The pre-trained GearNet-Edge model for Fold Classification

[arantir123]

Thank you for your amazing work! I found that for the Fold Classification task, the GearNet-Edge model was implemented based on the GearNetIEConv script rather than the GearNet script, which has some detail differences (e.g., extra input embedding and ieconv layers). Based on this, I would like to ask whether you could provide the pretrained GearNet-Edge model based on multiview contrast learning and the GearNetIEConv script for Fold Classification (rather than based on GearNet script for EC task)? Thank you.

[Oxer11]

Hi, the config file for GearNet-Edge-IEConv on Fold is config/Fold3D/gearnet_edge_ieconv.yaml. The pre-trained checkpoints of GearNet-Edge can be found at https://zenodo.org/record/7723075.

[arantir123]

Thank you. It seems that fold_mc_gearnet_edge_ieconv.pth includes the encoder and decoder parameters after finetuning. I just would like to do some experiments on my own, i.e., I would like to have the pretrained GearNet-Edge-IEConv encoder before finetuning, obtain the finetuning configuration script and corresponding running command (e.g., how many GPUs/batch size were actually used in finetuning), and do the finetuning experiment on my own. Whether it is convenient for you to provide these for me? Thank you very much.

[Oxer11]

I see. The original pre-trained checkpoints were deleted by my cluster. I've pre-trained a new GearNet-Edge-IEConv recently. You can download the checkpoint from this link and have a try. Please ping me if there is any problem with the checkpoint.

For finetuning, just use the following command

python script/downstream.py -c config/downstream/Fold3D/gearnet_edge_ieconv.yaml --gpus [0] --ckpt <path_to_your_model>

[arantir123]

I see. The original pre-trained checkpoints were deleted by my cluster. I've pre-trained a new GearNet-Edge-IEConv recently. You can download the checkpoint from this link and have a try. Please ping me if there is any problem with the checkpoint.

For finetuning, just use the following command

python script/downstream.py -c config/downstream/Fold3D/gearnet_edge_ieconv.yaml --gpus [0] --ckpt <path_to_your_model>

Thank you very much. I will have a try.

[arantir123]

I see. The original pre-trained checkpoints were deleted by my cluster. I've pre-trained a new GearNet-Edge-IEConv recently. You can download the checkpoint from this link and have a try. Please ping me if there is any problem with the checkpoint.

For finetuning, just use the following command

python script/downstream.py -c config/downstream/Fold3D/gearnet_edge_ieconv.yaml --gpus [0] --ckpt <path_to_your_model>

Hi, it seems that the model contained in the above link is not in line with/cannot fit the model (size) in official https://zenodo.org/record/7723075 (the hidden dimensions of each layer are different), I guess the model in https://zenodo.org/record/7723075 is based on the following new implementation version of GearNet-Edge-IEConv (with extra input embedding etc).

@R.register("models.GearNetIEConv")
class GearNetIEConv(nn.Module, core.Configurable):

def __init__(self, input_dim, embedding_dim, hidden_dims, num_relation, edge_input_dim=None,
             batch_norm=False, activation="relu", concat_hidden=False, short_cut=True, 
             readout="sum", dropout=0, num_angle_bin=None, layer_norm=False, use_ieconv=False):
    super(GearNetIEConv, self).__init__()
    print('using GearNetIEConv.')

    if not isinstance(hidden_dims, Sequence):
        hidden_dims = [hidden_dims]
    self.input_dim = input_dim
    self.embedding_dim = embedding_dim
    self.output_dim = sum(hidden_dims) if concat_hidden else hidden_dims[-1]
    self.dims = [embedding_dim if embedding_dim > 0 else input_dim] + list(hidden_dims)
    self.edge_dims = [edge_input_dim] + self.dims[:-1]
    self.num_relation = num_relation
    self.concat_hidden = concat_hidden
    self.short_cut = short_cut
    self.num_angle_bin = num_angle_bin
    self.short_cut = short_cut
    self.concat_hidden = concat_hidden
    self.layer_norm = layer_norm
    self.use_ieconv = use_ieconv  

    if embedding_dim > 0:
        self.linear = nn.Linear(input_dim, embedding_dim)
        self.embedding_batch_norm = nn.BatchNorm1d(embedding_dim)

