2_12_71', '2_12_72', '2_12_73', '2_12_75', '2_12_76', '2_12_77', '2_12_78', '2_12_79', '2_12_82', '2_12_83', '2_12_84', '2_12_85', '2_12_86', '2_12_87', '2_12_89', '2_12_90', '2_12_91', '2_12_92', '2_12_93', '2_12_94', '2_12_95', '2_12_96', '2_12_97', '2_12_98', '2_12_99', '2_12_100', '2_12_101', '2_12_102', '2_12_103', '2_12_104', '2_12_105', '2_12_106', '2_12_107', '2_12_108', '2_12_110', '2_12_112', '2_12_114', '2_12_115', '2_12_116', '2_12_117', '2_12_118', '2_12_119', '2_12_121', '2_12_122', '2_12_123', '2_12_125', '2_12_126', '2_12_127', '2_12_128', '2_12_129', '2_12_130', '2_12_132', '2_12_133', '2_12_134', '2_12_135', '2_12_136', '2_12_137', '2_12_139', '2_12_140', '2_12_141', '2_12_142', '2_12_143', '3_10_0', '3_10_2', '3_10_3', '3_10_4', '3_10_5', '3_10_6', '3_10_7', '3_10_8', '3_10_9', '3_10_10', '3_10_11', '3_10_12', '3_10_13', '3_10_14', '3_10_15', '3_10_16', '3_10_17', '3_10_19', '3_10_20', '3_10_21', '3_10_22', '3_10_23', '3_10_24', '3_10_26', '3_10_27', '3_10_28', '3_10_30', '3_10_33', '3_10_35', '3_10_36', '3_10_37', '3_10_38', '3_10_39', '3_10_40', '3_10_41', '3_10_43', '3_10_45', '3_10_46', '3_10_48', '3_10_49', '3_10_50', '3_10_51', '3_10_52', '3_10_53', '3_10_54', '3_10_55', '3_10_56', '3_10_57', '3_10_58', '3_10_59', '3_10_60', '3_10_61', '3_10_62', '3_10_63', '3_10_64', '3_10_65', '3_10_66', '3_10_67', '3_10_68', '3_10_69', '3_10_70', '3_10_72', '3_10_73', '3_10_74', '3_10_75', '3_10_76', '3_10_77', '3_10_78', '3_10_79', '3_10_80', '3_10_81', '3_10_82', '3_10_83', '3_10_84', '3_10_85', '3_10_86', '3_10_87', '3_10_88', '3_10_90', '3_10_91', '3_10_92', '3_10_93', '3_10_94', '3_10_95', '3_10_97', '3_10_99', '3_10_100', '3_10_101', '3_10_102', '3_10_103', '3_10_104', '3_10_105', '3_10_106', '3_10_108', '3_10_109', '3_10_110', '3_10_111', '3_10_112', '3_10_113', '3_10_114', '3_10_115', '3_10_116', '3_10_117', '3_10_118', '3_10_119', '3_10_120', '3_10_121', '3_10_122', '3_10_124', '3_10_125', '3_10_126', '3_10_127', '3_10_128', '3_10_129', '3_10_130', '3_10_131', '3_10_132', '3_10_133', '3_10_134', '3_10_135', '3_10_136', '3_10_137', '3_10_138', '3_10_139', '3_10_141', '3_10_142', '3_11_0', '3_11_2', '3_11_4', '3_11_5', '3_11_6', '3_11_7', '3_11_8', '3_11_9', '3_11_10', '3_11_11', '3_11_12', '3_11_13', '3_11_14', '3_11_15', '3_11_16', '3_11_17', '3_11_19', '3_11_20', '3_11_21', '3_11_22', '3_11_24', '3_11_25', '3_11_27', '3_11_28', '3_11_29', '3_11_30', '3_11_31', '3_11_32', '3_11_34', '3_11_35', '3_11_36', '3_11_37', '3_11_38', '3_11_39', '3_11_40', '3_11_42', '3_11_44', '3_11_45', '3_11_46', '3_11_47', '3_11_48', '3_11_50', '3_11_51', '3_11_52', '3_11_53', '3_11_54', '3_11_55', '3_11_57', '3_11_58', '3_11_59', '3_11_60', '3_11_62', '3_11_64', '3_11_65', '3_11_66', '3_11_68', '3_11_69', '3_11_71', '3_11_72', '3_11_73', '3_11_74', '3_11_75', '3_11_76', '3_11_77', '3_11_78', '3_11_79', '3_11_80', '3_11_81', '3_11_82', '3_11_83', '3_11_84', '3_11_85', '3_11_86', '3_11_87', '3_11_88', '3_11_89', '3_11_91', '3_11_92', '3_11_93', '3_11_94', '3_11_95', '3_11_96', '3_11_97', '3_11_98', '3_11_99', '3_11_100', '3_11_101', '3_11_102', '3_11_103', '3_11_104', '3_11_105', '3_11_106', '3_11_107', '3_11_109', '3_11_110', '3_11_111', '3_11_112', '3_11_113', '3_11_114', '3_11_115', '3_11_116', '3_11_117', '3_11_118', '3_11_119', '3_11_120', '3_11_121', '3_11_122', '3_11_123', '3_11_124', '3_11_125', '3_11_126', '3_11_127', '3_11_128', '3_11_131', '3_11_132', '3_11_133', '3_11_134', '3_11_135', '3_11_137', '3_11_138', '3_11_139', '3_11_140', '3_11_141', '3_11_142', '3_11_143', '3_12_0', '3_12_1', '3_12_2', '3_12_3', '3_12_4', '3_12_5', '3_12_6', '3_12_7', '3_12_8', '3_12_9', '3_12_10', '3_12_11', '3_12_12', '3_12_13', '3_12_14', '3_12_15', '3_12_16', '3_12_17', '3_12_18', '3_12_19', '3_12_20', '3_12_22', '3_12_23', '3_12_24', '3_12_25', '3_12_26', '3_12_27', '3_12_28', '3_12_29', '3_12_30', '3_12_31', '3_12_35', '3_12_36', '3_12_37', '3_12_38', '3_12_39', '3_12_40', '3_12_41', '3_12_42', '3_12_43', '3_12_44', '3_12_46', '3_12_47', '3_12_48', '3_12_49', '3_12_50', '3_12_52', '3_12_53', '3_12_54', '3_12_55', '3_12_58', '3_12_59', '3_12_60', '3_12_62', '3_12_63', '3_12_64', '3_12_65', '3_12_66', '3_12_67', '3_12_68', '3_12_69', '3_12_70', '3_12_71', '3_12_72', '3_12_73', '3_12_74', '3_12_77', '3_12_78', '3_12_79', '3_12_80', '3_12_81', '3_12_82', '3_12_83', '3_12_84', '3_12_85', '3_12_86', '3_12_87', '3_12_88', '3_12_89', '3_12_90', '3_12_91', '3_12_92', '3_12_93', 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'7_7_106', '7_7_107', '7_7_108', '7_7_109', '7_7_111', '7_7_112', '7_7_115', '7_7_116', '7_7_118', '7_7_119', '7_7_120', '7_7_121', '7_7_123', '7_7_124', '7_7_125', '7_7_126', '7_7_128', '7_7_129', '7_7_130', '7_7_131', '7_7_132', '7_7_133', '7_7_134', '7_7_135', '7_7_136', '7_7_137', '7_7_138', '7_7_139', '7_7_141', '7_7_142', '7_9_0', '7_9_2', '7_9_3', '7_9_4', '7_9_5', '7_9_6', '7_9_7', '7_9_8', '7_9_9', '7_9_10', '7_9_11', '7_9_12', '7_9_13', '7_9_15', '7_9_16', '7_9_17', '7_9_18', '7_9_20', '7_9_22', '7_9_23', '7_9_24', '7_9_25', '7_9_26', '7_9_27', '7_9_28', '7_9_30', '7_9_32', '7_9_33', '7_9_34', '7_9_35', '7_9_36', '7_9_38', '7_9_39', '7_9_40', '7_9_42', '7_9_43', '7_9_44', '7_9_45', '7_9_46', '7_9_47', '7_9_48', '7_9_49', '7_9_50', '7_9_51', '7_9_53', '7_9_54', '7_9_55', '7_9_56', '7_9_57', '7_9_58', '7_9_59', '7_9_60', '7_9_61', '7_9_62', '7_9_63', '7_9_64', '7_9_65', '7_9_66', '7_9_67', '7_9_68', '7_9_69', '7_9_70', '7_9_71', '7_9_72', '7_9_73', '7_9_74', '7_9_75', '7_9_76', '7_9_77', '7_9_78', '7_9_79', '7_9_81', '7_9_82', '7_9_83', '7_9_84', '7_9_86', '7_9_87', '7_9_88', '7_9_89', '7_9_90', '7_9_91', '7_9_92', '7_9_93', '7_9_95', '7_9_96', '7_9_97', '7_9_98', '7_9_99', '7_9_101', '7_9_102', '7_9_103', '7_9_104', '7_9_105', '7_9_106', '7_9_107', '7_9_108', '7_9_109', '7_9_110', '7_9_111', '7_9_112', '7_9_113', '7_9_116', '7_9_117', '7_9_118', '7_9_120', '7_9_121', '7_9_122', '7_9_123', '7_9_124', '7_9_125', '7_9_126', '7_9_128', '7_9_129', '7_9_130', '7_9_131', '7_9_132', '7_9_133', '7_9_135', '7_9_136', '7_9_137', '7_9_138', '7_9_139', '7_9_140', '7_9_142', '7_9_143', '7_11_0', '7_11_1', '7_11_2', '7_11_3', '7_11_4', '7_11_5', '7_11_6', '7_11_7', '7_11_8', '7_11_9', '7_11_10', '7_11_11', '7_11_13', '7_11_14', '7_11_15', '7_11_16', '7_11_17', '7_11_18', '7_11_19', '7_11_20', '7_11_23', '7_11_24', '7_11_25', '7_11_26', '7_11_27', '7_11_28', '7_11_29', '7_11_30', '7_11_31', '7_11_32', '7_11_33', '7_11_35', '7_11_36', '7_11_37', '7_11_38', '7_11_39', '7_11_40', '7_11_41', '7_11_42', '7_11_43', '7_11_44', '7_11_46', '7_11_47', '7_11_48', '7_11_49', '7_11_51', '7_11_52', '7_11_54', '7_11_55', '7_11_56', '7_11_57', '7_11_58', '7_11_59', '7_11_60', '7_11_61', '7_11_62', '7_11_63', '7_11_65', '7_11_66', '7_11_67', '7_11_68', '7_11_69', '7_11_70', '7_11_72', '7_11_73', '7_11_74', '7_11_75', '7_11_76', '7_11_77', '7_11_78', '7_11_80', '7_11_81', '7_11_82', '7_11_83', '7_11_84', '7_11_85', '7_11_86', '7_11_87', '7_11_89', '7_11_90', '7_11_92', '7_11_93', '7_11_95', '7_11_96', '7_11_97', '7_11_98', '7_11_99', '7_11_101', '7_11_102', '7_11_103', '7_11_104', '7_11_105', '7_11_106', '7_11_107', '7_11_108', '7_11_109', '7_11_110', '7_11_111', '7_11_112', '7_11_113', '7_11_114', '7_11_115', '7_11_116', '7_11_117', '7_11_118', '7_11_119', '7_11_121', '7_11_122', '7_11_123', '7_11_124', '7_11_125', '7_11_126', '7_11_127', '7_11_128', '7_11_129', '7_11_130', '7_11_131', '7_11_132', '7_11_133', '7_11_134', '7_11_135', '7_11_136', '7_11_138', '7_11_140', '7_11_141', '7_11_142', '7_12_0', '7_12_1', '7_12_3', '7_12_4', '7_12_6', '7_12_7', '7_12_8', '7_12_10', '7_12_11', '7_12_12', '7_12_13', '7_12_14', '7_12_15', '7_12_16', '7_12_17', '7_12_18', '7_12_19', '7_12_20', '7_12_21', '7_12_22', '7_12_23', '7_12_24', '7_12_25', '7_12_26', '7_12_27', '7_12_28', '7_12_29', '7_12_30', '7_12_31', '7_12_32', '7_12_33', '7_12_35', '7_12_36', '7_12_37', '7_12_38', '7_12_39', '7_12_40', '7_12_41', '7_12_42', '7_12_43', '7_12_44', '7_12_45', '7_12_46', '7_12_48', '7_12_49', '7_12_50', '7_12_51', '7_12_52', '7_12_53', '7_12_54', '7_12_55', '7_12_57', '7_12_58', '7_12_59', '7_12_60', '7_12_61', '7_12_62', '7_12_63', '7_12_64', '7_12_65', '7_12_66', '7_12_67', '7_12_68', '7_12_69', '7_12_71', '7_12_72', '7_12_73', '7_12_74', '7_12_75', '7_12_76', '7_12_77', '7_12_78', '7_12_79', '7_12_81', '7_12_83', '7_12_84', '7_12_85', '7_12_87', '7_12_88', '7_12_89', '7_12_90', '7_12_91', '7_12_93', '7_12_94', '7_12_96', '7_12_97', '7_12_98', '7_12_99', '7_12_100', '7_12_101', '7_12_103', '7_12_104', '7_12_105', '7_12_106', '7_12_107', '7_12_108', '7_12_109', '7_12_110', '7_12_111', '7_12_112', '7_12_114', '7_12_115', '7_12_117', '7_12_118', '7_12_120', '7_12_122', '7_12_123', '7_12_124', '7_12_125', '7_12_126', '7_12_127', '7_12_128', '7_12_129', '7_12_130', '7_12_131', '7_12_132', '7_12_134', '7_12_135', '7_12_136', '7_12_137', '7_12_138', '7_12_139', '7_12_140', '7_12_141', '7_12_142', '7_12_143']
#0.0625
# PL_ids = ['2_10_0', '2_10_1', '2_10_2', '2_10_3', '2_10_4', '2_10_5', '2_10_7', '2_10_8', '2_10_9', '2_10_10', '2_10_11', '2_10_12', '2_10_13', '2_10_14', '2_10_16', '2_10_17', '2_10_18', '2_10_19', '2_10_20', '2_10_21', '2_10_22', '2_10_23', '2_10_24', '2_10_25', '2_10_26', '2_10_27', '2_10_28', '2_10_29', '2_10_30', '2_10_31', '2_10_32', '2_10_33', '2_10_34', '2_10_36', '2_10_37', '2_10_38', '2_10_39', '2_10_40', '2_10_41', '2_10_42', '2_10_43', '2_10_44', '2_10_45', '2_10_46', '2_10_47', '2_10_48', '2_10_49', '2_10_50', '2_10_51', '2_10_52', '2_10_53', '2_10_54', '2_10_55', '2_10_56', '2_10_57', '2_10_58', '2_10_59', '2_10_60', '2_10_61', '2_10_62', '2_10_63', '2_10_64', '2_10_65', '2_10_66', '2_10_67', '2_10_68', '2_10_69', '2_10_70', '2_10_71', '2_10_72', '2_10_73', '2_10_74', '2_10_75', '2_10_76', '2_10_77', '2_10_78', '2_10_79', '2_10_80', '2_10_81', '2_10_82', '2_10_83', '2_10_84', '2_10_85', '2_10_86', '2_10_87', '2_10_88', '2_10_89', '2_10_90', '2_10_91', '2_10_92', '2_10_93', '2_10_94', '2_10_95', '2_10_96', '2_10_97', '2_10_98', '2_10_99', '2_10_100', '2_10_101', '2_10_102', '2_10_103', '2_10_104', '2_10_105', '2_10_106', '2_10_108', '2_10_109', '2_10_110', '2_10_111', '2_10_112', '2_10_113', '2_10_114', '2_10_115', '2_10_116', '2_10_117', '2_10_118', '2_10_119', '2_10_120', '2_10_122', '2_10_123', '2_10_124', '2_10_125', '2_10_127', '2_10_128', '2_10_129', '2_10_130', '2_10_132', '2_10_133', '2_10_135', '2_10_137', '2_10_138', '2_10_139', '2_10_140', '2_10_141', '2_10_142', '2_10_143', '2_11_0', '2_11_1', '2_11_2', '2_11_3', '2_11_4', '2_11_5', '2_11_6', '2_11_7', '2_11_9', '2_11_11', '2_11_12', '2_11_13', '2_11_14', '2_11_15', '2_11_16', '2_11_17', '2_11_18', '2_11_19', '2_11_20', '2_11_21', '2_11_22', '2_11_23', '2_11_24', '2_11_25', '2_11_26', '2_11_27', '2_11_28', '2_11_29', '2_11_30', '2_11_31', '2_11_32', '2_11_33', '2_11_34', '2_11_35', '2_11_36', '2_11_37', '2_11_38', '2_11_39', '2_11_40', '2_11_41', '2_11_42', '2_11_43', '2_11_44', '2_11_45', '2_11_46', '2_11_47', '2_11_49', '2_11_50', '2_11_51', '2_11_52', '2_11_53', '2_11_54', '2_11_55', '2