LiangXu123 / pretrained-models.pytorch

Pretrained ConvNets for pytorch: NASNet, ResNeXt101, ResNet152, InceptionV4, InceptionResnetV2, etc.

Pretrained models for Pytorch (Work in progress)

The goal of this repo is:

• to help to reproduce research papers results (transfer learning setups for instance),
• to access pretrained ConvNets with a unique interface/API inspired by torchvision.

News:

• 08/12/2017: update data url (/!\ git pull is needed)
• 30/11/2017: improve API (model.features(input), model.logits(features), model.forward(input), model.last_linear)
• 16/11/2017: nasnet-a-large pretrained model ported by T. Durand and R. Cadene
• 22/07/2017: torchvision pretrained models
• 22/07/2017: momentum in inceptionv4 and inceptionresnetv2 to 0.1
• 17/07/2017: model.input_range attribut
• 17/07/2017: BNInception pretrained on Imagenet

Summary

• Installation
• Quick examples
• Few use cases
  • Compute imagenet logits
  • Compute imagenet validation metrics
• Evaluation on ImageNet
  • Accuracy on valset
  • Reproducing results
• Documentation
  • Available models
    • AlexNet
    • BNInception
    • DenseNet121
    • DenseNet161
    • DenseNet169
    • DenseNet201
    • FBResNet152
    • InceptionResNetV2
    • InceptionV3
    • InceptionV4
    • NASNet-A-Large
    • ResNeXt101_32x4d
    • ResNeXt101_64x4d
    • ResNet101
    • ResNet152
    • ResNet18
    • ResNet34
    • ResNet50
    • SqueezeNet1_0
    • SqueezeNet1_1
    • VGG11
    • VGG13
    • VGG16
    • VGG19
    • VGG11_BN
    • VGG13_BN
    • VGG16_BN
    • VGG19_BN
  • Model API
    • model.input_size
    • model.input_space
    • model.input_range
    • model.mean
    • model.std
    • model.features
    • model.logits
    • model.forward
• Reproducing porting
  • ResNet*
  • ResNeXt*
  • Inception*

Installation

1. python3 with anaconda
2. pytorch with/out CUDA
3. git clone https://github.com/Cadene/pretrained-models.pytorch.git

Quick examples

• To import pretrainedmodels:

import sys
sys.path.append('yourdir/pretrained-models.pytorch') # if needed
import pretrainedmodels

• To print the available pretrained models:

model_names = sorted(name for name in pretrainedmodels.__dict__
    if not name.startswith("__")
    and name.islower()
    and callable(pretrainedmodels.__dict__[name]))
print(model_names)

• To load a pretrained models from imagenet:

model_name = 'nasnetalarge' # could be fbresnet152 or inceptionresnetv2
model = pretrainedmodels.__dict__[model_name](num_classes=1000, pretrained='imagenet')
model.eval()

Note: By default, models will be downloaded to your $HOME/.torch folder. You can modify this behavior using the $TORCH_MODEL_ZOO variable as follow: export TORCH_MODEL_ZOO="/local/pretrainedmodels

• To load an image and do a complete forward pass:

import torch
import pretrainedmodels.utils as utils

load_img = utils.LoadImage()

# transformations depending on the model
# rescale, center crop, normalize, and others (ex: ToBGR, ToRange255)
tf_img = utils.TransformImage(model) 

path_img = 'data/cat.jpg'

input_img = load_img(path_img)
input_tensor = tf_img(input_img)         # 3x400x225 -> 3x299x299 size may differ
input_tensor = input_tensor.unsqueeze(0) # 3x299x299 -> 1x3x299x299
input = torch.autograd.Variable(input_tensor,
    requires_grad=False)

output_logits = model(input) # 1x1000

• To extract features (beware this API is not available for all networks):

output_features = model.features(input) # 1x14x14x2048 size may differ
output_logits = model.logits(output_features) # 1x1000

Few use cases

Compute imagenet logits

• See examples/imagenet_logits.py to compute logits of classes appearance over a single image with a pretrained model on imagenet.

$ python examples/imagenet_logits.py -h
> nasnetalarge, resnet152, inceptionresnetv2, inceptionv4, ...

$ python examples/imagenet_logits.py -a nasnetalarge --path_img data/cat.png
> 'nasnetalarge': data/cat.png' is a 'tiger cat' 

Compute imagenet evaluation metrics

• See examples/imagenet_eval.py to evaluate pretrained models on imagenet valset.

