Python 3.7和visdom错误：不支持+：“NoneType”和“str”的操作数类型

# Python 2/3 compatiblity from __future__ import print_function from __future__ import division # Torch import torch import torch.utils.data as data from torchsummary import summary # Numpy, scipy, scikit-image, spectral import numpy as np import sklearn.svm import sklearn.model_selection from skimage import io # Visualization import seaborn as sns import visdom import os from utils import metrics, convert_to_color_, convert_from_color_,\ display_dataset, display_predictions, explore_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 models 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()] # 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" "SVM (linear), " "SVM_grid (grid search on linear, poly and RBF kernels), " "baseline (fully connected NN), " "hu (1D CNN), " "hamida (3D CNN + 1D classifier), " "lee (3D FCN), " "chen (3D CNN), " "li (3D CNN), " "he (3D CNN), " "luo (3D CNN), " "sharma (2D CNN), " "boulch (1D semi-supervised CNN), " "liu (3D semi-supervised CNN), " "mou (1D RNN)") parser.add_argument('--folder', type=str, help="Folder where to store the " "datasets (defaults to the current working directory).", default="./Datasets/") parser.add_argument('--cuda', type=int, default=-1, help="Specify CUDA device (defaults to -1, which learns on CPU)") parser.add_argument('--runs', type=int, default=1, help="Number of runs (default: 1)") parser.add_argument('--restore', type=str, default=None, help="Weights to use for initialization, e.g. a checkpoint") # Dataset options group_dataset = parser.add_argument_group('Dataset') group_dataset.add_argument('--training_sample', type=float, default=10, help="Percentage of samples to use for training (default: 10%%)") 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=None, help="Path to the train ground truth (optional, this " "supersedes the --sampling_mode option)") group_dataset.add_argument('--test_set', type=str, default=None, help="Path to the test set (optional, by default " "the test_set is the entire ground truth minus the training)") # Training options group_train = parser.add_argument_group('Training') group_train.add_argument('--epoch', type=int, help="Training epochs (optional, if" " absent will be set by the model)") group_train.add_argument('--patch_size', type=int, help="Size of the spatial neighbourhood (optional, if " "absent will be set by the model)") group_train.add_argument('--lr', type=float, help="Learning rate, set by the model if not specified.") group_train.add_argument('--class_balancing', action='store_true', help="Inverse median frequency class balancing (default = False)") group_train.add_argument('--batch_size', type=int, help="Batch size (optional, if absent will be set by the model") group_train.add_argument('--test_stride', type=int, default=1, help="Sliding window step stride during inference (default = 1)") # Data augmentation parameters group_da = parser.add_argument_group('Data augmentation') group_da.add_argument('--flip_augmentation', action='store_true', help="Random flips (if patch_size > 1)") group_da.add_argument('--radiation_augmentation', action='store_true', help="Random radiation noise (illumination)") group_da.add_argument('--mixture_augmentation', action='store_true', help="Random mixes between spectra") parser.add_argument('--with_exploration', action='store_true', help="See data exploration visualization") parser.add_argument('--download', type=str, default=None, nargs='+', choices=dataset_names, help="Download the specified datasets and quits.") args = parser.parse_args() CUDA_DEVICE = get_device(args.cuda) # % of training samples SAMPLE_PERCENTAGE = args.training_sample # Data augmentation ? FLIP_AUGMENTATION = args.flip_augmentation RADIATION_AUGMENTATION = args.radiation_augmentation MIXTURE_AUGMENTATION = args.mixture_augmentation # Dataset name DATASET = args.dataset # Model name MODEL = args.model # Number of runs (for cross-validation) N_RUNS = args.runs # Spatial context size (number of neighbours in each spatial direction) PATCH_SIZE = args.patch_size # Add some visualization of the spectra ? DATAVIZ = args.with_exploration # Target folder to store/download/load the datasets FOLDER = args.folder # Number of epochs to run EPOCH = args.epoch # Sampling mode, e.g random sampling SAMPLING_MODE = args.sampling_mode # Pre-computed weights to restore CHECKPOINT = args.restore # Learning rate for the SGD LEARNING_RATE = args.lr # Automated class balancing CLASS_BALANCING = args.class_balancing # Training ground truth file TRAIN_GT = args.train_set # Testing ground truth file TEST_GT = args.test_set TEST_STRIDE = args.test_stride if args.download is not None and len(args.download) > 0: for dataset in args.download: get_dataset(dataset, target_folder=FOLDER) quit() viz = visdom.Visdom(env=DATASET+ ' ' + MODEL) if not viz.check_connection: print("Visdom is not connected. Did you run 'python -m visdom.server' ?") hyperparams = vars(args) # Load the dataset img, gt, LABEL_VALUES, IGNORED_LABELS, RGB_BANDS, palette = get_dataset(DATASET, FOLDER) # Number of classes N_CLASSES = len(LABEL_VALUES) # Number of bands (last dimension of the image tensor) N_BANDS = img.shape[-1] # Parameters for the SVM grid search SVM_GRID_PARAMS = [{'kernel': ['rbf'], 'gamma': [1e-1, 1e-2, 1e-3], 'C': [1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [0.1, 1, 10, 100, 1000]}, {'kernel': ['poly'], 'degree': [3], 'gamma': [1e-1, 1e-2, 1e-3]}] if palette is None: # Generate color palette palette = {0: (0, 0, 0)} for k, color in enumerate(sns.color_palette("hls", len(LABEL_VALUES) - 1)): palette[k + 1] = tuple(np.asarray(255 * np.array(color), dtype='uint8')) invert_palette = {v: k for k, v in palette.items()} def convert_to_color(x): return convert_to_color_(x, palette=palette) def convert_from_color(x): return convert_from_color_(x, palette=invert_palette) # Instantiate the experiment based on predefined networks hyperparams.update({'n_classes': N_CLASSES, 'n_bands': N_BANDS, 'ignored_labels': IGNORED_LABELS, 'device': CUDA_DEVICE}) hyperparams = dict((k, v) for k, v in hyperparams.items() if v is not None) # Show the image and the ground truth display_dataset(img, gt, RGB_BANDS, LABEL_VALUES, palette, viz) color_gt = convert_to_color(gt) if DATAVIZ: # Data exploration : compute and show the mean spectrums mean_spectrums = explore_spectrums(img, gt, LABEL_VALUES, viz, ignored_labels=IGNORED_LABELS) plot_spectrums(mean_spectrums, viz, title='Mean spectrum/class') results = [] # run the experiment several times for run in range(N_RUNS): if TRAIN_GT is not None and TEST_GT is not None: train_gt = open_file(TRAIN_GT) test_gt = open_file(TEST_GT) elif TRAIN_GT is not None: train_gt = open_file(TRAIN_GT) test_gt = np.copy(gt) w, h = test_gt.shape test_gt[(train_gt > 0)[:w,:h]] = 0 elif TEST_GT is not None: test_gt = open_file(TEST_GT) else: # Sample random training spectra train_gt, test_gt = sample_gt(gt, SAMPLE_PERCENTAGE, mode=SAMPLING_MODE) print("{} samples selected (over {})".format(np.count_nonzero(train_gt), np.count_nonzero(gt))) print("Running an experiment with the {} model".format(MODEL), "run {}/{}".format(run + 