def __init__(self):
# ID and Name
self.id = 601
self.experiment_name = "ma_cotr_v{}".format(self.id)
self.debug = False
# System
self.checkpointsBasePath = "./checkpoints/"
self.checkpointsBasePathMod = self.checkpointsBasePath + 'models/'
# self.labelpath = "/local/DEEPLEARNING/MULTI_ATLAS/multi_atlas//512_512_256/"
self.labelpath = '/local/DEEPLEARNING/VP_multiorgan_v2/'
self.datapath = self.labelpath
self.input_shape = [512,512,256]
# filters = [4, 8, 16, 32]
# skip_idx = [1,3,5,6]
# self.patch_size=(128,128,128)
self.patch_size=(192,192,48)
# n_layers=6
self.clip = True
self.patched = True
# GPU
self.gpu = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# Model
self.n_classes = 8
self.net = ResTranUnet(norm_cfg='IN', activation_cfg='LeakyReLU', img_size=self.patch_size, num_classes=self.n_classes, weight_std=False, deep_supervision=True)
self.net.inference_apply_nonlin = softmax_helper
self.n_parameters = count_parameters(self.net)
print("N PARAMS : {}".format(self.n_parameters))
self.model_path = './checkpoints/models/vp_cotr.pth'
# self.model_path = './checkpoints/models/403/mod.pt'
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.start_epoch = 0
self.epoch = 1000
# self.loss = torch.nn.CrossEntropyLoss()
self.loss = DC_and_CE_loss({'batch_dice': True, 'smooth': 1e-5, 'do_bg': False}, {})
self.ds_scales = ((1, 1, 1), (0.5, 0.5, 1), (0.25, 0.25, 0.5))
################# Here we wrap the loss for deep supervision ############
# we need to know the number of outputs of the network
net_numpool = 4
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2 ** i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True] + [True if i < net_numpool - 1 else False for i in range(1, net_numpool)])
weights[~mask] = 0
weights = weights / weights.sum()
self.ds_loss_weights = weights
# now wrap the loss
self.loss = MultipleOutputLoss2(self.loss, self.ds_loss_weights)
################# END ###################
self.batchsize = 2
self.lr_rate = 2e-2
# self.final_lr_rate = 1e-5
# self.optimizer = optim.Adam(self.net.parameters(), lr = self.lr_rate)
self.optimizer = optim.SGD(self.net.parameters(), lr = self.lr_rate, weight_decay=3e-5, momentum=0.99)
self.optimizer.zero_grad()
self.validate_every_k_epochs = 10
# self.decay = (self.lr_rate/self.final_lr_rate - 1)/self.epoch
self.lr_scheduler = get_scheduler(self.optimizer, "poly", self.lr_rate, max_epochs=self.epoch)
self.load_model()
# Other
self.classes_name = ["Background", "Liver","Gallbladder","Spleen","Left_Kidney","Right_Kidney","Pancreas","Stomach"]
def __init__(self):
# ID and Name
self.id = "506b95"
self.experiment_name = "ma_crosstr_v{}".format(self.id)
self.debug = False
# System
self.checkpointsBasePath = "./checkpoints/"
self.checkpointsBasePathMod = self.checkpointsBasePath + 'models/'
self.labelpath = '/local/DEEPLEARNING/MULTI_ATLAS/MULTI_ATLAS/nnUNet_preprocessed/Task017_BCV/nnUNetData_plans_v2.1_stage1/'
self.datapath = self.labelpath
self.input_shape = [512,512,256]
# self.filters = [16, 32, 64, 128]
# self.filters = [64, 192, 448, 704]
# self.filters = [16, 32, 64, 128, 256]
self.filters = [32, 64, 128, 256, 512]
d_model = self.filters[-1]
# skip_idx = [1,3,5,6]
# self.patch_size=(128,128,128)
self.patch_size=(192,192,48)
# n_layers=6
self.clip = False
self.patched = True
# GPU
self.gpu = '1'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# torch.backends.cudnn.benchmark = False
# Model
number_of_cross_heads = 8
number_of_self_heads = 8
number_of_self_layer = 6
self.n_classes = 14
self.net = CrossPatch3DTr(filters=self.filters,patch_size=[1,1,1],
d_model=d_model,n_classes=self.n_classes,
n_cheads=1,n_sheads=number_of_self_heads,
