def predict(fold=0):
df = pd.read_csv("../input/plant-pathology-2020-fgvc7/test.csv")
device = "cuda" if torch.cuda.is_available() else "cpu"
imgs = df.image_id.values.tolist()
path = "../input/plant-images-224-224/"
test_imgs = [path + file + ".png" for file in imgs]
test_aug = Utils.get_aug("test")
test_tar = np.zeros((len(imgs), 4))
test_dataset = ClassificationLoader(image_paths=test_imgs,
targets=test_tar,
resize=None,
augmentations=test_aug)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=bs_test,
num_workers=0,
shuffle=False)
model = get_model(model_name)
model_save_path = f"./model_fold_{fold}.bin"
model.load_state_dict(torch.load(model_save_path))
model = model.to(device)
engine = Engine(model, None, device)
preds = engine.predict(test_loader)
preds = np.vstack(preds)
return preds
def predict(fold,model_name):
df=pd.read_csv("E:/kaggle_imgs/Plant-pathology-2020/Data/train_fold.csv")[0:10]
device="cuda" if torch.cuda.is_available() else "cpu"
imgs=df.image_id.values.tolist()
path="E:/kaggle_imgs/Plant-pathology-2020/images_224_224/"
test_imgs=[path+file+".png" for file in imgs]
utils=Utils(mode="test")
test_aug=utils.get_aug()
test_tar=np.zeros((len(imgs),4))
test_dataset=ClassificationLoader(
image_paths=test_imgs,targets=test_tar,resize=None,augmentations=test_aug
)
test_loader=torch.utils.data.DataLoader(
test_dataset,batch_size=bs_test,num_workers=4,shuffle=False
)
model=get_effinet(classes=2)
model_save_path=f"./model_fold_{fold}.bin"
model.load_state_dict(torch.load(model_save_path))
model=model.to(device)
engine=Engine(model,None,device,classes=2,weights=None)
preds=engine.predict(test_loader)
preds=np.vstack(preds)
#script to c++
sample=torch.rand(1,3,224,224)
model.to("cpu")
traced_script_module = torch.jit.trace(model,sample)
traced_script_module.save("E:/temp/saved_models/"+f"/traced_1015_fold_{fold}.pt")
return preds
def predict(fold):
df = train[0:10]
device = "cuda" if torch.cuda.is_available() else "cpu"
test_imgs = df.tar_path.values.tolist()
test_aug = Utils.get_aug("test")
test_tar = np.zeros((len(test_imgs), 2))
test_dataset = ClassificationLoader(image_paths=test_imgs,
targets=test_tar,
resize=None,
augmentations=test_aug)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=bs_valid,
num_workers=0,
shuffle=False)
model = get_model_effi_b4(2)
tm = datetime.datetime.now().strftime("%m%d")
model_save_path = f"E:/kaggle_imgs/H2/saved_models/model_fold_{fold}_{tm}.bin"
model.load_state_dict(torch.load(model_save_path))
model = model.to(device)
engine = Engine(model, None, device, classes=2, weights=None)
preds = engine.predict(test_loader)
preds = np.vstack(preds) #.argmax(axis=1)
#script to c++
sample = torch.rand(1, 3, 224, 224)
model.to("cpu")
model.set_swish(False)
traced_script_module = torch.jit.trace(model, sample)
traced_script_module.save(
f"E:/kaggle_imgs/H2/saved_models/traced_{model_name}_fold_{fold}_{tm}.pt"
)
return preds