Python torch.nn.Identity() Examples

def mlp(sizes, activation, output_activation=nn.Identity):
    """
    Build a multi-layer perceptron in PyTorch.

    Args:
        sizes: Tuple, list, or other iterable giving the number of units
            for each layer of the MLP. 

        activation: Activation function for all layers except last.

        output_activation: Activation function for last layer.

    Returns:
        A PyTorch module that can be called to give the output of the MLP.
        (Use an nn.Sequential module.)

    """
    #######################
    #                     #
    #   YOUR CODE HERE    #
    #                     #
    #######################
    pass 

def __init__(
        self,
        name: str = "resnet50",
        visual_feature_size: int = 2048,
        pretrained: bool = False,
        frozen: bool = False,
    ):
        super().__init__(visual_feature_size)

        self.cnn = getattr(torchvision.models, name)(
            pretrained, zero_init_residual=True
        )
        # Do nothing after the final residual stage.
        self.cnn.fc = nn.Identity()

        # Freeze all weights if specified.
        if frozen:
            for param in self.cnn.parameters():
                param.requires_grad = False
            self.cnn.eval()

        # Keep a list of intermediate layer names.
        self._stage_names = [f"layer{i}" for i in range(1, 5)] 

def __init__(self, in_size, out_size, hidden_size=128, depth=2,
               input_kwargs=None, internal_kwargs=None):
    super().__init__()
    self.depth = depth
    self.input_blocks = nn.ModuleList([
      self.make_block(in_size, hidden_size, **input_kwargs)
      for idx in range(depth)
    ])
    self.internal_blocks = nn.ModuleList([
      nn.Identity()
    ] + [
      self.make_block(hidden_size, hidden_size, **internal_kwargs)
      for idx in range(depth - 1)
    ])
    self.internal_constants = nn.ParameterList([
      self.make_constant(hidden_size)
      for idx in range(depth)
    ])

    self.output_block = self.make_block(hidden_size, out_size, **internal_kwargs)
    self.output_constant = self.make_constant(out_size) 

def forward(self, x):
        stages = self.get_stages()

        block_number = 0.
        drop_connect_rate = self._global_params.drop_connect_rate

        features = []
        for i in range(self._depth + 1):

            # Identity and Sequential stages
            if i < 2:
                x = stages[i](x)

            # Block stages need drop_connect rate
            else:
                for module in stages[i]:
                    drop_connect = drop_connect_rate * block_number / len(self._blocks)
                    block_number += 1.
                    x = module(x, drop_connect)

            features.append(x)

        return features 

def __init__(self, name, **params):

        super().__init__()

        if name is None or name == 'identity':
            self.activation = nn.Identity(**params)
        elif name == 'sigmoid':
            self.activation = nn.Sigmoid()
        elif name == 'softmax2d':
            self.activation = nn.Softmax(dim=1, **params)
        elif name == 'softmax':
            self.activation = nn.Softmax(**params)
        elif name == 'logsoftmax':
            self.activation = nn.LogSoftmax(**params)
        elif name == 'argmax':
            self.activation = ArgMax(**params)
        elif name == 'argmax2d':
            self.activation = ArgMax(dim=1, **params)
        elif callable(name):
            self.activation = name(**params)
        else:
            raise ValueError('Activation should be callable/sigmoid/softmax/logsoftmax/None; got {}'.format(name)) 

def body(cls, inputs, **kwargs):
        kwargs = cls.get_defaults('body', kwargs)
        encoder = kwargs.pop('encoder')
        embedding = kwargs.pop('embedding')
        decoder = kwargs.pop('decoder')

        layers = []
        encoder = cls.encoder(inputs=inputs, **{**kwargs, **encoder})
        encoder_outputs = encoder(inputs)
        layers.append(('encoder', encoder))

        if embedding is not None:
            embedding = cls.embedding(inputs=encoder_outputs, **{**kwargs, **embedding})
        else:
            embedding = nn.Identity()
        encoder_outputs = embedding(encoder_outputs)
        layers.append(('embedding', embedding))