_11_56', '2_11_57', '2_11_58', '2_11_59', '2_11_60', '2_11_61', '2_11_62', '2_11_63', '2_11_64', '2_11_65', '2_11_66', '2_11_67', '2_11_68', '2_11_69', '2_11_70', '2_11_71', '2_11_72', '2_11_73', '2_11_74', '2_11_75', '2_11_76', '2_11_77', '2_11_78', '2_11_80', '2_11_81', '2_11_83', '2_11_84', '2_11_85', '2_11_88', '2_11_89', '2_11_90', '2_11_91', '2_11_92', '2_11_93', '2_11_94', '2_11_95', '2_11_96', '2_11_97', '2_11_98', '2_11_99', '2_11_100', '2_11_101', '2_11_102', '2_11_103', '2_11_104', '2_11_105', '2_11_106', '2_11_107', '2_11_108', '2_11_109', '2_11_110', '2_11_111', '2_11_112', '2_11_113', '2_11_114', '2_11_115', '2_11_116', '2_11_117', '2_11_118', '2_11_119', '2_11_120', '2_11_121', '2_11_122', '2_11_123', '2_11_124', '2_11_125', '2_11_127', '2_11_128', '2_11_129', '2_11_131', '2_11_132', '2_11_133', '2_11_134', '2_11_135', '2_11_136', '2_11_137', '2_11_138', '2_11_139', '2_11_140', '2_11_142', '2_11_143', '2_12_0', '2_12_1', '2_12_2', '2_12_3', '2_12_4', '2_12_5', '2_12_6', '2_12_7', '2_12_8', '2_12_9', '2_12_11', '2_12_12', '2_12_13', '2_12_14', '2_12_15', '2_12_17', '2_12_18', '2_12_19', '2_12_20', '2_12_21', '2_12_22', '2_12_23', '2_12_24', '2_12_25', '2_12_26', '2_12_27', '2_12_28', '2_12_29', '2_12_30', '2_12_31', '2_12_32', '2_12_33', '2_12_34', '2_12_35', '2_12_36', '2_12_37', '2_12_38', '2_12_39', '2_12_40', '2_12_41', '2_12_42', '2_12_43', '2_12_44', '2_12_45', '2_12_46', '2_12_48', '2_12_50', '2_12_51', '2_12_52', '2_12_53', '2_12_54', '2_12_55', '2_12_56', '2_12_57', '2_12_58', '2_12_59', '2_12_60', '2_12_61', '2_12_62', '2_12_63', '2_12_64', '2_12_65', '2_12_66', '2_12_67', '2_12_68', '2_12_69', '2_12_70', '2_12_71', '2_12_73', '2_12_74', '2_12_75', '2_12_76', '2_12_77', '2_12_78', '2_12_79', '2_12_80', '2_12_81', '2_12_82', '2_12_83', '2_12_84', '2_12_85', '2_12_86', '2_12_87', '2_12_88', '2_12_89', '2_12_90', '2_12_91', '2_12_92', '2_12_93', '2_12_95', '2_12_96', '2_12_97', '2_12_98', '2_12_99', '2_12_100', '2_12_101', '2_12_102', '2_12_103', '2_12_104', '2_12_105', '2_12_106', '2_12_107', '2_12_109', '2_12_110', '2_12_111', '2_12_112', '2_12_113', '2_12_114', '2_12_116', '2_12_117', '2_12_118', '2_12_119', '2_12_120', '2_12_121', '2_12_122', '2_12_123', '2_12_124', '2_12_125', '2_12_126', '2_12_127', '2_12_128', '2_12_129', '2_12_130', '2_12_131', '2_12_133', '2_12_134', '2_12_135', '2_12_136', '2_12_137', '2_12_138', '2_12_139', '2_12_140', '2_12_141', '2_12_142', '2_12_143', '3_10_0', '3_10_1', '3_10_2', '3_10_3', '3_10_4', '3_10_5', '3_10_6', '3_10_7', '3_10_8', '3_10_9', '3_10_10', '3_10_11', '3_10_12', '3_10_13', '3_10_14', '3_10_16', '3_10_17', '3_10_19', '3_10_20', '3_10_21', '3_10_22', '3_10_23', '3_10_24', '3_10_25', '3_10_26', '3_10_27', '3_10_29', '3_10_30', '3_10_31', '3_10_32', '3_10_33', '3_10_35', '3_10_36', '3_10_37', '3_10_38', '3_10_39', '3_10_40', '3_10_41', '3_10_42', '3_10_43', '3_10_44', '3_10_45', '3_10_46', '3_10_48', '3_10_49', '3_10_50', '3_10_51', '3_10_52', '3_10_53', '3_10_54', '3_10_55', '3_10_56', '3_10_57', '3_10_58', '3_10_59', '3_10_60', '3_10_61', '3_10_62', '3_10_63', '3_10_64', '3_10_65', '3_10_66', '3_10_67', '3_10_68', '3_10_70', '3_10_71', '3_10_72', '3_10_73', '3_10_74', '3_10_75', '3_10_76', '3_10_77', '3_10_78', '3_10_79', '3_10_80', '3_10_81', '3_10_82', '3_10_83', '3_10_84', '3_10_85', '3_10_86', '3_10_87', '3_10_88', '3_10_89', '3_10_90', '3_10_91', '3_10_92', '3_10_93', '3_10_94', '3_10_95', '3_10_96', '3_10_97', '3_10_98', '3_10_99', '3_10_100', '3_10_101', '3_10_103', '3_10_104', '3_10_105', '3_10_106', '3_10_107', '3_10_108', '3_10_109', '3_10_110', '3_10_111', '3_10_112', '3_10_113', '3_10_114', '3_10_115', '3_10_116', '3_10_117', '3_10_118', '3_10_119', '3_10_120', '3_10_121', '3_10_124', '3_10_125', '3_10_126', '3_10_127', '3_10_128', '3_10_129', '3_10_130', '3_10_131', '3_10_132', '3_10_133', '3_10_134', '3_10_135', '3_10_136', '3_10_137', '3_10_138', '3_10_139', '3_10_140', '3_10_141', '3_10_142', '3_10_143', '3_11_0', '3_11_1', '3_11_2', '3_11_3', '3_11_4', '3_11_5', '3_11_6', '3_11_8', '3_11_9', '3_11_10', '3_11_11', '3_11_12', '3_11_13', '3_11_14', '3_11_15', '3_11_16', '3_11_17', '3_11_18', '3_11_19', '3_11_20', '3_11_21', '3_11_22', '3_11_24', '3_11_25', '3_11_26', '3_11_27', '3_11_28', '3_11_29', '3_11_30', '3_11_31', '3_11_32', '3_11_33', '3_11_34', '3_11_35', '3_11_36', '3_11_37', '3_11_38', '3_11_39', '3_11_40', '3_11_42', '3_11_43', '3_11_44', '3_11_45', '3_11_46', '3_11_47', '3_11_48', '3_11_49', '3_11_50', '3_11_51', '3_11_52', '3_11_53', '3_11_54', '3_11_55', '3_11_56', '3_11_57', '3_11_58', '3_11_59', '3_11_60', '3_11_61', '3_11_62', '3_11_63', '3_11_64', '3_11_65', '3_11_66', '3_11_67', '3_11_68', '3_11_69', '3_11_70', '3_11_71', '3_11_72', '3_11_73', '3_11_74', '3_11_75', '3_11_76', '3_11_77', '3_11_78', '3_11_79', '3_11_80', '3_11_81', '3_11_82', '3_11_83', '3_11_84', '3_11_85', '3_11_86', '3_11_88', '3_11_89', '3_11_90', '3_11_91', '3_11_92', '3_11_94', '3_11_95', '3_11_96', '3_11_98', '3_11_99', '3_11_100', '3_11_101', '3_11_102', '3_11_103', '3_11_104', '3_11_105', '3_11_106', '3_11_107', '3_11_109', '3_11_111', '3_11_112', '3_11_113', '3_11_114', '3_11_115', '3_11_116', '3_11_117', '3_11_118', '3_11_119', '3_11_120', '3_11_121', '3_11_122', '3_11_123', '3_11_124', '3_11_126', '3_11_127', '3_11_129', '3_11_130', '3_11_131', '3_11_132', '3_11_133', '3_11_134', '3_11_135', '3_11_136', '3_11_137', '3_11_138', '3_11_139', '3_11_140', '3_11_141', '3_11_142', '3_11_143', '3_12_0', '3_12_1', '3_12_2', '3_12_3', '3_12_4', '3_12_5', '3_12_6', '3_12_7', '3_12_8', '3_12_9', '3_12_10', '3_12_11', '3_12_12', '3_12_13', '3_12_14', '3_12_15', '3_12_16', '3_12_17', '3_12_18', '3_12_19', '3_12_20', '3_12_21', '3_12_22', '3_12_23', '3_12_24', '3_12_25', '3_12_26', '3_12_27', '3_12_28', '3_12_29', '3_12_30', '3_12_31', '3_12_32', '3_12_33', '3_12_34', '3_12_35', '3_12_37', 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'7_8_2', '7_8_3', '7_8_4', '7_8_5', '7_8_6', '7_8_7', '7_8_8', '7_8_9', '7_8_10', '7_8_11', '7_8_12', '7_8_13', '7_8_14', '7_8_15', '7_8_16', '7_8_17', '7_8_18', '7_8_19', '7_8_20', '7_8_21', '7_8_22', '7_8_23', '7_8_24', '7_8_25', '7_8_26', '7_8_27', '7_8_28', '7_8_29', '7_8_30', '7_8_31', '7_8_32', '7_8_33', '7_8_34', '7_8_35', '7_8_36', '7_8_37', '7_8_38', '7_8_39', '7_8_40', '7_8_41', '7_8_42', '7_8_43', '7_8_44', '7_8_45', '7_8_46', '7_8_47', '7_8_48', '7_8_49', '7_8_50', '7_8_51', '7_8_52', '7_8_53', '7_8_54', '7_8_55', '7_8_56', '7_8_57', '7_8_58', '7_8_59', '7_8_60', '7_8_61', '7_8_62', '7_8_63', '7_8_64', '7_8_65', '7_8_66', '7_8_67', '7_8_69', '7_8_70', '7_8_71', '7_8_72', '7_8_73', '7_8_74', '7_8_75', '7_8_77', '7_8_79', '7_8_80', '7_8_81', '7_8_82', '7_8_83', '7_8_84', '7_8_85', '7_8_86', '7_8_87', '7_8_88', '7_8_89', '7_8_90', '7_8_91', '7_8_92', '7_8_93', '7_8_94', '7_8_95', '7_8_97', '7_8_98', '7_8_99', '7_8_100', '7_8_101', '7_8_102', '7_8_103', '7_8_104', '7_8_105', '7_8_106', '7_8_107', '7_8_108', '7_8_109', '7_8_110', '7_8_111', '7_8_113', '7_8_114', '7_8_116', '7_8_118', '7_8_119', '7_8_120', '7_8_121', '7_8_122', '7_8_123', '7_8_124', '7_8_125', '7_8_126', '7_8_127', '7_8_128', '7_8_129', '7_8_130', '7_8_131', '7_8_132', '7_8_134', '7_8_135', '7_8_136', '7_8_137', '7_8_138', '7_8_139', '7_8_141', '7_8_142', '7_8_143', '7_7_0', '7_7_1', '7_7_2', '7_7_3', '7_7_4', '7_7_5', '7_7_6', '7_7_7', '7_7_8', '7_7_10', '7_7_11', '7_7_12', '7_7_13', '7_7_14', '7_7_15', '7_7_16', '7_7_17', '7_7_19', '7_7_20', '7_7_22', '7_7_23', '7_7_24', '7_7_29', '7_7_30', '7_7_31', '7_7_32', '7_7_33', '7_7_34', '7_7_35', '7_7_36', '7_7_37', '7_7_38', '7_7_39', '7_7_40', '7_7_42', '7_7_43', '7_7_44', '7_7_45', '7_7_46', '7_7_47', '7_7_48', '7_7_49', '7_7_50', '7_7_51', '7_7_52', '7_7_53', '7_7_54', '7_7_55', '7_7_56', '7_7_57', '7_7_58', '7_7_59', '7_7_60', '7_7_61', '7_7_62', '7_7_63', '7_7_64', '7_7_65', '7_7_66', '7_7_67', '7_7_68', '7_7_69', '7_7_70', '7_7_71', '7_7_72', '7_7_73', '7_7_74', '7_7_75', '7_7_76', '7_7_77', '7_7_78', '7_7_79', '7_7_80', '7_7_81', '7_7_82', '7_7_83', '7_7_84', '7_7_85', '7_7_86', '7_7_87', '7_7_88', '7_7_89', '7_7_90', '7_7_91', '7_7_92', '7_7_93', '7_7_94', '7_7_95', '7_7_96', '7_7_97', '7_7_98', '7_7_99', '7_7_100', '7_7_101', '7_7_102', '7_7_103', '7_7_104', '7_7_105', '7_7_106', '7_7_107', '7_7_108', '7_7_109', '7_7_110', '7_7_111', '7_7_112', '7_7_113', '7_7_114', '7_7_116', '7_7_117', '7_7_118', '7_7_119', '7_7_120', '7_7_121', '7_7_122', '7_7_123', '7_7_124', '7_7_125', '7_7_126', '7_7_127', '7_7_128', '7_7_129', '7_7_130', '7_7_131', '7_7_132', '7_7_133', '7_7_134', '7_7_135', '7_7_136', '7_7_137', '7_7_138', '7_7_139', '7_7_140', '7_7_141', '7_7_142', '7_7_143', '7_9_0', '7_9_1', '7_9_2', '7_9_3', '7_9_4', '7_9_5', '7_9_7', '7_9_9', '7_9_10', '7_9_11', '7_9_12', '7_9_13', '7_9_14', '7_9_15', '7_9_16', '7_9_17', '7_9_18', '7_9_19', '7_9_20', '7_9_21', '7_9_22', '7_9_23', '7_9_24', '7_9_25', '7_9_26', '7_9_28', '7_9_29', '7_9_30', '7_9_31', '7_9_32', '7_9_33', '7_9_34', '7_9_35', '7_9_36', '7_9_37', '7_9_38', '7_9_39', '7_9_40', '7_9_41', '7_9_42', '7_9_43', '7_9_44', '7_9_45', '7_9_46', '7_9_47', '7_9_48', '7_9_49', '7_9_50', '7_9_51', '7_9_52', '7_9_53', '7_9_54', '7_9_55', '7_9_56', '7_9_57', '7_9_58', '7_9_59', '7_9_60', '7_9_62', '7_9_63', '7_9_64', '7_9_65', '7_9_66', '7_9_67', '7_9_68', '7_9_69', '7_9_70', '7_9_71', '7_9_72', '7_9_73', '7_9_74', '7_9_75', '7_9_76', '7_9_77', '7_9_78', '7_9_79', '7_9_80', '7_9_81', '7_9_82', '7_9_84', '7_9_86', '7_9_87', '7_9_88', '7_9_89', '7_9_90', '7_9_91', '7_9_92', '7_9_93', '7_9_94', '7_9_95', '7_9_96', '7_9_97', '7_9_99', '7_9_100', '7_9_101', '7_9_102', '7_9_103', '7_9_104', '7_9_105', '7_9_106', '7_9_107', '7_9_108', '7_9_109', '7_9_110', '7_9_111', '7_9_112', '7_9_113', '7_9_115', '7_9_116', '7_9_117', '7_9_118', '7_9_119', '7_9_120', '7_9_121', '7_9_122', '7_9_123', '7_9_124', '7_9_125', '7_9_126', '7_9_128', '7_9_129', '7_9_130', '7_9_132', '7_9_133', '7_9_135', '7_9_136', '7_9_137', '7_9_138', '7_9_139', '7_9_140', '7_9_141', '7_9_142', '7_9_143', '7_11_0', '7_11_1', '7_11_2', '7_11_3', '7_11_4', '7_11_6', '7_11_7', '7_11_8', '7_11_9', '7_11_10', '7_11_11', '7_11_13', '7_11_14', '7_11_15', '7_11_16', '7_11_17', '7_11_18', '7_11_19', '7_11_20', '7_11_21', '7_11_22', '7_11_23', '7_11_24', '7_11_25', '7_11_26', '7_11_27', '7_11_28', '7_11_29', '7_11_30', '7_11_31', '7_11_32', '7_11_33', '7_11_34', '7_11_35', '7_11_36', '7_11_37', '7_11_38', '7_11_39', '7_11_40', '7_11_41', '7_11_42', '7_11_43', '7_11_44', '7_11_45', '7_11_46', '7_11_47', '7_11_48', '7_11_49', '7_11_50', '7_11_51', '7_11_52', '7_11_53', '7_11_54', '7_11_55', '7_11_56', '7_11_57', '7_11_58', '7_11_59', '7_11_60', '7_11_61', '7_11_63', '7_11_64', '7_11_65', '7_11_67', '7_11_68', '7_11_69', '7_11_70', '7_11_71', '7_11_72', '7_11_73', '7_11_74', '7_11_75', '7_11_76', '7_11_77', '7_11_78', '7_11_79', '7_11_80', '7_11_81', '7_11_82', '7_11_83', '7_11_84', '7_11_85', '7_11_86', '7_11_87', '7_11_88', '7_11_89', '7_11_90', '7_11_91', '7_11_92', '7_11_93', '7_11_94', '7_11_95', '7_11_96', '7_11_97', '7_11_99', '7_11_100', '7_11_101', '7_11_102', '7_11_103', '7_11_104', '7_11_105', '7_11_107', '7_11_108', '7_11_110', '7_11_111', '7_11_112', '7_11_113', '7_11_115', '7_11_116', '7_11_117', '7_11_118', '7_11_119', '7_11_120', '7_11_121', '7_11_122', '7_11_123', '7_11_124', '7_11_125', '7_11_126', '7_11_127', '7_11_128', '7_11_129', '7_11_130', '7_11_131', '7_11_132', '7_11_133', '7_11_134', '7_11_135', '7_11_136', '7_11_137', '7_11_138', '7_11_139', '7_11_141', '7_11_142', '7_11_143', '7_12_0', '7_12_1', '7_12_2', '7_12_3', '7_12_4', '7_12_5', '7_12_6', '7_12_7', '7_12_8', '7_12_9', '7_12_11', '7_12_12', '7_12_13', '7_12_14', '7_12_15', '7_12_16', '7_12_17', '7_12_18', '7_12_19', '7_12_20', '7_12_22', '7_12_23', '7_12_24', '7_12_25', '7_12_27', '7_12_28', '7_12_29', '7_12_30', '7_12_31', '7_12_32', '7_12_33', '7_12_34', '7_12_35', '7_12_36', '7_12_37', '7_12_38', '7_12_39', '7_12_40', '7_12_41', '7_12_42', '7_12_43', '7_12_45', '7_12_46', '7_12_47', '7_12_49', '7_12_50', '7_12_51', '7_12_52', '7_12_53', '7_12_55', '7_12_56', '7_12_57', '7_12_58', '7_12_59', '7_12_60', '7_12_61', '7_12_62', '7_12_63', '7_12_64', '7_12_65', '7_12_66', '7_12_67', '7_12_68', '7_12_69', '7_12_70', '7_12_71', '7_12_72', '7_12_73', '7_12_74', '7_12_75', '7_12_76', '7_12_78', '7_12_79', '7_12_80', '7_12_81', '7_12_82', '7_12_83', '7_12_84', '7_12_85', '7_12_86', '7_12_87', '7_12_88', '7_12_89', '7_12_90', '7_12_91', '7_12_92', '7_12_93', '7_12_94', '7_12_95', '7_12_96', '7_12_97', '7_12_98', '7_12_99', '7_12_100', '7_12_101', '7_12_102', '7_12_103', '7_12_104', '7_12_105', '7_12_106', '7_12_107', '7_12_108', '7_12_109', '7_12_110', '7_12_111', '7_12_112', '7_12_113', '7_12_114', '7_12_115', '7_12_116', '7_12_117', '7_12_119', '7_12_120', '7_12_121', '7_12_122', '7_12_123', '7_12_124', '7_12_125', '7_12_126', '7_12_127', '7_12_128', '7_12_129', '7_12_130', '7_12_131', '7_12_132', '7_12_133', '7_12_135', '7_12_136', '7_12_137', '7_12_138', '7_12_139', '7_12_140', '7_12_141', '7_12_142', '7_12_143']
Stride_Size = 128
FOLDER = "/home/SSRS-main/SAM_RS/"
LABELS = ["roads", "buildings", "low veg.", "trees", "cars", "clutter"]
N_CLASSES = len(LABELS) # Number of classes
WEIGHTS = torch.ones(N_CLASSES) # Weights for class balancing
MAIN_FOLDER = FOLDER + 'Postdam/'
DATA_FOLDER = MAIN_FOLDER + 'img_0.25/{}.tif'
LABEL_FOLDER = MAIN_FOLDER + 'label_0.25/{}.tif'
BOUNDARY_FOLDER = MAIN_FOLDER + 'sam2_sgb/{}.png'
OBJECT_FOLDER = MAIN_FOLDER + 'sam2_3sgo//{}.png'
ERODED_FOLDER = MAIN_FOLDER + 'label_0.25/{}.tif'
# palette = {0 : (255, 255, 255), # background
# 1 : (255, 0, 0), # building
# 2 : (255, 255, 0), # road
# 3 : (0, 0, 255), # water
# 4 : (159, 129, 183), # barren
# 5 : (0, 255, 0), # forest
# 6 : (255, 195, 128)} # agriculture
palette = {0: (255, 255, 255), # Impervious surfaces (white)
1: (0, 0, 255), # Buildings (blue)
2: (0, 255, 255), # Low vegetation (cyan)
3: (0, 255, 0), # Trees (green)
4: (255, 255, 0), # Cars (yellow)
5: (255, 0, 0), # Clutter (red)
6: (0, 0, 0)} # Undefined (black)
invert_palette = {v: k for k, v in palette.items()}
if DATASET == 'Vaihingen':
#Vaihingen数据集
#all
# train_ids = ['1_0', '1_1', '1_10', '1_11', '1_12', '1_13', '1_14', '1_15', '1_16', '1_17', '1_18', '1_19', '1_2', '1_20', '1_21', '1_22', '1_23', '1_24', '1_25', '1_26', '1_27', '1_28', '1_29', '1_3', '1_30', '1_31', '1_32', '1_33', '1_34', '1_4', '1_5', '1_6', '1_7', '1_8', '1_9', '3_0', '3_1', '3_10', '3_11', '3_12', '3_13', '3_14', '3_15', '3_16', '3_17', '3_18', '3_19', '3_2', '3_20', '3_21', '3_22', '3_23', '3_24', '3_25', '3_26', '3_27', '3_28', '3_29', '3_3', '3_30', '3_31', '3_32', '3_33', '3_34', '3_35', '3_36', '3_37', '3_38', '3_39', '3_4', '3_5', '3_6', '3_7', '3_8', '3_9', '5_0', '5_1', '5_10', '5_11', '5_12', '5_13', '5_14', '5_15', '5_16', '5_17', '5_18', '5_19', '5_2', '5_20', '5_21', '5_22', '5_23', '5_24', '5_25', '5_26', '5_27', '5_28', '5_29', '5_3', '5_4', '5_5', '5_6', '5_7', '5_8', '5_9', '7_0', '7_1', '7_10', '7_11', '7_12', '7_13', '7_14', '7_15', '7_16', '7_17', '7_18', '7_19', '7_2', '7_20', '7_21', '7_22', '7_23', '7_24', '7_25', '7_26', '7_27', '7_28', '7_29', '7_3', '7_4', '7_5', '7_6', '7_7', '7_8', '7_9', '11_0', '11_1', '11_10', '11_11', '11_12', '11_13', '11_14', '11_15', '11_16', '11_17', '11_18', '11_19', '11_2', '11_20', '11_21', '11_22', '11_23', '11_24', '11_25', '11_26', '11_27', '11_28', '11_29', '11_3', '11_30', '11_31', '11_32', '11_33', '11_34', '11_4', '11_5', '11_6', '11_7', '11_8', '11_9', '13_0', '13_1', '13_10', '13_11', '13_12', '13_13', '13_14', '13_15', '13_16', '13_17', '13_18', '13_19', '13_2', '13_20', '13_21', '13_22', '13_23', '13_24', '13_25', '13_26', '13_27', '13_28', '13_29', '13_3', '13_30', '13_31', '13_32', '13_33', '13_34', '13_35', '13_36', '13_37', '13_38', '13_39', '13_4', '13_40', '13_41', '13_42', '13_43', '13_44', '13_45', '13_46', '13_47', '13_48', '13_5', '13_6', '13_7', '13_8', '13_9', '15_0', '15_1', '15_10', '15_11', '15_12', '15_13', '15_14', '15_15', '15_16', '15_17', '15_18', '15_19', '15_2', '15_20', '15_21', '15_22', '15_23', '15_24', '15_25', '15_26', '15_27', '15_28', '15_29', '15_3', '15_30', '15_31', '15_32', '15_33', '15_34', '15_4', '15_5', '15_6', '15_7', '15_8', '15_9', '17_0', '17_1', '17_10', '17_11', '17_12', '17_13', '17_14', '17_15', '17_16', '17_17', '17_2', '17_3', '17_4', '17_5', '17_6', '17_7', '17_8', '17_9', '21_0', '21_1', '21_10', '21_11', '21_12', '21_13', '21_14', '21_15', '21_16', '21_17', '21_18', '21_19', '21_2', '21_20', '21_21', '21_22', '21_23', '21_24', '21_25', '21_26', '21_27', '21_28', '21_29', '21_3', '21_4', '21_5', '21_6', '21_7', '21_8', '21_9', '23_0', '23_1', '23_10', '23_11', '23_12', '23_13', '23_14', '23_15', '23_16', '23_17', '23_18', '23_19', '23_2', '23_20', '23_21', '23_22', '23_23', '23_24', '23_25', '23_26', '23_27', '23_28', '23_29', '23_3', '23_4', '23_5', '23_6', '23_7', '23_8', '23_9', '26_0', '26_1', '26_10', '26_11', '26_12', '26_13', '26_14', '26_15', '26_16', '26_17', '26_18', '26_19', '26_2', '26_20', '26_21', '26_22', '26_23', '26_24', '26_25', '26_26', '26_27', '26_28', '26_29', '26_3', '26_30', '26_31', '26_32', '26_33', '26_34', '26_35', '26_36', '26_37', '26_38', '26_39', '26_4', '26_5', '26_6', '26_7', '26_8', '26_9', '28_0', '28_1', '28_10', '28_11', '28_12', '28_13', '28_14', '28_15', '28_16', '28_17', '28_18', '28_19', '28_2', '28_20', '28_21', '28_22', '28_23', '28_24', '28_25', '28_26', '28_27', '28_28', '28_29', '28_3', '28_30', '28_31', '28_32', '28_33', '28_34', '28_4', '28_5', '28_6', '28_7', '28_8', '28_9', '30_0', '30_1', '30_10', '30_11', '30_12', '30_13', '30_14', '30_15', '30_16', '30_17', '30_18', '30_19', '30_2', '30_20', '30_21', '30_22', '30_23', '30_24', '30_25', '30_26', '30_27', '30_28', '30_29', '30_3', '30_30', '30_31', '30_32', '30_33', '30_34', '30_4', '30_5', '30_6', '30_7', '30_8', '30_9', '32_0', '32_1', '32_10', '32_11', '32_12', '32_13', '32_14', '32_15', '32_16', '32_17', '32_18', '32_19', '32_2', '32_20', '32_21', '32_22', '32_23', '32_24', '32_25', '32_26', '32_27', '32_28', '32_29', '32_3', '32_4', '32_5', '32_6', '32_7', '32_8', '32_9', '34_0', '34_1', '34_10', '34_11', '34_12', '34_13', '34_14', '34_15', '34_16', '34_17', '34_18', '34_19', '34_2', '34_20', '34_21', '34_22', '34_23', '34_3', '34_4', '34_5', '34_6', '34_7', '34_8', '34_9', '37_0', '37_1', '37_10', '37_11', '37_12', '37_13', '37_14', '37_15', '37_16', '37_17', '37_18', '37_19', '37_2', '37_20', '37_21', '37_22', '37_23', '37_24', '37_3', '37_4', '37_5', '37_6', '37_7', '37_8', '37_9']
# #0.25
train_ids = ['1_29', '1_8', '1_17', '1_22', '1_23', '1_13', '1_19', '1_30', '3_24', '3_28', '3_23', '3_9', '3_33', '3_15', '3_25', '3_11', '3_29', '3_31', '5_14', '5_16', '5_1', '5_13', '5_27', '5_23', '5_22', '7_1', '7_17', '7_2', '7_26', '7_11', '7_22', '7_15', '11_8', '11_18', '11_14', '11_21', '11_31', '11_17', '11_16', '11_15', '13_26', '13_38', '13_48', '13_21', '13_7', '13_5', '13_39', '13_29', '13_16', '13_4', '13_47', '13_1', '15_22', '15_7', '15_28', '15_27', '15_10', '15_13', '15_8', '15_11', '17_14', '17_7', '17_8', '17_12', '21_11', '21_20', '21_25', '21_4', '21_19', '21_21', '21_6', '23_13', '23_4', '23_0', '23_9', '23_14', '23_8', '23_15', '26_16', '26_32', '26_36', '26_17', '26_5', '26_1', '26_23', '26_10', '26_25', '26_22', '28_17', '28_33', '28_7', '28_34', '28_21', '28_10', '28_12', '28_26', '30_32', '30_31', '30_20', '30_22', '30_26', '30_33', '30_29', '30_19', '32_15', '32_16', '32_27', '32_20', '32_11', '32_23', '32_7', '34_16', '34_6', '34_1', '34_19', '34_2', '34_17', '37_22', '37_3', '37_18', '37_11', '37_8', '37_19']
# #0.125
# train_ids = ['1_12', '1_1', '1_14', '1_11', '3_36', '3_24', '3_16', '3_23', '3_4', '5_19', '5_28', '5_20', '7_0', '7_16', '7_6', '11_15', '11_25', '11_9', '11_2', '13_24', '13_38', '13_3', '13_30', '13_43', '13_39', '15_18', '15_24', '15_2', '15_28', '17_12', '17_9', '21_0', '21_22', '21_21', '23_8', '23_25', '23_27', '26_37', '26_32', '26_26', '26_36', '26_9', '28_23', '28_32', '28_11', '28_34', '30_3', '30_4', '30_19', '30_13', '32_19', '32_29', '32_5', '34_13', '34_3', '34_8', '37_6', '37_14', '37_2']
# #0.0625
# train_ids = ['1_32', '1_17', '3_24', '3_10', '5_26', '7_5', '11_19', '11_18', '13_18', '13_30', '13_17', '15_2', '15_17', '17_11', '21_3', '23_9', '26_16', '26_8', '28_24', '28_4', '30_30', '30_20', '32_17', '34_16', '37_7']
#all
test_ids = ['2_0', '2_1', '2_10', '2_11', '2_12', '2_13', '2_14', '2_15', '2_16', '2_17', '2_18', '2_19', '2_2', '2_20', '2_21', '2_22', '2_23', '2_24', '2_25', '2_26', '2_27', '2_28', '2_29', '2_3', '2_30', '2_31', '2_32', '2_33', '2_34', '2_35', '2_36', '2_37', '2_38', '2_39', '2_4', '2_40', '2_41', '2_5', '2_6', '2_7', '2_8', '2_9', '4_0', '4_1', '4_10', '4_11', '4_12', '4_13', '4_14', '4_15', '4_16', '4_17', '4_18', '4_19', '4_2', '4_20', '4_21', '4_22', '4_23', '4_24', '4_25', '4_26', '4_27', '4_28', '4_29', '4_3', '4_4', '4_5', '4_6', '4_7', '4_8', '4_9', '6_0', '6_1', '6_10', '6_11', '6_12', '6_13', '6_14', '6_15', '6_16', '6_17', '6_18', '6_19', '6_2', '6_20', '6_21', '6_22', '6_23', '6_24', '6_25', '6_26', '6_27', '6_28', '6_29', '6_3', '6_4', '6_5', '6_6', '6_7', '6_8', '6_9', '8_0', '8_1', '8_10', '8_11', '8_12', '8_13', '8_14', '8_15', '8_16', '8_17', '8_18', '8_19', '8_2', '8_20', '8_21', '8_22', '8_23', '8_24', '8_25', '8_26', '8_27', '8_28', '8_29', '8_3', '8_4', '8_5', '8_6', '8_7', '8_8', '8_9', '10_0', '10_1', '10_10', '10_11', '10_12', '10_13', '10_14', '10_15', '10_16', '10_17', '10_18', '10_19', '10_2', '10_20', '10_21', '10_22', '10_23', '10_24', '10_25', '10_26', '10_27', '10_28', '10_29', '10_3', '10_4', '10_5', '10_6', '10_7', '10_8', '10_9', '12_0', '12_1', '12_10', '12_11', '12_12', '12_13', '12_14', '12_15', '12_16', '12_17', '12_18', '12_19', '12_2', '12_20', '12_21', '12_22', '12_23', '12_24', '12_25', '12_26', '12_27', '12_28', '12_29', '12_3', '12_30', '12_31', '12_32', '12_33', '12_34', '12_4', '12_5', '12_6', '12_7', '12_8', '12_9', '14_0', '14_1', '14_10', '14_11', '14_12', '14_13', '14_14', '14_15', '14_16', '14_17', '14_18', '14