def forward(self, pos, batch):
radius = 0.2
edge_index = radius_graph(pos, r=radius, batch=batch)
x = F.relu(self.features[0](None, pos, edge_index))
idx = fps(pos, batch, ratio=0.5)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 0.4
edge_index = radius_graph(pos, r=radius, batch=batch)
x = F.relu(self.features[1](x, pos, edge_index))
idx = fps(pos, batch, ratio=0.25)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 1
edge_index = radius_graph(pos, r=radius, batch=batch)
x = F.relu(self.features[2](x, pos, edge_index))
x = global_max_pool(x, batch)
feat = x
x = F.relu(self.classifier[0](x))
x = F.relu(self.classifier[1](x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.classifier[2](x)
x2 = F.relu(self.discriminator[0](feat))
x2 = F.dropout(x2, p=0.5, training=self.training)
x2 = self.discriminator[1](x2)
return F.log_softmax(x, dim=-1), F.log_softmax(x2, dim=-1)
def forward(self, pos, batch):
radius = 0.2
edge_index = radius_graph(pos, r=radius, batch=batch)
x = F.relu(self.conv1(None, pos, edge_index))
idx = fps(pos, batch, ratio=0.5)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 0.4
edge_index = radius_graph(pos, r=radius, batch=batch)
x = F.relu(self.conv2(x, pos, edge_index))
idx = fps(pos, batch, ratio=0.25)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 1
edge_index = radius_graph(pos, r=radius, batch=batch)
x = F.relu(self.conv3(x, pos, edge_index))
x = global_max_pool(x, batch)
x = F.relu(self.lin1(x))
x = F.relu(self.lin2(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
return F.log_softmax(x, dim=-1)
def forward(self, pos, batch):
x = pos.new_ones((pos.size(0), 1))
radius = 0.2
edge_index = radius_graph(pos, r=radius, batch=batch)
pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
pseudo = pseudo.clamp(min=0, max=1)
x = F.elu(self.conv1(x, edge_index, pseudo))
idx = fps(pos, batch, ratio=0.5)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 0.4
edge_index = radius_graph(pos, r=radius, batch=batch)
pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
pseudo = pseudo.clamp(min=0, max=1)
x = F.elu(self.conv2(x, edge_index, pseudo))
idx = fps(pos, batch, ratio=0.25)
x, pos, batch = x[idx], pos[idx], batch[idx]
radius = 1
edge_index = radius_graph(pos, r=radius, batch=batch)
pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
pseudo = pseudo.clamp(min=0, max=1)
x = F.elu(self.conv3(x, edge_index, pseudo))
x = global_mean_pool(x, batch)
x = F.elu(self.lin1(x))
x = F.elu(self.lin2(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin3(x)
return F.log_softmax(x, dim=-1)
def get(self, idx):
adj_all = torch.zeros((self.max_num_nodes, self.max_num_nodes),
dtype=torch.float)
# transform
if self.dynamic_graph:
data = torch.load(osp.join(self.processed_root, self.idxlist[idx]))
num_nodes = data.num_nodes
dist_path = os.path.join(self.root, 'proto', 'distance',
self.setting.dataset, self.idxlist[idx])
choice, sample_num_node = self._sampling(num_nodes,
self.sampling_ratio,
dist_path)
for key, item in data:
if torch.is_tensor(item) and item.size(0) == num_nodes:
data[key] = item[choice]
# generate the graph
if self.graph_sampler == 'knn':
edge_index = radius_graph(data.pos, self.max_edge_distance,
None, True, self.max_neighbours)
adj = sparse_to_dense(edge_index)
else:
raise NotImplementedError
adj[adj > 0] = 1
adj_all[:sample_num_node, :sample_num_node] = adj
else:
data = torch.load(
osp.join(self.processed_fix_data_root, str(self.epoch),
self.idxlist[idx]))
if self.graph_sampler == 'knn':
edge_index = radius_graph(data.pos, self.max_edge_distance,
None, True, self.max_neighbours)
adj = sparse_to_dense(edge_index)
else:
raise NotImplementedError
num_nodes = adj.shape[0]
adj_all[:num_nodes, :num_nodes] = adj
feature = data.x
if self.feature_type == 'c':
feature = feature[:, -2:]
elif self.feature_type == 'a':
feature = feature[:, :-2]
num_feature = feature.shape[1]
feature = (feature - self.mean) / self.std
feature_all = torch.zeros((self.max_num_nodes, num_feature),
dtype=torch.float)
if self.dynamic_graph:
feature_all[:sample_num_node] = feature
else:
feature_all[:num_nodes] = feature
label = data.y
idx = torch.tensor(idx)
return {
'adj': adj_all,
'feats': feature_all,
'label': label,
'num_nodes': sample_num_node if self.dynamic_graph else num_nodes,
'patch_idx': idx
}
def forward(self, batch_data):
z, pos, batch = batch_data.z, batch_data.pos, batch_data.batch
if self.energy_and_force:
pos.requires_grad_()
edge_index = radius_graph(pos, r=self.cutoff, batch=batch)
num_nodes = z.size(0)
dist, angle, torsion, i, j, idx_kj, idx_ji = xyztodat(pos,
edge_index,
num_nodes,
use_torsion=True)
emb = self.emb(dist, angle, torsion, idx_kj)
