def get_transform(pixmean, pixstd):
# Data transforms
logging.info('==> Preparing data..') # Random Crop, Zero out, flip, scale,
transform_train = transforms.Compose([
transforms.RandomCrop(CROPSIZE, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomYFlip(),
transforms.RandomZFlip(),
transforms.ZeroOut(4),
transforms.ToTensor(),
transforms.Normalize((pixmean), (pixstd)), # need to cal mean and std, revise norm func
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((pixmean), (pixstd)),
])
return transform_train, transform_test
def load_data(trained_data_path, test_data_path, fold, batch_size,
num_workers):
crop_size = 32
black_list = []
preprocess_path = trained_data_path
pix_value, npix = 0, 0
for file_name in os.listdir(preprocess_path):
if file_name.endswith('.npy'):
if file_name[:-4] in black_list:
continue
data = np.load(os.path.join(preprocess_path, file_name))
pix_value += np.sum(data)
npix += np.prod(data.shape)
pix_mean = pix_value / float(npix)
pix_value = 0
for file_name in os.listdir(preprocess_path):
if file_name.endswith('.npy'):
if file_name[:-4] in black_list: continue
data = np.load(os.path.join(preprocess_path, file_name)) - pix_mean
pix_value += np.sum(data * data)
pix_std = np.sqrt(pix_value / float(npix))
print(pix_mean, pix_std)
transform_train = transforms.Compose([
# transforms.RandomScale(range(28, 38)),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomYFlip(),
transforms.RandomZFlip(),
transforms.ZeroOut(4),
transforms.ToTensor(),
transforms.Normalize(
(pix_mean),
(pix_std)), # need to cal mean and std, revise norm func
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((pix_mean), (pix_std)),
])
# load data list
train_file_name_list = []
train_label_list = []
train_feat_list = []
test_file_name_list = []
test_label_list = []
test_feat_list = []
data_frame = pd.read_csv('./data/annotationdetclsconvfnl_v3.csv',
names=[
'seriesuid', 'coordX', 'coordY', 'coordZ',
'diameter_mm', 'malignant'
])
all_list = data_frame['seriesuid'].tolist()[1:]
label_list = data_frame['malignant'].tolist()[1:]
crdx_list = data_frame['coordX'].tolist()[1:]
crdy_list = data_frame['coordY'].tolist()[1:]
crdz_list = data_frame['coordZ'].tolist()[1:]
dim_list = data_frame['diameter_mm'].tolist()[1:]
# test id
test_id_list = []
for file_name in os.listdir(test_data_path + str(fold) + '/'):
if file_name.endswith('.mhd'):
test_id_list.append(file_name[:-4])
mxx = mxy = mxz = mxd = 0
for srsid, label, x, y, z, d in zip(all_list, label_list, crdx_list,
crdy_list, crdz_list, dim_list):
mxx = max(abs(float(x)), mxx)
mxy = max(abs(float(y)), mxy)
mxz = max(abs(float(z)), mxz)
mxd = max(abs(float(d)), mxd)
if srsid in black_list:
continue
# crop raw pixel as feature
data = np.load(os.path.join(preprocess_path, srsid + '.npy'))
bgx = int(data.shape[0] / 2 - crop_size / 2)
bgy = int(data.shape[1] / 2 - crop_size / 2)
bgz = int(data.shape[2] / 2 - crop_size / 2)
data = np.array(data[bgx:bgx + crop_size, bgy:bgy + crop_size,
bgz:bgz + crop_size])
y, x, z = np.ogrid[-crop_size / 2:crop_size / 2,
-crop_size / 2:crop_size / 2,
-crop_size / 2:crop_size / 2]
mask = abs(y**3 + x**3 + z**3) <= abs(float(d))**3
feat = np.zeros((crop_size, crop_size, crop_size), dtype=float)
feat[mask] = 1
if srsid.split('-')[0] in test_id_list:
test_file_name_list.append(srsid + '.npy')
test_label_list.append(int(label))
test_feat_list.append(feat)
else:
train_file_name_list.append(srsid + '.npy')
train_label_list.append(int(label))
train_feat_list.append(feat)
for idx in range(len(train_feat_list)):
train_feat_list[idx][-1] /= mxd
for idx in range(len(test_feat_list)):
test_feat_list[idx][-1] /= mxd
train_set = lunanod(preprocess_path,
train_file_name_list,
train_label_list,
train_feat_list,
train=True,
download=True,
transform=transform_train)
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
test_set = lunanod(preprocess_path,
test_file_name_list,
test_label_list,
test_feat_list,
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
return train_loader, test_loader
if fname[:-4] in blklst: continue
data = np.load(os.path.join(preprocesspath, fname)) - pixmean
pixvlu += np.sum(data * data)
pixstd = np.sqrt(pixvlu / float(npix))
# pixstd /= 255
print(pixmean, pixstd)
logging.info('mean ' + str(pixmean) + ' std ' + str(pixstd))
# Datatransforms
logging.info('==> Preparing data..') # Random Crop, Zero out, x z flip, scale,
transform_train = transforms.Compose([
