Python tensorflow.contrib.layers 模块，avg_pool2d() 实例源码

def aux_logit_layer( inputs, num_classes, is_training ):
    with tf.variable_scope("pool2d"):
        pooled = layers.avg_pool2d(inputs, [ 5, 5 ], stride = 3 )
    with tf.variable_scope("conv11"):
        conv11 = layers.conv2d( pooled, 128, [1, 1] )
    with tf.variable_scope("flatten"):
        flat = tf.reshape( conv11, [-1, 2048] )
    with tf.variable_scope("fc"):
        fc = layers.fully_connected( flat, 1024, activation_fn=None )
    with tf.variable_scope("drop"):
        drop = layers.dropout( fc, 0.3, is_training = is_training )
    with tf.variable_scope( "linear" ):
        linear = layers.fully_connected( drop, num_classes, activation_fn=None )
    with tf.variable_scope("soft"):
        soft = tf.nn.softmax( linear )
    return soft

def _block_a(net, scope='BlockA'):
    # 35 x 35 x 384 grid
    # default padding = SAME
    # default stride = 1
    with tf.variable_scope(scope):
        with tf.variable_scope('Br1_Pool'):
            br1 = layers.avg_pool2d(net, [3, 3], scope='Pool1_3x3')
            br1 = layers.conv2d(br1, 96, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br2_1x1'):
            br2 = layers.conv2d(net, 96, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br3_3x3'):
            br3 = layers.conv2d(net, 64, [1, 1], scope='Conv1_1x1')
            br3 = layers.conv2d(br3, 96, [3, 3], scope='Conv2_3x3')
        with tf.variable_scope('Br4_3x3Dbl'):
            br4 = layers.conv2d(net, 64, [1, 1], scope='Conv1_1x1')
            br4 = layers.conv2d(br4, 96, [3, 3], scope='Conv2_3x3')
            br4 = layers.conv2d(br4, 96, [3, 3], scope='Conv3_3x3')
        net = tf.concat(3, [br1, br2, br3, br4], name='Concat1')
        # 35 x 35 x 384
    return net

def _block_b(net, scope='BlockB'):
    # 17 x 17 x 1024 grid
    # default padding = SAME
    # default stride = 1
    with tf.variable_scope(scope):
        with tf.variable_scope('Br1_Pool'):
            br1 = layers.avg_pool2d(net, [3, 3], scope='Pool1_3x3')
            br1 = layers.conv2d(br1, 128, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br2_1x1'):
            br2 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br3_7x7'):
            br3 = layers.conv2d(net, 192, [1, 1], scope='Conv1_1x1')
            br3 = layers.conv2d(br3, 224, [1, 7], scope='Conv2_1x7')
            br3 = layers.conv2d(br3, 256, [7, 1], scope='Conv3_7x1')
        with tf.variable_scope('Br4_7x7Dbl'):
            br4 = layers.conv2d(net, 192, [1, 1], scope='Conv1_1x1')
            br4 = layers.conv2d(br4, 192, [1, 7], scope='Conv2_1x7')
            br4 = layers.conv2d(br4, 224, [7, 1], scope='Conv3_7x1')
            br4 = layers.conv2d(br4, 224, [1, 7], scope='Conv4_1x7')
            br4 = layers.conv2d(br4, 256, [7, 1], scope='Conv5_7x1')
        net = tf.concat(3, [br1, br2, br3, br4], name='Concat1')
        # 17 x 17 x 1024
    return net

