Extract image features from a CNN layer

[miladf2]

I am trying to extract features from an image using the ssd_vgg_300 code by outputing the weights multiplied by inputs. Can you let me know what part of the code I need to change to output features from the CNN layers?

[ryanjay0]

Doesn't sound like you need ssd at all. You can just take the net output from the bottom layer of vgg (or a better network) in tensorflow slim. https://github.com/tensorflow/models/tree/master/slim

[miladf2]

@ryanjay0 So that means if I want to use the same balancap code I can change this part only?

def ssd_net(inputs,
            num_classes=SSDNet.default_params.num_classes,
            feat_layers=SSDNet.default_params.feat_layers,
            anchor_sizes=SSDNet.default_params.anchor_sizes,
            anchor_ratios=SSDNet.default_params.anchor_ratios,
            normalizations=SSDNet.default_params.normalizations,
            is_training=True,
            dropout_keep_prob=0.5,
            prediction_fn=slim.softmax,
            reuse=None,
            scope='ssd_300_vgg'):
    """SSD net definition.
    """
    # if data_format == 'NCHW':
    #     inputs = tf.transpose(inputs, perm=(0, 3, 1, 2))

    # End_points collect relevant activations for external use.
    end_points = {}
    with tf.variable_scope(scope, 'ssd_300_vgg', [inputs], reuse=reuse):
        # Original VGG-16 blocks.
        net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
        end_points['block1'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool1')
        # Block 2.
        net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
        end_points['block2'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool2')
        # Block 3.
        net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
        end_points['block3'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool3')
        # Block 4.
        net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
        end_points['block4'] = net
        net = slim.max_pool2d(net, [2, 2], scope='pool4')
        # Block 5.
        net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
        end_points['block5'] = net
        net = slim.max_pool2d(net, [3, 3], stride=1, scope='pool5')

        # Additional SSD blocks.
        # Block 6: let's dilate the hell out of it!
        net = slim.conv2d(net, 1024, [3, 3], rate=6, scope='conv6')
        end_points['block6'] = net
        net = tf.layers.dropout(net, rate=dropout_keep_prob, training=is_training)
        # Block 7: 1x1 conv. Because the fuck.
        net = slim.conv2d(net, 1024, [1, 1], scope='conv7')
        end_points['block7'] = net
        net = tf.layers.dropout(net, rate=dropout_keep_prob, training=is_training)

        # Block 8/9/10/11: 1x1 and 3x3 convolutions stride 2 (except lasts).
        end_point = 'block8'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 256, [1, 1], scope='conv1x1')
            net = custom_layers.pad2d(net, pad=(1, 1))
            net = slim.conv2d(net, 512, [3, 3], stride=2, scope='conv3x3', padding='VALID')
        end_points[end_point] = net
        end_point = 'block9'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
            net = custom_layers.pad2d(net, pad=(1, 1))
            net = slim.conv2d(net, 256, [3, 3], stride=2, scope='conv3x3', padding='VALID')
        end_points[end_point] = net
        end_point = 'block10'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
            net = slim.conv2d(net, 256, [3, 3], scope='conv3x3', padding='VALID')
        end_points[end_point] = net
        end_point = 'block11'
        with tf.variable_scope(end_point):
            net = slim.conv2d(net, 128, [1, 1], scope='conv1x1')
            net = slim.conv2d(net, 256, [3, 3], scope='conv3x3', padding='VALID')
        end_points[end_point] = net

        # Prediction and localisations layers.
        predictions = []
        logits = []
        localisations = []
        for i, layer in enumerate(feat_layers):
            with tf.variable_scope(layer + '_box'):
                p, l = ssd_multibox_layer(end_points[layer],
                                          num_classes,
                                          anchor_sizes[i],
                                          anchor_ratios[i],
                                          normalizations[i])
            predictions.append(prediction_fn(p))
            logits.append(p)
            localisations.append(l)

return predictions, localisations, logits, end_points

[whigy]

I tried to extract from feature maps in end_points as feature, but seems like they are not so useful.

[akhiljalan]

@ryanjay0 The link https://github.com/tensorflow/models/tree/master/slim returns a 404 error.

Did you mean to post this?https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim