def __init__(self):
# ID and Name
self.experiment_name = "unet_3D"
self.id = 1
# System
self.checkpointsBasePath = "./models/checkpoints"
self.labelpath = "/local/SSD_DEEPLEARNING/brats/processed/data_3D_size_240_240_155_res_1.0_1.0_1.0.hdf5"
self.datapath = "/local/SSD_DEEPLEARNING/brats/processed/data_3D_size_160_192_155_res_1.0_1.0_1.0.hdf5"
# GPU
self.gpu = '1'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# Model
self.channels = [64, 128, 256, 512, 1024]
self.channels = [int(x / 4) for x in self.channels]
self.net = unet_3D(
self.channels,
n_classes=3,
in_channels=4,
interpolation=(
240, 240,
155)) #self.channels, 3, interpolation = (240,240,155))
# Data
self.nn_augmentation = False
self.soft_augmentation = False
self.do_rotate = False
self.rot_degrees = 20
self.do_scale = False
self.scale_factor = False
self.do_flip = False
self.do_elastic_aug = False
self.sigma = 10
self.do_intensity_shift = False
self.max_intensity_shift = 0.1
# Training
self.train_original_classes = False
self.epoch = 1000
def loss(outputs, labels):
return bratsUtils.bratsDiceLoss(outputs, labels, nonSquared=True)
self.loss = loss
self.batchsize = 2
# self.optimizer = optim.SGD(self.net.parameters(),
# lr= 0.01, #to do
# momentum=0.9,
# nesterov=True,
# weight_decay=1e-5) #todo
self.optimizer = optim.Adam(self.net.parameters(),
lr=0.0001,
weight_decay=1e-5)
self.validate_every_k_epochs = 1
# Scheduler list : [lambdarule_1]
# self.lr_scheduler = get_scheduler(self.optimizer, "lambdarule_e1000")
self.lr_scheduler = get_scheduler(self.optimizer, "multistep")
def main():
# print(convert_byte(1000*1000*1000*3*4))
# print(convert_byte(1000*1000*1000*4*4))
# print(convert_byte(1000*1000*1000*5*4))
# exit(0)
inchan = 1
chanscale = 2
chans = [i // chanscale for i in [64, 128, 256, 512, 1024]]
outsize = 14
# interp = (512,512,198)
mod = unet_3D(chans,
n_classes=outsize,
in_channels=inchan,
interpolation=None)
layers = get_mod_details(mod)
fact = 0.1
# s = (80,80,32)
# s = (112,112,48)
s = (256, 256, 112)
# x = torch.from_numpy(np.random.rand(1,1,int(round(512*fact)),int(round(512*fact)),int(round(198*fact)))).float()
# y = torch.from_numpy(np.random.rand(1,outsize,int(round(512*fact)),int(round(512*fact)),int(round(198*fact)))).float()
# argmax = torch.from_numpy(np.random.rand(1,outsize,int(round(512*fact)),int(round(512*fact)),int(round(198*fact)))).float()
x = torch.from_numpy(np.random.rand(1, 1, s[0], s[1], s[2])).float()
y = torch.from_numpy(np.random.rand(1, outsize, s[0], s[1], s[2])).float()
argmax = torch.from_numpy(np.random.rand(1, outsize, s[0], s[1],
s[2])).float()
acts = get_activations_shapes_as_dict(layers, x)
mod_m = model_memory(mod)
lab_m = labels_mem(y)
argm_m = validat_arg_memory(argmax)
inp_m = labels_mem(x)
cur_m = forward_memory_cosumption_with_peak(acts) - inp_m
back_m = chans[0] * np.prod(s) * 4 * 2 - outsize * np.prod(s) * 4 * 3
# print(convert_byte(cur_m*2 + mod_m + lab_m))
# print(convert_byte(lab_m))
# print(convert_byte(cur_m),convert_byte(mod_m),convert_byte(lab_m))
# print(convert_byte(mod_m))
# print(convert_byte(mod_m+ labels_mem(x)))
# print(convert_byte(mod_m+ labels_mem(x) + lab_m))
# print(convert_byte(mod_m+ labels_mem(x) + lab_m + (cur_m + lab_m)))
print('model :', convert_byte(mod_m))
print('input :', convert_byte(mod_m + inp_m))
print('label :', convert_byte(mod_m + inp_m + lab_m))
print('forwa :', convert_byte(mod_m + inp_m + lab_m + cur_m))
print('backw :', convert_byte(mod_m + inp_m + lab_m + cur_m + back_m))
print(
'max :',
convert_byte(
max(
mod_m + inp_m + lab_m + cur_m, mod_m + inp_m + lab_m + cur_m +
back_m - 2 * chans[0] * np.prod(s) * 4)))
def __init__(self):
# ID and Name
self.id = 100
self.experiment_name = "multi_atlas_unet_016_e1000_CE_adam_wd6_da_id{}".format(self.id)
self.debug = False
# System
self.checkpointsBasePath = "./checkpoints/"
self.checkpointsBasePathMod = self.checkpointsBasePath + 'models/'
# self.labelpath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_512_512_198_res_1.0_1.0_1.0.hdf5"
# self.labelpath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_256_256_99_res_0.5_0.5.hdf5"
self.labelpath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_80_80_32_res_0.16.hdf5"