1, N_RUNS)) display_predictions(convert_to_color(train_gt), viz, caption="Train ground truth") display_predictions(convert_to_color(test_gt), viz, caption="Test ground truth") if MODEL == 'SVM_grid': print("Running a grid search SVM") # Grid search SVM (linear and RBF) X_train, y_train = build_dataset(img, train_gt, ignored_labels=IGNORED_LABELS) class_weight = 'balanced' if CLASS_BALANCING else None clf = sklearn.svm.SVC(class_weight=class_weight) clf = sklearn.model_selection.GridSearchCV(clf, SVM_GRID_PARAMS, verbose=5, n_jobs=4) clf.fit(X_train, y_train) print("SVM best parameters : {}".format(clf.best_params_)) prediction = clf.predict(img.reshape(-1, N_BANDS)) save_model(clf, MODEL, DATASET) prediction = prediction.reshape(img.shape[:2]) elif MODEL == 'SVM': X_train, y_train = build_dataset(img, train_gt, ignored_labels=IGNORED_LABELS) class_weight = 'balanced' if CLASS_BALANCING else None clf = sklearn.svm.SVC(class_weight=class_weight) clf.fit(X_train, y_train) save_model(clf, MODEL, DATASET) prediction = clf.predict(img.reshape(-1, N_BANDS)) prediction = prediction.reshape(img.shape[:2]) elif MODEL == 'SGD': X_train, y_train = build_dataset(img, train_gt, ignored_labels=IGNORED_LABELS) X_train, y_train = sklearn.utils.shuffle(X_train, y_train) scaler = sklearn.preprocessing.StandardScaler() X_train = scaler.fit_transform(X_train) class_weight = 'balanced' if CLASS_BALANCING else None clf = sklearn.linear_model.SGDClassifier(class_weight=class_weight, learning_rate='optimal', tol=1e-3, average=10) clf.fit(X_train, y_train) save_model(clf, MODEL, DATASET) prediction = clf.predict(scaler.transform(img.reshape(-1, N_BANDS))) prediction = prediction.reshape(img.shape[:2]) elif MODEL == 'nearest': X_train, y_train = build_dataset(img, train_gt, ignored_labels=IGNORED_LABELS) X_train, y_train = sklearn.utils.shuffle(X_train, y_train) class_weight = 'balanced' if CLASS_BALANCING else None clf = sklearn.neighbors.KNeighborsClassifier(weights='distance') clf = sklearn.model_selection.GridSearchCV(clf, {'n_neighbors': [1, 3, 5, 10, 20]}, verbose=5, n_jobs=4) clf.fit(X_train, y_train) clf.fit(X_train, y_train) save_model(clf, MODEL, DATASET) prediction = clf.predict(img.reshape(-1, N_BANDS)) prediction = prediction.reshape(img.shape[:2]) else: # Neural network model, optimizer, loss, hyperparams = get_model(MODEL, **hyperparams) if CLASS_BALANCING: weights = compute_imf_weights(train_gt, N_CLASSES, IGNORED_LABELS) hyperparams['weights'] = torch.from_numpy(weights) # Split train set in train/val train_gt, val_gt = sample_gt(train_gt, 0.95, mode='random') # Generate the dataset train_dataset = HyperX(img, train_gt, **hyperparams) train_loader = data.DataLoader(train_dataset, batch_size=hyperparams['batch_size'], #pin_memory=hyperparams['device'], shuffle=True) val_dataset = HyperX(img, val_gt, **hyperparams) val_loader = data.DataLoader(val_dataset, #pin_memory=hyperparams['device'], batch_size=hyperparams['batch_size']) print(hyperparams) print("Network :") with torch.no_grad(): for input, _ in train_loader: break summary(model.to(hyperparams['device']), input.size()[1:]) # We would like to use device=hyperparams['device'] altough we have # to wait for torchsummary to be fixed first. if CHECKPOINT is not None: model.load_state_dict(torch.load(CHECKPOINT)) try: train(model, optimizer, loss, train_loader, hyperparams['epoch'], scheduler=hyperparams['scheduler'], device=hyperparams['device'], supervision=hyperparams['supervision'], val_loader=val_loader, display=viz) except KeyboardInterrupt: # Allow the user to stop the training pass probabilities = test(model, img, hyperparams) prediction = np.argmax(probabilities, axis=-1) run_results = metrics(prediction, test_gt, ignored_labels=hyperparams['ignored_labels'], n_classes=N_CLASSES) mask = np.zeros(gt.shape, dtype='bool') for l in IGNORED_LABELS: mask[gt == l] = True prediction[mask] = 0 color_prediction = convert_to_color(prediction) display_predictions(color_prediction, viz, gt=convert_to_color(test_gt), caption="Prediction vs. test ground truth") results.append(run_results) show_results(run_results, viz, label_values=LABEL_VALUES) if N_RUNS > 1: show_results(results, viz, label_values=LABEL_VALUES, agregated=True)

# -*- coding: utf-8 -*- """ This file contains the PyTorch dataset for hyperspectral images and related helpers. """ import spectral import numpy as np import torch import torch.utils import torch.utils.data import os from tqdm import tqdm try: # Python 3 from urllib.request import urlretrieve except ImportError: # Python 2 from urllib import urlretrieve from utils import open_file DATASETS_CONFIG = { 'PaviaC': { 'urls': ['http://www.ehu.eus/ccwintco/uploads/e/e3/Pavia.mat', 'http://www.ehu.eus/ccwintco/uploads/5/53/Pavia_gt.mat'], 'img': 'Pavia.mat', 'gt': 'Pavia_gt.mat' }, 'Salinas': { 'urls': ['http://www.ehu.eus/ccwintco/uploads/a/a3/Salinas_corrected.mat', 'http://www.ehu.eus/ccwintco/uploads/f/fa/Salinas_gt.mat'], 'img': 'Salinas_corrected.mat', 'gt': 'Salinas_gt.mat' }, 'PaviaU': { 'urls': ['http://www.ehu.eus/ccwintco/uploads/e/ee/PaviaU.mat', 'http://www.ehu.eus/ccwintco/uploads/5/50/PaviaU_gt.mat'], 'img': 'PaviaU.mat', 'gt': 'PaviaU_gt.mat' }, 'KSC': { 'urls': ['http://www.ehu.es/ccwintco/uploads/2/26/KSC.mat', 'http://www.ehu.es/ccwintco/uploads/a/a6/KSC_gt.mat'], 'img': 'KSC.mat', 'gt': 'KSC_gt.mat' }, 'IndianPines': { 'urls': ['http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat', 'http://www.ehu.eus/ccwintco/uploads/c/c4/Indian_pines_gt.mat'], 'img': 'Indian_pines_corrected.mat', 'gt': 'Indian_pines_gt.mat' }, 'Botswana': { 'urls': ['http://www.ehu.es/ccwintco/uploads/7/72/Botswana.mat', 'http://www.ehu.es/ccwintco/uploads/5/58/Botswana_gt.mat'], 'img': 'Botswana.mat', 'gt': 'Botswana_gt.mat', } } try: from custom_datasets import CUSTOM_DATASETS_CONFIG DATASETS_CONFIG.update(CUSTOM_DATASETS_CONFIG) except ImportError: pass class TqdmUpTo(tqdm): """Provides `update_to(n)` which uses `tqdm.update(delta_n)`.""" def update_to(self, b=1, bsize=1, tsize=None): """ b : int, optional Number of blocks transferred so far [default: 1]. bsize : int, optional Size of each block (in tqdm units) [default: 1]. tsize : int, optional Total size (in tqdm units). If [default: None] remains unchanged. """ if tsize is not None: self.total = tsize self.update(b * bsize - self.n) # will also set self.n = b * bsize def get_dataset(dataset_name, target_folder="./", datasets=DATASETS_CONFIG): """ Gets the dataset specified by name and return the related components. Args: dataset_name: string with the name of the dataset target_folder (optional): folder to store the datasets, defaults to ./ datasets (optional): dataset configuration dictionary, defaults to prebuilt one Returns: img: 3D hyperspectral image (WxHxB) gt: 2D int array of labels label_values: list of class names ignored_labels: list of int classes to ignore rgb_bands: int tuple that correspond to red, green and blue bands """ palette = None if dataset_name not in datasets.keys(): raise ValueError("{} dataset is unknown.".format(dataset_name)) dataset = datasets[dataset_name] folder = target_folder + datasets[dataset_name].get('folder', dataset_name + '/') if dataset.get('download', True): # Download