# transforms.RandomScale(range(28, 38)),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomYFlip(),
transforms.RandomZFlip(),
transforms.ZeroOut(4),
transforms.ToTensor(),
transforms.Normalize((pixmean), (pixstd)), # need to cal mean and std, revise norm func
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((pixmean), (pixstd)),
])
# load data list
trfnamelst = []
trlabellst = []
trfeatlst = []
tefnamelst = []
telabellst = []
def __init__(self):
self.best_acc = 0
self.best_acc_gbt = 0
self.use_cuda = torch.cuda.is_available()
pixvlu, npix = 0, 0
for fname in os.listdir(preprocesspath):
if fname.endswith('.npy'):
if fname[:-4] in blklst: continue
data = np.load(os.path.join(preprocesspath, fname))
pixvlu += np.sum(data)
npix += np.prod(data.shape)
pixmean = pixvlu / float(npix)
pixvlu = 0
for fname in os.listdir(preprocesspath):
if fname.endswith('.npy'):
if fname[:-4] in blklst: continue
data = np.load(os.path.join(preprocesspath, fname)) - pixmean
pixvlu += np.sum(data * data)
pixstd = np.sqrt(pixvlu / float(npix))
print('pixmean:%.3f, pixstd:%.3f' % (pixmean, pixstd))
logging.info('mean ' + str(pixmean) + ' std ' + str(pixstd))
# Datatransforms
logging.info(
'==> Preparing data..') # Random Crop, Zero out, x z flip, scale,
transform_train = transforms.Compose([
# transforms.RandomScale(range(28, 38)),
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.RandomYFlip(),
transforms.RandomZFlip(),
transforms.ZeroOut(4),
transforms.ToTensor(),
transforms.Normalize(
(pixmean),
(pixstd)), # need to cal mean and std, revise norm func
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((pixmean), (pixstd)),
])
# load data list
self.trfnamelst = []
trlabellst = []
trfeatlst = []
self.tefnamelst = []
telabellst = []
tefeatlst = []
dataframe = pd.read_csv(csvfilepath,
names=[
'seriesuid', 'coordX', 'coordY', 'coordZ',
'diameter_mm', 'malignant'
])
alllst = dataframe['seriesuid'].tolist()[1:]
labellst = dataframe['malignant'].tolist()[1:]
crdxlst = dataframe['coordX'].tolist()[1:]
crdylst = dataframe['coordY'].tolist()[1:]
crdzlst = dataframe['coordZ'].tolist()[1:]
dimlst = dataframe['diameter_mm'].tolist()[1:]
# test id
teidlst = []
for fname in os.listdir(luna16path + '/subset' + str(fold) + '/'):
if fname.endswith('.mhd'):
teidlst.append(fname[:-4])
mxx = mxy = mxz = mxd = 0
for srsid, label, x, y, z, d in zip(alllst, labellst, crdxlst, crdylst,
crdzlst, dimlst):
mxx = max(abs(float(x)), mxx)
mxy = max(abs(float(y)), mxy)
mxz = max(abs(float(z)), mxz)
mxd = max(abs(float(d)), mxd)
if srsid in blklst: continue
# crop raw pixel as feature
if os.path.exists(os.path.join(preprocesspath, srsid + '.npy')):
data = np.load(os.path.join(preprocesspath, srsid + '.npy'))
bgx = data.shape[0] / 2 - CROPSIZE / 2
bgy = data.shape[1] / 2 - CROPSIZE / 2
bgz = data.shape[2] / 2 - CROPSIZE / 2
data = np.array(data[bgx:bgx + CROPSIZE, bgy:bgy + CROPSIZE,
bgz:bgz + CROPSIZE])
feat = np.hstack((np.reshape(data, (-1, )) / 255, float(d)))
if srsid.split('-')[0] in teidlst:
self.tefnamelst.append(srsid + '.npy')
telabellst.append(int(label))
tefeatlst.append(feat)
else:
self.trfnamelst.append(srsid + '.npy')
trlabellst.append(int(label))
trfeatlst.append(feat)
for idx in xrange(len(trfeatlst)):
trfeatlst[idx][-1] /= mxd
for idx in xrange(len(tefeatlst)):
tefeatlst[idx][-1] /= mxd
trainset = lunanod(preprocesspath,
self.trfnamelst,
trlabellst,
trfeatlst,
train=True,
download=True,
transform=transform_train)
self.trainloader = torch.utils.data.DataLoader(trainset,
batch_size=16,
shuffle=True,
num_workers=30)
testset = lunanod(preprocesspath,
self.tefnamelst,
telabellst,
tefeatlst,
train=False,
download=True,
transform=transform_test)
self.testloader = torch.utils.data.DataLoader(testset,
batch_size=16,
shuffle=False,
num_workers=30)
# Model
if args.resume:
# Load checkpoint.
logging.info('==> Resuming from checkpoint..')
# assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load(savemodelpath + 'ckpt.t7')
self.net = checkpoint['net']
best_acc = checkpoint['acc']
start_epoch = checkpoint['epoch']
else:
logging.info('==> Building model..')
self.net = dpn3d.DPN92_3D()
if self.use_cuda:
self.net.cuda()
self.net = torch.nn.DataParallel(self.net,
device_ids=range(
torch.cuda.device_count()))
cudnn.benchmark = False # True
self.criterion = nn.CrossEntropyLoss()
self.optimizer = optim.SGD(self.net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=5e-4)
pass