def _block_c(net, scope='BlockC'):
    # 8 x 8 x 1536 grid
    # default padding = SAME
    # default stride = 1
    with tf.variable_scope(scope):
        with tf.variable_scope('Br1_Pool'):
            br1 = layers.avg_pool2d(net, [3, 3], scope='Pool1_3x3')
            br1 = layers.conv2d(br1, 256, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br2_1x1'):
            br2 = layers.conv2d(net, 256, [1, 1], scope='Conv1_1x1')
        with tf.variable_scope('Br3_3x3'):
            br3 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
            br3a = layers.conv2d(br3, 256, [1, 3], scope='Conv2_1x3')
            br3b = layers.conv2d(br3, 256, [3, 1], scope='Conv3_3x1')
        with tf.variable_scope('Br4_7x7Dbl'):
            br4 = layers.conv2d(net, 384, [1, 1], scope='Conv1_1x1')
            br4 = layers.conv2d(br4, 448, [1, 7], scope='Conv2_1x7')
            br4 = layers.conv2d(br4, 512, [7, 1], scope='Conv3_7x1')
            br4a = layers.conv2d(br4, 256, [1, 7], scope='Conv4a_1x7')
            br4b = layers.conv2d(br4, 256, [7, 1], scope='Conv4b_7x1')
        net = tf.concat(3, [br1, br2, br3a, br3b, br4a, br4b], name='Concat1')
        # 8 x 8 x 1536
    return net

def __call__(self, inputs, reuse = True):
        with tf.variable_scope(self.name) as vs:
            tf.get_variable_scope()
            if reuse:
                vs.reuse_variables()

            conv1 = tcl.conv2d(inputs,
                               num_outputs = 64,
                               kernel_size = (7, 7),
                               stride = (2, 2),
                               padding = 'SAME')
            conv1 = tcl.batch_norm(conv1)
            conv1 = tf.nn.relu(conv1)
            conv1 = tcl.max_pool2d(conv1,
                                   kernel_size = (3, 3),
                                   stride = (2, 2),
                                   padding = 'SAME')

            x = conv1
            filters = 64
            first_layer = True
            for i, r in enumerate(self.repetitions):
                x = _residual_block(self.block_fn,
                                    filters = filters,
                                    repetition = r,
                                    is_first_layer = first_layer)(x)
                filters *= 2
                if first_layer:
                    first_layer = False

            _, h, w, ch = x.shape.as_list()
            outputs = tcl.avg_pool2d(x,
                                     kernel_size = (h, w),
                                     stride = (1, 1))
            outputs = tcl.flatten(outputs)
            logits = tcl.fully_connected(outputs, num_outputs = self.num_output,
                                         activation_fn = None)
            return logits

def _output(net, endpoints, num_classes, pre_act=False):
    with tf.variable_scope('Output'):
        if pre_act:
            net = layers.batch_norm(net, activation_fn=tf.nn.relu)
        shape = net.get_shape()
        net = layers.avg_pool2d(net, shape[1:3], scope='Pool1_Global')
        endpoints['OutputPool1'] = net
        net = layers.flatten(net)
        net = layers.fully_connected(net, num_classes, activation_fn=None, scope='Logits')
        endpoints['Logits'] = net
        # num_classes
    return net

def _block_a_reduce(net, endpoints, k=192, l=224, m=256, n=384, scope='BlockReduceA'):
    # 35 x 35 -> 17 x 17 reduce
    # inception-v4: k=192, l=224, m=256, n=384
    # inception-resnet-v1: k=192, l=192, m=256, n=384
    # inception-resnet-v2: k=256, l=256, m=384, n=384
    # default padding = VALID
    # default stride = 1
    with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
        with tf.variable_scope(scope):
            with tf.variable_scope('Br1_Pool'):
                br1 = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
                # 17 x 17 x input
            with tf.variable_scope('Br2_3x3'):
                br2 = layers.conv2d(net, n, [3, 3], stride=2, scope='Conv1_3x3/2')
                # 17 x 17 x n
            with tf.variable_scope('Br3_3x3Dbl'):
                br3 = layers.conv2d(net, k, [1, 1], padding='SAME', scope='Conv1_1x1')
                br3 = layers.conv2d(br3, l, [3, 3], padding='SAME', scope='Conv2_3x3')
                br3 = layers.conv2d(br3, m, [3, 3], stride=2, scope='Conv3_3x3/2')
                # 17 x 17 x m
            net = tf.concat(3, [br1, br2, br3], name='Concat1')
            # 17 x 17 x input + n + m
            # 1024 for v4 (384 + 384 + 256)
            # 896 for res-v1 (256 + 384 +256)
            # 1152 for res-v2 (384 + 384 + 384)
            endpoints[scope] = net
            print('%s output shape: %s' % (scope, net.get_shape()))
    return net

