nshaud
commented
4 years ago That's weird. Do you mind sharing your custom_datasets.py file?
Closed bbop1983 closed 3 years ago
nshaud
commented
4 years ago That's weird. Do you mind sharing your custom_datasets.py file?
bbop1983
commented
4 years ago No worries thanks Daniel. I'm attempting to define separate train and test masks. The code defines the labels for the gt but if I use --train_set I don't think the separate mask gets the labels.
from deprecated import deprecated
from utils import open_file
import numpy as np
CUSTOM_DATASETS_CONFIG = {
# DFC 2018 HSI dataset is nowhere to be found for download, so skip this entirely
'DFC2018_HSI': {
'img': '2018_IEEE_GRSS_DFC_HSI_TR.HDR',
'gt': '2018_IEEE_GRSS_DFC_GT_TR.tif',
'download': False,
'loader': lambda folder: dfc2018_loader(folder)
},
'Salinas': {
'img': 'Salinas_corrected.mat',
'gt': 'Salinas_gt.mat',
'download': False,
'loader': lambda folder: salinas_loader(folder)
},
'SalinasA': {
'img': 'SalinasA_corrected.mat',
'gt': 'SalinasA_gt.mat',
'download': False,
'loader': lambda folder: salinas_a_loader(folder)
},
# https://rslab.ut.ac.ir/data
# Cuprite: After removing the noisy channels (1-2 and 221-224) and water absorption channels (104-113 and 148-167), 188 channels remain.
'Cuprite-224': {
'img': 'CupriteS1_F224.mat',
'gt': 'groundTruth_Cuprite_nEnd12.mat',
'download': False,
'loader': lambda folder: cuprite_224_loader(folder)
},
'Cuprite-188': {
'img': 'CupriteS1_R188.mat',
'gt': 'groundTruth_Cuprite_nEnd12.mat',
'download': False,
'loader': lambda folder: cuprite_188_loader(folder)
},
'Samson': {
'img': 'samson_1.mat',
'gt': 'end3_gt.mat',
'download': False,
'loader': lambda folder: samson_loader(folder)
},
'JasperRidge-198': {
'img': 'jasperRidge2_R198.mat',
'gt': 'end4.mat',
'download': False,
'loader': lambda folder: jasper_ridge_198_loader(folder)
},
'JasperRidge-224': {
'img': 'jasperRidge2_F224_2.mat',
'gt': 'end4.mat',
'download': False,
'loader': lambda folder: jasper_ridge_224_loader(folder)
},
'Urban-162': {
'img': 'Urban_R162.mat',
'gt': 'end6_groundTruth.mat',
'download': False,
'loader': lambda folder: urban_162_loader(folder)
},
'Urban-210': {
'img': 'Urban_F210.mat',
'gt': 'end6_groundTruth.mat',
'download': False,
'loader': lambda folder: urban_210_loader(folder)
},
'China': {
'img': 'China_Change_Dataset.mat',
'download': False,
'loader': lambda folder: china_loader(folder)
},
'USA': {
'img': 'USA_Change_Dataset.mat',
'download': False,
'loader': lambda folder: usa_loader(folder)
},
'Washington': {
'img': 'DC.tif',
'gt': 'GT.tif',
'download': False,
'loader': lambda folder: washington_loader(folder)
},
'Selene1': {
'img': 'H23VnirQuacSub1HyResPy.mat',
'gt': 'H23VNIRSub1TargetMapPy.mat',
'download': False,
'loader': lambda folder: selene1_loader(folder)
},
'Selene2': {
'img': 'H23VnirQuacSub2HyResPy.mat',
'gt': 'H23VNIRSub2TargetMapPy.mat',
'download': False,
'loader': lambda folder: selene2_loader(folder)
},
'Selene3': {
'img': 'H23VnirQuacSub3HyResPy.mat',
'gt': 'H23VNIRSub3TargetMapPy.mat',
'download': False,
'loader': lambda folder: selene3_loader(folder)
},
'Selene1NC': {
'img': 'H23VnirQuacSub1HyResPyNC.mat',
'gt': 'Sub1TargetMapPy1.mat',
'download': False,
'loader': lambda folder: selene1NC_loader(folder)
} ,
'Selene1NCGP': {
'img': 'H23VnirQuacSub1HyResPyNC.mat',
'gt': 'H23VNIRSub1TargetMapPyNCGP.mat',
'download': False,
'loader': lambda folder: selene1NCGP_loader(folder)
},
'Selene1NCGyC': {
'img': 'H23VnirQuacSub1HyResPyNC.mat',
'gt': 'H23VNIRSub1TargetMapPyNCGyC.mat',
