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Luigi edited this page Oct 2, 2018 · 1 revision

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/CeLuigi/models-comparison.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)
> ['mobilenet', 'mobilenetv2', 'shufflenet', 'googlenet', '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']
  • 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_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

$ 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

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

Reproducing results

Please see Compute imagenet validation metrics

Documentation

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)

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