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: - 27/10/2018: Fix compatibility issues, Add tests, Add travis - 04/06/2018: PolyNet and PNASNet-5-Large thanks to Alex Parinov - 16/04/2018: SE-ResNet and SE-ResNeXt thanks to Alex Parinov - 09/04/2018: SENet154 thanks to Alex Parinov - 22/03/2018: CaffeResNet101 (good for localization with FasterRCNN) - 21/03/2018: NASNet Mobile thanks to Veronika Yurchuk and Anastasiia - 25/01/2018: DualPathNetworks thanks to Ross Wightman, Xception thanks to T Standley, improved TransformImage API - 13/01/2018: pip install pretrainedmodels, pretrainedmodels.model_names, pretrainedmodels.pretrained_settings - 12/01/2018: python setup.py install - 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
    • CaffeResNet101
    • DenseNet121
    • DenseNet161
    • DenseNet169
    • DenseNet201
    • DenseNet201
    • DualPathNet68
    • DualPathNet92
    • DualPathNet98
    • DualPathNet107
    • DualPathNet113
    • FBResNet152
    • InceptionResNetV2
    • InceptionV3
    • InceptionV4
    • NASNet-A-Large
    • NASNet-A-Mobile
    • PNASNet-5-Large
    • PolyNet
    • ResNeXt101_32x4d
    • ResNeXt101_64x4d
    • ResNet101
    • ResNet152
    • ResNet18
    • ResNet34
    • ResNet50
    • SENet154
    • SE-ResNet50
    • SE-ResNet101
    • SE-ResNet152
    • SE-ResNeXt50_32x4d
    • SE-ResNeXt101_32x4d
    • SqueezeNet1_0
    • SqueezeNet1_1
    • VGG11
    • VGG13
    • VGG16
    • VGG19
    • VGG11_BN
    • VGG13_BN
    • VGG16_BN
    • VGG19_BN
    • Xception
  • 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

Install from pip

1. pip install pretrainedmodels

Install from repo

1. git clone https://github.com/Cadene/pretrained-models.pytorch.git
2. cd pretrained-models.pytorch
3. python setup.py install

Quick examples

• To import pretrainedmodels:

import pretrainedmodels

• To print the available pretrained models:

print(pretrainedmodels.model_names)
> ['fbresnet152', 'bninception', 'resnext101_32x4d', 'resnext101_64x4d', 'inceptionv4', 'inceptionresnetv2', 'alexnet', 'densenet121', 'densenet169', 'densenet201', 'densenet161', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'inceptionv3', 'squeezenet1_0', 'squeezenet1_1', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn', 'vgg19_bn', 'vgg19', 'nasnetalarge', 'nasnetamobile', 'cafferesnet101', 'senet154',  'se_resnet50', 'se_resnet101', 'se_resnet152', 'se_resnext50_32x4d', 'se_resnext101_32x4d', 'cafferesnet101', 'polynet', 'pnasnet5large']

• To print the available pretrained settings for a chosen model:

print(pretrainedmodels.pretrained_settings['nasnetalarge'])
> {'imagenet': {'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1000}, 'imagenet+background': {'url': 'http://data.lip6.fr/cadene/pretrainedmodels/nasnetalarge-a1897284.pth', 'input_space': 'RGB', 'input_size': [3, 331, 331], 'input_range': [0, 1], 'mean': [0.5, 0.5, 0.5], 'std': [0.5, 0.5, 0.5], 'num_classes': 1001}}

• 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_HOME variable as follow: export TORCH_HOME="/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.jpg
> 'nasnetalarge': data/cat.jpg' 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 82.693, Acc@5 96.13

Evaluation on imagenet

Accuracy on validation set (single model)

Results were obtained using (center cropped) images of the same size than during the training process.