DenseNet

import torch
model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet121', pretrained=True)
# or any of these variants
# model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet169', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet201', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.10.0', 'densenet161', pretrained=True)
model.eval()

所有預訓練模型都要求輸入影像以相同的方式進行歸一化，即由形狀為 (3 x H x W) 的 3 通道 RGB 影像組成的小批次資料，其中 H 和 W 預計至少為 224。影像必須載入到 [0, 1] 範圍內，然後使用 mean = [0.485, 0.456, 0.406] 和 std = [0.229, 0.224, 0.225] 進行歸一化。

這是一個示例執行。

# 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

密集卷積網路 (DenseNet) 以一種前饋方式將每個層連線到所有其他層。傳統的具有 L 層的卷積網路有 L 個連線——每個層與其後續層之間一個連線——而我們的網路有 L(L+1)/2 個直接連線。對於每個層，所有前一個層的特徵圖都被用作輸入，其自身的特徵圖被用作所有後續層的輸入。DenseNet 具有幾個引人注目的優點：它們緩解了梯度消失問題，加強了特徵傳播，鼓勵了特徵重用，並大大減少了引數數量。

下面列出了使用預訓練模型在 ImageNet 資料集上的 1-裁剪錯誤率。

模型結構	Top-1 錯誤率	Top-5 錯誤率
densenet121	25.35	7.83
densenet169	24.00	7.00
densenet201	22.80	6.43
densenet161	22.35	6.20

參考文獻

• 密集連線卷積網路.