        decoder = cls.decoder(inputs=encoder_outputs, **{**kwargs, **decoder})
        layers.append(('decoder', decoder))

        return nn.Sequential(OrderedDict(layers)) 

def __init__(self, in_channels, out_channels, expansion=1, kernel_size=3,
                 stride=1, padding=1, se_ratio=0.25, hard_act=False):
        expanded = in_channels * expansion
        super(MBConv, self).__init__()
        self.add_res = stride == 1 and in_channels == out_channels
        self.block = nn.Sequential(
            ConvBNAct(in_channels, expanded, 1,
                      hard_act=hard_act) if expanded != in_channels else nn.Identity(),
            ConvBNAct(expanded, expanded, kernel_size,
                      stride=stride, padding=padding, groups=expanded, hard_act=hard_act),
            SESwishBlock(expanded, expanded, int(in_channels*se_ratio),
                         hard_act=hard_act) if se_ratio > 0 else nn.Identity(),
            nn.Conv2d(expanded, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels)
        )
        self.drop_prob = 0 

def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        if num_classes:
            num_features = self.num_features * self.global_pool.feat_mult()
            self.fc = nn.Conv2d(num_features, num_classes, kernel_size=1, bias=True)
        else:
            self.fc = nn.Identity() 

def __init__(self, config):
        super(SequenceSummary, self).__init__()

        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
        if config.summary_type == 'attn':
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = nn.Identity()
        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = nn.Identity()
        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
            self.activation = nn.Tanh()

        self.first_dropout = nn.Identity()
        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = nn.Identity()
        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, config):
        super().__init__()

        self.summary_type = config.summary_type if hasattr(config, "summary_type") else "last"
        if self.summary_type == "attn":
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = Identity()
        if hasattr(config, "summary_use_proj") and config.summary_use_proj:
            if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = Identity()
        if hasattr(config, "summary_activation") and config.summary_activation == "tanh":
            self.activation = nn.Tanh()

        self.first_dropout = Identity()
        if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = Identity()
        if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, config):
        super().__init__()

        self.summary_type = config.summary_type if hasattr(config, "summary_type") else "last"
        if self.summary_type == "attn":
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = Identity()
        if hasattr(config, "summary_use_proj") and config.summary_use_proj:
            if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = Identity()
        if hasattr(config, "summary_activation") and config.summary_activation == "tanh":
            self.activation = nn.Tanh()

        self.first_dropout = Identity()
        if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = Identity()
        if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, config):
        super(SequenceSummary, self).__init__()

        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
        if self.summary_type == 'attn':
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = Identity()
        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = Identity()
        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
            self.activation = nn.Tanh()

        self.first_dropout = Identity()
        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = Identity()
        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, config):
        super(SequenceSummary, self).__init__()

        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
        if self.summary_type == 'attn':
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = Identity()
        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = Identity()
        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
            self.activation = nn.Tanh()

        self.first_dropout = Identity()
        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = Identity()
        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, num_embeddings, embedding_dim, base_dim):
        super(PrEmbeddingBag, self).__init__()
        self.embs = nn.EmbeddingBag(
            num_embeddings, embedding_dim, mode="sum", sparse=True)
        torch.nn.init.xavier_uniform_(self.embs.weight)
        if embedding_dim < base_dim:
            self.proj = nn.Linear(embedding_dim, base_dim, bias=False)
            torch.nn.init.xavier_uniform_(self.proj.weight)
        elif embedding_dim == base_dim:
            self.proj = nn.Identity()
        else:
            raise ValueError(
                "Embedding dim " + str(embedding_dim) + " > base dim " + str(base_dim)
            ) 

def reset_classifier(self, num_classes, global_pool='avg'):
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        self.num_classes = num_classes
        if num_classes:
            num_features = self.num_features * self.global_pool.feat_mult()
            self.classif = nn.Linear(num_features, num_classes)
        else:
            self.classif = nn.Identity() 

