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update predict.py.

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Brainway
2022-09-27 02:30:54 +00:00
committed by Gitee
parent 597882178e
commit 1d4997bd42
1 changed files with 26 additions and 24 deletions
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predict.py
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@ -10,14 +10,14 @@ from PIL import Image
from torchvision import transforms
from models.modeling import VisionTransformer, CONFIGS
#模型预测
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--img_size", default=448, type=int, help="Resolution size")
parser.add_argument("--img_size", default=600, type=int, help="Resolution size")
parser.add_argument('--split', type=str, default='overlap', help="Split method") # non-overlap
parser.add_argument('--slide_step', type=int, default=12, help="Slide step for overlap split")
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/emptyjudge5_checkpoint.bin", help="load pretrained model")
parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_checkpoint_good.pth", help="load pretrained model")
return parser.parse_args()
@ -32,7 +32,7 @@ class Predictor(object):
self.num_classes = 0
self.model = None
self.prepare_model()
self.test_transform = transforms.Compose([transforms.Resize((448, 448), Image.BILINEAR),
self.test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
@ -40,28 +40,18 @@ class Predictor(object):
config = CONFIGS["ViT-B_16"]
config.split = self.args.split
config.slide_step = self.args.slide_step
model_name = os.path.basename(self.args.pretrained_model).replace("_checkpoint.bin", "")
print("use model_name: ", model_name)
if model_name.lower() == "emptyJudge5".lower():
self.num_classes = 5
self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"}
elif model_name.lower() == "emptyJudge4".lower():
self.num_classes = 4
self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "stack"}
elif model_name.lower() == "emptyJudge3".lower():
self.num_classes = 3
self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard"}
elif model_name.lower() == "emptyJudge2".lower():
self.num_classes = 2
self.cls_dict = {0: "noemp", 1: "yesemp"}
self.num_classes = 5
self.cls_dict = {0: "noemp", 1: "yesemp", 2: "hard", 3: "fly", 4: "stack"}
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():
pretrained_model = torch.load(self.args.pretrained_model, map_location=torch.device('cpu'))['model']
self.model.load_state_dict(pretrained_model)
self.model = torch.load(self.args.pretrained_model)
else:
pretrained_model = torch.load(self.args.pretrained_model)['model']
self.model.load_state_dict(pretrained_model)
self.model = torch.load(self.args.pretrained_model,map_location='cpu')
self.model.to(self.args.device)
self.model.eval()
@ -89,7 +79,7 @@ if __name__ == "__main__":
y_true = []
y_pred = []
test_dir = "/data/pfc/fineGrained/test_5cls"
test_dir = "./emptyJudge5/images/"
dir_dict = {"noemp":"0", "yesemp":"1", "hard": "2", "fly": "3", "stack": "4"}
total = 0
num = 0
@ -125,6 +115,18 @@ if __name__ == "__main__":
print(rst_C)
print(rst_f1)
'''
所有数据集
The cast of time is :160.738966 seconds
The classification accuracy is 0.986836
[[4923 58]
[ 34 1974]]
0.9839851634589902
'''
'''
test_imgs: yesemp=145, noemp=453 大图