Python resnet_v1.resnet_v1_block方法代码示例

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def restnet_head(input, is_training, scope_name):
    block4 = [resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    with slim.arg_scope(resnet_arg_scope(is_training=is_training)):
        C5, _ = resnet_v1.resnet_v1(input,
                                    block4,
                                    global_pool=False,
                                    include_root_block=False,
                                    scope=scope_name)
        # C5 = tf.Print(C5, [tf.shape(C5)], summarize=10, message='C5_shape')
        C5_flatten = tf.reduce_mean(C5, axis=[1, 2], keep_dims=False, name='global_average_pooling')
        # C5_flatten = tf.Print(C5_flatten, [tf.shape(C5_flatten)], summarize=10, message='C5_flatten_shape')

    # global average pooling C5 to obtain fc layers
    return C5_flatten 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def _decide_blocks(self):
        # choose different blocks for different number of layers
        if self._num_layers == 50:
            self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                            # block 2 use stride 1
                            resnet_v1_block('block2', base_depth=128, num_units=4, stride=1),
                            resnet_v1_block('block3', base_depth=256, num_units=6, stride=2),
                            # use stride 1 for the last conv4 layer
                            resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

        elif self._num_layers == 101:
            self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                            resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                            # use stride 1 for the last conv4 layer
                            resnet_v1_block('block3', base_depth=256, num_units=23, stride=1),
                            resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

        elif self._num_layers == 152:
            self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                            resnet_v1_block('block2', base_depth=128, num_units=8, stride=2),
                            # use stride 1 for the last conv4 layer
                            resnet_v1_block('block3', base_depth=256, num_units=36, stride=1),
                            resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

        else:
            # other numbers are not supported
            raise NotImplementedError 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def _decide_blocks(self):
        # choose different blocks for different number of layers
        if self._num_layers == 50:
            self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                            resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                            # use stride 1 for the last conv4 layer
                            resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                            resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

        elif self._num_layers == 101:
            self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                            resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                            # use stride 1 for the last conv4 layer
                            resnet_v1_block('block3', base_depth=256, num_units=23, stride=1),
                            resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

        elif self._num_layers == 152:
            self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                            resnet_v1_block('block2', base_depth=128, num_units=8, stride=2),
                            # use stride 1 for the last conv4 layer
                            resnet_v1_block('block3', base_depth=256, num_units=36, stride=1),
                            resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

        else:
            # other numbers are not supported
            raise NotImplementedError 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def _decide_blocks(self):
    # choose different blocks for different number of layers
    if self._num_layers == 50:
      self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                      resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                      # use stride 1 for the last conv4 layer
                      resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    elif self._num_layers == 101:
      self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                      resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                      # use stride 1 for the last conv4 layer
                      resnet_v1_block('block3', base_depth=256, num_units=23, stride=1),
                      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    elif self._num_layers == 152:
      self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                      resnet_v1_block('block2', base_depth=128, num_units=8, stride=2),
                      # use stride 1 for the last conv4 layer
                      resnet_v1_block('block3', base_depth=256, num_units=36, stride=1),
                      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    else:
      # other numbers are not supported
      raise NotImplementedError 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def _decide_blocks(self):
    # choose different blocks for different number of layers
    if self._num_layers == 50:
      if tf.__version__ == '1.1.0':
        self._blocks     = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256,   64, 1)] * 2 + [(256,   64, 2)]),
                               resnet_utils.Block('block2', resnet_v1.bottleneck,[(512,  128, 1)] * 3 + [(512,  128, 2)]),
                               resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]),
                               resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)]
      else:
        from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
        self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                       resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                       resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                       resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    elif self._num_layers == 101:
      self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                      resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                      # use stride 1 for the last conv4 layer
                      resnet_v1_block('block3', base_depth=256, num_units=23, stride=1),
                      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    elif self._num_layers == 152:
      self._blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
                      resnet_v1_block('block2', base_depth=128, num_units=8, stride=2),
                      # use stride 1 for the last conv4 layer
                      resnet_v1_block('block3', base_depth=256, num_units=36, stride=1),
                      resnet_v1_block('block4', base_depth=512, num_units=3, stride=1)]

