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update predict.py.

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Brainway
2022-11-09 07:07:07 +00:00
committed by Gitee
parent 611db26c8a
commit 2a89533188
1 changed files with 26 additions and 17 deletions
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43
predict.py
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@ -9,6 +9,7 @@ from sklearn.metrics import f1_score
from PIL import Image
from torchvision import transforms
from models.modeling import VisionTransformer, CONFIGS
import lightrise
#模型预测
def parse_args():
@ -17,16 +18,15 @@ def parse_args():
parser.add_argument('--split', type=str, default='overlap', help="Split method") # non-overlap
parser.add_argument('--slide_step', type=int, default=2, help="Slide step for overlap split")
parser.add_argument('--smoothing_value', type=float, default=0.0, help="Label smoothing value\n")
parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_vit_checkpoint.pth", help="load pretrained model")
#parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/now/emptyjudge5_checkpoint.bin", help="load pretrained model")
#parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_vitgood_checkpoint.pth", help="load pretrained model")
parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/new/ieemooempty_vit_checkpoint.pth", help="load pretrained model")
#parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_vit_checkpoint.pth", help="load pretrained model") #使用自定义VIT
return parser.parse_args()
class Predictor(object):
def __init__(self, args):
self.args = args
self.args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.args.device = torch.device("cuda")
print("self.args.device =", self.args.device)
self.args.nprocs = torch.cuda.device_count()
@ -43,16 +43,12 @@ class Predictor(object):
config.split = self.args.split
config.slide_step = self.args.slide_step
self.num_classes = 5
self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"}
self.cls_dict = {0: "noemp", 1: "yesemp"}
self.model = VisionTransformer(config, self.args.img_size, zero_head=True, num_classes=self.num_classes, smoothing_value=self.args.smoothing_value)
if self.args.pretrained_model is not None:
if not torch.cuda.is_available():
self.model = torch.load(self.args.pretrained_model)
else:
self.model = torch.load(self.args.pretrained_model,map_location='cpu')
self.model = torch.load(self.args.pretrained_model,map_location='cpu')
self.model.to(self.args.device)
self.model.eval()
@ -65,9 +61,7 @@ class Predictor(object):
"Image file failed to read: {}".format(img_path))
else:
x = self.test_transform(img)
if torch.cuda.is_available():
x = x.cuda()
part_logits = self.model(x.unsqueeze(0))
part_logits = self.model(x.unsqueeze(0).to(args.device))
probs = torch.nn.Softmax(dim=-1)(part_logits)
topN = torch.argsort(probs, dim=-1, descending=True).tolist()
clas_ids = topN[0][0]
@ -81,10 +75,12 @@ if __name__ == "__main__":
y_true = []
y_pred = []
# test_dir = "./emptyJudge5/images/"
# dir_dict = {"noemp":"0", "yesemp":"1", "hard": "2", "fly": "3", "stack": "4"}
test_dir = "../emptyJudge2/images"
dir_dict = {"noempty":"0", "empty":"1"}
test_dir = "./emptyJudge5/images/"
dir_dict = {"noemp":"0", "yesemp":"1", "hard": "2", "fly": "3", "stack": "4"}
# test_dir = "../emptyJudge2/images"
# dir_dict = {"noempty":"0", "empty":"1"}
total = 0
num = 0
t0 = time.time()
@ -100,6 +96,19 @@ if __name__ == "__main__":
cur_pred, pred_score = predictor.normal_predict(cur_img_file)
label = 0 if 2 == int(label) or 3 == int(label) or 4 == int(label) else int(label)
riseresult = lightrise.riseempty(Image.open(cur_img_file))
if(label==1):
if(int(riseresult["rst_cls"])==1):
label=1
else:
label=0
# else:
# if(riseresult["rst_cls"]==0):
# label=0
# else:
# label=1
cur_pred = 0 if 2 == int(cur_pred) or 3 == int(cur_pred) or 4 == int(cur_pred) else int(cur_pred)
y_true.append(int(label))
y_pred.append(int(cur_pred))