_19', '14_2', '14_20', '14_21', '14_22', '14_23', '14_24', '14_25', '14_26', '14_27', '14_28', '14_29', '14_3', '14_30', '14_31', '14_32', '14_33', '14_34', '14_4', '14_5', '14_6', '14_7', '14_8', '14_9', '16_0', '16_1', '16_10', '16_11', '16_12', '16_13', '16_14', '16_15', '16_16', '16_17', '16_18', '16_19', '16_2', '16_20', '16_21', '16_22', '16_23', '16_24', '16_25', '16_26', '16_27', '16_28', '16_29', '16_3', '16_30', '16_31', '16_32', '16_33', '16_34', '16_4', '16_5', '16_6', '16_7', '16_8', '16_9', '20_0', '20_1', '20_10', '20_11', '20_12', '20_13', '20_14', '20_15', '20_16', '20_17', '20_18', '20_19', '20_2', '20_20', '20_21', '20_22', '20_23', '20_24', '20_25', '20_26', '20_27', '20_28', '20_29', '20_3', '20_4', '20_5', '20_6', '20_7', '20_8', '20_9', '22_0', '22_1', '22_10', '22_11', '22_12', '22_13', '22_14', '22_15', '22_16', '22_17', '22_18', '22_19', '22_2', '22_20', '22_21', '22_22', '22_23', '22_24', '22_25', '22_26', '22_27', '22_28', '22_29', '22_3', '22_4', '22_5', '22_6', '22_7', '22_8', '22_9', '24_0', '24_1', '24_10', '24_11', '24_12', '24_13', '24_14', '24_15', '24_16', '24_17', '24_18', '24_19', '24_2', '24_20', '24_21', '24_22', '24_23', '24_24', '24_25', '24_26', '24_27', '24_28', '24_29', '24_3', '24_4', '24_5', '24_6', '24_7', '24_8', '24_9', '27_0', '27_1', '27_10', '27_11', '27_12', '27_13', '27_14', '27_15', '27_16', '27_17', '27_18', '27_19', '27_2', '27_20', '27_21', '27_22', '27_23', '27_24', '27_25', '27_26', '27_27', '27_28', '27_29', '27_3', '27_30', '27_31', '27_32', '27_33', '27_34', '27_35', '27_36', '27_37', '27_38', '27_39', '27_4', '27_5', '27_6', '27_7', '27_8', '27_9', '29_0', '29_1', '29_10', '29_11', '29_12', '29_13', '29_14', '29_15', '29_16', '29_17', '29_18', '29_19', '29_2', '29_20', '29_21', '29_22', '29_23', '29_24', '29_25', '29_26', '29_27', '29_28', '29_29', '29_3', '29_30', '29_31', '29_32', '29_33', '29_34', '29_4', '29_5', '29_6', '29_7', '29_8', '29_9', '31_0', '31_1', '31_10', '31_11', '31_12', '31_13', '31_14', '31_15', '31_16', '31_17', '31_18', '31_19', '31_2', '31_20', '31_21', '31_22', '31_23', '31_24', '31_25', '31_26', '31_27', '31_28', '31_29', '31_3', '31_4', '31_5', '31_6', '31_7', '31_8', '31_9', '33_0', '33_1', '33_10', '33_11', '33_12', '33_13', '33_14', '33_15', '33_16', '33_17', '33_18', '33_19', '33_2', '33_20', '33_21', '33_22', '33_23', '33_3', '33_4', '33_5', '33_6', '33_7', '33_8', '33_9', '35_0', '35_1', '35_10', '35_11', '35_12', '35_13', '35_14', '35_15', '35_16', '35_17', '35_18', '35_19', '35_2', '35_20', '35_21', '35_22', '35_23', '35_24', '35_25', '35_26', '35_27', '35_28', '35_29', '35_3', '35_30', '35_31', '35_32', '35_33', '35_34', '35_4', '35_5', '35_6', '35_7', '35_8', '35_9', '38_0', '38_1', '38_10', '38_11', '38_12', '38_13', '38_14', '38_15', '38_16', '38_17', '38_18', '38_19', '38_2', '38_20', '38_21', '38_22', '38_23', '38_24', '38_25', '38_26', '38_27', '38_28', '38_29', '38_3', '38_30', '38_31', '38_32', '38_33', '38_34', '38_35', '38_36', '38_37', '38_38', '38_39', '38_4', '38_40', '38_41', '38_42', '38_43', '38_44', '38_45', '38_46', '38_47', '38_48', '38_49', '38_5', '38_50', '38_51', '38_52', '38_53', '38_54', '38_55', '38_56', '38_57', '38_58', '38_59', '38_6', '38_7', '38_8', '38_9']
# #0.25
PL_ids = ['1_2', '1_11', '1_6', '1_21', '1_5', '1_15', '1_34', '1_33', '1_10', '1_7', '1_18', '1_16', '1_12', '1_27', '1_4', '1_1', '1_9', '1_14', '1_20', '1_28', '1_24', '1_25', '1_31', '1_0', '1_32', '1_3', '1_26', '3_20', '3_4', '3_0', '3_10', '3_27', '3_35', '3_12', '3_1', '3_37', '3_8', '3_39', '3_13', '3_7', '3_21', '3_38', '3_17', '3_32', '3_14', '3_30', '3_16', '3_3', '3_6', '3_19', '3_36', '3_26', '3_5', '3_2', '3_18', '3_22', '3_34', '5_0', '5_24', '5_29', '5_17', '5_7', '5_6', '5_2', '5_8', '5_11', '5_4', '5_18', '5_21', '5_19', '5_10', '5_15', '5_26', '5_3', '5_28', '5_20', '5_12', '5_5', '5_25', '5_9', '7_8', '7_3', '7_23', '7_18', '7_29', '7_10', '7_9', '7_6', '7_16', '7_5', '7_7', '7_4', '7_25', '7_0', '7_27', '7_24', '7_14', '7_20', '7_12', '7_21', '7_28', '7_13', '7_19', '11_29', '11_20', '11_2', '11_4', '11_23', '11_33', '11_25', '11_22', '11_27', '11_5', '11_28', '11_1', '11_12', '11_7', '11_11', '11_32', '11_19', '11_10', '11_9', '11_13', '11_24', '11_3', '11_6', '11_30', '11_26', '11_34', '11_0', '13_2', '13_11', '13_33', '13_27', '13_41', '13_8', '13_34', '13_25', '13_12', '13_6', '13_40', '13_35', '13_18', '13_36', '13_42', '13_20', '13_28', '13_45', '13_24', '13_14', '13_19', '13_0', '13_9', '13_30', '13_44', '13_31', '13_10', '13_13', '13_46', '13_3', '13_37', '13_15', '13_22', '13_32', '13_43', '13_23', '13_17', '15_3', '15_2', '15_31', '15_26', '15_30', '15_0', '15_21', '15_24', '15_34', '15_20', '15_33', '15_23', '15_25', '15_16', '15_12', '15_29', '15_15', '15_6', '15_19', '15_18', '15_17', '15_32', '15_1', '15_5', '15_4', '15_14', '15_9', '17_10', '17_15', '17_6', '17_3', '17_16', '17_9', '17_0', '17_5', '17_1', '17_2', '17_17', '17_4', '17_11', '17_13', '21_0', '21_24', '21_3', '21_8', '21_15', '21_17', '21_29', '21_1', '21_23', '21_16', '21_12', '21_9', '21_13', '21_27', '21_18', '21_22', '21_28', '21_26', '21_7', '21_2', '21_10', '21_5', '21_14', '23_3', '23_16', '23_27', '23_1', '23_5', '23_21', '23_23', '23_28', '23_25', '23_26', '23_10', '23_12', '23_7', '23_2', '23_19', '23_20', '23_6', '23_22', '23_29', '23_18', '23_17', '23_11', '23_24', '26_35', '26_4', '26_6', '26_24', '26_2', '26_38', '26_27', '26_34', '26_30', '26_31', '26_7', '26_11', '26_0', '26_14', '26_8', '26_39', '26_29', '26_21', '26_3', '26_19', '26_12', '26_13', '26_33', '26_15', '26_18', '26_9', '26_28', '26_37', '26_26', '26_20', '28_20', '28_9', '28_0', '28_32', '28_23', '28_25', '28_2', '28_28', '28_29', '28_14', '28_27', '28_13', '28_16', '28_11', '28_31', '28_8', '28_22', '28_18', '28_1', '28_19', '28_3', '28_30', '28_4', '28_5', '28_24', '28_6', '28_15', '30_3', '30_21', '30_0', '30_7', '30_4', '30_5', '30_11', '30_28', '30_15', '30_9', '30_30', '30_17', '30_14', '30_16', '30_34', '30_12', '30_27', '30_2', '30_23', '30_8', '30_6', '30_25', '30_24', '30_10', '30_13', '30_18', '30_1', '32_14', '32_9', '32_17', '32_21', '32_4', '32_18', '32_12', '32_24', '32_0', '32_22', '32_29', '32_19', '32_6', '32_25', '32_26', '32_2', '32_1', '32_13', '32_8', '32_10', '32_5', '32_3', '32_28', '34_18', '34_4', '34_9', '34_13', '34_3', '34_15', '34_12', '34_14', '34_10', '34_23', '34_11', '34_20', '34_22', '34_21', '34_8', '34_5', '34_7', '34_0', '37_10', '37_5', '37_23', '37_0', '37_1', '37_12', '37_4', '37_9', '37_21', '37_24', '37_6', '37_2', '37_20', '37_15', '37_17', '37_16', '37_14', '37_7', '37_13']
#
# #0.125
# PL_ids = ['1_9', '1_24', '1_34', '1_27', '1_20', '1_33', '1_10', '1_16', '1_6', '1_3', '1_25', '1_5', '1_19', '1_31', '1_29', '1_15', '1_26', '1_17', '1_18', '1_13', '1_30', '1_0', '1_21', '1_32', '1_22', '1_2', '1_7', '1_8', '1_28', '1_4', '1_23', '3_27', '3_2', '3_34', '3_25', '3_10', '3_29', '3_7', '3_9', '3_17', '3_39', '3_22', '3_12', '3_37', '3_32', '3_33', '3_11', '3_30', '3_6', '3_1', '3_31', '3_38', '3_18', '3_5', '3_21', '3_15', '3_28', '3_8', '3_26', '3_19', '3_3', '3_0', '3_20', '3_13', '3_14', '3_35', '5_8', '5_4', '5_25', '5_29', '5_5', '5_15', '5_21', '5_3', '5_18', '5_16', '5_0', '5_1', '5_13', '5_7', '5_9', '5_11', '5_12', '5_6', '5_27', '5_24', '5_17', '5_26', '5_10', '5_22', '5_14', '5_2', '5_23', '7_12', '7_1', '7_20', '7_9', '7_28', '7_4', '7_10', '7_7', '7_25', '7_19', '7_21', '7_22', '7_11', '7_13', '7_18', '7_26', '7_15', '7_29', '7_8', '7_5', '7_14', '7_27', '7_3', '7_24', '7_2', '7_17', '7_23', '11_33', '11_8', '11_1', '11_29', '11_34', '11_7', '11_4', '11_13', '11_23', '11_16', '11_27', '11_18', '11_20', '11_17', '11_10', '11_21', '11_12', '11_24', '11_22', '11_0', '11_3', '11_19', '11_5', '11_14', '11_31', '11_32', '11_28', '11_26', '11_30', '11_11', '11_6', '13_22', '13_20', '13_28', '13_37', '13_21', '13_25', '13_10', '13_41', '13_12', '13_46', '13_16', '13_15', '13_2', '13_8', '13_18', '13_19', '13_44', '13_9', '13_48', '13_7', '13_27', '13_33', '13_36', '13_47', '13_1', '13_32', '13_17', '13_45', '13_34', '13_6', '13_14', '13_0', '13_4', '13_42', '13_40', '13_13', '13_11', '13_23', '13_5', '13_26', '13_29', '13_35', '13_31', '15_6', '15_4', '15_26', '15_17', '15_14', '15_10', '15_20', '15_1', '15_16', '15_34', '15_21', '15_7', '15_22', '15_19', '15_27', '15_5', '15_0', '15_25', '15_31', '15_12', '15_29', '15_30', '15_23', '15_33', '15_13', '15_3', '15_32', '15_11', '15_8', '15_9', '15_15', '17_4', '17_10', '17_15', '17_0', '17_1', '17_16', '17_2', '17_8', '17_6', '17_13', '17_3', '17_17', '17_14', '17_7', '17_5', '17_11', '21_3', '21_1', '21_23', '21_10', '21_17', '21_13', '21_15', '21_14', '21_24', '21_2', '21_9', '21_8', '21_19', '21_12', '21_25', '21_16', '21_11', '21_6', '21_27', '21_26', '21_29', '21_4', '21_5', '21_18', '21_20', '21_7', '21_28', '23_21', '23_15', '23_0', '23_26', '23_29', '23_10', '23_16', '23_14', '23_9', '23_7', '23_17', '23_19', '23_20', '23_3', '23_11', '23_28', '23_13', '23_4', '23_24', '23_12', '23_5', '23_2', '23_1', '23_23', '23_22', '23_18', '23_6', '26_7', '26_31', '26_17', '26_25', '26_6', '26_2', '26_38', '26_14', '26_13', '26_30', '26_5', '26_34', '26_39', '26_8', '26_0', '26_35', '26_3', '26_24', '26_21', '26_27', '26_33', '26_12', '26_18', '26_11', '26_1', '26_20', '26_28', '26_22', '26_15', '26_4', '26_16', '26_10', '26_19', '26_29', '26_23', '28_10', '28_26', '28_1', '28_6', '28_13', '28_15', '28_0', '28_27', '28_20', '28_9', '28_31', '28_4', '28_29', '28_18', '28_33', '28_14', '28_28', '28_21', '28_25', '28_12', '28_2', '28_19', '28_17', '28_8', '28_3', '28_22', '28_5', '28_24', '28_30', '28_7', '28_16', '30_7', '30_15', '30_28', '30_27', '30_33', '30_0', '30_23', '30_22', '30_24', '30_25', '30_31', '30_2', '30_34', '30_8', '30_32', '30_14', '30_16', '30_9', '30_6', '30_10', '30_11', '30_30', '30_5', '30_18', '30_29', '30_21', '30_1', '30_17', '30_26', '30_12', '30_20', '32_18', '32_25', '32_0', '32_7', '32_23', '32_11', '32_26', '32_8', '32_22', '32_9', '32_4', '32_10', '32_21', '32_27', '32_1', '32_2', '32_15', '32_20', '32_13', '32_16', '32_6', '32_17', '32_3', '32_24', '32_14', '32_28', '32_12', '34_11', '34_12', '34_21', '34_22', '34_10', '34_0', '34_5', '34_16', '34_7', '34_17', '34_2', '34_23', '34_9', '34_6', '34_14', '34_18', '34_20', '34_15', '34_1', '34_4', '34_19', '37_22', '37_7', '37_18', '37_9', '37_3', '37_17', '37_15', '37_0', '37_8', '37_1', '37_20', '37_4', '37_16', '37_13', '37_5', '37_19', '37_24', '37_21', '37_23', '37_10', '37_11', '37_12']
#
# #0.0625
# PL_ids = ['1_5', '1_2', '1_18', '1_20', '1_13', '1_21', '1_25', '1_3', '1_33', '1_12', '1_0', '1_26', '1_27', '1_11', '1_30', '1_23', '1_8', '1_6', '1_4', '1_29', '1_19', '1_15', '1_24', '1_1', '1_22', '1_16', '1_9', '1_31', '1_10', '1_7', '1_28', '1_14', '1_34', '3_31', '3_1', '3_32', '3_20', '3_37', '3_17', '3_27', '3_28', '3_13', '3_16', '3_30', '3_33', '3_6', '3_39', '3_12', '3_29', '3_36', '3_4', '3_8', '3_14', '3_35', '3_22', '3_21', '3_15', '3_9', '3_19', '3_0', '3_34', '3_3', '3_7', '3_18', '3_25', '3_5', '3_11', '3_38', '3_26', '3_23', '3_2', '5_7', '5_25', '5_28', '5_20', '5_5', '5_3', '5_12', '5_16', '5_17', '5_27', '5_1', '5_11', '5_23', '5_8', '5_22', '5_0', '5_9', '5_21', '5_2', '5_29', '5_24', '5_14', '5_13', '5_19', '5_4', '5_10', '5_15', '5_18', '5_6', '7_26', '7_14', '7_9', '7_20', '7_3', '7_25', '7_27', '7_18', '7_28', '7_16', '7_8', '7_22', '7_1', '7_12', '7_4', '7_29', '7_10', '7_17', '7_6', '7_11', '7_2', '7_0', '7_21', '7_19', '7_15', '7_23', '7_13', '7_24', '7_7', '11_33', '11_1', '11_9', '11_6', '11_28', '11_22', '11_21', '11_4', '11_30', '11_13', '11_8', '11_20', '11_2', '11_17', '11_34', '11_15', '11_29', '11_26', '11_3', '11_25', '11_16', '11_14', '11_24', '11_27', '11_10', '11_5', '11_23', '11_0', '11_12', '11_31', '11_32', '11_11', '11_7', '13_3', '13_22', '13_10', '13_44', '13_39', '13_29', '13_15', '13_46', '13_33', '13_45', '13_43', '13_48', '13_31', '13_20', '13_8', '13_21', '13_41', '13_36', '13_9', '13_13', '13_5', '13_28', '13_14', '13_42', '13_27', '13_25', '13_34', '13_40', '13_1', '13_37', '13_35', '13_6', '13_19', '13_16', '13_12', '13_26', '13_2', '13_11', '13_4', '13_47', '13_32', '13_0', '13_38', '13_23', '13_24', '13_7', '15_1', '15_34', '15_27', '15_13', '15_19', '15_12', '15_5', '15_18', '15_10', '15_0', '15_15', '15_25', '15_33', '15_28', '15_4', '15_6', '15_26', '15_23', '15_21', '15_11', '15_9', '15_32', '15_16', '15_8', '15_31', '15_24', '15_30', '15_14', '15_7', '15_22', '15_29', '15_20', '15_3', '17_15', '17_10', '17_14', '17_9', '17_1', '17_6', '17_16', '17_2', '17_7', '17_12', '17_5', '17_0', '17_3', '17_4', '17_13', '17_8', '17_17', '21_23', '21_11', '21_6', '21_16', '21_14', '21_15', '21_0', '21_27', '21_29', '21_5', '21_18', '21_20', '21_19', '21_2', '21_9', '21_8', '21_7', '21_28', '21_12', '21_22', '21_26', '21_25', '21_24', '21_17', '21_1', '21_4', '21_13', '21_21', '21_10', '23_12', '23_5', '23_0', '23_23', '23_28', '23_18', '23_29', '23_27', '23_13', '23_17', '23_4', '23_16', '23_8', '23_26', '23_3', '23_7', '23_25', '23_11', '23_2', '23_20', '23_15', '23_14', '23_22', '23_21', '23_24', '23_19', '23_6', '23_10', '23_1', '26_34', '26_3', '26_32', '26_9', '26_35', '26_38', '26_33', '26_23', '26_30', '26_29', '26_24', '26_6', '26_31', '26_28', '26_15', '26_1', '26_39', '26_5', '26_19', '26_22', '26_36', '26_21', '26_11', '26_18', '26_25', '26_27', '26_20', '26_26', '26_0', '26_14', '26_4', '26_10', '26_17', '26_7', '26_37', '26_13', '26_2', '26_12', '28_15', '28_28', '28_31', '28_7', '28_22', '28_11', '28_33', '28_32', '28_18', '28_0', '28_17', '28_13', '28_9', '28_19', '28_3', '28_10', '28_34', '28_21', '28_1', '28_23', '28_16', '28_30', '28_29', '28_20', '28_5', '28_8', '28_6', '28_14', '28_2', '28_27', '28_25', '28_26', '28_12', '30_32', '30_19', '30_7', '30_6', '30_2', '30_14', '30_25', '30_10', '30_8', '30_34', '30_33', '30_26', '30_27', '30_5', '30_11', '30_12', '30_18', '30_9', '30_13', '30_31', '30_17', '30_23', '30_0', '30_16', '30_3', '30_28', '30_4', '30_1', '30_15', '30_29', '30_21', '30_24', '30_22', '32_21', '32_24', '32_16', '32_14', '32_8', '32_23', '32_20', '32_18', '32_25', '32_29', '32_10', '32_2', '32_9', '32_13', '32_27', '32_19', '32_22', '32_26', '32_12', '32_11', '32_1', '32_6', '32_3', '32_4', '32_0', '32_5', '32_7', '32_15', '32_28', '34_19', '34_7', '34_6', '34_13', '34_2', '34_3', '34_9', '34_0', '34_11', '34_1', '34_18', '34_15', '34_8', '34_20', '34_17', '34_4', '34_5', '34_21', '34_14', '34_23', '34_12', '34_22', '34_10', '37_0', '37_13', '37_19', '37_23', '37_14', '37_24', '37_22', '37_2', '37_5', '37_20', '37_11', '37_8', '37_12', '37_3', '37_21', '37_10', '37_15', '37_6', '37_1', '37_18', '37_9', '37_17', '37_4', '37_16']
Stride_Size = 128
FOLDER = "/data5/open_code/Datasets/"
LABELS = ["roads", "buildings", "low veg.", "trees", "cars", "clutter"]
N_CLASSES = len(LABELS) # Number of classes
WEIGHTS = torch.ones(N_CLASSES) # Weights for class balancing
MAIN_FOLDER = FOLDER + 'Vaihingen/'
DATA_FOLDER = MAIN_FOLDER + 'img_0.25/{}.tif'
LABEL_FOLDER = MAIN_FOLDER + 'label_0.25/{}.tif'
BOUNDARY_FOLDER = MAIN_FOLDER + 'sam2_sgb/{}.png'
OBJECT_FOLDER = MAIN_FOLDER + 'sam2_3sgo/{}.png'
ERODED_FOLDER = MAIN_FOLDER + 'label_0.25/{}.tif'
#### Please review and modify the code on line 818 when using the LoveDA dataset
if DATASET == 'Loveda':
# train_ids = 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# 0.25
train_ids = ['2064', '1759', '144', '1320', '1746', '292', '677', '321', '1205', '1277', '941', '526', '1617', '1443', '816', '329', '558', '1667', '177', '2172', '354', '1781', '841', '1008', '1276', '2041', '857', '1000', '1776', '246', '313', '1453', '2502', '1922', '278', '1466', '1310', '2210', '2437', '798', '1469', '1977', '992', '1842', '346', '466', '1301', '290', '1151', '2369', '1195', '1158', '455', '1623', '1003', '388', '2446', '172', '1082', '1891', '715', '1545', '1919', '1846', '2499', '1761', '2130', '1566', '1998', '1093', '94', '2508', '768', '2070', '219', '239', '999', '956', '1062', '1754', '614', '1470', '705', '35', '1345', '2311', '286', '471', '1654', '935', '1764', '1073', '1498', '951', '1569', '1428', '2120', '379', '515', '1527', '1894', '1507', '126', '1780', '2263', '1092', '2456', '918', '2320', '1393', '2364', '1128', '975', '1628', '1777', '1118', '772', '1673', '333', '162', '1011', '75', '233', '2255', '948', '1164', '275', '915', '1505', '2223', '487', '656', '903', '77', '1605', '1586', '889', '1983', '234', '2233', '1728', '2384', '1362', '528', '1938', '1611', '2324', '2418', '1782', '599', '2477', '535', '1540', '949', '937', '742', '1963', '410', '836', '633', '2286', '1664', '45', '1434', '2186', '2299', '306', '650', '1697', '1060', '1700', '2376', '129', '1224', '1572', '341', '2415', '2250', '1595', '850', '416', '2121', '1518', '2391', '1559', '308', '2401', '2459', '2133', '447', '1669', '2300', '584', '309', '2010', '1896', '229', '176', '2269', '1429', '1046', '111', '200', '1867', '1973', '1298', '2207', '2108', '1342', '1495', '2162', '1255', '2520', '1463', '1783', '362', '244', '1874', '799', '530', '970', '1420', '672', '73', '1373', '812', '215', '1535', '606', '1178', '1088', '1377', '136', '131', '958', '2092', '2362', '1907', '2117', '208', '1710', '1189', '2216', '2192', '2248', '803', '2179', '2423', '1585', '1864', '1262', '1655', '2019', '818', '631', '933', '1130', '158', '706', '1068', '101', '1032', '1057', '137', '2312', '2406', '1704', '2204', '1913', '754', '1844', '993', '2356', '2473', '214', '2079', '2055', '780', '213', '986', '1895', '470', '2218', '307', '2138', '345', '1987', '1818', '1937', '93', '1686', '296', '671', '386', '2483', '1208', '1142', '1191', '1038', '1384', '1985', '259', '1975', '253', '2237', '1125', '2412', '352', '1263', '2371', '852', '1737', '1012', '464', '1836', '367', '701', '1942', '391', '301', '201', '21', '1383', '453', '1630', '127', '1707', '124', '2075', '509', '1108', '1788', '505', '838', '1386', '804', '1557', '2382', '2234', '1978', '493', '797', '2253', '1132', '2318', '592', '340', '718', '756', '100', '1370', '543', '2464', '1098', '171', '444', '834', '62', '1837', '1984', '1394', '666', '2032', '2333', '2101', '2449', '152', '1353', '866', '2266', '2063', '2090', '1279', '1712', '1227', '1408', '249', '312', '53', '2126', '2405', '1805', '1616', '406', '657', '2112', '917', '1716', '868', '151', '380', '1305', '1308', '327', '604', '1407', '205', '2165', '79', '1525', '906', '2008', '1622', '1526', '2338', '1127', '1791', '1204', '1964', '2377', '522', '245', '1030', '749', '2478', '2111', '1968', '2343', '1708', '39', '1141', '1590', '2140', '569', '678', '1917', '2102', '1513', '1725', '1852', '1356', '1225', '1720', '1862', '1702', '2467', '1389', '972', '56', '2352', '1237', '2114', '2115', '555', '103', '548', '317', '2367', '1773', '553', '1823', '745', '1675', '1789', '1672', '1500', '1567', '1880', '1722', '418', '1025', '1610', '2347', '1945', '199', '1768', '1464', '1213', '15', '2431', '134', '156', '1643', '1733', '2461', '1069', '667', '1115', '2492', '764', '1056', '2109', '102', '2231', '393', '2342', '1160', '2470', '1043', '147', '1624', '398', '482', '1972', '337', '1850', '1691', '2518', '849', '532', '1833', '419', '642', '2219', '1114', '1857', '383', '1531', '630', '1814', '1753', '808', '1102', '1883', '1550', '844', '467', '1647', '1419', '242', '1620', '710', '2390', '20', '726', '488', '820', '1014', '2124', '415', '363', '1516', '1543', '1948', '1401', '2168', '96', '78', '187', '559', '813', '1847', '622', '1903', '1222', '95', '451', '2143', '141', '722', '1268', '1487', '1711', '537', '1772', '218', '2073', '2386', '1607', '11', '322', '384', '2261', '2303', '1632', '174', '486', '1760', '1757', '1748', '1953', '2426', '1307', '1250', '1494', '2212', '2330', '552', '1741', '1400', '87', '1096', '2129', '59', '829', '947', '753', '1321', '243', '181', '1140', '1856', '1797', '1216', '716', '605', '2317', '1176', '347', '762', '1685', '2359', '1876', '854', '1041', '14', '1574', '681', '547', '541', '225', '1066', '303', '571', '2003', '1288', '37', '2351', '978', '428', '135', '2099', '2104', '1893', '1740', '1285', '717', '1544', '965', '966']
# 0.125
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# PL_0.25
PL_ids = ['2203', '982', '897', '1019', '1182', '452', '1371', '107', '413', '516', '267', '1238', '2271', '680', '8', '1930', '2211', '139', '577', '699', '1520', '873', '1905', '465', '579', '1676', '779', '801', '84', '628', '1009', '583', '2265', '2421', '2085', '1899', '503', '789', '608', '740', '1209', '1841', '832', '1561', '1018', '791', '490', '1501', '1367', '1052', '540', '2163', '252', '1099', '2224', '1724', '597', '679', '863', '2497', '538', '807', '2201', '1563', '2394', '814', '549', '2098', '2013', '886', '1976', '85', '1603', '1980', '2146', '519', '2354', '1886', '636', '1309', '1504', '659', '2213', '2252', '1959', '279', '939', '2494', '288', '2069', '1291', '1157', '1359', '1375', '2026', '998', '1925', '1798', '1070', '1820', '1067', '2123', '2409', '368', '1674', '2141', '2257', '2298', '1422', '781', '743', '1547', '1765', '685', '332', '1081', '2482', '2027', '424', '2127', '778', '1480', '2015', '1300', '2328', '212', '1650', '477', '1835', '1829', '2453', 