    else:
      # other numbers are not supported
      raise NotImplementedError 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def __init__(self):
        self.visualize = {}
        self.intermediate = {}
        self.predictions = {}
        self.score_summaries = {}
        self.event_summaries = {}
        self.train_summaries = []
        self.losses = {}

        self.image       = tf.placeholder(tf.float32, shape=[1, None, None, 3], name = 'image')
        self.spatial     = tf.placeholder(tf.float32, shape=[None, 64, 64, 2], name = 'sp')
        self.Hsp_boxes   = tf.placeholder(tf.float32, shape=[None, 5], name = 'Hsp_boxes')
        self.O_boxes     = tf.placeholder(tf.float32, shape=[None, 5], name = 'O_boxes')
        self.gt_class_H  = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_H')
        self.gt_class_HO = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_HO')
        self.gt_class_sp = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_sp')
        self.Mask_HO     = tf.placeholder(tf.float32, shape=[None, 29], name = 'HO_mask')
        self.Mask_H      = tf.placeholder(tf.float32, shape=[None, 29], name = 'H_mask')
        self.Mask_sp     = tf.placeholder(tf.float32, shape=[None, 29], name = 'sp_mask')
        self.H_num       = tf.placeholder(tf.int32)
        self.num_classes = 29
        self.num_fc      = 1024
        self.scope       = 'resnet_v1_50'
        self.stride      = [16, ]
        self.lr          = tf.placeholder(tf.float32)
        if tf.__version__ == '1.1.0':
            self.blocks     = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256,   64, 1)] * 2 + [(256,   64, 2)]),
                               resnet_utils.Block('block2', resnet_v1.bottleneck,[(512,  128, 1)] * 3 + [(512,  128, 2)]),
                               resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]),
                               resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3),
                               resnet_utils.Block('block5', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)]
        else:
            from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
            self.blocks = [resnet_v1_block('block1', base_depth=64,  num_units=3, stride=2),
                           resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                           resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                           resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
                           resnet_v1_block('block5', base_depth=512, num_units=3, stride=1)] 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def __init__(self):
        self.visualize = {}
        self.intermediate = {}
        self.predictions = {}
        self.score_summaries = {}
        self.event_summaries = {}
        self.train_summaries = []
        self.losses = {}

        self.image       = tf.placeholder(tf.float32, shape=[1, None, None, 3], name = 'image')
        self.spatial     = tf.placeholder(tf.float32, shape=[None, 64, 64, 2], name = 'sp')
        self.H_boxes     = tf.placeholder(tf.float32, shape=[None, 5], name = 'H_boxes')
        self.O_boxes     = tf.placeholder(tf.float32, shape=[None, 5], name = 'O_boxes')
        self.gt_class_H  = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_H')
        self.gt_class_HO = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_HO')
        self.Mask_HO     = tf.placeholder(tf.float32, shape=[None, 29], name = 'HO_mask')
        self.Mask_H      = tf.placeholder(tf.float32, shape=[None, 29], name = 'H_mask')
        self.H_num       = tf.placeholder(tf.int32)
        self.num_classes = 29
        self.num_fc      = 1024
        self.scope       = 'resnet_v1_50'
        self.stride      = [16, ]
        self.lr          = tf.placeholder(tf.float32)
        if tf.__version__ == '1.1.0':
            self.blocks     = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256,   64, 1)] * 2 + [(256,   64, 2)]),
                               resnet_utils.Block('block2', resnet_v1.bottleneck,[(512,  128, 1)] * 3 + [(512,  128, 2)]),
                               resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]),
                               resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3),
                               resnet_utils.Block('block5', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)]
        else:
            from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
            self.blocks = [resnet_v1_block('block1', base_depth=64,  num_units=3, stride=2),
                           resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                           resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                           resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
                           resnet_v1_block('block5', base_depth=512, num_units=3, stride=1)] 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def __init__(self):
        self.visualize = {}
        self.intermediate = {}
        self.predictions = {}
        self.score_summaries = {}
        self.event_summaries = {}
        self.train_summaries = []
        self.losses = {}

