from skimage import io
# VisualizationSE-
import seaborn as sns
import visdom # 可视化工具
import torchvision
import scipy.io as scio
import os
from utils import metrics, convert_to_color_, convert_from_color_,\
display_dataset, display_predictions, explore_spectrums, plot_spectrums, plot_spectrums_, \
sample_gt, build_dataset, show_results, compute_imf_weights, get_device
from datasets import get_dataset, HyperX, open_file, DATASETS_CONFIG
from CBW import get_model, train, test, save_model
import argparse
dataset_names = [
v['name'] if 'name' in v.keys() else k for k, v in DATASETS_CONFIG.items()
] # 提取dataset的名称
# 利用argparse设置参数
# Argument parser for CLI interaction
parser = argparse.ArgumentParser(description="Run deep learning experiments on"
" various hyperspectral datasets")
parser.add_argument(
'--dataset',
type=str,
default='Salinas',
choices=dataset_names, # 数据集！！！
help="Dataset to use. IndianPines; PaviaU; Salinas")
parser.add_argument('--model',
type=str,
default="CBW",
sample_gt,
build_dataset,
show_results,
compute_imf_weights,
get_device,
)
from datasets import get_dataset, HyperX, open_file, DATASETS_CONFIG
from models import get_model, train, test, save_model
import argparse
# save train/test split
import scipy.io
dataset_names = [
v["name"] if "name" in v.keys() else k for k, v in DATASETS_CONFIG.items()
]
# Argument parser for CLI interaction
parser = argparse.ArgumentParser(description="Run deep learning experiments on"
" various hyperspectral datasets")
parser.add_argument("--dataset",
type=str,
default=None,
choices=dataset_names,
help="Dataset to use.")
parser.add_argument(
"--model",
type=str,
default=None,
help="Model to train. Available:\n"