'download': False,
'loader': lambda folder: selene1NCGyC_loader(folder)
},
'Selene1Train': {
'img': 'H23VnirQuacSub1HyResPyTrain1.mat',
'gt': 'Sub1TargetMapPyTrainCal1.mat',
'download': False,
'loader': lambda folder: selene1Train_loader(folder)
}
,
'Selene1TrainX': {
'img': 'H23VnirQuacSub1HyResPy.mat',
'gt': 'Sub1TargetMapPyTrainX.mat',
'download': False,
'loader': lambda folder: selene1TrainX_loader(folder)
}
,
'Selene1TestX': {
'img': 'H23VnirQuacSub1HyResPy.mat',
'gt': 'Sub1TargetMapPyTestX.mat',
'download': False,
'loader': lambda folder: selene1TestX_loader(folder)
}
}
@deprecated(reason="the dataset is nowhere to be found / downloaded, please use a different dataset")
def dfc2018_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['DFC2018_HSI']['img'])[:,:,:-2]
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['DFC2018_HSI']['gt'])
gt = gt.astype('uint8')
rgb_bands = (47, 31, 15)
label_values = ["Unclassified",
"Healthy grass",
"Stressed grass",
"Artificial turf",
"Evergreen trees",
"Deciduous trees",
"Bare earth",
"Water",
"Residential buildings",
"Non-residential buildings",
"Roads",
"Sidewalks",
"Crosswalks",
"Major thoroughfares",
"Highways",
"Railways",
"Paved parking lots",
"Unpaved parking lots",
"Cars",
"Trains",
"Stadium seats"]
ignored_labels = [0]
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def salinas_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Salinas']['img'])['salinas_corrected']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Salinas']['gt'])['salinas_gt']
gt = gt.astype('uint8')
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Brocoli_green_weeds_1",
"Brocoli_green_weeds_2",
"Fallow",
"Fallow_rough_plow",
"Fallow_smooth",
"Stubble",
"Celery",
"Grapes_untrained",
"Soil_vinyard_develop",
"Corn_senesced_green_weeds",
"Lettuce_romaine_4wk",
"Lettuce_romaine_5wk",
"Lettuce_romaine_6wk",
"Lettuce_romaine_7wk",
"Vinyard_untrained",
"Vinyard_vertical_trellis"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def salinas_a_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['SalinasA']['img'])['salinasA_corrected']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['SalinasA']['gt'])['salinasA_gt']
gt = gt.astype('uint8')
# remap for contiguous integers to avoid index out of bounds
salinas_a_remap = {0: 0, 1: 1, 10: 2, 11: 3, 12: 4, 13: 5, 14: 6}
for k, v in salinas_a_remap.items():
gt = np.where(gt == k, v, gt)
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Brocoli_green_weeds_1",
"Corn_senesced_green_weeds",
"Lettuce_romaine_4wk",
"Lettuce_romaine_5wk",
"Lettuce_romaine_6wk",
"Lettuce_romaine_7wk"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
@deprecated("gt is incomplete")
def cuprite_224_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Cuprite-224']['img'])['Y']
img = np.reshape(img, (250, 190, 224)) # only includes GT: endmembers.
gt = np.asarray(np.matrix(open_file(folder + CUSTOM_DATASETS_CONFIG['Cuprite-224']['gt'])['M'].argmax(1)))
gt = np.transpose(gt)
gt = np.reshape(gt, (224))
rgb_bands = (183, 193, 203) # not sure but does not matter
label_values = ["Alunite",
"Andradite",
"Buddingtonite",
"Dumortierite",
"Kaolinite1",
"Kaolinite2",
"Muscovite",
"Montmorillonite",
"Nontronite",
"Pyrope",
"Sphene",
"Chalcedony"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
@deprecated("gt is incomplete")
def cuprite_188_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Cuprite-188']['img'])['Y']
img = np.reshape(img, (250, 190, 188)) # only includes GT: endmembers.