        self.image       = tf.placeholder(tf.float32, shape=[1, None, None, 3], name = 'image')
        self.spatial     = tf.placeholder(tf.float32, shape=[None, 64, 64, 2], name = 'sp')
        self.H_boxes     = tf.placeholder(tf.float32, shape=[None, 5], name = 'H_boxes')
        self.O_boxes     = tf.placeholder(tf.float32, shape=[None, 5], name = 'O_boxes')
        self.gt_class_HO = tf.placeholder(tf.float32, shape=[None, 600], name = 'gt_class_HO')
        self.H_num       = tf.placeholder(tf.int32)
        self.num_classes = 600
        self.num_fc      = 1024
        self.scope       = 'resnet_v1_50'
        self.stride      = [16, ]
        self.lr          = tf.placeholder(tf.float32)
        if tf.__version__ == '1.1.0':
            self.blocks     = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256,   64, 1)] * 2 + [(256,   64, 2)]),
                               resnet_utils.Block('block2', resnet_v1.bottleneck,[(512,  128, 1)] * 3 + [(512,  128, 2)]),
                               resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]),
                               resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3),
                               resnet_utils.Block('block5', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)]
        else:
            from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
            self.blocks = [resnet_v1_block('block1', base_depth=64,  num_units=3, stride=2),
                           resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                           resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                           resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
                           resnet_v1_block('block5', base_depth=512, num_units=3, stride=1)] 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def resnet_base(img_batch, scope_name, is_training=True):
    '''
    this code is derived from light-head rcnn.
    https://github.com/zengarden/light_head_rcnn

    It is convenient to freeze blocks. So we adapt this mode.
    '''
    if scope_name == 'resnet_v1_50':
        middle_num_units = 6
    elif scope_name == 'resnet_v1_101':
        middle_num_units = 23
    else:
        raise NotImplementedError('We only support resnet_v1_50 or resnet_v1_101. Check your network name....yjr')

    blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2),
              resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
              # use stride 1 for the last conv4 layer.

              resnet_v1_block('block3', base_depth=256, num_units=middle_num_units, stride=1)]
              # when use fpn . stride list is [1, 2, 2]

    with slim.arg_scope(resnet_arg_scope(is_training=False)):
        with tf.variable_scope(scope_name, scope_name):
            # Do the first few layers manually, because 'SAME' padding can behave inconsistently
            # for images of different sizes: sometimes 0, sometimes 1
            net = resnet_utils.conv2d_same(
                img_batch, 64, 7, stride=2, scope='conv1')
            net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
            net = slim.max_pool2d(
                net, [3, 3], stride=2, padding='VALID', scope='pool1')

    not_freezed = [False] * cfgs.FIXED_BLOCKS + (4-cfgs.FIXED_BLOCKS)*[True]
    # Fixed_Blocks can be 1~3

    with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[0]))):
        C2, _ = resnet_v1.resnet_v1(net,
                                    blocks[0:1],
                                    global_pool=False,
                                    include_root_block=False,
                                    scope=scope_name)

    # C2 = tf.Print(C2, [tf.shape(C2)], summarize=10, message='C2_shape')

    with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[1]))):
        C3, _ = resnet_v1.resnet_v1(C2,
                                    blocks[1:2],
                                    global_pool=False,
                                    include_root_block=False,
                                    scope=scope_name)

    # C3 = tf.Print(C3, [tf.shape(C3)], summarize=10, message='C3_shape')

    with slim.arg_scope(resnet_arg_scope(is_training=(is_training and not_freezed[2]))):
        C4, _ = resnet_v1.resnet_v1(C3,
                                    blocks[2:3],
                                    global_pool=False,
                                    include_root_block=False,
                                    scope=scope_name)