def _block_stem_res(net, endpoints, scope='Stem'):
    # Simpler _stem for inception-resnet-v1 network
    # NOTE observe endpoints of first 3 layers
    # default padding = VALID
    # default stride = 1
    with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
        with tf.variable_scope(scope):
            # 299 x 299 x 3
            net = layers.conv2d(net, 32, [3, 3], stride=2, scope='Conv1_3x3/2')
            endpoints[scope + '/Conv1'] = net
            # 149 x 149 x 32
            net = layers.conv2d(net, 32, [3, 3], scope='Conv2_3x3')
            endpoints[scope + '/Conv2'] = net
            # 147 x 147 x 32
            net = layers.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv3_3x3')
            endpoints[scope + '/Conv3'] = net
            # 147 x 147 x 64
            net = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
            # 73 x 73 x 64
            net = layers.conv2d(net, 80, [1, 1], padding='SAME', scope='Conv4_1x1')
            # 73 x 73 x 80
            net = layers.conv2d(net, 192, [3, 3], scope='Conv5_3x3')
            # 71 x 71 x 192
            net = layers.conv2d(net, 256, [3, 3], stride=2, scope='Conv6_3x3/2')
            # 35 x 35 x 256
            endpoints[scope] = net
            print('%s output shape: %s' % (scope, net.get_shape()))
    return net

def _block_b_reduce_res(net, endpoints, ver=2, scope='BlockReduceB'):
    # 17 x 17 -> 8 x 8 reduce

    # configure branch filter numbers
    br3_num = 256
    br4_num = 256
    if ver == 1:
        br3_inc = 0
        br4_inc = 0
    else:
        br3_inc = 32
        br4_inc = 32

    with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
        with tf.variable_scope(scope):
            with tf.variable_scope('Br1_Pool'):
                br1 = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
            with tf.variable_scope('Br2_3x3'):
                br2 = layers.conv2d(net, 256, [1, 1], padding='SAME', scope='Conv1_1x1')
                br2 = layers.conv2d(br2, 384, [3, 3], stride=2, scope='Conv2_3x3/2')
            with tf.variable_scope('Br3_3x3'):
                br3 = layers.conv2d(net, br3_num, [1, 1], padding='SAME', scope='Conv1_1x1')
                br3 = layers.conv2d(br3, br3_num + br3_inc, [3, 3], stride=2, scope='Conv2_3x3/2')
            with tf.variable_scope('Br4_3x3Dbl'):
                br4 = layers.conv2d(net, br4_num, [1, 1], padding='SAME', scope='Conv1_1x1')
                br4 = layers.conv2d(br4, br4_num + 1*br4_inc, [3, 3], padding='SAME', scope='Conv2_3x3')
                br4 = layers.conv2d(br4, br4_num + 2*br4_inc, [3, 3], stride=2, scope='Conv3_3x3/2')
            net = tf.concat(3, [br1, br2, br3, br4], name='Concat1')
            # 8 x 8 x 1792 v1, 2144 v2 (paper indicates 2048 but only get this if we use a v1 config for this block)
            endpoints[scope] = net
            print('%s output shape: %s' % (scope, net.get_shape()))
    return net

def _block_output(net, endpoints, num_classes=1000, dropout_keep_prob=0.5, scope='Output'):
    with tf.variable_scope(scope):
        # 8 x 8 x 1536
        shape = net.get_shape()
        net = layers.avg_pool2d(net, shape[1:3], padding='VALID', scope='Pool1_Global')
        endpoints['Output/Pool1'] = net
        # 1 x 1 x 1536
        net = layers.dropout(net, dropout_keep_prob)
        net = layers.flatten(net)
        # 1536
        net = layers.fully_connected(net, num_classes, activation_fn=None, scope='Logits')
        # num classes
        endpoints['Logits'] = net
    return net

