Python layers.avg_pool2d方法代码示例

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
def _squeezenet(images, num_classes=1000, data_format='NCHW'):
        net = conv2d(images, 96, [2, 2], stride=2, scope='conv1')
        net = max_pool2d(net, [3, 3], stride=2, scope='maxpool1')
        net = fire_module(net, 16, 64, scope='fire2', data_format=data_format)
        net = fire_module(net, 16, 64, scope='fire3', data_format=data_format)
        net = fire_module(net, 32, 128, scope='fire4', data_format=data_format)
        net = max_pool2d(net, [3, 3], stride=2, scope='maxpool4')
        net = fire_module(net, 32, 128, scope='fire5', data_format=data_format)
        net = fire_module(net, 48, 192, scope='fire6', data_format=data_format)
        net = fire_module(net, 48, 192, scope='fire7', data_format=data_format)
        net = fire_module(net, 64, 256, scope='fire8', data_format=data_format)
        net = max_pool2d(net, [3, 3], stride=2, scope='maxpool8')
        net = fire_module(net, 64, 256, scope='fire9', data_format=data_format)
        net = conv2d(net, num_classes, [1, 1], stride=1, scope='conv10')
        net = avg_pool2d(net, [13, 13], stride=1, scope='avgpool10')

        squeeze_axes = [2, 3] if data_format == 'NCHW' else [1, 2]
        logits = tf.squeeze(net, squeeze_axes, name='logits')
        return logits 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
def _squeezenet(images, num_classes=1000):
        net = conv2d(images, 96, [7, 7], stride=2, scope='conv1')
        net = max_pool2d(net, [3, 3], stride=2, scope='maxpool1')
        net = fire_module(net, 16, 64, scope='fire2')
        net = fire_module(net, 16, 64, scope='fire3')
        net = fire_module(net, 32, 128, scope='fire4')
        net = max_pool2d(net, [3, 3], stride=2, scope='maxpool4')
        net = fire_module(net, 32, 128, scope='fire5')
        net = fire_module(net, 48, 192, scope='fire6')
        net = fire_module(net, 48, 192, scope='fire7')
        net = fire_module(net, 64, 256, scope='fire8')
        net = max_pool2d(net, [3, 3], stride=2, scope='maxpool8')
        net = fire_module(net, 64, 256, scope='fire9')
        net = conv2d(net, num_classes, [1, 1], stride=1, scope='conv10')
        net = avg_pool2d(net, [13, 13], stride=1, scope='avgpool10')
        logits = tf.squeeze(net, [2], name='logits')
        return logits 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
def avg_pool2d(self, *args, **kwargs):
    return self._pass_through_mask(
        self._function_dict['avg_pool2d'], *args, **kwargs) 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
def inception_v3_figure5_downsample(
	name, x, end_points, 
	act_fn, norm_fn, norm_params, winit_fn, 
	filters=[[64,96,96,], [384,],],
	is_avg_pooling=True):


	with tf.variable_scope(name):
		with tf.variable_scope('branch0'):
			branch_0 = tcl.conv2d(x, filters[0][0], 1, 
						stride=1, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='SAME', weights_initializer=winit_fn, scope='conv2d_0a_1x1')
			branch_0 = tcl.conv2d(branch_0, filters[0][1], 3,
						stride=1, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='SAME', weights_initializer=winit_fn, scope='conv2d_0b_3x3')
			branch_0 = tcl.conv2d(branch_0, filters[0][2], 3,
						stride=2, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='VALID', weights_initializer=winit_fn, scope='conv2d_0c_3x3')

		with tf.variable_scope('branch1'):
			branch_1 = tcl.conv2d(x, filters[1][0], 3, 
						stride=2, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='VALID', weights_initializer=winit_fn, scope='conv2d_1a_3x3')

		with tf.variable_scope('branch2'):
			if is_avg_pooling:
				branch_2 = tcl.avg_pool2d(x, 3, stride=2,
						padding='VALID', scope='avgpool_2a_3x3')
			else:
				branch_2 = tcl.max_pool2d(x, 3, stride=2, 
						padding='VALID', scope='maxpool_2a_3x3')

		x = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])

		end_points[name] = x
	return x, end_points 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
def inception_v3_figure6_downsample(
	name, x, end_points, n,
	act_fn, norm_fn, norm_params, winit_fn, 
	filters=[[192,192,192,192,], [192,320], [192], [192]],
	is_avg_pooling=True):

	with tf.variable_scope(name):
		with tf.variable_scope('branch0'):
			branch_0 = tcl.conv2d(x, filters[0][0], 1, 
						stride=1, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='SAME', weights_initializer=winit_fn, scope='conv2d_0a_1x1')
			branch_0 = tcl.conv2d(branch_0, filters[0][1], [1, n],
						stride=1, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='SAME', weights_initializer=winit_fn, scope='conv2d_0b_1xn')
			branch_0 = tcl.conv2d(branch_0, filters[0][2], [n, 1],
						stride=1, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='SAME', weights_initializer=winit_fn, scope='conv2d_0c_nx1')
			branch_0 = tcl.conv2d(branch_0, filters[0][3], [3, 3],
						stride=2, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='VALID', weights_initializer=winit_fn, scope='conv2d_0d_3x3')

		with tf.variable_scope('branch1'):
			branch_1 = tcl.conv2d(x, filters[1][0],  1, 
						stride=1, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='SAME', weights_initializer=winit_fn, scope='conv2d_1a_1x1')
			branch_1 = tcl.conv2d(branch_1, filters[1][1], [3, 3], 
						stride=2, activation_fn=act_fn, normalizer_fn=norm_fn, normalizer_params=norm_params,
						padding='VALID', weights_initializer=winit_fn, scope='conv2d_1b_3x3')

		with tf.variable_scope('branch2'):
			if is_avg_pooling:
				branch_2 = tcl.avg_pool2d(x, 3, 
						padding='VALID', stride=2, scope='avgpool_2a_3x3')
			else:
				branch_2 = tcl.max_pool2d(x, 3, 
						padding='VALID', stride=2, scope='maxpool_2a_3x3')

		x = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])

		end_points[name] = x
	return x, end_points 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
def avg_pool(_input, k=2, s=2):
    padding = 'VALID'
    output = avg_pool2d(
        _input, kernel_size=[
            k, k], stride=[
            s, s], padding=padding, data_format='NHWC')
    return output 

# 需要导入模块: from tensorflow.contrib import layers [as 别名]
# 或者: from tensorflow.contrib.layers import avg_pool2d [as 别名]
def _arg_scope(is_training, weight_decay, bn_decay, data_format):
    with arg_scope([conv2d],
                   weights_regularizer=l2_regularizer(weight_decay),
                   normalizer_fn=batch_norm,
                   normalizer_params={'is_training': is_training,
                                      'fused': True,
                                      'decay': bn_decay}):
        with arg_scope([conv2d, avg_pool2d, max_pool2d, batch_norm],
                       data_format=data_format) as sc:
                return sc