gt = np.asarray(np.matrix(open_file(folder + CUSTOM_DATASETS_CONFIG['Cuprite-188']['gt'])['M'].argmax(1)))
gt = np.transpose(gt)
gt = np.reshape(gt, (188))
rgb_bands = (183, 193, 203) # not sure but does not matter
label_values = ["Alunite",
"Andradite",
"Buddingtonite",
"Dumortierite",
"Kaolinite1",
"Kaolinite2",
"Muscovite",
"Montmorillonite",
"Nontronite",
"Pyrope",
"Sphene",
"Chalcedony"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def samson_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Samson']['img'])['V']
img = np.reshape(img, (95,95,156))
gt = np.asarray(np.matrix(open_file(folder + CUSTOM_DATASETS_CONFIG['Samson']['gt'])['A']).argmax(0))
gt = np.reshape(gt, (95, 95))
rgb_bands = (9, 44, 54) # manually calculated, assuming linear distribution of bands among wavelengths
label_values = ["Rock",
"Tree",
"Water"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def jasper_ridge_198_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['JasperRidge-198']['img'])['Y']
img = np.reshape(img, (100, 100, 198))
gt = np.asarray(np.matrix(open_file(folder + CUSTOM_DATASETS_CONFIG['JasperRidge-198']['gt'])['A'].argmax(0)))
gt = np.reshape(gt, (100, 100))
rgb_bands = (5, 15, 18) # manually calculated, assuming linear distribution of bands among wavelengths
label_values = ["Road",
"Soil",
"Water",
"Tree"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def jasper_ridge_224_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['JasperRidge-224']['img'])['Y']
img = np.reshape(img, (100, 100, 224))
gt = np.asarray(np.matrix(open_file(folder + CUSTOM_DATASETS_CONFIG['JasperRidge-224']['gt'])['A'].argmax(0)))
gt = np.reshape(gt, (100, 100))
rgb_bands = (5, 16, 20) # manually calculated, assuming linear distribution of bands among wavelengths
label_values = ["Road",
"Soil",
"Water",
"Tree"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def urban_162_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Urban-162']['img'])['Y']
img = np.reshape(img, (307, 307, 162))
gt = np.asarray(np.matrix(open_file(folder + CUSTOM_DATASETS_CONFIG['Urban-162']['gt'])['A'].argmax(0)))
gt = np.reshape(gt, (307, 307))
rgb_bands = (13, 11, 2) # manually calculated, assuming linear distribution of bands among wavelengths
label_values = ["Asphalt",
"Grass",
"Tree",
"Roof",
"Metal",
"Dirt"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def urban_210_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Urban-210']['img'])['Y']
img = np.reshape(img, (307, 307, 210))
gt = np.asarray(np.matrix(open_file(folder + CUSTOM_DATASETS_CONFIG['Urban-210']['gt'])['A'].argmax(0)))
gt = np.reshape(gt, (307, 307))
rgb_bands = (17, 14, 3) # manually calculated, assuming linear distribution of bands among wavelengths
label_values = ["Asphalt",
"Grass",
"Tree",
"Roof",
"Metal",
"Dirt"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
""" Please cite using these datasets as below
Published in: International Journal of Remote Sensing, vol. ?, no. ?, p. ?, April. 2018.
Title: "Hyperspectral Change Detection: An Experimental Comparative Study
https://doi.org/10.1080/01431161.2018.1466079
Authors: M. Hasanlou and S. T. Seyedi """
def china_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['China']['img'])['T2']
gt_preprocess_me = open_file(folder + CUSTOM_DATASETS_CONFIG['China']['img'])['Multiple']
# need to preprocess gt
gt = np.zeros(shape=(gt_preprocess_me.shape[0], gt_preprocess_me.shape[1]))
for i in range(gt_preprocess_me.shape[0]):
for j in range(gt_preprocess_me.shape[1]):
if (gt_preprocess_me[i][j] == [254,0,0]).all():
gt[i][j] = 0
elif (gt_preprocess_me[i][j] == [0,254,0]).all():
gt[i][j] = 1
elif (gt_preprocess_me[i][j] == [0,0,254]).all():
gt[i][j] = 2
elif (gt_preprocess_me[i][j] == [254,254,0]).all():
gt[i][j] = 3
# from here on, unused elifs
elif (gt_preprocess_me[i][j] == [254,0,254]).all():
gt[i][j] = 4
elif (gt_preprocess_me[i][j] == [0,254,254]).all():
gt[i][j] = 5
elif (gt_preprocess_me[i][j] == [0, 0, 0]).all():
gt[i][j] = 6
elif (gt_preprocess_me[i][j] == [254, 254, 254]).all():
gt[i][j] = 7
gt = gt.astype(dtype='uint8')
rgb_bands = (0,0,0) # not given
label_values = ["soil",
"river",
"tree",
"building",
"road",
"agricultural field"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
""" Please cite using these datasets as below
Published in: International Journal of Remote Sensing, vol. ?, no. ?, p. ?, April. 2018.