the dataset if is not present if not os.path.isdir(folder): os.mkdir(folder) for url in datasets[dataset_name]['urls']: # download the files filename = url.split('/')[-1] if not os.path.exists(folder + filename): with TqdmUpTo(unit='B', unit_scale=True, miniters=1, desc="Downloading {}".format(filename)) as t: urlretrieve(url, filename=folder + filename, reporthook=t.update_to) elif not os.path.isdir(folder): print("WARNING: {} is not downloadable.".format(dataset_name)) if dataset_name == 'PaviaC': # Load the image img = open_file(folder + 'Pavia.mat')['pavia'] rgb_bands = (55, 41, 12) gt = open_file(folder + 'Pavia_gt.mat')['pavia_gt'] label_values = ["Undefined", "Water", "Trees", "Asphalt", "Self-Blocking Bricks", "Bitumen", "Tiles", "Shadows", "Meadows", "Bare Soil"] ignored_labels = [0] elif dataset_name == 'PaviaU': # Load the image img = open_file(folder + 'PaviaU.mat')['paviaU'] rgb_bands = (55, 41, 12) gt = open_file(folder + 'PaviaU_gt.mat')['paviaU_gt'] label_values = ['Undefined', 'Asphalt', 'Meadows', 'Gravel', 'Trees', 'Painted metal sheets', 'Bare Soil', 'Bitumen', 'Self-Blocking Bricks', 'Shadows'] ignored_labels = [0] elif dataset_name == 'Salinas': img = open_file(folder + 'Salinas.mat')['Salinas_corrected'] rgb_bands = (43, 21, 11) # AVIRIS sensor gt = open_file(folder + 'Salinas_gt.mat')['Salinas_gt'] label_values = ['Undefined','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 = [0] elif dataset_name == 'IndianPines': # Load the image img = open_file(folder + 'Indian_pines_corrected.mat') img = img['indian_pines_corrected'] rgb_bands = (43, 21, 11) # AVIRIS sensor gt = open_file(folder + 'Indian_pines_gt.mat')['indian_pines_gt'] label_values = ["Undefined", "Alfalfa", "Corn-notill", "Corn-mintill", "Corn", "Grass-pasture", "Grass-trees", "Grass-pasture-mowed", "Hay-windrowed", "Oats", "Soybean-notill", "Soybean-mintill", "Soybean-clean", "Wheat", "Woods", "Buildings-Grass-Trees-Drives", "Stone-Steel-Towers"] ignored_labels = [0] elif dataset_name == 'Botswana': # Load the image img = open_file(folder + 'Botswana.mat')['Botswana'] rgb_bands = (75, 33, 15) gt = open_file(folder + 'Botswana_gt.mat')['Botswana_gt'] label_values = ["Undefined", "Water", "Hippo grass", "Floodplain grasses 1", "Floodplain grasses 2", "Reeds", "Riparian", "Firescar", "Island interior", "Acacia woodlands", "Acacia shrublands", "Acacia grasslands", "Short mopane", "Mixed mopane", "Exposed soils"] ignored_labels = [0] elif dataset_name == 'KSC': # Load the image img = open_file(folder + 'KSC.mat')['KSC'] rgb_bands = (43, 21, 11) # AVIRIS sensor gt = open_file(folder + 'KSC_gt.mat')['KSC_gt'] label_values = ["Undefined", "Scrub", "Willow swamp", "Cabbage palm hammock", "Cabbage palm/oak hammock", "Slash pine", "Oak/broadleaf hammock", "Hardwood swamp", "Graminoid marsh", "Spartina marsh", "Cattail marsh", "Salt marsh", "Mud flats", "Wate"] ignored_labels = [0] else: # Custom dataset img, gt, rgb_bands, ignored_labels, label_values, palette = CUSTOM_DATASETS_CONFIG[dataset_name]['loader'](folder) # Filter NaN out nan_mask = np.isnan(img.sum(axis=-1)) if np.count_nonzero(nan_mask) > 0: print("Warning: NaN have been found in the data. It is preferable to remove them beforehand. Learning on NaN data is disabled.") img[nan_mask] = 0 gt[nan_mask] = 0 ignored_labels.append(0) ignored_labels = list(set(ignored_labels)) # Normalization img = np.asarray(img, dtype='float32') img = (img - np.min(img)) / (np.max(img) - np.min(img)) return img, gt, label_values, ignored_labels, rgb_bands, palette

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