tmp = smp.Unet(
encoder_name="efficientnet-b7",
encoder_weights="imagenet",
in_channels=3,
classes=2,
)
net = torch.nn.Sequential()
net.add_module("core", tmp)
net.add_module("softnms", dataloader.SoftNMS())
net.add_module("softnmsbis", dataloader.SoftNMS())
net = net.cuda()
net.train()
print("load data")
if whereIam == "wdtim719z":
cia = dataloader.CIA("custom", custom=["isprs/train", "saclay/train"])
else:
cia = dataloader.CIA("train")
batchsize = 32
print("train")
import collections
import random
class Gscore(torch.nn.Module):
def __init__(self):
super(Gscore, self).__init__()
self.tot = torch.zeros(1).cuda()
self.correct = torch.zeros(1).cuda()
self.good = torch.zeros(1).cuda()
sys.path.append("/d/achanhon/github/segmentation_models.pytorch")
import segmentation_models_pytorch
with torch.no_grad():
if len(sys.argv) > 1:
net = torch.load(sys.argv[1])
else:
net = torch.load("build/model.pth")
net = net.to(device)
net.eval()
print("load data")
import dataloader
cia = dataloader.CIA("test")
earlystopping = cia.getrandomtiles(128, 16)
print("test on testing crop")
def accu(cm):
return 100.0 * (cm[0][0] + cm[1][1]) / np.sum(cm)
def f1(cm):
return 50.0 * cm[0][0] / (cm[0][0] + cm[1][0] + cm[0][1]) + 50.0 * cm[1][1] / (
cm[1][1] + cm[1][0] + cm[0][1]
)
else:
print("no cuda")
quit()
with torch.no_grad():
if len(sys.argv) > 1:
net = torch.load(sys.argv[1])
else:
net = torch.load("build/model.pth")
net = net.cuda()
net.eval()
print("load data")
import dataloader
if whereIam in ["super", "wdtim719z"]:
cia = dataloader.CIA(flag="custom", custom=["isprs/test", "saclay/test"])
else:
cia = dataloader.CIA("test")
print("test")
import numpy
import PIL
from PIL import Image
def accu(cm):
return 100.0 * (cm[0][0] + cm[1][1]) / numpy.sum(cm)
def iou(cm):
return 50.0 * cm[0][0] / (cm[0][0] + cm[1][0] +
else:
print("no cuda")
quit()
with torch.no_grad():
if len(sys.argv) > 1:
net = torch.load(sys.argv[1])
else:
net = torch.load("build/model.pth")
net = net.cuda()
net.eval()
print("load data")
import dataloader
if whereIam == "wdtis719z" or True:
miniworld = dataloader.CIA(flag="custom", custom=["isprs/test"])
else:
miniworld = dataloader.CIA("test")
print("test")
import numpy
import PIL
from PIL import Image
def accu(cm):
return 100.0 * (cm[0][0] + cm[1][1]) / numpy.sum(cm)
def iou(cm):
return 50.0 * cm[0][0] / (cm[0][0] + cm[1][0] +
net = torchvision.models.vgg16()
net = net.features
net._modules["30"] = torch.nn.Identity()
dummy = torch.zeros(1, 3, 16 * 5, 16 * 5)
dummy = net(dummy)
assert dummy.shape == (1, 512, 5, 5)
net.add_module("31", torch.nn.Conv2d(512, 1024, kernel_size=1, padding=0, stride=1))
net.add_module("32", torch.nn.LeakyReLU())
net.add_module("33", torch.nn.Conv2d(1024, 2, kernel_size=1, padding=0, stride=1))
net = net.cuda()
net.train()
print("load data")
cia = dataloader.CIA(flag="custom", custom=["isprs/train", "vedai/train", "dfc/train"])
print("train")
import collections
import random
optimizer = torch.optim.Adam(net.parameters(), lr=0.0001)
meanloss = collections.deque(maxlen=200)
nbepoch = 800
batchsize = 32
for epoch in range(nbepoch):
print("epoch=", epoch, "/", nbepoch)
XY = cia.getrandomtiles(192, batchsize)
stats = torch.zeros(3).cuda()
sys.path.append("/d/achanhon/github/segmentation_models.pytorch")
import segmentation_models_pytorch
with torch.no_grad():
if len(sys.argv) > 1:
net = torch.load(sys.argv[1])
else:
net = torch.load("build/model.pth")
net = net.to(device)
net.eval()
print("eval in transfert setting")
import dataloader
cia = dataloader.CIA("test")
def accu(cm):
return 100.0 * (cm[0][0] + cm[1][1]) / np.sum(cm)
def f1(cm):
return 50.0 * cm[0][0] / (cm[0][0] + cm[1][0] + cm[0][1]) + 50.0 * cm[1][1] / (
cm[1][1] + cm[1][0] + cm[0][1]
)
cmforlogging = []
cm = {}
with torch.no_grad():
import detectionhead
import dataloader
print("define model")
net = detectionhead.DetectionHead(
smp.Unet(
encoder_name="efficientnet-b7",
encoder_weights="imagenet",
in_channels=3,
classes=2,
))
net = net.cuda()
net.train()
print("load data")
cia = dataloader.CIA(flag="custom", custom=["isprs/train", "saclay/train"])
print("train")
import collections
import random
optimizer = torch.optim.Adam(net.parameters(), lr=0.0001)
meanloss = collections.deque(maxlen=200)
nbepoch = 800
batchsize = 32
for epoch in range(nbepoch):
print("epoch=", epoch, "/", nbepoch)
XY = cia.getrandomtiles(128, batchsize)
stats = torch.zeros(3).cuda()
classes=2,
)
else:
net = smp.Unet(
encoder_name="efficientnet-b7",
encoder_weights="imagenet",
in_channels=3,
classes=2,
)
net = net.cuda()
net.train()
print("load data")
import dataloader
cia = dataloader.CIA("train")
earlystopping = cia.getrandomtiles(128, 16)
weights = torch.Tensor([1, cia.balance / 2, 0.000001]).to(device)
criterion = torch.nn.CrossEntropyLoss(weight=weights)
criterionbis = smp.losses.dice.DiceLoss(mode="multiclass", ignore_index=[2])
print("train")
def accu(cm):
return 100.0 * (cm[0][0] + cm[1][1]) / np.sum(cm)
def f1(cm):
