def get_learn(data):
# create model
model = nb_resnet_unet.get_unet_res18(1, True)
model.load_state_dict(torch.load('./models/unet_res18_allres_init.pth'))
# create learner
learn = Learner(data, model)
# split model
learn.layer_groups = split_model(learn.model)
# set multi-gpu
if data.device.type == 'cuda':
learn.model = torch.nn.DataParallel(learn.model,
device_ids=[0, 1, 2, 3])
# set loss func
# learn.loss_func = partial(nb_loss_metrics.combo_loss, balance_ratio=1)
# learn.loss_func = nb_loss_metrics.dice_loss
learn.loss_func = partial(nb_loss_metrics.balance_bce, balance_ratio=1)
# 添加metrics
learn.metrics += [nb_loss_metrics.dice_loss]
learn.metrics += [partial(nb_loss_metrics.balance_bce, balance_ratio=1)]
learn.metrics += [nb_loss_metrics.mask_iou]
return learn
def get_learn_detectsym_17clas(data, gaf, clas_weights=weights):
'''
用的符号检测的17个类别的数据集
'''
# create model
model = resnet_ssd.get_resnet18_1ssd(num_classes=17)
model.load_state_dict(torch.load('./models/pretrained_res18_1ssd.pth'))
# create learner
learn = Learner(data, model)
# split model
learn.layer_groups = split_model(learn.model)
# set multi-gpu
if data.device.type == 'cuda':
learn.model = torch.nn.DataParallel(
learn.model, device_ids=device_ids) #device_ids=[0,1,2,3,4,5])
# set loss func
learn.loss_func = partial(anchors_loss_metrics.yolo_L,
gaf=gaf,
conf_th=1,
clas_weights=clas_weights,
lambda_nconf=10)
# 添加metrics
learn.metrics += [
partial(anchors_loss_metrics.clas_L,
gaf=gaf,
clas_weights=clas_weights)
]
learn.metrics += [
partial(anchors_loss_metrics.cent_L,
gaf=gaf,
clas_weights=clas_weights)
]
learn.metrics += [
partial(anchors_loss_metrics.pConf_L,
gaf=gaf,
clas_weights=clas_weights)
]
learn.metrics += [
partial(anchors_loss_metrics.nConf_L, gaf=gaf, conf_th=1)
]
learn.metrics += [partial(anchors_loss_metrics.clas_acc, gaf=gaf)]
learn.metrics += [partial(anchors_loss_metrics.cent_d, gaf=gaf)]
return learn
v_data = DLDataLoader(v_chain,
collate_fn=dlc.gdf_col,
pin_memory=False,
num_workers=0)
databunch = DataBunch(t_data, v_data, collate_fn=dlc.gdf_col, device="cuda")
t_final = time() - start
print(t_final)
print("Creating model")
start = time()
model = TabularModel(emb_szs=embeddings,
n_cont=len(cont_names),
out_sz=2,
layers=[512, 256])
learn = Learner(databunch, model, metrics=[accuracy])
learn.loss_func = torch.nn.CrossEntropyLoss()
t_final = time() - start
print(t_final)
print("Finding learning rate")
start = time()
learn.lr_find()
learn.recorder.plot(show_moms=True, suggestion=True)
learning_rate = 1.32e-2
epochs = 1
t_final = time() - start
print(t_final)
print("Running Training")
start = time()
learn.fit_one_cycle(epochs, learning_rate)
t_final = time() - start
print(t_final)
#learn.load("/scratch/leuven/412/vsc41276/mp/CNN_16epochs", strict=False)
learn.split(_schnet_split)
learn.freeze()
summary(model, device)
if args.sdr:
learn.opt_func = Adam_sdr#partial(SGD_sdr, weight_decay=0)#
else:
learn.opt_func = Adam
if args.triplets_online:
learn.loss_func = TripletLoss(margin=1.0)
learn.callbacks.append(TripletSetter(model, train_loader, train_loader2, semihard_negative, margin=1.0, triplets_per_class=100))
learn.callbacks.append(TripletSetter(model, val_loader , val_loader2 , semihard_negative, margin=1.0, triplets_per_class=125))
else:
learn.loss_func = OnlineTripletLoss(1.0 ,SemihardNegativeTripletSelector(margin=1.0))
#learn.lr_find(start_lr=1e-6,end_lr=5e-2,no_grad_val = False,num_it=300)
#plot_recorder(learn.recorder)
plt.show()
torch.cuda.empty_cache()
learn.fit_one_cycle(cyc_len=1, max_lr=1e-2, div_factor=20.0, pct_start=0.25,no_grad_val=False) #,wd=#1e-7#,moms=(0.95, 0.85)
#learn.save('16epochs_triplets')