Title: "Hyperspectral Change Detection: An Experimental Comparative Study
https://doi.org/10.1080/01431161.2018.1466079
Authors: M. Hasanlou and S. T. Seyedi """
def usa_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['USA']['img'])['T2']
gt_preprocess_me = open_file(folder + CUSTOM_DATASETS_CONFIG['USA']['img'])['Multiple']
# need to preprocess gt
gt = np.zeros(shape=(gt_preprocess_me.shape[0], gt_preprocess_me.shape[1]))
for i in range(gt_preprocess_me.shape[0]):
for j in range(gt_preprocess_me.shape[1]):
if (gt_preprocess_me[i][j] == [255,0,0]).all():
gt[i][j] = 0
elif (gt_preprocess_me[i][j] == [0,255,0]).all():
gt[i][j] = 1
elif (gt_preprocess_me[i][j] == [0,0,255]).all():
gt[i][j] = 2
elif (gt_preprocess_me[i][j] == [255,255,0]).all():
gt[i][j] = 3
elif (gt_preprocess_me[i][j] == [255,0,255]).all():
gt[i][j] = 4
elif (gt_preprocess_me[i][j] == [0,255,255]).all():
gt[i][j] = 5
# from here on, unused elifs
elif (gt_preprocess_me[i][j] == [0, 0, 0]).all():
gt[i][j] = 6
elif (gt_preprocess_me[i][j] == [255, 255, 255]).all():
gt[i][j] = 7
gt = gt.astype(dtype='uint8')
rgb_bands = (0,0,0) # not given
label_values = ["soil",
"irrigated fields",
"river",
"building",
"type of cultivated land",
"grassland"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
@deprecated("cannot identify image file")
def washington_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Washington']['img'])
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Washington']['gt'])
rgb_bands = (60,27,17)
# http://sugs.u-strasbg.fr/omiv/imagemining/documents/IMAGEMINING-DallaMurra-practicals.pdf
label_values = ["Roofs",
"Street",
"Path",
"Grass",
"Trees",
"Water",
"Shadow"]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene1_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1']['img'])['H23VnirQuacSub1HyResPy']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1']['gt'])['H23VNIRSub1TargetMapPy']
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Green Ceramic",
"Grey Ceramic",
"Orange Perspex",
"Green Perspex",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene2_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene2']['img'])['H23VnirQuacSub2HyResPy']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene2']['gt'])['H23VNIRSub2TargetMapPy']
gt = gt.astype('uint8')
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Beige Carpet",
"Green Ceramic",
"Orange Perspex",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene3_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene3']['img'])['H23VnirQuacSub3HyResPy']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene3']['gt'])['H23VNIRSub3TargetMapPy']
gt = gt.astype('uint8')
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Beige Carpet",
"Green Carpet",
"White Perspex",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene1NC_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1NC']['img'])['H23VnirQuacSub1HyResPyNC']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1NC']['gt'])['Sub1TargetMapPy1']
gt = gt.astype('uint8')
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Green Ceramic",
"Grey Ceramic",
"Orange Perspex",
"Green Perspex",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene1NCGP_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1NC']['img'])['H23VnirQuacSub1HyResPyNC']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1NC']['gt'])['H23VNIRSub1TargetMapPyNCGP']
gt = gt.astype('uint8')
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Green Perspex",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene1NCGyC_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1NC']['img'])['H23VnirQuacSub1HyResPyNC']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1NC']['gt'])['H23VNIRSub1TargetMapPyNCGyC']
gt = gt.astype('uint8')
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Grey Ceramic",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene1Train_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1Train']['img'])['H23VnirQuacSub1HyResPyTrain1']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1Train']['gt'])['Sub1TargetMapPyTrainCal1']
