VGG16预训练模型#

Author: Pytorch Team

Award winning ConvNets from 2014 Imagenet ILSVRC challenge

alt

https://pytorch.org/hub/pytorch_vision_vgg/

https://pytorch.org/docs/stable/torchvision/models.html

Run it in colab#

https://colab.research.google.com/drive/1epVRmNLeoAenypwM1ffGeHv9pk1xtEek

This notebook is optionally accelerated with a GPU runtime.

If you would like to use this acceleration, please

  • select the menu option “Runtime” -> “Change runtime type”,

  • select “Hardware Accelerator” -> “GPU” and click “SAVE”

Load Pretrained Models#

import torch
model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg16', pretrained=True)
# or any of these variants
# model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg11_bn', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg13', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg13_bn', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg16', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg16_bn', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg19', pretrained=True)
# model = torch.hub.load('pytorch/vision:v0.6.0', 'vgg19_bn', pretrained=True)
model.eval()

Downloading: "https://github.com/pytorch/vision/archive/v0.6.0.zip" to /Users/datalab/.cache/torch/hub/v0.6.0.zip
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /Users/datalab/.cache/torch/hub/checkpoints/vgg16-397923af.pth

100% 528M/528M [00:02<00:00, 223MB/s]

VGG(
  (features): Sequential(
    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU(inplace=True)
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU(inplace=True)
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (8): ReLU(inplace=True)
    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(inplace=True)
    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (13): ReLU(inplace=True)
    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (15): ReLU(inplace=True)
    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (18): ReLU(inplace=True)
    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (20): ReLU(inplace=True)
    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (22): ReLU(inplace=True)
    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (25): ReLU(inplace=True)
    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (27): ReLU(inplace=True)
    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (29): ReLU(inplace=True)
    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
  (classifier): Sequential(
    (0): Linear(in_features=25088, out_features=4096, bias=True)
    (1): ReLU(inplace=True)
    (2): Dropout(p=0.5, inplace=False)
    (3): Linear(in_features=4096, out_features=4096, bias=True)
    (4): ReLU(inplace=True)
    (5): Dropout(p=0.5, inplace=False)
    (6): Linear(in_features=4096, out_features=1000, bias=True)
  )
)

All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224.

The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].

Here’s a sample execution.

# 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)

pytorch/hub

from PIL import Image
input_image = Image.open(filename)
input_image

# sample execution (requires torchvision)
from torchvision import transforms

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.

input_prob  = torch.nn.functional.softmax(output[0], dim=0)
torch.argmax(input_prob)

tensor(258, device='cuda:0')

# ImageNet挑战使用了一个“修剪”的1000个非重叠类的列表
import pandas as pd

url = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json'
imagenet_df = pd.read_json(url).T

imagenet_df
0 1
0 n01440764 tench
1 n01443537 goldfish
2 n01484850 great_white_shark
3 n01491361 tiger_shark
4 n01494475 hammerhead
... ... ...
995 n13044778 earthstar
996 n13052670 hen-of-the-woods
997 n13054560 bolete
998 n13133613 ear
999 n15075141 toilet_tissue

1000 rows × 2 columns

imagenet_df.iloc[int(torch.argmax(input_prob))]

0    n02111889
1      Samoyed
Name: 258, dtype: object

Model Description#

Here we have implementations for the models proposed in Very Deep Convolutional Networks for Large-Scale Image Recognition, for each configurations and their with bachnorm version.

For example, configuration A presented in the paper is vgg11, configuration B is vgg13, configuration D is vgg16 and configuration E is vgg19. Their batchnorm version are suffixed with _bn.

Their 1-crop error rates on imagenet dataset with pretrained models are listed below.

Model structure

Top-1 error

Top-5 error

vgg11

30.98

11.37

vgg11_bn

26.70

8.58

vgg13

30.07

10.75

vgg13_bn

28.45

9.63

vgg16

28.41

9.62

vgg16_bn

26.63

8.50

vgg19

27.62

9.12

vgg19_bn

25.76

8.15

References