Function MobileNetV1

tensorlayer/models/mobilenetv1.py:54–118

Pre-trained MobileNetV1 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 [conv, depth1, depth2 ... depth13, g

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

def MobileNetV1(pretrained=False, end_with='out', name=None):
    """Pre-trained MobileNetV1 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 [conv, depth1, depth2 ... depth13, globalmeanpool, out]. Default ``out`` i.e. the whole model.
    name : None or str
        Name for this model.

    Examples
    ---------
    Classify ImageNet classes, see `tutorial_models_mobilenetv1.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_models_mobilenetv1.py>`__

    >>> # get the whole model with pretrained weights
    >>> mobilenetv1 = tl.models.MobileNetV1(pretrained=True)
    >>> # use for inferencing
    >>> output = mobilenetv1(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.MobileNetV1(pretrained=True, end_with='reshape').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), name='out')(nn)
    >>> nn = Flatten(name='flatten')(nn)
    >>> model = tl.models.Model(inputs=ni, outputs=nn)
    >>> # train your own classifier (only update the last layer)
    >>> train_params = model.get_layer('out').trainable_weights

    Returns
    -------
        static MobileNetV1.
    """
    ni = Input([None, 224, 224, 3], name="input")

    for i in range(len(layer_names)):
        if i == 0:
            n = conv_block(ni, n_filters[i], strides=(2, 2), name=layer_names[i])
        elif layer_names[i] in ['depth2', 'depth4', 'depth6', 'depth12']:
            n = depthwise_conv_block(n, n_filters[i], strides=(2, 2), name=layer_names[i])
        elif layer_names[i] == 'globalmeanpool':
            n = GlobalMeanPool2d(name='globalmeanpool')(n)
        elif layer_names[i] == 'reshape':
            n = Reshape([-1, 1, 1, 1024], name='reshape')(n)
        elif layer_names[i] == 'out':
            n = Conv2d(1000, (1, 1), (1, 1), name='out')(n)
            n = Flatten(name='flatten')(n)
        else:
            n = depthwise_conv_block(n, n_filters[i], name=layer_names[i])

        if layer_names[i] == end_with:
            break