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Beige Carpet",
"Green Carpet",
"Green Ceramic",
"Grey Ceramic",
"White Perspex",
"Orange Perspex",
"Green Perspex",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene1TrainX_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1TrainX']['img'])['H23VnirQuacSub1HyResPy']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1TrainX']['gt'])['Sub1TargetMapPyTrainX']
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Beige Carpet",
"Green Carpet",
"Green Ceramic",
"Grey Ceramic",
"White Perspex",
"Orange Perspex",
"Green Perspex",
"Reflector",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
def selene1TestX_loader(folder):
img = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1TestX']['img'])['H23VnirQuacSub1HyResPy']
gt = open_file(folder + CUSTOM_DATASETS_CONFIG['Selene1TestX']['gt'])['Sub1TargetMapPyTestX']
rgb_bands = (47, 27, 13)
label_values = ["Unclassified",
"Background",
"Beige Carpet",
"Green Carpet",
"Green Ceramic",
"Grey Ceramic",
"White Perspex",
"Orange Perspex",
"Green Perspex",
"Reflector",]
ignored_labels = []
palette = None
return img, gt, rgb_bands, ignored_labels, label_values, palette
zy-wyhym
commented
3 years ago Hi,
Anyone know how to define the training and test set?
I have the GT defined in a mat file and I put the path in but it comes back with the following error:
python main.py --model nn --dataset Selene1TestX --train_set C:\Users\bbop1\hsi-toolbox-master\DeepHyperX\Datasets\Selene1TrainX\Sub1TargetMapPyTrainX.mat --cuda 0
Setting up a new session... Image has dimensions 1250x1596 and 134 channels Traceback (most recent call last): File "main.py", line 275, in test_gt[(train_gt > 0)[:w,:h]] = 0 TypeError: '>' not supported between instances of 'dict' and 'int'
I have a feeling the train gt needs to be defined as a dictionary. Has anyone done this?
Cheers,
Bop
Hi, did you resolve your problem? I also have the similar problem of defining train set and test set separately.
zy-wyhym
commented
3 years ago Hi, I wanted to specify the training samples and testing samples except for “random”. I’ve tried to add the paths for training and testing samples in the main.py as followed:
# Dataset options
group_dataset = parser.add_argument_group('Dataset')
group_dataset.add_argument('--training_sample', type=float, default=r'D:\PyCharm-code\pytorch\Datasets\trainall.mat',
help="all samples use for training,default is the path for training samples")
group_dataset.add_argument('--testing_sample', type=float, default=r'D:\PyCharm-code\pytorch\Datasets\testall.mat',
help="all samples use for testing,default is the path for testing samples")
# group_dataset.add_argument('--sampling_mode', type=str, help="Sampling mode"
# " (random sampling or disjoint, default: random)",
# default='random')
group_dataset.add_argument('--train_set', type=str, default=r'D:\PyCharm-code\pytorch\Datasets\trainall_ref.mat',
help="Path to the train ground truth")
group_dataset.add_argument('--test_set', type=str, default=r'D:\PyCharm-code\pytorch\Datasets\testall_ref.mat',
help="Path to the test set")When I used “python main.py --model nn”, it showed SyntaxError: invalid syntax
Could you tell me what was wrong with it. Thanks for your great help.
nshaud
commented
3 years ago @zy-wyhym you shouldn't modify these options in the code. Use the command line to change them, e.g. :
python main.py --model nn --train_set path/to/trainall_ref.mat --test_set path/to/testall_ref.mat
Hi,
Anyone know how to define the training and test set?
I have the GT defined in a mat file and I put the path in but it comes back with the following error:
python main.py --model nn --dataset Selene1TestX --train_set C:\Users\bbop1\hsi-toolbox-master\DeepHyperX\Datasets\Selene1TrainX\Sub1TargetMapPyTrainX.mat --cuda 0
Setting up a new session... Image has dimensions 1250x1596 and 134 channels Traceback (most recent call last): File "main.py", line 275, in
test_gt[(train_gt > 0)[:w,:h]] = 0
TypeError: '>' not supported between instances of 'dict' and 'int'
I have a feeling the train gt needs to be defined as a dictionary. Has anyone done this?
Cheers,
Bop