    self.layers = nn.ModuleList()
    self.ieconvs = nn.ModuleList()
    for i in range(len(self.dims) - 1):
        # note that these layers are from gearnet.layer instead of torchdrug.layers
        self.layers.append(layer.GeometricRelationalGraphConv(self.dims[i], self.dims[i + 1], num_relation,
                                                               None, batch_norm, activation))
        if use_ieconv:
            self.ieconvs.append(layer.IEConvLayer(self.dims[i], self.dims[i] // 4, 
                                self.dims[i+1], edge_input_dim=14, kernel_hidden_dim=32))
    if num_angle_bin:
        self.spatial_line_graph = layers.SpatialLineGraph(num_angle_bin)
        self.edge_layers = nn.ModuleList()
        for i in range(len(self.edge_dims) - 1):
            self.edge_layers.append(layer.GeometricRelationalGraphConv(
                self.edge_dims[i], self.edge_dims[i + 1], num_angle_bin, None, batch_norm, activation))

    if layer_norm:
        self.layer_norms = nn.ModuleList()
        for i in range(len(self.dims) - 1):
            self.layer_norms.append(nn.LayerNorm(self.dims[i + 1]))

    self.dropout = nn.Dropout(dropout)

    if readout == "sum":
        self.readout = layers.SumReadout()
    elif readout == "mean":
        self.readout = layers.MeanReadout()
    else:
        raise ValueError("Unknown readout `%s`" % readout)

def get_ieconv_edge_feature(self, graph):
    u = torch.ones_like(graph.node_position)
    u[1:] = graph.node_position[1:] - graph.node_position[:-1]
    u = F.normalize(u, dim=-1)
    b = torch.ones_like(graph.node_position)
    b[:-1] = u[:-1] - u[1:]
    b = F.normalize(b, dim=-1)
    n = torch.ones_like(graph.node_position)
    n[:-1] = torch.cross(u[:-1], u[1:])
    n = F.normalize(n, dim=-1)

    local_frame = torch.stack([b, n, torch.cross(b, n)], dim=-1)

    node_in, node_out = graph.edge_list.t()[:2]
    t = graph.node_position[node_out] - graph.node_position[node_in]
    t = torch.einsum('ijk, ij->ik', local_frame[node_in], t)
    r = torch.sum(local_frame[node_in] * local_frame[node_out], dim=1)
    delta = torch.abs(graph.atom2residue[node_in] - graph.atom2residue[node_out]).float() / 6
    delta = delta.unsqueeze(-1)

    return torch.cat([
        t, r, delta, 
        1 - 2 * t.abs(), 1 - 2 * r.abs(), 1 - 2 * delta.abs()
    ], dim=-1)

def forward(self, graph, input, all_loss=None, metric=None):
    hiddens = []
    layer_input = input
    if self.embedding_dim > 0:
        layer_input = self.linear(layer_input)
        layer_input = self.embedding_batch_norm(layer_input)
    if self.num_angle_bin:
        line_graph = self.spatial_line_graph(graph)
        edge_hidden = line_graph.node_feature.float()
    else:
        edge_hidden = None
    ieconv_edge_feature = self.get_ieconv_edge_feature(graph)

    for i in range(len(self.layers)):
        # edge message passing
        if self.num_angle_bin:
            edge_hidden = self.edge_layers[i](line_graph, edge_hidden)
        hidden = self.layers[i](graph, layer_input, edge_hidden)
        # ieconv layer
        if self.use_ieconv:
            hidden = hidden + self.ieconvs[i](graph, layer_input, ieconv_edge_feature)
        hidden = self.dropout(hidden)

        if self.short_cut and hidden.shape == layer_input.shape:
            hidden = hidden + layer_input

        if self.layer_norm:
            hidden = self.layer_norms[i](hidden)
        hiddens.append(hidden)
        layer_input = hidden

    if self.concat_hidden:
        node_feature = torch.cat(hiddens, dim=-1)
    else:
        node_feature = hiddens[-1]
    graph_feature = self.readout(graph, node_feature)

    return {
        "graph_feature": graph_feature,
        "node_feature": node_feature
    }