$ python examples/imagenet_eval.py /local/common-data/imagenet_2012/images -a nasnetalarge -b 20 -e
> * Acc@1 92.693, Acc@5 96.13

Evaluation on imagenet

Accuracy on validation set (single model)

Model	Version	Acc@1	Acc@5
NASNet-A-Large	Tensorflow	82.693	96.163
NASNet-A-Large	Our porting	82.566	96.086
InceptionResNetV2	Tensorflow	80.4	95.3
InceptionV4	Tensorflow	80.2	95.3
InceptionResNetV2	Our porting	80.170	95.234
InceptionV4	Our porting	80.062	94.926
ResNeXt101_64x4d	Torch7	79.6	94.7
ResNeXt101_64x4d	Our porting	78.956	94.252
ResNeXt101_32x4d	Torch7	78.8	94.4
ResNet152	Pytorch	78.428	94.110
ResNeXt101_32x4d	Our porting	78.188	93.886
FBResNet152	Torch7	77.84	93.84
DenseNet161	Pytorch	77.560	93.798
FBResNet152	Our porting	77.386	93.594
InceptionV3	Pytorch	77.294	93.454
DenseNet201	Pytorch	77.152	93.548
ResNet101	Pytorch	77.438	93.672
DenseNet169	Pytorch	76.026	92.992
ResNet50	Pytorch	76.002	92.980
DenseNet121	Pytorch	74.646	92.136
VGG19_BN	Pytorch	74.266	92.066
ResNet34	Pytorch	73.554	91.456
BNInception	Our porting	73.522	91.560
VGG16_BN	Pytorch	73.518	91.608
VGG19	Pytorch	72.080	90.822
VGG16	Pytorch	71.636	90.354
VGG13_BN	Pytorch	71.508	90.494
VGG11_BN	Pytorch	70.452	89.818
ResNet18	Pytorch	70.142	89.274
VGG13	Pytorch	69.662	89.264
VGG11	Pytorch	68.970	88.746
SqueezeNet1_1	Pytorch	58.250	80.800
SqueezeNet1_0	Pytorch	58.108	80.428
Alexnet	Pytorch	56.432	79.194

Note: the Pytorch version of ResNet152 is not a porting of the Torch7 but has been retrained by facebook.

Beware, the accuracy reported here is not always representative of the transferable capacity of the network on other tasks and datasets. You must try them all! :P

Reproducing results

Please see Compute imagenet validation metrics

Documentation

Available models

NASNet*

Source: TensorFlow Slim repo

• nasnetalarge(num_classes=1000, pretrained='imagenet')
• nasnetalarge(num_classes=1001, pretrained='imagenet+background')

FaceBook ResNet*

Source: Torch7 repo of FaceBook

There are a bit different from the ResNet* of torchvision. ResNet152 is currently the only one available.

• fbresnet152(num_classes=1000, pretrained='imagenet')

Inception*

Source: TensorFlow Slim repo and Pytorch/Vision repo for inceptionv3

• inceptionresnetv2(num_classes=1000, pretrained='imagenet')
• inceptionresnetv2(num_classes=1001, pretrained='imagenet+background')
• inceptionv4(num_classes=1000, pretrained='imagenet')
• inceptionv4(num_classes=1001, pretrained='imagenet+background')
• inceptionv3(num_classes=1000, pretrained='imagenet')

BNInception

Source: Trained with Caffe by Xiong Yuanjun

• bninception(num_classes=1000, pretrained='imagenet')

ResNeXt*

Source: ResNeXt repo of FaceBook

• resnext101_32x4d(num_classes=1000, pretrained='imagenet')
• resnext101_62x4d(num_classes=1000, pretrained='imagenet')

TorchVision

Source: Pytorch/Vision repo

(inceptionv3 included in Inception*)

