import torch
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.makeAIRSdataset(datasetpath="/data/AIRS",train=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)
pred = net(image.to(device),datatest.metadata())
if sys.argv[1] == "BRUGES_lod0":
data = segsemdata.makeDFC2015(datasetpath=root + "DFC2015",
labelflag="lod0",
weightflag="iou",
dataflag="train")
if sys.argv[1] == "TOULOUSE":
data = segsemdata.makeSEMCITY(datasetpath=root + "SEMCITY_TOULOUSE",
dataflag="train")
if sys.argv[1] == "TOULOUSE_lod0":
data = segsemdata.makeSEMCITY(datasetpath=root + "SEMCITY_TOULOUSE",
dataflag="train",
labelflag="lod0",
weightflag="iou")
if sys.argv[1] == "AIRS":
data = segsemdata.makeAIRSdataset(datasetpath=root + "AIRS",
dataflag="train",
weightflag="iou")
if sys.argv[1] in ["TOULOUSE", "TOULOUSE_lod0"] or len(
sys.argv) == 2 or sys.argv[2] not in ["grey", "normalize"]:
data = data.copyTOcache(outputresolution=50)
else:
if sys.argv[2] == "grey":
data = data.copyTOcache(outputresolution=50, color=False)
else:
data = data.copyTOcache(outputresolution=50,
color=False,
normalize=True)
nbclasses = len(data.setofcolors)
cm = np.zeros((nbclasses, nbclasses), dtype=int)
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
print("load model")
net = embedding.Embedding(pretrained="/data/vgg16-00b39a1b.pth")
net = net.to(device)
print("load data")
datatrain = segsemdata.makeAIRSdataset(datasetpath="/data/AIRS", train=True)
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()