Python tensorflow.contrib.layers 模块，max_pool2d() 实例源码

def image_processing_layers(self) -> List[tf.Tensor]:
        """Do all convolutions and return the last conditional map.

        Applies convolutions on the input tensor with optional max pooling.
        All the intermediate layers are stored in the `image_processing_layers`
        attribute.  There is not dropout between the convolutional layers, by
        default the activation function is ReLU.
        """
        last_layer = self.image_input
        image_processing_layers = []  # type: List[tf.Tensor]

        with tf.variable_scope("convolutions"):
            for i, (filter_size,
                    n_filters,
                    pool_size) in enumerate(self.convolutions):
                with tf.variable_scope("cnn_layer_{}".format(i)):
                    last_layer = conv2d(last_layer, n_filters, filter_size)
                    image_processing_layers.append(last_layer)

                    if pool_size:
                        last_layer = max_pool2d(last_layer, pool_size)
                        image_processing_layers.append(last_layer)

        return image_processing_layers

def image_processing_layers(self) -> List[tf.Tensor]:
        """Do all convolutions and return the last conditional map.

        Applies convolutions on the input tensor with optional max pooling.
        All the intermediate layers are stored in the `image_processing_layers`
        attribute.  There is not dropout between the convolutional layers, by
        default the activation function is ReLU.
        """
        last_layer = self.image_input
        image_processing_layers = []  # type: List[tf.Tensor]

        with tf.variable_scope("convolutions"):
            for i, (filter_size,
                    n_filters,
                    pool_size) in enumerate(self.convolutions):
                with tf.variable_scope("cnn_layer_{}".format(i)):
                    last_layer = conv2d(last_layer, n_filters, filter_size)
                    image_processing_layers.append(last_layer)

                    if pool_size:
                        last_layer = max_pool2d(last_layer, pool_size)
                        image_processing_layers.append(last_layer)

        return image_processing_layers

def image_processing_layers(self) -> List[tf.Tensor]:
        """Do all convolutions and return the last conditional map.

        Applies convolutions on the input tensor with optional max pooling.
        All the intermediate layers are stored in the `image_processing_layers`
        attribute.  There is not dropout between the convolutional layers, by
        default the activation function is ReLU.
        """
        last_layer = self.image_input
        image_processing_layers = []  # type: List[tf.Tensor]

        with tf.variable_scope("convolutions"):
            for i, (filter_size,
                    n_filters,
                    pool_size) in enumerate(self.convolutions):
                with tf.variable_scope("cnn_layer_{}".format(i)):
                    last_layer = conv2d(last_layer, n_filters, filter_size)
                    image_processing_layers.append(last_layer)

                    if pool_size:
                        last_layer = max_pool2d(last_layer, pool_size)
                        image_processing_layers.append(last_layer)

        return image_processing_layers

def get_inception_layer( inputs, conv11_size, conv33_11_size, conv33_size,
                         conv55_11_size, conv55_size, pool11_size ):
    with tf.variable_scope("conv_1x1"):
        conv11 = layers.conv2d( inputs, conv11_size, [ 1, 1 ] )
    with tf.variable_scope("conv_3x3"):
        conv33_11 = layers.conv2d( inputs, conv33_11_size, [ 1, 1 ] )
        conv33 = layers.conv2d( conv33_11, conv33_size, [ 3, 3 ] )
    with tf.variable_scope("conv_5x5"):
        conv55_11 = layers.conv2d( inputs, conv55_11_size, [ 1, 1 ] )
        conv55 = layers.conv2d( conv55_11, conv55_size, [ 5, 5 ] )
    with tf.variable_scope("pool_proj"):
        pool_proj = layers.max_pool2d( inputs, [ 3, 3 ], stride = 1 )
        pool11 = layers.conv2d( pool_proj, pool11_size, [ 1, 1 ] )
    if tf.__version__ == '0.11.0rc0':
        return tf.concat(3, [conv11, conv33, conv55, pool11])
    return tf.concat([conv11, conv33, conv55, pool11], 3)

