Function SqueezeNetV1

tensorlayer/models/squeezenetv1.py:45–111

Pre-trained SqueezeNetV1 model (static mode). Input shape [?, 224, 224, 3], value range [0, 1]. Parameters ------------ pretrained : boolean Whether to load pretrained weights. Default False. end_with : str The end point of the model [conv1, maxpool1, fire2, fire3, f

(pretrained=False, end_with='out', name=None)

def SqueezeNetV1(pretrained=False, end_with='out', name=None):
    """Pre-trained SqueezeNetV1 model (static mode). Input shape [?, 224, 224, 3], value range [0, 1].

    Parameters
    ------------
    pretrained : boolean
        Whether to load pretrained weights. Default False.
    end_with : str
        The end point of the model [conv1, maxpool1, fire2, fire3, fire4, ..., out]. Default ``out`` i.e. the whole model.
    name : None or str
        Name for this model.

    Examples
    ---------
    Classify ImageNet classes, see `tutorial_models_squeezenetv1.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_squeezenetv1.py>`__

    >>> # get the whole model
    >>> squeezenet = tl.models.SqueezeNetV1(pretrained=True)
    >>> # use for inferencing
    >>> output = squeezenet(img1, is_train=False)
    >>> prob = tf.nn.softmax(output)[0].numpy()

    Extract features and Train a classifier with 100 classes

    >>> # get model without the last layer
    >>> cnn = tl.models.SqueezeNetV1(pretrained=True, end_with='drop1').as_layer()
    >>> # add one more layer and build new model
    >>> ni = Input([None, 224, 224, 3], name="inputs")
    >>> nn = cnn(ni)
    >>> nn = Conv2d(100, (1, 1), (1, 1), padding='VALID', name='conv10')(nn)
    >>> nn = GlobalMeanPool2d(name='globalmeanpool')(nn)
    >>> model = tl.models.Model(inputs=ni, outputs=nn)
    >>> # train your own classifier (only update the last layer)
    >>> train_params = model.get_layer('conv10').trainable_weights

    Returns
    -------
        static SqueezeNetV1.

    """
    ni = Input([None, 224, 224, 3], name="input")
    n = Lambda(lambda x: x * 255, name='scale')(ni)

    for i in range(len(layer_names)):
        if layer_names[i] == 'conv1':
            n = Conv2d(64, (3, 3), (2, 2), tf.nn.relu, 'SAME', name='conv1')(n)
        elif layer_names[i] == 'maxpool1':
            n = MaxPool2d((3, 3), (2, 2), 'VALID', name='maxpool1')(n)
        elif layer_names[i] == 'drop1':
            n = Dropout(keep=0.5, name='drop1')(n)
        elif layer_names[i] == 'out':
            n = Conv2d(1000, (1, 1), (1, 1), padding='VALID', name='conv10')(n)  # 13, 13, 1000
            n = GlobalMeanPool2d(name='globalmeanpool')(n)
        elif layer_names[i] in ['fire3', 'fire5']:
            n = fire_block(n, n_filters[i - 2], max_pool=True, name=layer_names[i])
        else:
            n = fire_block(n, n_filters[i - 2], max_pool=False, name=layer_names[i])