import torch
# load WRN-50-2:
model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet50_2', pretrained=True)
# or WRN-101-2
model = torch.hub.load('pytorch/vision:v0.10.0', 'wide_resnet101_2', pretrained=True)
model.eval()

所有预训练模型都期望以相同方式归一化输入图像，即形状为 (3 x H x W) 的三通道 RGB 图像 mini-batches，其中 H 和 W 预计至少为 224。图像必须加载到 [0, 1] 的范围内，然后使用 mean = [0.485, 0.456, 0.406] 和 std = [0.229, 0.224, 0.225] 进行归一化。

这是一个示例执行。

# Download an example image from the pytorch website
import urllib
url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
try: urllib.URLopener().retrieve(url, filename)
except: urllib.request.urlretrieve(url, filename)

# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open(filename)
preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

# move the input and model to GPU for speed if available
if torch.cuda.is_available():
    input_batch = input_batch.to('cuda')
    model.to('cuda')

with torch.no_grad():
    output = model(input_batch)
# Tensor of shape 1000, with confidence scores over ImageNet's 1000 classes
print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
probabilities = torch.nn.functional.softmax(output[0], dim=0)
print(probabilities)

# Download ImageNet labels
!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt

# Read the categories
with open("imagenet_classes.txt", "r") as f:
    categories = [s.strip() for s in f.readlines()]
# Show top categories per image
top5_prob, top5_catid = torch.topk(probabilities, 5)
for i in range(top5_prob.size(0)):
    print(categories[top5_catid[i]], top5_prob[i].item())

模型描述

宽残差网络相比 ResNet 简单地增加了通道数量。除此之外，架构是相同的。带有瓶颈块的更深层 ImageNet 模型增加了内部 3x3 卷积的通道数量。

wide_resnet50_2 和 wide_resnet101_2 模型使用带有暖启动 (warm restarts) 的 SGD 进行混合精度训练，并在 FP16 中训练。检查点（checkpoints）的权重采用半精度（批量归一化除外）以减小大小，并且也可以在 FP32 模型中使用。

模型结构 Top-1 错误率 Top-5 错误率 参数数量
wide_resnet50_2 21.49 5.91 68.9M
wide_resnet101_2 21.16 5.72 126.9M

参考文献