def _block_b_reduce(net, endpoints, scope='BlockReduceB'):
    # 17 x 17 -> 8 x 8 reduce
    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, 192, [1, 1], padding='SAME', scope='Conv1_1x1')
                br2 = layers.conv2d(br2, 192, [3, 3], stride=2, scope='Conv2_3x3/2')
            with tf.variable_scope('Br3_7x7x3'):
                br3 = layers.conv2d(net, 256, [1, 1], padding='SAME', scope='Conv1_1x1')
                br3 = layers.conv2d(br3, 256, [1, 7], padding='SAME', scope='Conv2_1x7')
                br3 = layers.conv2d(br3, 320, [7, 1], padding='SAME', scope='Conv3_7x1')
                br3 = layers.conv2d(br3, 320, [3, 3], stride=2, scope='Conv4_3x3/2')
            net = tf.concat(3, [br1, br2, br3], name='Concat1')
            endpoints[scope] = net
            print('%s output shape: %s' % (scope, net.get_shape()))
    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()

            x = tcl.conv2d(inputs,
                           num_outputs = 64,
                           kernel_size = (4, 4),
                           stride = (1, 1),
                           padding = 'SAME')
            x = tcl.batch_norm(x)
            x = tf.nn.relu(x)
            x = tcl.max_pool2d(x, (2, 2), (2, 2), 'SAME')
            x = tcl.conv2d(x,
                           num_outputs = 128,
                           kernel_size = (4, 4),
                           stride = (1, 1),
                           padding = 'SAME')
            x = tcl.batch_norm(x)
            x = tf.nn.relu(x)
            x = tcl.max_pool2d(x, (2, 2), (2, 2), 'SAME')
            x = tcl.flatten(x)
            logits = tcl.fully_connected(x, num_outputs = self.num_output)

            return logits

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 down_block(block_fn, filters):
    def f(inputs):
        x = block_fn(filters)(inputs)
        x = block_fn(filters)(x)
        down = tcl.max_pool2d(x,
                              kernel_size = (3, 3),
                              stride = (2, 2),
                              padding = 'SAME')
        return x, down # x:same size of inputs, down: downscaled
    return f

def model(H, x, training):
  net = dropout(x, 0.5, is_training = training)
  # net = conv2d(net, 64, [3, 3], activation_fn = tf.nn.relu)
  # net = conv2d(net, 64, [3, 3], activation_fn = tf.nn.relu)
  # net = max_pool2d(net, [2, 2], padding = 'VALID')
  # net = conv2d(net, 128, [3, 3], activation_fn = tf.nn.relu)
  # net = conv2d(net, 128, [3, 3], activation_fn = tf.nn.relu)
  # net = max_pool2d(net, [2, 2], padding = 'VALID')
  # ksize = net.get_shape().as_list()
  # net = max_pool2d(net, [ksize[1], ksize[2]])
  net = fully_connected(flatten(net), 256, activation_fn = tf.nn.relu)
  net = dropout(net, 0.5, is_training = training)
  logits = fully_connected(net, 1, activation_fn = tf.nn.sigmoid)
  preds = tf.cast(tf.greater(logits, 0.5), tf.int64)
  return logits, preds

def vgg_16_fn(input_tensor: tf.Tensor, scope='vgg_16', blocks=5, weight_decay=0.0005) \
        -> (tf.Tensor, list):  # list of tf.Tensors (layers)
    intermediate_levels = []
    # intermediate_levels.append(input_tensor)
    with slim.arg_scope(nets.vgg.vgg_arg_scope(weight_decay=weight_decay)):
        with tf.variable_scope(scope, 'vgg_16', [input_tensor]) as sc:
            input_tensor = mean_substraction(input_tensor)
            end_points_collection = sc.original_name_scope + '_end_points'
            # Collect outputs for conv2d, fully_connected and max_pool2d.
            with slim.arg_scope(
                    [layers.conv2d, layers.fully_connected, layers.max_pool2d],
                    outputs_collections=end_points_collection):
                net = layers.repeat(
                    input_tensor, 2, layers.conv2d, 64, [3, 3], scope='conv1')
                intermediate_levels.append(net)
                net = layers.max_pool2d(net, [2, 2], scope='pool1')
                if blocks >= 2:
                    net = layers.repeat(net, 2, layers.conv2d, 128, [3, 3], scope='conv2')
                    intermediate_levels.append(net)
                    net = layers.max_pool2d(net, [2, 2], scope='pool2')
                if blocks >= 3:
                    net = layers.repeat(net, 3, layers.conv2d, 256, [3, 3], scope='conv3')
                    intermediate_levels.append(net)
                    net = layers.max_pool2d(net, [2, 2], scope='pool3')
                if blocks >= 4:
                    net = layers.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv4')
                    intermediate_levels.append(net)
                    net = layers.max_pool2d(net, [2, 2], scope='pool4')
                if blocks >= 5:
                    net = layers.repeat(net, 3, layers.conv2d, 512, [3, 3], scope='conv5')
                    intermediate_levels.append(net)
                    net = layers.max_pool2d(net, [2, 2], scope='pool5')

                return net, intermediate_levels

def _stem(inputs, endpoints, num_filters=64):
    with tf.variable_scope('Stem'):
        net = layers.conv2d(inputs, num_filters, [7, 7], stride=2, scope='Conv1_7x7')
        net = layers.max_pool2d(net, [3, 3], stride=2, scope='Pool1_3x3')
    endpoints['Stem'] = net
    print("Stem output size: ", net.get_shape())
    return net