    # C4 = tf.Print(C4, [tf.shape(C4)], summarize=10, message='C4_shape')
    return C4 

# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import resnet_v1 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block [as 别名]
def __init__(self):
        self.visualize = {}
        self.intermediate = {}
        self.predictions = {}
        self.score_summaries = {}
        self.event_summaries = {}
        self.train_summaries = []
        self.losses = {}

        self.image       = tf.placeholder(tf.float32, shape=[1, None, None, 3], name = 'image')
        self.spatial     = tf.placeholder(tf.float32, shape=[None, 64, 64, 3], name = 'sp') # Pattern.reshape( num_pos_neg, 64, 64, 3) 
        self.Hsp_boxes   = tf.placeholder(tf.float32, shape=[None, 5], name = 'Hsp_boxes')
        self.O_boxes     = tf.placeholder(tf.float32, shape=[None, 5], name = 'O_boxes') #Object_augmented[:num_pos].reshape(num_pos, 5)
        self.gt_class_H  = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_H')
        self.gt_class_HO = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_HO')
        self.gt_class_sp = tf.placeholder(tf.float32, shape=[None, 29], name = 'gt_class_sp')
        self.Mask_HO     = tf.placeholder(tf.float32, shape=[None, 29], name = 'HO_mask')
        self.Mask_H      = tf.placeholder(tf.float32, shape=[None, 29], name = 'H_mask')
        self.Mask_sp     = tf.placeholder(tf.float32, shape=[None, 29], name = 'sp_mask')
        self.gt_binary_label = tf.placeholder(tf.float32, shape=[None, 2], name = 'gt_binary_label')
        self.H_num       = tf.placeholder(tf.int32)
        self.HO_weight   = np.array([3.3510249, 3.4552405, 4.0257854, 0.0, 4.088436, 
                                    3.4370995, 3.85842, 4.637334, 3.5487218, 3.536237, 
                                    2.5578923, 3.342811, 3.8897269, 4.70686, 3.3952892, 
                                    3.9706533, 4.504736, 0.0, 1.4873443, 3.700363, 
                                    4.1058283, 3.6298118, 0.0, 6.490651, 5.0808263, 
                                    1.520838, 3.3888445, 0.0, 3.9899964], dtype = 'float32').reshape(1,29)
        self.H_weight   = np.array([4.0984106, 4.102459, 4.0414762, 4.060745, 4.0414762, 
                                    3.9768186, 4.23686, 5.3542085, 3.723717, 3.4699364, 
                                    2.4587274, 3.7167964, 4.08836, 5.050695, 3.9077065, 
                                    4.534647, 3.4699364, 2.9466882, 1.8585607, 3.9433942, 
                                    3.9433942, 4.3523254, 3.8368235, 6.4963055, 5.138182, 
                                    1.7807873, 4.080392, 1.9544303, 4.5761204],dtype = 'float32').reshape(1,29)
        self.binary_weight = np.array([1.0986122886681098, 0.4054651081081644], dtype = 'float32').reshape(1,2)
        self.num_classes = 29
        self.num_binary  = 2 # existence (0 or 1) of HOI
        self.num_fc      = 1024
        self.scope       = 'resnet_v1_50'
        self.stride      = [16, ]
        self.lr          = tf.placeholder(tf.float32)
        if tf.__version__ == '1.1.0':
            self.blocks     = [resnet_utils.Block('block1', resnet_v1.bottleneck,[(256,   64, 1)] * 2 + [(256,   64, 2)]),
                               resnet_utils.Block('block2', resnet_v1.bottleneck,[(512,  128, 1)] * 3 + [(512,  128, 2)]),
                               resnet_utils.Block('block3', resnet_v1.bottleneck,[(1024, 256, 1)] * 5 + [(1024, 256, 1)]),
                               resnet_utils.Block('block4', resnet_v1.bottleneck,[(2048, 512, 1)] * 3),
                               resnet_utils.Block('block5', resnet_v1.bottleneck,[(2048, 512, 1)] * 3)]
        else:
            from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_block
            self.blocks = [resnet_v1_block('block1', base_depth=64,  num_units=3, stride=2),
                           resnet_v1_block('block2', base_depth=128, num_units=4, stride=2),
                           resnet_v1_block('block3', base_depth=256, num_units=6, stride=1),
                           resnet_v1_block('block4', base_depth=512, num_units=3, stride=1),
                           resnet_v1_block('block5', base_depth=512, num_units=3, stride=1)]