预训练权重下载
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
进行分类测试
def img_transform(img_rgb, transform=None):
"""
将数据转换为模型读取的形式
:param img_rgb: PIL Image
:param transform: torchvision.transform
:return: tensor
"""
if transform is None:
raise ValueError("找不到transform!必须有transform对img进行处理")
img_t = transform(img_rgb)
return img_t
def process_img(path_img):
# hard code
norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]
inference_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop((224, 224)),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
# path --> img
img_rgb = Image.open(path_img).convert('RGB')
# img --> tensor
img_tensor = img_transform(img_rgb, inference_transform)
img_tensor.unsqueeze_(0) # chw --> bchw
img_tensor = img_tensor.to(device)
return img_tensor, img_rgb
def load_class_names(p_clsnames, p_clsnames_cn):
"""
加载标签名
:param p_clsnames:
:param p_clsnames_cn:
:return:
"""
with open(p_clsnames, "r") as f:
class_names = json.load(f)
with open(p_clsnames_cn, encoding='UTF-8') as f: # 设置文件对象
class_names_cn = f.readlines()
return class_names, class_names_cn
if __name__ == "__main__":
# config
path_state_dict = os.path.join(BASE_DIR, "..", "data", "vgg16-397923af.pth")
# path_img = os.path.join(BASE_DIR, "..", "..", "Data","Golden Retriever from baidu.jpg")
path_img = os.path.join(BASE_DIR, "..", "data", "Golden Retriever from baidu.jpg")
path_classnames = os.path.join(BASE_DIR, "..", "data", "imagenet1000.json")
path_classnames_cn = os.path.join(BASE_DIR, "..", "data","imagenet_classnames.txt")
# load class names
cls_n, cls_n_cn = load_class_names(path_classnames, path_classnames_cn)
# 1/5 load img
img_tensor, img_rgb = process_img(path_img)
# 2/5 load model
vgg_model = get_vgg16(path_state_dict, device, True)
# 3/5 inference tensor --> vector
with torch.no_grad():
time_tic = time.time()
outputs = vgg_model(img_tensor)
time_toc = time.time()
# 4/5 index to class names
_, pred_int = torch.max(outputs.data, 1)
_, top5_idx = torch.topk(outputs.data, 5, dim=1)
pred_idx = int(pred_int.cpu().numpy())
pred_str, pred_cn = cls_n[pred_idx], cls_n_cn[pred_idx]
print("img: {} is: {}\n{}".format(os.path.basename(path_img), pred_str, pred_cn))
print("time consuming:{:.2f}s".format(time_toc - time_tic))
# 5/5 visualization
plt.imshow(img_rgb)
plt.title("predict:{}".format(pred_str))
top5_num = top5_idx.cpu().numpy().squeeze()
text_str = [cls_n[t] for t in top5_num]
for idx in range(len(top5_num)):
plt.text(5, 15+idx*30, "top {}:{}".format(idx+1, text_str[idx]), bbox=dict(fc='yellow'))
plt.show()