#@add_arg_scope

def _block_a(net, endpoints, d=64, scope='BlockA'):
    with tf.variable_scope(scope):
        net = endpoints[scope+'/Conv1'] = layers.conv2d(net, d, [3, 3], scope='Conv1_3x3')
        net = endpoints[scope+'/Conv2'] = layers.conv2d(net, d, [3, 3], scope='Conv2_3x3')
        net = endpoints[scope+'/Pool1'] = layers.max_pool2d(net, [2, 2], stride=2, scope='Pool1_2x2/2')
    return net

def _block_b(net, endpoints, d=256, scope='BlockB'):
    with tf.variable_scope(scope):
        net = endpoints[scope+'/Conv1'] = layers.conv2d(net, d, [3, 3], scope='Conv1_3x3')
        net = endpoints[scope+'/Conv2'] = layers.conv2d(net, d, [3, 3], scope='Conv2_3x3')
        net = endpoints[scope+'/Conv3'] = layers.conv2d(net, d, [3, 3], scope='Conv3_3x3')
        net = endpoints[scope+'/Pool1'] = layers.max_pool2d(net, [2, 2], stride=2, scope='Pool1_2x2/2')
    return net

def _block_c(net, endpoints, d=256, scope='BlockC'):
    with tf.variable_scope(scope):
        net = endpoints[scope+'/Conv1'] = layers.conv2d(net, d, [3, 3], scope='Conv1_3x3')
        net = endpoints[scope+'/Conv2'] = layers.conv2d(net, d, [3, 3], scope='Conv2_3x3')
        net = endpoints[scope+'/Conv3'] = layers.conv2d(net, d, [3, 3], scope='Conv3_3x3')
        net = endpoints[scope+'/Conv4'] = layers.conv2d(net, d, [3, 3], scope='Conv4_3x3')
        net = endpoints[scope+'/Pool1'] = layers.max_pool2d(net, [2, 2], stride=2, scope='Pool1_2x2/2')
    return net

def _build_vgg16(
        inputs,
        num_classes=1000,
        dropout_keep_prob=0.5,
        is_training=True,
        scope=''):
    """Blah"""

    endpoints = {}
    with tf.name_scope(scope, 'vgg16', [inputs]):
        with arg_scope(
                [layers.batch_norm, layers.dropout], is_training=is_training):
            with arg_scope(
                    [layers.conv2d, layers.max_pool2d], 
                    stride=1,
                    padding='SAME'):

                net = _block_a(inputs, endpoints, d=64, scope='Scale1')
                net = _block_a(net, endpoints, d=128, scope='Scale2')
                net = _block_b(net, endpoints, d=256, scope='Scale3')
                net = _block_b(net, endpoints, d=512, scope='Scale4')
                net = _block_b(net, endpoints, d=512, scope='Scale5')
                logits = _block_output(net, endpoints, num_classes, dropout_keep_prob)

                endpoints['Predictions'] = tf.nn.softmax(logits, name='Predictions')
                return logits, endpoints

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 discriminator(inputs, reuse=False):
    with tf.variable_scope('discriminator'):
        if reuse:
            tf.get_variable_scope().reuse_variables()
        net = lays.conv2d_transpose(inputs, 64, 3, stride=1, scope='conv1', padding='SAME', activation_fn=leaky_relu)
        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max1')
        net = lays.conv2d_transpose(net, 128, 3, stride=1, scope='conv2', padding='SAME', activation_fn=leaky_relu)
        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max2')
        net = lays.conv2d_transpose(net, 256, 3, stride=1, scope='conv3', padding='SAME', activation_fn=leaky_relu)
        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max3')
        net = tf.reshape(net, (batch_size, 4 * 4 * 256))
        net = lays.fully_connected(net, 128, scope='fc1', activation_fn=leaky_relu)
        net = lays.dropout(net, 0.5)
        net = lays.fully_connected(net, 1, scope='fc2', activation_fn=None)
        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