bn=True,up_mode='deconv',
n_strans=number_of_self_layer, do_cross=True,
enc_grad=False)
self.net.inference_apply_nonlin = softmax_helper
self.n_parameters = count_parameters(self.net)
print("N PARAMS : {}".format(self.n_parameters))
# self.model_path = './checkpoints/models/deep_crosstr.pth'
self.model_path = './checkpoints/models/506/modlast.pt'
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.start_epoch = 1000
self.epoch = 2000
# self.loss = torch.nn.CrossEntropyLoss()
self.loss = DC_and_CE_loss({'batch_dice': True, 'smooth': 1e-5, 'do_bg': False}, {})
self.ds_scales = ((1, 1, 1), (0.5, 0.5, 0.5), (0.25, 0.25, 0.25), (0.125,0.125,0.125))
################# Here we wrap the loss for deep supervision ############
# we need to know the number of outputs of the network
net_numpool = 4
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2 ** i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True] + [True if i < net_numpool - 1 else False for i in range(1, net_numpool)])
weights[~mask] = 0
weights = weights / weights.sum()
self.ds_loss_weights = weights
# now wrap the loss
self.loss = MultipleOutputLoss2(self.loss, self.ds_loss_weights)
################# END ###################
self.batchsize = 2
self.lr_rate = 1e-3
self.load_lr = False
self.load_model()
self.net.reinit_decoder()
self.net.reinit_crostrans(dim=d_model, depth=1, heads=number_of_cross_heads, dim_head=1024, mlp_dim=1024, dropout = 0.1)
self.optimizer = optim.SGD(self.net.parameters(), lr = self.lr_rate, weight_decay=3e-5, momentum=0.99, nesterov=True)
self.optimizer.zero_grad()
self.validate_every_k_epochs = 1
# self.decay = (self.lr_rate/self.final_lr_rate - 1)/self.epoch
self.lr_scheduler = get_scheduler(self.optimizer, "poly", self.lr_rate, max_epochs=self.epoch)
# 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']
def __init__(self):
# ID and Name
self.id = -1
self.experiment_name = "ma_cotr_pred_v{}".format(self.id)
self.debug = False
# System
self.checkpointsBasePath = "./checkpoints/"
self.checkpointsBasePathMod = self.checkpointsBasePath + 'models/'
self.labelpath = "/local/DEEPLEARNING/MULTI_ATLAS/multi_atlas//512_512_256/"
self.datapath = self.labelpath
self.input_shape = [512,512,256]
# filters = [4, 8, 16, 32]
# skip_idx = [1,3,5,6]
self.patch_size=(128,128,128)
# self.patch_size=(192,192,48)
# n_layers=6
self.clip = True
self.patched = True
# GPU
self.gpu = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = self.gpu
# Model
self.n_classes = 14
self.net = ResTranUnet(norm_cfg='IN', activation_cfg='LeakyReLU', img_size=self.patch_size, num_classes=self.n_classes, weight_std=False, deep_supervision=False)
self.net.inference_apply_nonlin = softmax_helper
self.n_parameters = count_parameters(self.net)
print("N PARAMS : {}".format(self.n_parameters))
# self.model_path = './checkpoints/models/cotr.pth'
self.model_path = './checkpoints/models/400/mod.pt'
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.start_epoch = 1000
self.epoch = 1000
self.loss = torch.nn.CrossEntropyLoss()
self.loss = DC_and_CE_loss({'batch_dice': True, 'smooth': 1e-5, 'do_bg': False}, {})
self.batchsize = 2
self.lr_rate = 2e-2
# self.final_lr_rate = 1e-5
# self.optimizer = optim.Adam(self.net.parameters(), lr = self.lr_rate)
self.optimizer = optim.SGD(self.net.parameters(), lr = self.lr_rate, weight_decay=3e-5, momentum=0.99)
self.optimizer.zero_grad()
self.validate_every_k_epochs = 10
# self.decay = (self.lr_rate/self.final_lr_rate - 1)/self.epoch
self.lr_scheduler = get_scheduler(self.optimizer, "poly", self.lr_rate, max_epochs=self.epoch)
self.load_model()
# 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']