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'1493', '2198', '1955', '1048', '1940', '2161', '209', '724', '1156', '1481', '2505', '1079', '618', '1410', '1417', '1581', '578', '2095', '2308', '1248', '19', '2294', '2147', '315', '1592', '1461', '689', '529', '2278', '356', '2445', '1599', '289', '1254', '1690', '2355', '261', '1579', '1858', '1723', '1627', '1826', '314', '1943', '620', '707', '1947', '1435', '2368', '693', '1587', '923', '2279', '1872', '945', '1558', '1982', '1107', '637', '913', '1344', '110', '431', '1016', '148', '1811', '1731', '271', '1683', '684', '967', '1391', '116', '469', '1220', '1368', '1445', '459', '1533', '1659', '1631', '890', '2187', '1084', '2428', '2251', '2074', '2440', '533', '2113', '2068', '1194', '2474', '1086', '295', '737', '1859', '2139', '725', '2148', '2018', '1694', '1949', '1337', '2329', '1890', '1979', '1122', '1873', '1150', '2128', '2051', '27', '1869', '1137', '1795', '1269', '729', '733', '113', '654', '565', '1477', '1571', '1413', '2448', '2290', '1449', '1794', '1570', '1129', '848', '1486', '2056', '273', '142', '81', '2184', '359', '895', '258', '1004', '2149', '1021', '2039', '550', '1639', '339', '378', '1931', '760', '237', '1197', '905', '1542', '1351', '1554', '731', '40', '409', '1412', '2034', '1035', '1071', '665', '2004', '202', '1074', '576', '883', '823', '570', '2420', '621', '1299', '1993', '351', '1169', '1094', '1223', '196', '1465', '344', '695', '927', '1612', '1010', '1878', '1636', '2054', '2422', '856', '257', '2487', '41', '456', '281', '2293', '1001', '2249', '2316', '901', '186', '1406', '1576', '330', '687', '165', '997', '1171', '1539', '2071', '475', '61', '1822', '1670', '1652', '2240', '360', '1243', '1090', '626', '600', '1813', '1324', '1693', '1934', '2319', '2031', '839', '241', '112', '1514', '2521', '1199', '500', '2160', '2036', '2232', '376', '765', '67', '457', '1662', '1727', '206', '1206', '1929', '2164', '964', '2334', '1771', '1005', '757', '981', '874', '2155', '1198', '1104', '880', '294', '2195', '1641', '1378', '1692', '34', '635', '2315', '920', '1366', '2150', '1302', '1259', '1181', '2323', '192', '310', '1904', '2062', '32', '1860', '1089', '2089', '1750', '566', '1072', '1682', '299', '2217', '1933', '483', '1448', '669', '1769', '1875', '2175', '1898', '316', '1468', '2469', '2450', '157', '1136', '1289', '1364', '1476', '1589', '926', '1242', '1995', '732', '761', '1472', '2242', '1629', '1606', '1341', '1452', '167', '417', '2480', '16', '276', '925', '2093', '1646', '268', '2393', '2463', '609', '1677', '1403', '2260', '2142', '50', '2476', '1499', '1801', '1215', '627', '2151', '2336', '48', '586', '1230', '497', '2280', '1582', '1909', '511', '862', '1638', '2484', '2443', '1121', '1145', '365', '514', '236', '2096', '1398', '1044', '502', '591', '1170', '1796', '560', '1163', '587', '43', '2190', '448', '888', '662', '1840', '1459', '1958', '382', '1969', '1885', '1446', '2509', '1684', '472', '106', '845', '2205', '1923', '2177', '2345', '952', '806', '1471', '1424', '646', '1548', '623', '2025', '282', '291', '2097', '2385', '153', '2517', '2167', '692', '222', '819', '750', '160', '7', '1053', '1911', '2262', '1126', '2472', '894', '374', '1138', '1232', '1450', '1565', '235', '1584', '1133', '66', '1952', '394', '403', '1802', '2481', '266', '23', '2082', '1986', '305', '1245', '596', '2229', '2152', '1162', '1427', '98', '865', '47', '1790', '184', '1597', '746', '1475', '2507', '1144', '302', '720', '1109', '1713', '2506', '350', '2077', '2442', '661', '1730', '2241', '1203', '1992', '932', '1803', '1029', '2372', '2030', '392', '859', '2392', '944', '995', '612', '1644', '2287', '2310', '649', '1609', '602', '1698', '518', '1031', '387', '641', '1848', '240', '3', '2173', '508', '92', '676', '1478', '580', '2193', '1095', '1879', '1281', '33', '1914', '1521', '648', '389', '2011', '1604', '2083', '954', '1752', '2340', '2235', '712', '65', '166', '1267', '1326', '675', '461', '507', '976', '1921', '2302', '2471', '52', '485', '879', '2106', '2398', '220', '371', '755', '1091', '269', '1436', '63', '336', '442', '2292', '300', '1173', '773', '198', '531', '2375', '427', '690', '784', '1211', '1120', '1313', '501', '335', '2254', '2335', '1329', '1613', '2498', '161', '601', '882', '1549', '1926', '2370', '1871', '2196', '792', '221', '90', '1578', '1849', '2512', '1989', '1474', '788', '826', '1260', '1996', '1360', '1678', '1414', '2137', '2496', '1508', '1190', '499', '795', '2209', '1456', '1405', '872', '454', '2430', '484', '1442', '1747', '2503', '1744', '1085', '2040', '1123', '1758', '719', '639', '961', '1482', '343', '188', '1075', '1179', '1188', '462', '1390', '542', '2313', '1981', '2174', '1608', '1249', '1217', '1023', '1064', '1532', '1537', '414', '495', '60', '2188', '2500', '1828', '2007', '632', '2107', '175', '1050', '1418', '400', '1568', '422', '1385', '1161', '492', '1830', '304', '928', '846', '960', '1297', '319', '1392', '751', '2045', '2353', '1512', '88', '1530', '449', '774', '1779', '298', '2157', '1601', '1296', '1396', '921', '1155', '1490', '1681', '1473', '734', '1235', '1339', '1454', '1718', '274', '2402', '36', '1106', '1824', '2282', '655', '1861', '2321', '272', '49', '943', '1124', '13', '1970', '2403', '1889', '2256', '2226', '2081', '1119', '2395', '1536', '968', '2490', '776', '2134', '2291', '912', '1967', '1918', '51', '575', '1275', '545', '1810', '2349', '1184', '1045', '1033', '247', '1458', '853', '2427', '1402', '76', '1455', '1439', '562', '2460', '1717', '262', '2058', '523', '2144', '2438', '2033', '907', '2275', '2002', '326', '191', '468', '2411', '833', '1580', '207', '2452', '775', '1653', '1787', '256', '711', '2176', '1600', '210', '1936', '1489', '1701', '22', '855', '17', '2285', '1770', '1257', '280', '771', '1', '793', '860', '1799', '694', '904', '25', '44', '1024', '1626', '563', '703', '1334', '2274', '1330', '2513', '358', '180', '805', '1946', '568', '802', '899', '613', '1441', '1153', '108', '984', '1971', '1143', '590', '980', '1022', '1049', '1634', '1343', '847', '1510', '479', '68', '840', '1809', '851', '1244', '2206', '1553', '1002', '1059', '2024', '1552', '1187', '1808', '1185', '72', '91', '610', '1900', '434', '1395', '1246', '2247', '506', '1932', '1965', '473', '1063', '1467', '1105', '2486', '668', '2363', '2457', '353', '2379', '458', '1619', '228', '1614', '2023', '370', '1827', '835', '1113', '1312', '517', '2050', '1340', '1515', '437', '293', '1036', '2125', '1061', '355', '2416', '2194', '1658', '1290', '1047', '2105', '619', '858', '323', '440', '436', '1256', '763', '133', '2404', '1743', '2076', '2042', '1149', '248', '581', '1966', '1649', '2047', '411', '1167', '491', '1517', '338', '1714', '1382', '1915', '782', '1186', '1462', '2466', '653', '870', '934', '2236', '700', '2413', '1214', '1175', '2227', '496', '638', '395', '311', '74', '1147', '709', '1920', '381', '476', '1196', '130', '817', '924', '2005', '2331', '1180', '1200', '1409', '1042', '828', '1146', '1583', '179', '1735', '713', '557', '2360', '2100', '2110', '1040', '822', '2170', '2447', '1541', '1906', '1485', '1266', '2020', '1821', '2044', '1497', '1519', '1077', '168', '2387', '2397', '1804', '1812', '1327', '955', '1165', '1866', '1666', '1834', '2408', '1888', '1908', '2214', '1950', '2326', '1705', '1028', '348', '815', '1404', '1831', '2116', '1331', '1838', '1625', '1637', '2454', '435', '1865', '86', '953', '2504', '1621', '2273', '1887', '173', '401', '1280', '1734', '1201', '603', '988', '1183', '377', '1825', '190', '2238', '1192', '2436', '2288', '617', '1912', '1233', '1039', '238', '5', '629', '38', '728', '1361', '119', '1843', '104', '673', '1387', '787', '1350', '2202', '1447', '2495', '2399', '1349', '2091', '777', '1306', '24', '1374', '2049', '2396', '2276', '1332', '1742', '1935', '264', '325', '1054', '1738', '1117', '2489', '277', '2012', '97', '909', '2325', '1017', '1228', '910', '940', '2327', '109', '2458', '1355', '644', '1352', '285', '2389', '950', '1916', '2455', '71', '1502', '145', '1668', '2158', '796', '375', '2087', '1699', '2135', '2057', '708', '2283', '1739', '2153', '994', '1679', '1956', '1007', '987', '738', '195', '1845', '2180', '1766', '189', '929', '1483', '2154', '1240', '372', '971', '1807', '1026', '115', '183', '2348', '1177', '117', '1479', '1336', '2215', '744']
# PL_0.125
# PL_ids = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '36', '37', '38', '39', '40', '41', '42', '43', '44', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '74', '76', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '95', '96', '97', '98', '99', '100', '102', '103', '104', '105', '106', '107', '108', '109', '110', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '127', '128', '130', '132', '133', '134', '135', '138', '139', '140', '141', '142', '143', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '159', '160', '161', '163', '164', '165', '166', '167', '168', '169', '170', '171', '173', '174', '175', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '201', '202', '203', '204', '205', '206', '207', '209', '210', '211', '212', '216', '217', '218', '220', '221', '222', '223', '224', '225', '226', '227', '228', '230', '231', '232', '235', '236', '237', '238', '240', '241', '242', '243', '245', '247', '248', '249', '250', '251', '252', '254', '255', '256', '257', '258', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '276', '277', '279', '280', '281', '282', '283', '284', '285', '287', '288', '289', '291', '293', '294', '295', '297', '298', '299', '300', '301', '302', '303', '304', '305', '310', '311', '312', '314', '315', '316', '317', '318', '319', '320', '322', '323', '324', '325', '326', '327', '328', '330', '331', '332', '334', '335', '336', '337', '338', '339', '340', '342', '343', '344', '347', '348', '349', '350', '351', '353', '355', '356', '357', '358', '359', '360', '361', '363', '364', '365', '366', '367', '368', '369', '370', '371', '372', '373', '374', '375', '376', '377', '378', '380', '381', '382', '383', '384', '385', '387', '389', '390', '391', '392', '393', '394', '395', '396', '397', '398', '399', '400', '401', '402', '403', '404', '405', '406', '407', '408', '409', '411', '412', '413', '414', '415', '417', '418', '419', '420', '421', '422', '423', '424', '425', '426', '427', '428', '429', '430', '431', '432', '433', '434', '435', '436', '437', '438', '439', '440', '441', '442', '443', '444', '445', '446', '448', '449', '450', '451', '452', '453', '454', '456', '457', '458', '459', '460', '461', '462', '463', '465', '467', '468', '469', '472', '473', '474', '475', '476', '477', '478', '479', '480', '481', '482', '483', '484', '485', '486', '488', '489', '490', '491', '492', '493', '494', '495', '496', '497', '498', '499', '500', '501', '502', '503', '504', '505', '506', '507', '508', '509', '510', '511', '512', '513', '514', '516', '517', '518', '519', '520', '521', '522', '523', '524', '525', '527', '529', '531', '532', '533', '534', '536', '537', '538', '539', '540', '541', '542', '543', '544', '545', '546', '547', '548', '549', '550', '551', '552', '553', '554', '555', '556', '557', '559', '560', '561', '562', '563', '564', '565', '566', '567', '568', '569', '570', '571', '572', '573', '574', '575', '576', '577', '578', '579', '580', '581', '582', '583', '585', '586', '587', '588', '589', '590', '591', '592', '593', '594', '595', '596', '597', '598', '600', '601', '602', '603', '604', '605', '607', '608', '609', '610', '611', '612', '613', '615', '616', '617', '618', '619', '620', '621', '622', '623', '624', '625', '626', '627', '628', '629', '630', '632', '634', '635', '636', '637', '638', '639', '640', '641', '642', '643', '644', '645', '646', '647', '648', '649', '651', '652', '653', '654', '655', '657', '658', '659', '660', '661', '662', '663', '664', '665', '666', '667', '668', '669', '670', '673', '674', '675', '676', '678', '679', '680', '681', '682', '683', '684', '685', '686', '687', '688', '689', '690', '691', '692', '693', '694', '695', '696', '697', '698', '699', '700', '701', '702', '703', '704', '707', '708', '709', '710', '711', '712', '713', '714', '716', '717', '718', '719', '720', '721', '722', '723', '724', '725', '726', '727', '728', '729', '730', '731', '732', '733', '734', '735', '736', '737', '738', '739', '740', '741', '743', '744', '745', '746', '747', '748', '749', '750', '751', '752', '753', '755', '756', '757', '758', '759', '760', '761', '762', '763', '764', '765', '766', '767', '769', '770', '771', '773', '774', '775', '776', '777', '778', '779', '781', '782', '783', '784', '785', '786', '787', '788', '789', '790', '791', '792', '793', '794', '795', '796', '797', '800', '801', '802', '804', '805', '806', '807', '808', '809', '810', '811', '813', '814', '815', '817', '819', '820', '821', '822', '823', '824', 