self.datapath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_80_80_32_res_0.16.hdf5"
# GPU
self.gpu = '1'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# Model
self.channels = [64, 128, 256, 512, 1024]
self.channels = [int(x) for x in self.channels]
self.net = unet_3D(self.channels, n_classes=14, is_batchnorm=False, in_channels=1, interpolation = None)#1, self.channels, 12, interpolation = (512,512,198))
# self.net = RevUnet3D(1, self.channels, 12, interpolation = (256,256,99))
self.n_parameters = count_parameters(self.net)
print("N PARAMS : {}".format(self.n_parameters))
self.n_classes = 14
max_displacement = 5,5,5
deg = (0,5,10)
scales = 0
self.transform = tio.Compose([
tio.RandomElasticDeformation(max_displacement=max_displacement),
tio.RandomAffine(scales=scales, degrees=deg)
])
# Training
self.train_original_classes = False
self.epoch = 1000
# def loss(outputs, labels):
# return atlasUtils.atlasDiceLoss(outputs, labels, n_classe = self.n_classes)
# self.loss = loss
# self.loss = SoftDiceLoss(self.n_classes)
self.loss = torch.nn.CrossEntropyLoss()
self.hot = 0
self.batchsize = 1
# self.optimizer = optim.Ada(self.net.parameters(),
# lr= 0.01, #to do
# momentum=0.9,
# nesterov=True,
# weight_decay=1e-5) #todo
self.lr_rate = 5e-4
self.optimizer = optim.Adam(self.net.parameters(), lr = self.lr_rate, weight_decay=1e-6)
# self.optimizer = optim.SGD(self.net.parameters(),
# lr=self.lr_rate)
self.optimizer.zero_grad()
self.validate_every_k_epochs = 1
# Scheduler list : [lambdarule_1]
# self.lr_scheduler = get_scheduler(self.optimizer, "multistep")
self.lr_scheduler = get_scheduler(self.optimizer, "multistep", self.lr_rate)
# self.lr_scheduler = get_scheduler(self.optimizer, "lambdarule_1", self.lr_rate)
# Other
self.classes_name = ['background','spleen','right kidney','left kidney','gallbladder','esophagus','liver','stomach','aorta','inferior vena cava','portal vein and splenic vein','pancreas','right adrenal gland','left adrenal gland']
self.look_small = False
def __init__(self):
# ID and Name
self.experiment_name = "atlas_unet_3D"
self.id = 5
# System
self.checkpointsBasePath = "./models/checkpoints"
self.labelpath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_512_512_198_res_1.0_1.0_1.0.hdf5"
self.datapath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_256_256_99_res_0.5_0.5.hdf5"
# GPU
self.gpu = '1'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# Model
self.channels = [64, 128, 256, 512, 1024]
self.channels = [int(x / 1) for x in self.channels]
self.net = unet_3D(
self.channels,
n_classes=13,
in_channels=1,
interpolation=(
512, 512,
198)) #1, self.channels, 12, interpolation = (512,512,198))
# Data
self.nn_augmentation = False
self.soft_augmentation = False
self.do_rotate = False
self.rot_degrees = 20
self.do_scale = False
self.scale_factor = False
self.do_flip = False
self.do_elastic_aug = False
self.sigma = 10
self.do_intensity_shift = False
self.max_intensity_shift = 0.1
# Training
self.train_original_classes = False
self.epoch = 1000
def loss(outputs, labels):
return atlasUtils.atlasDiceLoss(outputs, labels, nonSquared=True)
self.loss = loss
self.batchsize = 1
# self.optimizer = optim.Ada(self.net.parameters(),
# lr= 0.01, #to do
# momentum=0.9,
# nesterov=True,
# weight_decay=1e-5) #todo
self.optimizer = optim.Adam(self.net.parameters(),
lr=5e-4,
weight_decay=1e-5)
self.validate_every_k_epochs = 1
# Scheduler list : [lambdarule_1]
self.lr_scheduler = get_scheduler(self.optimizer, "multistep")
# Other
self.classes_name = [
'spleen', 'right kidney', 'left kidney', 'gallbladder',
'esophagus', 'liver', 'stomach', 'aorta', 'inferior vena cava',
'portal vein and splenic vein', 'pancreas', 'right adrenal gland',
'left adrenal gland'
]
self.look_small = False
def __init__(self):
# ID and Name
self.id = 211
self.experiment_name = "tcia_unet_03_e1000_dice_sgd_wd6_da_f16_id{}".format(
self.id)
self.debug = False
# System
self.checkpointsBasePath = "./checkpoints/"
self.checkpointsBasePathMod = self.checkpointsBasePath + 'models/'
self.labelpath = "/local/SSD_DEEPLEARNING/PANCREAS_MULTI_RES/160_160_64/"