def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
        if num_classes:
            num_features = self.num_features * self.global_pool.feat_mult()
            self.last_linear = nn.Linear(num_features, num_classes)
        else:
            self.last_linear = nn.Identity() 

def __init__(self, in_chs, out_chs, dw_kernel_size=3,
                 stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False,
                 pw_kernel_size=1, pw_act=False, se_ratio=0., se_kwargs=None,
                 norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0.):
        super(DepthwiseSeparableConv, self).__init__()
        norm_kwargs = norm_kwargs or {}
        has_se = se_ratio is not None and se_ratio > 0.
        self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
        self.has_pw_act = pw_act  # activation after point-wise conv
        self.drop_path_rate = drop_path_rate

        self.conv_dw = create_conv2d(
            in_chs, in_chs, dw_kernel_size, stride=stride, dilation=dilation, padding=pad_type, depthwise=True)
        self.bn1 = norm_layer(in_chs, **norm_kwargs)
        self.act1 = act_layer(inplace=True)

        # Squeeze-and-excitation
        if has_se:
            se_kwargs = resolve_se_args(se_kwargs, in_chs, act_layer)
            self.se = SqueezeExcite(in_chs, se_ratio=se_ratio, **se_kwargs)
        else:
            self.se = None

        self.conv_pw = create_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
        self.bn2 = norm_layer(out_chs, **norm_kwargs)
        self.act2 = act_layer(inplace=True) if self.has_pw_act else nn.Identity() 

def reset_classifier(self, num_classes, global_pool='avg'):
        self.num_classes = num_classes
        self.avg_pool = SelectAdaptivePool2d(pool_type=global_pool)
        if num_classes:
            num_features = self.num_features * self.avg_pool.feat_mult()
            self.last_linear = nn.Linear(num_features, num_classes)
        else:
            self.last_linear = nn.Identity() 

def __init__(self, *args, **kwargs):
            super(Identity, self).__init__() 

def __init__(self, config):
        super(SequenceSummary, self).__init__()

        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
        if self.summary_type == 'attn':
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = Identity()
        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = Identity()
        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
            self.activation = nn.Tanh()

        self.first_dropout = Identity()
        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = Identity()
        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, *args, **kwargs):
            super(Identity, self).__init__() 

def __init__(self, config):
        super(SequenceSummary, self).__init__()

        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
        if config.summary_type == 'attn':
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = nn.Identity()
        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = nn.Identity()
        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
            self.activation = nn.Tanh()

        self.first_dropout = nn.Identity()
        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = nn.Identity()
        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, config: PretrainedConfig):
        super().__init__()

        self.summary_type = getattr(config, "summary_type", "last")
        if self.summary_type == "attn":
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = Identity()
        if hasattr(config, "summary_use_proj") and config.summary_use_proj:
            if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        activation_string = getattr(config, "summary_activation", None)
        self.activation: Callable = (get_activation(activation_string) if activation_string else Identity())

        self.first_dropout = Identity()
        if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = Identity()
        if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def identity(inputs, **kwargs):
        """ Return tensor unchanged. """
        _ = inputs, kwargs
        return nn.Identity() 

def __init__(self, inputs, layout='cna', filters=None, kernel_size=1, pool_op='mean',
                 pyramid=(0, 1, 2, 3, 6), **kwargs):
        super().__init__()

        spatial_shape = np.array(get_shape(inputs)[2:])
        filters = filters if filters else 'same // {}'.format(len(pyramid))

        modules = nn.ModuleList()
        for level in pyramid:
            if level == 0:
                module = nn.Identity()
            else:
                x = inputs
                pool_size = tuple(np.ceil(spatial_shape / level).astype(np.int32).tolist())
                pool_strides = tuple(np.floor((spatial_shape - 1) / level + 1).astype(np.int32).tolist())

                layer = ConvBlock(inputs=x, layout='p' + layout, filters=filters, kernel_size=kernel_size,
                                  pool_op=pool_op, pool_size=pool_size, pool_strides=pool_strides, **kwargs)
                x = layer(x)