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'989', '990', '991', '994', '995', '996', '997', '998', '1001', '1002', '1004', '1005', '1006', '1007', '1009', '1010', '1013', '1014', '1015', '1016', '1017', '1018', '1019', '1020', '1021', '1022', '1023', '1024', '1025', '1026', '1027', '1028', '1029', '1030', '1031', '1033', '1034', '1035', '1036', '1037', '1039', '1040', '1041', '1042', '1043', '1044', '1045', '1047', '1048', '1049', '1050', '1051', '1052', '1053', '1054', '1055', '1056', '1058', '1059', '1061', '1063', '1064', '1065', '1066', '1067', '1069', '1070', '1071', '1072', '1074', '1075', '1076', '1077', '1078', '1079', '1080', '1081', '1083', '1084', '1085', '1086', '1087', '1089', '1090', '1091', '1094', '1095', '1096', '1097', '1098', '1099', '1100', '1101', '1102', '1103', '1104', '1105', '1106', '1107', '1108', '1109', '1110', '1111', '1112', '1113', '1114', '1115', '1116', '1117', '1119', '1120', '1121', '1122', '1123', '1124', '1126', '1127', '1129', '1131', '1132', '1133', '1134', '1135', '1136', '1137', '1138', 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Stride_Size = 128
FOLDER = "/home/SSRS-main/SAM_RS_all/"
LABELS = ["background", "building", "road", "water", "barren", "forest", "agriculture"]
N_CLASSES = len(LABELS) # Number of classes
WEIGHTS = torch.ones(N_CLASSES) # Weights for class balancing
MAIN_FOLDER = FOLDER + 'loveda_all/'
DATA_FOLDER = MAIN_FOLDER + 'img_0.25/{}.png'
LABEL_FOLDER = MAIN_FOLDER + 'label_0.25/{}.png'
BOUNDARY_FOLDER = MAIN_FOLDER + 'sam2_sgb/{}.png'
OBJECT_FOLDER = MAIN_FOLDER + 'sam2_3sgo/{}.png'
ERODED_FOLDER = MAIN_FOLDER + 'label_0.25/{}.png'
palette = {0 : (255, 255, 255), # background
1 : (255, 0, 0), # building
2 : (255, 255, 0), # road
3 : (0, 0, 255), # water
4 : (159, 129, 183), # barren
5 : (0, 255, 0), # forest
6 : (255, 195, 128)} # agriculture
invert_palette = {v: k for k, v in palette.items()}
# palette_1 = { # background
# 0 : (255, 0, 0), # building
# 1: (255, 255, 0), # road
# 2 : (0, 0, 255), # water
# 3 : (159, 129, 183), # barren
# 4 : (0, 255, 0), # forest
# 5 : (255, 195, 128)} # agriculture
#
# invert_palette_1 = {v: k for k, v in palette_1.items()}
def convert_to_color(arr_2d, palette=palette):
""" Numeric labels to RGB-color encoding """
arr_3d = np.zeros((arr_2d.shape[0], arr_2d.shape[1], 3), dtype=np.uint8)
for c, i in palette.items():
m = arr_2d == c
arr_3d[m] = i
return arr_3d
# def convert_to_color_1(arr_2d, palette_1=palette_1):
# """ Numeric labels to RGB-color encoding """
# arr_3d = np.zeros((arr_2d.shape[0], arr_2d.shape[1], 3), dtype=np.uint8)
#
# for c, i in palette_1.items():
# m = arr_2d == c
# arr_3d[m] = i
#
# return arr_3d
# def convert_to_color(arr_2d, palette=palette):
# """ Numeric labels to RGB-color encoding """
# arr_3d = np.zeros((arr_2d.shape[0], arr_2d.shape[1], 3), dtype=np.uint8)
#
# for x in range(arr_2d.shape[0]):
# for y in range(arr_2d.shape[1]):
# label = arr_2d[x, y]
# arr_3d[x, y] = palette[label]
#
# return arr_3d
def convert_from_color(arr_3d, palette=invert_palette):
""" RGB-color encoding to grayscale labels """
arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
for c, i in palette.items():
m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
arr_2d[m] = i
return arr_2d
# def convert_from_color_1(arr_3d, palette_1=invert_palette_1):
# """ RGB-color encoding to grayscale labels """
# arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
#
# for c, i in palette_1.items():
# m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
# arr_2d[m] = i
#
# return arr_2d
def convert_rgb_to_indices(rgb_label):
indices_label = np.zeros_like(rgb_label, dtype=np.int64)
for color, index in invert_palette.items():
# 找到 RGB 图像中与当前颜色对应的像素，并将其标签设置为对应的索引值
indices_label[np.all(rgb_label == color, axis=-1)] = index
return indices_label
def save_img(tensor, name):
tensor = tensor.cpu() .permute((1, 0, 2, 3))
im = make_grid(tensor, normalize=True, scale_each=True, nrow=8, padding=2).permute((1, 2, 0))
im = (im.data.numpy() * 255.).astype(np.uint8)
Image.fromarray(im).save(name + '.jpg')
def object_process(object):
ids = np.unique(object)
new_id = 1
for id in ids[1:]:
object = np.where(object == id, new_id, object)
new_id += 1
return object
class ISPRS_dataset1(torch.utils.data.Dataset):
def __init__(self, ids, data_files=DATA_FOLDER, label_files=LABEL_FOLDER,
cache=False, augmentation=True):
super(ISPRS_dataset1, self).__init__()
self.augmentation = augmentation
self.cache = cache
# List of files
self.data_files = [DATA_FOLDER.format(id) for id in ids]
self.boundary_files = [BOUNDARY_FOLDER.format(id) for id in ids]
self.object_files = [OBJECT_FOLDER.format(id) for id in ids]
self.label_files = [LABEL_FOLDER.format(id) for id in ids]
# Sanity check : raise an error if some files do not exist
for f in self.data_files + self.label_files:
if not os.path.isfile(f):
raise KeyError('{} is not a file !'.format(f))
# Initialize cache dicts
self.data_cache_ = {}
self.boundary_cache_ = {}
self.object_cache_ = {}
self.label_cache_ = {}
def __len__(self):
# Default epoch size is 10 000 samples
# return BATCH_SIZE * 100
return BATCH_SIZE * 1000
@classmethod
def data_augmentation(cls, *arrays, flip=True, mirror=True):
will_flip, will_mirror = False, False
if flip and random.random() < 0.5:
will_flip = True
if mirror and random.random() < 0.5:
will_mirror = True
results = []
for array in arrays:
if will_flip:
if len(array.shape) == 2:
array = array[::-1, :]
else:
array = array[:, ::-1, :]
if will_mirror:
if len(array.shape) == 2:
array = array[:, ::-1]
else:
array = array[:, :, ::-1]
results.append(np.copy(array))
return tuple(results)
def __getitem__(self, i):
# Pick a random image
random_idx = random.randint(0, len(self.data_files) - 1)
# If the tile hasn't been loaded yet, put in cache
if random_idx in self.data_cache_.keys():
data = self.data_cache_[random_idx]
else:
# Data is normalized in [0, 1]
## Potsdam IRRG
if DATASET == 'Potsdam':
## RGB
data = io.imread(self.data_files[random_idx])[:, :, :3].transpose((2, 0, 1))
## IRRG
# data = io.imread(self.data_files[random_idx])[:, :, (3, 0, 1, 2)][:, :, :3].transpose((2, 0, 1))
data = 1 / 255 * np.asarray(data, dtype='float32')
else:
## Vaihingen IRRG
data = io.imread(self.data_files[random_idx])
data = 1 / 255 * np.asarray(data.transpose((2, 0, 1)), dtype='float32')
if self.cache:
self.data_cache_[random_idx] = data
if random_idx in self.boundary_cache_.keys():
boundary = self.boundary_cache_[random_idx]
else:
boundary = np.asarray(io.imread(self.boundary_files[random_idx])) / 255
boundary = boundary.astype(np.int64)
if self.cache:
self.boundary_cache_[random_idx] = boundary
if random_idx in self.object_cache_.keys():
object = self.object_cache_[random_idx]
else:
object = np.asarray(io.imread(self.object_files[random_idx]))
object = object.astype(np.int64)
if self.cache:
self.object_cache_[random_idx] = object
if random_idx in self.label_cache_.keys():
label = self.label_cache_[random_idx]
else:
# Labels are converted from RGB to their numeric values
if DATASET == 'Loveda':
label = np.asarray(io.imread(self.label_files[random_idx]), dtype='int64') - 1
else:
label = np.asarray(convert_from_color(io.imread(self.label_files[random_idx])), dtype='int64')
if self.cache:
self.label_cache_[random_idx] = label
# Get a random patch
x1, x2, y1, y2 = get_random_pos(data, WINDOW_SIZE)
data_p = data[:, x1:x2, y1:y2]
boundary_p = boundary[x1:x2, y1:y2]
object_p = object[x1:x2, y1:y2]
label_p = label[x1:x2, y1:y2]
# Data augmentation
# data_p, boundary_p, label_p = self.data_augmentation(data_p, boundary_p, label_p)
data_p, boundary_p, object_p, label_p = self.data_augmentation(data_p, boundary_p, object_p, label_p)
object_p = object_process(object_p)
# Data augmentation
# data_p, boundary_p, label_p = self.data_augmentation(data_p, boundary_p, label_p)
data_p, boundary_p, object_p, label_p = self.data_augmentation(data_p, boundary_p, object_p, label_p)
object_p = object_process(object_p)
# Return the torch.Tensor values
return (torch.from_numpy(data_p),
torch.from_numpy(boundary_p),
torch.from_numpy(object_p),
torch.from_numpy(label_p))
class ISPRS_dataset2(torch.utils.data.Dataset):
def __init__(self, ids, data_files=DATA_FOLDER, label_files=LABEL_FOLDER,
cache=False, augmentation=True):
super(ISPRS_dataset2, self).__init__()
self.augmentation = augmentation
self.cache = cache
# List of files
self.data_files = [DATA_FOLDER.format(id) for id in ids]
self.boundary_files = [BOUNDARY_FOLDER.format(id) for id in ids]
self.object_files = [OBJECT_FOLDER.format(id) for id in ids]
self.label_files = [LABEL_FOLDER.format(id) for id in ids]
# Sanity check : raise an error if some files do not exist
for f in self.data_files + self.label_files:
if not os.path.isfile(f):
raise KeyError('{} is not a file !'.format(f))
# Initialize cache dicts
self.data_cache_ = {}
self.boundary_cache_ = {}
self.object_cache_ = {}
self.label_cache_ = {}
def __len__(self):
# Default epoch size is 10 000 samples
# return BATCH_SIZE * 100
return BATCH_SIZE * 1000
@classmethod
def data_augmentation(cls, *arrays, flip=True, mirror=True):
will_flip, will_mirror = False, False
if flip and random.random() < 0.5:
will_flip = True
if mirror and random.random() < 0.5:
will_mirror = True
results = []
for array in arrays:
if will_flip:
if len(array.shape) == 2:
array = array[::-1, :]
else:
array = array[:, ::-1, :]
if will_mirror:
if len(array.shape) == 2:
array = array[:, ::-1]
else:
array = array[:, :, ::-1]
results.append(np.copy(array))
return tuple(results)
def __getitem__(self, i):
# Pick a random image
random_idx = random.randint(0, len(self.data_files) - 1)
# If the tile hasn't been loaded yet, put in cache
if random_idx in self.data_cache_.keys():
data = self.data_cache_[random_idx]
else:
# Data is normalized in [0, 1]
## Potsdam IRRG
if DATASET == 'Potsdam':
## RGB
data = io.imread(self.data_files[random_idx])[:, :, :3].transpose((2, 0, 1))
## IRRG
# data = io.imread(self.data_files[random_idx])[:, :, (3, 0, 1, 2)][:, :, :3].transpose((2, 0, 1))
data = 1 / 255 * np.asarray(data, dtype='float32')
