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utils_1.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/" # Replace with your "/path/to/the/ISPRS/dataset/folder/"
BATCH_SIZE = 16 # Number of samples in a mini-batch
# BATCH_SIZE = 16 # Number of samples in a mini-batch
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'
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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# #0.25
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_22','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_46','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_134','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','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']
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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/{}.tif'
LABEL_FOLDER = MAIN_FOLDER + 'label/{}.tif'
BOUNDARY_FOLDER = MAIN_FOLDER + 'SGB/{}.tif'
OBJECT_FOLDER = MAIN_FOLDER + 'SGO/{}.tif'
ERODED_FOLDER = MAIN_FOLDER + 'label/{}.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']
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/{}.tif'
LABEL_FOLDER = MAIN_FOLDER + 'label/{}.tif'
BOUNDARY_FOLDER = MAIN_FOLDER + 'SGB/{}.png'
OBJECT_FOLDER = MAIN_FOLDER + 'SGO/{}.png'
ERODED_FOLDER = MAIN_FOLDER + 'label/{}.tif'
if DATASET == 'Loveda':
#LoveDa 整体
# train_ids = 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#0.25
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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/{}.png'
LABEL_FOLDER = MAIN_FOLDER + 'label/{}.png'
BOUNDARY_FOLDER = MAIN_FOLDER + 'SGB/{}.png'
OBJECT_FOLDER = MAIN_FOLDER + 'SGO/{}.png'
ERODED_FOLDER = MAIN_FOLDER + 'label/{}.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()}
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(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_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_dataset(torch.utils.data.Dataset):
def __init__(self, ids, data_files=DATA_FOLDER, label_files=LABEL_FOLDER,
cache=False, augmentation=True):
super(ISPRS_dataset, 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])
print('mean MIoU: %.4f' % (MIoU))
print("---")
return MIoU