Comments (4)
最后一层直接取出的结果没有经过softmax,可以用一个softmax得到归一化的概率值
from pytorch_classification.
from pytorch_classification.
这个是我的推理代码
`img = cv2.imread(img_dir + img_name)
img_input = img[..., ::-1] # BGR to RGB
img_input = (np.float32(img)/255.0-[0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
img_input = img_input.transpose((2, 0, 1))
img_input = torch.from_numpy(img_input).unsqueeze(0)
img_input = img_input.type(torch.FloatTensor)
print(img_input.shape)
pred = model(img_input.cuda())
pred_score = torch.max(pred, 1)[0]
pred_index = torch.max(pred, 1)[1]`
from pytorch_classification.
请问您测试同一张图片得出的pre值是一样的吗。我用训练的模型测试同一张图片,每次输出的pre值都有差异,想问一下您有没有出现这种情况,可能是什么原因导致的
from pytorch_classification.
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from pytorch_classification.