else:
## Vaihingen IRRG
data = io.imread(self.data_files[random_idx])
data = 1 / 255 * np.asarray(data.transpose((2, 0, 1)), dtype='float32')
if self.cache:
self.data_cache_[random_idx] = data
if random_idx in self.boundary_cache_.keys():
boundary = self.boundary_cache_[random_idx]
else:
boundary = np.asarray(io.imread(self.boundary_files[random_idx])) / 255
boundary = boundary.astype(np.int64)
if self.cache:
self.boundary_cache_[random_idx] = boundary
if random_idx in self.object_cache_.keys():
object = self.object_cache_[random_idx]
else:
object = np.asarray(io.imread(self.object_files[random_idx]))
object = object.astype(np.int64)
if self.cache:
self.object_cache_[random_idx] = object
if random_idx in self.label_cache_.keys():
label = self.label_cache_[random_idx]
else:
# Labels are converted from RGB to their numeric values
if DATASET == 'Loveda':
label = np.asarray(io.imread(self.label_files[random_idx]), dtype='int64') - 1
else:
label = np.asarray(convert_from_color(io.imread(self.label_files[random_idx])), dtype='int64')
if self.cache:
self.label_cache_[random_idx] = label
# Get a random patch
x1, x2, y1, y2 = get_random_pos(data, WINDOW_SIZE)
data_p = data[:, x1:x2, y1:y2]
boundary_p = boundary[x1:x2, y1:y2]
object_p = object[x1:x2, y1:y2]
label_p = label[x1:x2, y1:y2]
# Data augmentation
# data_p, boundary_p, label_p = self.data_augmentation(data_p, boundary_p, label_p)
data_p, boundary_p, object_p, label_p = self.data_augmentation(data_p, boundary_p, object_p, label_p)
object_p = object_process(object_p)
# Data augmentation
# data_p, boundary_p, label_p = self.data_augmentation(data_p, boundary_p, label_p)
data_p, boundary_p, object_p, label_p = self.data_augmentation(data_p, boundary_p, object_p, label_p)
object_p = object_process(object_p)
# Return the torch.Tensor values
return (torch.from_numpy(data_p),
torch.from_numpy(boundary_p),
torch.from_numpy(object_p),
torch.from_numpy(label_p))
## We load one tile from the dataset and we display it
# img = io.imread('./ISPRS_dataset/Vaihingen/top/top_mosaic_09cm_area11.tif')
# fig = plt.figure()
# fig.add_subplot(121)
# plt.imshow(img)
#
# # We load the ground truth
# gt = io.imread('./ISPRS_dataset/Vaihingen/gts_for_participants/top_mosaic_09cm_area11.tif')
# fig.add_subplot(122)
# plt.imshow(gt)
# plt.show()
#
# # We also check that we can convert the ground truth into an array format
# array_gt = convert_from_color(gt)
# print("Ground truth in numerical format has shape ({},{}) : \n".format(*array_gt.shape[:2]), array_gt)
# Utils
def get_random_pos(img, window_shape):
""" Extract of 2D random patch of shape window_shape in the image """
w, h = window_shape
W, H = img.shape[-2:]
x1 = random.randint(0, W - w - 1)
x2 = x1 + w
y1 = random.randint(0, H - h - 1)
y2 = y1 + h
return x1, x2, y1, y2
class CrossEntropy2d_ignore(nn.Module):
def __init__(self, size_average=True, ignore_label=255):
super(CrossEntropy2d_ignore, self).__init__()
self.size_average = size_average
self.ignore_label = ignore_label
def forward(self, predict, target, weight=None):
"""
Args:
predict:(n, c, h, w)
target:(n, h, w)
weight (Tensor, optional): a manual rescaling weight given to each class.
If given, has to be a Tensor of size "nclasses"
"""
assert not target.requires_grad
assert predict.dim() == 4
assert target.dim() == 3
assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0))
assert predict.size(2) == target.size(1), "{0} vs {1} ".format(predict.size(2), target.size(1))
assert predict.size(3) == target.size(2), "{0} vs {1} ".format(predict.size(3), target.size(3))
n, c, h, w = predict.size()
target_mask = (target >= 0) * (target != self.ignore_label)
target = target[target_mask]
if not target.data.dim():
return Variable(torch.zeros(1))
predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
loss = F.cross_entropy(predict, target, weight=weight, size_average=self.size_average)
return loss
def loss_calc(pred, label, weights):
"""
This function returns cross entropy loss for semantic segmentation
"""
# out shape batch_size x channels x h x w -> batch_size x channels x h x w
# label shape h x w x 1 x batch_size -> batch_size x 1 x h x w
label = Variable(label.long()).cuda()
criterion = CrossEntropy2d_ignore().cuda()
return criterion(pred, label, weights)
def CrossEntropy2d(input, target, weight=None, size_average=True):
""" 2D version of the cross entropy loss """
dim = input.dim()
if dim == 2:
return F.cross_entropy(input, target, weight, size_average)
elif dim == 4:
output = input.view(input.size(0), input.size(1), -1)
output = torch.transpose(output, 1, 2).contiguous()
output = output.view(-1, output.size(2))
target = target.view(-1)
return F.cross_entropy(output, target, weight, size_average)
else:
raise ValueError('Expected 2 or 4 dimensions (got {})'.format(dim))
def accuracy(input, target):
return 100 * float(np.count_nonzero(input == target)) / target.size
def sliding_window(top, step=10, window_size=(20, 20)):
""" Slide a window_shape window across the image with a stride of step """
for x in range(0, top.shape[0], step):
if x + window_size[0] > top.shape[0]:
x = top.shape[0] - window_size[0]
for y in range(0, top.shape[1], step):
if y + window_size[1] > top.shape[1]:
y = top.shape[1] - window_size[1]
yield x, y, window_size[0], window_size[1]
def count_sliding_window(top, step=10, window_size=(20, 20)):
""" Count the number of windows in an image """
c = 0
for x in range(0, top.shape[0], step):
if x + window_size[0] > top.shape[0]:
x = top.shape[0] - window_size[0]
for y in range(0, top.shape[1], step):
if y + window_size[1] > top.shape[1]:
y = top.shape[1] - window_size[1]
c += 1
return c
def grouper(n, iterable):
""" Browse an iterator by chunk of n elements """
it = iter(iterable)
while True:
chunk = tuple(itertools.islice(it, n))
if not chunk:
return
yield chunk
def one_hot(label, n_classes, requires_grad=True):
"""Return One Hot Label"""
one_hot_label = torch.eye(
n_classes, device='cuda', requires_grad=requires_grad)[label]
one_hot_label = one_hot_label.transpose(1, 3).transpose(2, 3)
return one_hot_label
class ObjectLoss(nn.Module):
def __init__(self, max_object=50):
super().__init__()
self.max_object = max_object
def forward(self, pred, gt):
num_object = int(torch.max(gt)) + 1
num_object = min(num_object, self.max_object)
total_object_loss = 0
for object_index in range(1, num_object):
mask = torch.where(gt == object_index, 1, 0).unsqueeze(1).to('cuda')
num_point = mask.sum(2).sum(2).unsqueeze(2).unsqueeze(2).to('cuda')
avg_pool = mask / (num_point + 1)
object_feature = pred.mul(avg_pool)
avg_feature = object_feature.sum(2).sum(2).unsqueeze(2).unsqueeze(2).repeat(1, 1, gt.shape[1], gt.shape[2])
avg_feature = avg_feature.mul(mask)
object_loss = torch.nn.functional.mse_loss(num_point * object_feature, avg_feature, reduction='mean')
total_object_loss = total_object_loss + object_loss
return total_object_loss
class BoundaryLoss(nn.Module):
def __init__(self, theta0=3, theta=5):
super().__init__()
self.theta0 = theta0
self.theta = theta
def forward(self, pred, gt):
"""
Input:
- pred: the output from model (before softmax)
shape (N, C, H, W)
- gt: ground truth map
shape (N, H, w)
Return:
- boundary loss, averaged over mini-bathc
"""
n, c, _, _ = pred.shape
# softmax so that predicted map can be distributed in [0, 1]
pred = torch.softmax(pred, dim=1)
# one-hot vector of ground truth
one_hot_gt = one_hot(gt, c)
# boundary map
gt_b = F.max_pool2d(
1 - one_hot_gt, kernel_size=self.theta0, stride=1, padding=(self.theta0 - 1) // 2)
gt_b -= 1 - one_hot_gt
pred_b = F.max_pool2d(
1 - pred, kernel_size=self.theta0, stride=1, padding=(self.theta0 - 1) // 2)
pred_b -= 1 - pred
# extended boundary map
gt_b_ext = F.max_pool2d(
gt_b, kernel_size=self.theta, stride=1, padding=(self.theta - 1) // 2)
pred_b_ext = F.max_pool2d(
pred_b, kernel_size=self.theta, stride=1, padding=(self.theta - 1) // 2)
# reshape
gt_b = gt_b.view(n, c, -1)
pred_b = pred_b.view(n, c, -1)
gt_b_ext = gt_b_ext.view(n, c, -1)
pred_b_ext = pred_b_ext.view(n, c, -1)
# Precision, Recall
P = torch.sum(pred_b * gt_b_ext, dim=2) / (torch.sum(pred_b, dim=2) + 1e-7)
R = torch.sum(pred_b_ext * gt_b, dim=2) / (torch.sum(gt_b, dim=2) + 1e-7)
# Boundary F1 Score
BF1 = 2 * P * R / (P + R + 1e-7)
# summing BF1 Score for each class and average over mini-batch
loss = torch.mean(1 - BF1)
return loss
def metrics(predictions, gts, label_values=LABELS):
cm = confusion_matrix(
gts,
predictions,
labels=range(len(label_values)))
print("Confusion matrix :")
print(cm)
# Compute global accuracy
total = sum(sum(cm))
accuracy = sum([cm[x][x] for x in range(len(cm))])
accuracy *= 100 / float(total)
print("%d pixels processed" % (total))
print("Total accuracy : %.2f" % (accuracy))
Acc = np.diag(cm) / cm.sum(axis=1)
for l_id, score in enumerate(Acc):
print("%s: %.4f" % (label_values[l_id], score))
print("---")
# Compute F1 score
F1Score = np.zeros(len(label_values))
for i in range(len(label_values)):
try:
F1Score[i] = 2. * cm[i, i] / (np.sum(cm[i, :]) + np.sum(cm[:, i]))
except:
# Ignore exception if there is no element in class i for test set
pass
print("F1Score :")
for l_id, score in enumerate(F1Score):
print("%s: %.4f" % (label_values[l_id], score))
print('mean F1Score: %.4f' % (np.nanmean(F1Score[:5])))
print("---")
# Compute kappa coefficient
total = np.sum(cm)
pa = np.trace(cm) / float(total)
pe = np.sum(np.sum(cm, axis=0) * np.sum(cm, axis=1)) / float(total * total)
kappa = (pa - pe) / (1 - pe)
print("Kappa: %.4f" % (kappa))
# Compute MIoU coefficient
MIoU = np.diag(cm) / (np.sum(cm, axis=1) + np.sum(cm, axis=0) - np.diag(cm))
print(MIoU)
MIoU = np.nanmean(MIoU[:5]) ##################### when you use LoveDA dataset, please set MIoU = np.nanmean(MIoU[:]) ###############
print('mean MIoU: %.4f' % (MIoU))
print("---")
return MIoU
def generate_cutmix_mask(img_size, ratio=2):
cut_area = img_size[0] * img_size[1] / ratio
w = np.random.randint(img_size[1] / ratio + 1, img_size[1])
h = np.round(cut_area / w)
x_start = np.random.randint(0, img_size[1] - w + 1)
y_start = np.random.randint(0, img_size[0] - h + 1)
x_end = int(x_start + w)
y_end = int(y_start + h)
mask = torch.ones(img_size)
mask[y_start:y_end, x_start:x_end] = 0
return mask.long()
def generate_unsup_aug_sdc(conf_w, mask_w, data_s1, data_s2):
b, _, im_h, im_w = data_s1.shape
device = data_s1.device
new_conf_w, new_mask_w, new_data_s = [], [], []
for i in range(b):
augmix_mask = generate_cutmix_mask([im_h, im_w]).to(device)
new_conf_w.append((conf_w[i] * augmix_mask + conf_w[(i + 1) % b] * (1 - augmix_mask)).unsqueeze(0))
new_mask_w.append((mask_w[i] * augmix_mask + mask_w[(i + 1) % b] * (1 - augmix_mask)).unsqueeze(0))
if i % 2 == 0:
new_data_s.append((data_s1[i] * augmix_mask + data_s2[(i + 1) % b] * (1 - augmix_mask)).unsqueeze(0))
else:
new_data_s.append((data_s2[i] * augmix_mask + data_s1[(i + 1) % b] * (1 - augmix_mask)).unsqueeze(0))
new_conf_w, new_mask_w, new_data_s = (torch.cat(new_conf_w), torch.cat(new_mask_w), torch.cat(new_data_s))
return new_conf_w, new_mask_w, new_data_s