                upsample_layer = Upsample(inputs=x, factor=None, layout='b',
                                          shape=tuple(spatial_shape.tolist()), **kwargs)
                module = nn.Sequential(layer, upsample_layer)
            modules.append(module)

        self.blocks = modules
        self.combine = Combine(op='concat') 

def __init__(self, inputs=None, **kwargs):
        super().__init__()

        if kwargs.get('layout'):
            self.layer = ConvBlock(inputs=inputs, **kwargs)
        else:
            self.layer = nn.Identity() 

def _make_modules(self, inputs, **kwargs):
        num_stages = kwargs.pop('num_stages')
        encoder_layout = ''.join([item[0] for item in kwargs.pop('order')])

        block_args = kwargs.pop('blocks')
        downsample_args = kwargs.pop('downsample')
        self.layout = ''

        for i in range(num_stages):
            for letter in encoder_layout:

                if letter in ['b']:
                    args = {**kwargs, **block_args, **unpack_args(block_args, i, num_stages)}

                    layer = ConvBlock(inputs=inputs, **args)
                    inputs = layer(inputs)
                    layer_desc = 'block-{}'.format(i)

                elif letter in ['d', 'p']:
                    args = {**kwargs, **downsample_args, **unpack_args(downsample_args, i, num_stages)}

                    layer = ConvBlock(inputs=inputs, **args)
                    inputs = layer(inputs)
                    layer_desc = 'downsample-{}'.format(i)

                elif letter in ['s']:
                    layer = nn.Identity()
                    layer_desc = 'skip-{}'.format(i)
                else:
                    raise ValueError('Unknown letter in order {}, use one of "b", "d", "p", "s"'
                                     .format(letter))

                self.update([(layer_desc, layer)])
                self.layout += letter 

def __init__(self, config):
        super(SequenceSummary, self).__init__()

        self.summary_type = config.summary_type if hasattr(config, 'summary_use_proj') else 'last'
        if self.summary_type == 'attn':
            # We should use a standard multi-head attention module with absolute positional embedding for that.
            # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276
            # We can probably just use the multi-head attention module of PyTorch >=1.1.0
            raise NotImplementedError

        self.summary = Identity()
        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
            if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                num_classes = config.num_labels
            else:
                num_classes = config.hidden_size
            self.summary = nn.Linear(config.hidden_size, num_classes)

        self.activation = Identity()
        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
            self.activation = nn.Tanh()

        self.first_dropout = Identity()
        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
            self.first_dropout = nn.Dropout(config.summary_first_dropout)

        self.last_dropout = Identity()
        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
            self.last_dropout = nn.Dropout(config.summary_last_dropout) 

def __init__(self, *args, **kwargs):
            super(Identity, self).__init__() 

def __init__(
            self,
            encoder_channels,
            decoder_channels,
            n_blocks=5,
            use_batchnorm=True,
            attention_type=None,
            center=False,
    ):
        super().__init__()

        if n_blocks != len(decoder_channels):
            raise ValueError(
                "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format(
                    n_blocks, len(decoder_channels)
                )
            )

        encoder_channels = encoder_channels[1:]  # remove first skip with same spatial resolution
        encoder_channels = encoder_channels[::-1]  # reverse channels to start from head of encoder

        # computing blocks input and output channels
        head_channels = encoder_channels[0]
        in_channels = [head_channels] + list(decoder_channels[:-1])
        skip_channels = list(encoder_channels[1:]) + [0]
        out_channels = decoder_channels

        if center:
            self.center = CenterBlock(
                head_channels, head_channels, use_batchnorm=use_batchnorm
            )
        else:
            self.center = nn.Identity()

        # combine decoder keyword arguments
        kwargs = dict(use_batchnorm=use_batchnorm, attention_type=attention_type)
        blocks = [
            DecoderBlock(in_ch, skip_ch, out_ch, **kwargs)
            for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels)
        ]
        self.blocks = nn.ModuleList(blocks)