self.datapath = self.labelpath
self.im_dim = (160, 160, 64)
# GPU
self.gpu = '1'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# Model
self.n_classes = 2
self.channels = [64, 128, 256, 512, 1024]
self.channels = [int(x / 16) for x in self.channels]
self.net = unet_3D(
self.channels,
n_classes=self.n_classes,
is_batchnorm=False,
in_channels=1,
interpolation=None
) #1, self.channels, 12, interpolation = (512,512,198))
# self.net = RevUnet3D(1, self.channels, 12, interpolation = (256,256,99))
self.n_parameters = count_parameters(self.net)
print("N PARAMS : {}".format(self.n_parameters))
self.model_path = './checkpoints/models/unet_tcia_160_160_64_d3_f16.pth'
self.load_model()
self.split = 1
max_displacement = 5, 5, 5
deg = (0, 5, 10)
scales = 0
self.transform = tio.Compose([
tio.RandomElasticDeformation(max_displacement=max_displacement),
tio.RandomAffine(scales=scales, degrees=deg)
])
# Training
self.train_original_classes = False
self.epoch = 1000
# self.loss = torch.nn.CrossEntropyLoss()
self.loss = SoftDiceLoss(self.n_classes)
self.hot = 0
self.batchsize = 2
self.lr_rate = 1e-2 #5e-4
# self.optimizer = optim.Adam(self.net.parameters(), lr = self.lr_rate, weight_decay=1e-5)
self.optimizer = optim.SGD(self.net.parameters(),
lr=self.lr_rate,
momentum=0.9,
nesterov=True,
weight_decay=5e-4)
self.optimizer.zero_grad()
self.validate_every_k_epochs = 1
self.lr_scheduler = get_scheduler(self.optimizer, "multistep",
self.lr_rate)
# self.lr_scheduler = get_scheduler(self.optimizer, "lambdarule_1", self.lr_rate)
# Other
self.classes_name = ['background', 'pancreas']
self.look_small = False
def __init__(self):
# ID and Name
self.id = 46
self.experiment_name = "atlas_unet_3D_016_id{}".format(self.id)
self.debug = False
# System
self.checkpointsBasePath = "./checkpoints/"
self.checkpointsBasePathMod = self.checkpointsBasePath + 'models/'
# self.labelpath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_512_512_198_res_1.0_1.0_1.0.hdf5"
# self.labelpath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_256_256_99_res_0.5_0.5.hdf5"
self.labelpath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_80_80_32_res_0.16.hdf5"
self.datapath = "/local/SSD_DEEPLEARNING/MULTI_ATLAS/multi_atlas/data_3D_size_80_80_32_res_0.16.hdf5"
# GPU
self.gpu = '1'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# Model
self.channels = [64, 128, 256, 512, 1024]
self.channels = [int(x) for x in self.channels]
self.net = unet_3D(
self.channels,
n_classes=14,
is_batchnorm=False,
in_channels=1,
interpolation=None
) #1, self.channels, 12, interpolation = (512,512,198))
# self.net = RevUnet3D(1, self.channels, 12, interpolation = (256,256,99))
self.n_parameters = count_parameters(self.net)
self.n_classes = 14
self.nn_augmentation = False
self.soft_augmentation = False
self.do_rotate = False
self.rot_degrees = 20
self.do_scale = False
self.scale_factor = False
self.do_flip = False
self.do_elastic_aug = False
self.sigma = 10
self.do_intensity_shift = False
self.max_intensity_shift = 0.1
# Training
self.train_original_classes = False
self.epoch = 300
def loss(outputs, labels):
return atlasUtils.atlasDiceLoss(outputs,
labels,
n_classe=self.n_classes)
self.loss = loss
# self.loss = SoftDiceLoss(self.n_classes)
self.hot = 1
self.batchsize = 1
# self.optimizer = optim.Ada(self.net.parameters(),
# lr= 0.01, #to do
# momentum=0.9,
# nesterov=True,
# weight_decay=1e-5) #todo
# self.optimizer = optim.Adam(self.net.parameters(), lr = 5e-4, weight_decay=1e-5)
self.lr_rate = 5e-3
self.optimizer = optim.SGD(self.net.parameters(), lr=self.lr_rate)
self.optimizer.zero_grad()
self.validate_every_k_epochs = 1
# Scheduler list : [lambdarule_1]
# self.lr_scheduler = get_scheduler(self.optimizer, "multistep")
self.lr_scheduler = get_scheduler(self.optimizer, "multistep",
self.lr_rate)
# self.lr_scheduler = get_scheduler(self.optimizer, "lambdarule_1", self.lr_rate)
# Other
self.classes_name = [
'background', 'spleen', 'right kidney', 'left kidney',
'gallbladder', 'esophagus', 'liver', 'stomach', 'aorta',
'inferior vena cava', 'portal vein and splenic vein', 'pancreas',
'right adrenal gland', 'left adrenal gland'
]
self.look_small = False