import torch.backends.cudnn as cudnn device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cuda": torch.cuda.empty_cache() cudnn.benchmark = True import segsemdata import embedding import numpy as np import PIL from PIL import Image from sklearn.metrics import confusion_matrix print("load data") datatest = segsemdata.makeISPRS(datasetpath="/data/POSTDAM", lod0=False, dataflag="test", POTSDAM=True) datatest = datatest.copyTOcache(outputresolution=50) nbclasses = len(datatest.setofcolors) cm = np.zeros((nbclasses, nbclasses), dtype=int) names = datatest.getnames() with torch.no_grad(): print("load model") net = torch.load("build/model.pth") net = net.to(device) net.eval() print("test") for name in names: image, label = datatest.getImageAndLabel(name, innumpy=False)
if device == "cuda": torch.cuda.empty_cache() cudnn.benchmark = True sys.path.append('../..') import segsemdata print("load data") root = "/data/" assert (sys.argv[1] in [ "VAIHINGEN", "POTSDAM", "BRUGES", "TOULOUSE", "VAIHINGEN_lod0", "POTSDAM_lod0", "BRUGES_lod0", "TOULOUSE_lod0", "AIRS" ]) if sys.argv[1] == "VAIHINGEN": data = segsemdata.makeISPRS(datasetpath=root + "ISPRS_VAIHINGEN", dataflag="train", POTSDAM=False) if sys.argv[1] == "VAIHINGEN_lod0": data = segsemdata.makeISPRS(datasetpath=root + "ISPRS_VAIHINGEN", labelflag="lod0", weightflag="iou", dataflag="train", POTSDAM=False) if sys.argv[1] == "POTSDAM": data = segsemdata.makeISPRS(datasetpath=root + "ISPRS_POTSDAM", dataflag="train", POTSDAM=True) if sys.argv[1] == "POTSDAM_lod0": data = segsemdata.makeISPRS(datasetpath=root + "ISPRS_POTSDAM", labelflag="lod0", weightflag="iou",
device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cuda": torch.cuda.empty_cache() cudnn.benchmark = True import segsemdata import embedding import numpy as np print("load model") net = embedding.Embedding(pretrained="/data/vgg16-00b39a1b.pth") net = net.to(device) print("load data") datatrain = segsemdata.makeISPRS(datasetpath="/data/ISPRS_VAIHINGEN", lod0=False, POTSDAM=False, dataflag="train") datatrain = datatrain.copyTOcache(outputresolution=50) net.adddataset(datatrain.metadata()) net = net.to(device) nbclasses = len(datatrain.setofcolors) earlystopping = datatrain.getrandomtiles(1000, 128, 16) print("train setting") import torch.nn as nn import collections import random from sklearn.metrics import confusion_matrix criterion = nn.CrossEntropyLoss() optimizer = net.getoptimizer()
import torch.backends.cudnn as cudnn device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cuda": torch.cuda.empty_cache() cudnn.benchmark = True import segsemdata import embedding import numpy as np import PIL from PIL import Image from sklearn.metrics import confusion_matrix print("load data") datatest = segsemdata.makeISPRS(datasetpath="/data/ISPRS_VAIHINGEN", lod0=False, dataflag="test", POTSDAM=False) datatest = datatest.copyTOcache(outputresolution=50) nbclasses = len(datatest.setofcolors) cm = np.zeros((nbclasses, nbclasses), dtype=int) names = datatest.getnames() with torch.no_grad(): print("load model") net = torch.load("build/model.pth") net = net.to(device) net.eval() print("test") for name in names: image, label = datatest.getImageAndLabel(name, innumpy=False)
device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cuda": torch.cuda.empty_cache() cudnn.benchmark = True import segsemdata import embedding import numpy as np print("load model") net = embedding.Embedding(pretrained="/data/vgg16-00b39a1b.pth") net = net.to(device) print("load data") datatrain = segsemdata.makeISPRS(datasetpath="/data/POSTDAM", lod0=False, POTSDAM=True, dataflag="train") datatrain = datatrain.copyTOcache(outputresolution=50) net.adddataset(datatrain.metadata()) net = net.to(device) nbclasses = len(datatrain.setofcolors) earlystopping = datatrain.getrandomtiles(1000, 128, 16) print("train setting") import torch.nn as nn import collections import random from sklearn.metrics import confusion_matrix criterion = nn.CrossEntropyLoss() optimizer = net.getoptimizer()
device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cuda": torch.cuda.empty_cache() cudnn.benchmark = True import segsemdata import embedding import numpy as np print("load model") net = embedding.Embedding(pretrained="/data/vgg16-00b39a1b.pth") net = net.to(device) print("load data") datatrain1 = segsemdata.makeISPRS(datasetpath="/data/ISPRS_VAIHINGEN", POTSDAM=False) datatrain1 = datatrain1.copyTOcache(outputresolution=50) net.adddataset(datatrain1.metadata()) datatrain2 = segsemdata.makeDFC2015() datatrain2 = datatrain2.copyTOcache(outputresolution=50) net.adddataset(datatrain2.metadata()) net = net.to(device) earlystopping1 = datatrain1.getrandomtiles(1000, 128, 16) earlystopping2 = datatrain2.getrandomtiles(1000, 128, 16) print("train setting") import torch.nn as nn import collections
import torch.backends.cudnn as cudnn device = "cuda" if torch.cuda.is_available() else "cpu" if device == "cuda": torch.cuda.empty_cache() cudnn.benchmark = True import segsemdata import embedding import numpy as np import PIL from PIL import Image from sklearn.metrics import confusion_matrix print("load data") datatest = segsemdata.makeISPRS(datasetpath="/data/ISPRS_VAIHINGEN", trainData=False, POTSDAM=False) datatest = datatest.copyTOcache(outputresolution=50) nbclasses = len(datatest.setofcolors) cm = np.zeros((nbclasses, nbclasses), dtype=int) names = datatest.getnames() with torch.no_grad(): print("load model") net = torch.load("build/model.pth") net = net.to(device) net.eval() print("test") for name in names: image, label = datatest.getImageAndLabel(name, innumpy=False)