def _block_stem(net, endpoints, scope='Stem'):
    # Stem shared by inception-v4 and inception-resnet-v2 (resnet-v1 uses simpler _stem below)
    # NOTE observe endpoints of first 3 layers
    with arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], padding='VALID'):
        with tf.variable_scope(scope):
            # 299 x 299 x 3
            net = layers.conv2d(net, 32, [3, 3], stride=2, scope='Conv1_3x3/2')
            endpoints[scope + '/Conv1'] = net
            # 149 x 149 x 32
            net = layers.conv2d(net, 32, [3, 3], scope='Conv2_3x3')
            endpoints[scope + '/Conv2'] = net
            # 147 x 147 x 32
            net = layers.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv3_3x3')
            endpoints[scope + '/Conv3'] = net
            # 147 x 147 x 64
            with tf.variable_scope('Br1A_Pool'):
                br1a = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3/2')
            with tf.variable_scope('Br1B_3x3'):
                br1b = layers.conv2d(net, 96, [3, 3], stride=2, scope='Conv4_3x3/2')
            net = tf.concat(3, [br1a, br1b], name='Concat1')
            endpoints[scope + '/Concat1'] = net
            # 73 x 73 x 160
            with tf.variable_scope('Br2A_3x3'):
                br2a = layers.conv2d(net, 64, [1, 1], padding='SAME', scope='Conv5_1x1')
                br2a = layers.conv2d(br2a, 96, [3, 3], scope='Conv6_3x3')
            with tf.variable_scope('Br2B_7x7x3'):
                br2b = layers.conv2d(net, 64, [1, 1], padding='SAME', scope='Conv5_1x1')
                br2b = layers.conv2d(br2b, 64, [7, 1], padding='SAME', scope='Conv6_7x1')
                br2b = layers.conv2d(br2b, 64, [1, 7], padding='SAME', scope='Conv7_1x7')
                br2b = layers.conv2d(br2b, 96, [3, 3], scope='Conv8_3x3')
            net = tf.concat(3, [br2a, br2b], name='Concat2')
            endpoints[scope + '/Concat2'] = net
            # 71 x 71 x 192
            with tf.variable_scope('Br3A_3x3'):
                br3a = layers.conv2d(net, 192, [3, 3], stride=2, scope='Conv9_3x3/2')
            with tf.variable_scope('Br3B_Pool'):
                br3b = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool2_3x3/2')
            net = tf.concat(3, [br3a, br3b], name='Concat3')
            endpoints[scope + '/Concat3'] = net
            print('%s output shape: %s' % (scope, net.get_shape()))
            # 35x35x384
    return net

def _build_inception_v4(
        inputs,
        stack_counts=[4, 7, 3],
        dropout_keep_prob=0.8,
        num_classes=1000,
        is_training=True,
        scope=''):
    """Inception v4 from http://arxiv.org/abs/

    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      dropout_keep_prob: dropout keep_prob.
      num_classes: number of predicted classes.
      is_training: whether is training or not.
      scope: Optional scope for op_scope.

    Returns:
      a list containing 'logits' Tensors and a dict of Endpoints.
    """
    # endpoints will collect relevant activations for external use, for example, summaries or losses.
    endpoints = {}
    name_scope_net = tf.name_scope(scope, 'Inception_v4', [inputs])
    arg_scope_train = arg_scope([layers.batch_norm, layers.dropout], is_training=is_training)
    arg_scope_conv = arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], stride=1, padding='SAME')
    with name_scope_net, arg_scope_train, arg_scope_conv:

        net = _block_stem(inputs, endpoints)
        # 35 x 35 x 384

        with tf.variable_scope('Scale1'):
            net = _stack(net, endpoints, fn=_block_a, count=stack_counts[0], scope='BlockA')
            # 35 x 35 x 384

        with tf.variable_scope('Scale2'):
            net = _block_a_reduce(net, endpoints)
            # 17 x 17 x 1024
            net = _stack(net, endpoints, fn=_block_b, count=stack_counts[1], scope='BlockB')
            # 17 x 17 x 1024

        with tf.variable_scope('Scale3'):
            net = _block_b_reduce(net, endpoints)
            # 8 x 8 x 1536
            net = _stack(net, endpoints, fn=_block_c, count=stack_counts[2], scope='BlockC')
            # 8 x 8 x 1536

        logits = _block_output(net, endpoints, num_classes, dropout_keep_prob, scope='Output')
        endpoints['Predictions'] = tf.nn.softmax(logits, name='Predictions')

        return logits, endpoints