• resnet18(num_classes=1000, pretrained='imagenet')
• resnet34(num_classes=1000, pretrained='imagenet')
• resnet50(num_classes=1000, pretrained='imagenet')
• resnet101(num_classes=1000, pretrained='imagenet')
• resnet152(num_classes=1000, pretrained='imagenet')
• densenet121(num_classes=1000, pretrained='imagenet')
• densenet161(num_classes=1000, pretrained='imagenet')
• densenet169(num_classes=1000, pretrained='imagenet')
• densenet201(num_classes=1000, pretrained='imagenet')
• squeezenet1_0(num_classes=1000, pretrained='imagenet')
• squeezenet1_1(num_classes=1000, pretrained='imagenet')
• alexnet(num_classes=1000, pretrained='imagenet')
• vgg11(num_classes=1000, pretrained='imagenet')
• vgg13(num_classes=1000, pretrained='imagenet')
• vgg16(num_classes=1000, pretrained='imagenet')
• vgg19(num_classes=1000, pretrained='imagenet')
• vgg11_bn(num_classes=1000, pretrained='imagenet')
• vgg13_bn(num_classes=1000, pretrained='imagenet')
• vgg16_bn(num_classes=1000, pretrained='imagenet')
• vgg19_bn(num_classes=1000, pretrained='imagenet')

Model API

Once a pretrained model has been loaded, you can use it that way.

Important note: All image must be loaded using PIL which scales the pixel values between 0 and 1.

model.input_size

Attribut of type list composed of 3 numbers:

• number of color channels,
• height of the input image,
• width of the input image.

Example:

• [3, 299, 299] for inception* networks,
• [3, 224, 224] for resnet* networks.

model.input_space

Attribut of type str representating the color space of the image. Can be RGB or BGR.

model.input_range

Attribut of type list composed of 2 numbers:

• min pixel value,
• max pixel value.

Example:

• [0, 1] for resnet* and inception* networks,
• [0, 255] for bninception network.

model.mean

Attribut of type list composed of 3 numbers which are used to normalize the input image (substract "color-channel-wise").

Example:

• [0.5, 0.5, 0.5] for inception* networks,
• [0.485, 0.456, 0.406] for resnet* networks.

model.std

Attribut of type list composed of 3 numbers which are used to normalize the input image (divide "color-channel-wise").

Example:

• [0.5, 0.5, 0.5] for inception* networks,
• [0.229, 0.224, 0.225] for resnet* networks.

model.features

/!\ work in progress (may not be available)

Method which is used to extract the features from the image.

Example when the model is loaded using fbresnet152:

print(input_224.size())            # (1,3,224,224)
output = model.features(input_224) 
print(output.size())               # (1,2048,1,1)

# print(input_448.size())          # (1,3,448,448)
output = model.features(input_448)
# print(output.size())             # (1,2048,7,7)

model.logits

/!\ work in progress (may not be available)

Method which is used to classify the features from the image.

Example when the model is loaded using fbresnet152:

output = model.features(input_224) 
print(output.size())               # (1,2048, 1, 1)
output = model.logits(output)
print(output.size())               # (1,1000)

model.forward

Method used to call model.features and model.logits. It can be overwritten as desired.

Note: A good practice is to use model.__call__ as your function of choice to forward an input to your model. See the example bellow.

# Without model.__call__
output = model.forward(input_224)
print(output.size())      # (1,1000)

# With model.__call__
output = model(input_224)
print(output.size())      # (1,1000)

model.last_linear

Attribut of type nn.Linear. This module is the last one to be called during the forward pass.

• Can be replaced by an adapted nn.Linear for fine tuning.
• Can be replaced by pretrained.utils.Identity for features extraction.

Example when the model is loaded using fbresnet152:

print(input_224.size())            # (1,3,224,224)
output = model.features(input_224) 
print(output.size())               # (1,2048,1,1)
output = model.logits(output)
print(output.size())               # (1,1000)

# fine tuning
dim_feats = model.last_linear.in_features # =2048
nb_classes = 4
model.last_linear = nn.Linear(dim_feats, nb_classes)
output = model(input_224)
print(output.size())               # (1,4)

# features extraction
model.last_linear = pretrained.utils.Identity()
output = model(input_224)
print(output.size())               # (1,2048)

Reproducing

Hand porting of ResNet152

th pretrainedmodels/fbresnet/resnet152_dump.lua
python pretrainedmodels/fbresnet/resnet152_load.py

Automatic porting of ResNeXt

https://github.com/clcarwin/convert_torch_to_pytorch

Hand porting of NASNet, InceptionV4 and InceptionResNetV2

https://github.com/Cadene/tensorflow-model-zoo.torch

Acknowledgement

Thanks to the deep learning community and especially to the contributers of the pytorch ecosystem.