#Initialize edge, node, graph features
e = self.init_e(z, emb, i, j)
v = self.init_v(e, i, num_nodes=pos.size(0))
u = self.init_u(torch.zeros_like(scatter(v, batch, dim=0)), v,
batch) #scatter(v, batch, dim=0)
for update_e, update_v, update_u in zip(self.update_es, self.update_vs,
self.update_us):
e = update_e(e, emb, idx_kj, idx_ji)
v = update_v(e, i)
u = update_u(u, v, batch) #u += scatter(v, batch, dim=0)
return u
def forward(self, pos, p, f=None, batch=None):
N = pos.shape[0]
# build graph
# col, row = knn_graph(pos, k=self.kernel_size, batch=batch)
col, row = radius_graph(pos,
r=self.radius,
batch=batch,
max_num_neighbors=self.kernel_size)
u = -torch.log(p) # unary
# compute weights
f = f.unsqueeze(0).repeat(self.num_kernels, 1, 1)
f = torch.bmm(f, self.F) # [K, N, H]
f = f.permute((1, 0, 2)) # [N, K, H]
f = f[col] - f[row] # [E, K, H]
w = torch.exp(-torch.sum(f**2, dim=-1)) # [E, K]
w = torch.mm(w, self.W) # [E, 1]
# mean field steps
q = p # initialize q [N, L]
for _ in range(self.steps):
q = scatter_add(q[col] * w, row, dim=0,
dim_size=N) # message passing [N, L]
q = torch.mm(q, self.C) # compatibility transformation [N, L]
q = torch.softmax(-u - q, dim=-1)
return q
def forward(self, data, phase):
# pdb.set_trace()
x, edge_index, pseudo, batch = data.x, data.edge_index, data.pos, data.batch
# radius = np.random.randint(1,4) if phase == 'train' else 4
radius = 4
edge_index = radius_graph(pseudo.float(), r=radius, batch=batch)
x = self.lin01(x)
x_g1 = F.relu(self.conv1(x, edge_index))
x = self.graph_norm_1(x_g1, batch)
x = F.dropout(x, p=0.8, training=phase == 'train')
x_g2 = F.relu(self.conv2(x, edge_index))
x = self.graph_norm_2(x_g2, batch)
x = F.dropout(x, p=0.8, training=phase == 'train')
x_g3 = F.relu(self.conv3(x, edge_index))
x = self.graph_norm_3(x_g3, batch)
# pdb.set_trace()
x = self.pool(x, batch)
x = self.lin2(x)
x = self.lin3(x)
# pdb.set_trace()
return {'A2': x}
def forward(self, data):
# Get node features
if self.embedding.device != data.atomic_numbers.device:
self.embedding = self.embedding.to(data.atomic_numbers.device)
data.x = self.embedding[data.atomic_numbers.long() - 1]
# Construct graph, get edge features
data.edge_index = radius_graph(data.pos,
r=self.cutoff,
batch=data.batch)
row, col = data.edge_index
pos = data.pos
if self.regress_forces:
pos = pos.requires_grad_(True)
data.edge_weight = (pos[row] - pos[col]).norm(dim=-1)
data.edge_attr = self.distance_expansion(data.edge_weight)
# Forward pass through the network
mol_feats = self._convolve(data)
mol_feats = self.conv_to_fc(mol_feats)
if hasattr(self, "fcs"):
mol_feats = self.fcs(mol_feats)
energy = self.fc_out(mol_feats)
if self.regress_forces:
forces = -1 * (torch.autograd.grad(
energy,
pos,
grad_outputs=torch.ones_like(energy),
create_graph=True,
)[0])
return energy, forces
else:
return energy
def __call__(self, sample):
if self._visualization:
keys = sorted([x for x in dir(sample) if 'edge_index' == x])
else:
keys = sorted([
x for x in dir(sample)
if 'edge_index' in x and 'dilated' not in x
])
pos_keys = sorted([x for x in dir(sample) if 'pos' in x])
for level, key in enumerate(keys):
radius_edges = radius_graph(sample[pos_keys[level]],
self._radius[level],
max_num_neighbors=self._max_neigh)
if not self._override:
sample[key.replace('edge_index',
'euclidean_edge_index')] = radius_edges
else:
# in a single conv architecture, we only have to keep track of one edge set
sample[key] = radius_edges
if not self._keep_pos:
# we do not need to now actual spatial positions of later layers -> delete them
keys = sorted([x for x in dir(sample) if 'pos_' in x])
for key in keys:
delattr(sample, key)
return sample
def get(self, idx):
# only support batch size = 1
data = torch.load(osp.join(self.processed_root, self.idxlist[idx]))
# generate the graph
if self.graph_sampler == 'knn':
edge_index = radius_graph(data.pos, self.max_edge_distance, None,
True, self.max_neighbours)
adj_all = sparse_to_dense(edge_index)
else:
raise NotImplementedError
adj_all[adj_all > 0] = 1
num_nodes = adj_all.shape[0]
feature = data.x
if self.feature_type == 'c':
feature = feature[:, -2:]
elif self.feature_type == 'a':
feature = feature[:, :-2]
feature = (feature - self.mean) / self.std
label = data.y
idx = torch.tensor([idx])
return {
'adj': adj_all,
'feats': feature,
'label': label,
'num_nodes': num_nodes if self.dynamic_graph else num_nodes,
'patch_idx': idx
}
def forward(self, atomic_ns, coords, batch_node_vec):
# processing sequence
# dist -> rbf
# dist+angle -> sbf
#
# rbf -> emb
# rbf + sbf -> interaction
#
# sum(emb, interactions) -> output mlp
# functions needed
# interatomic dist
# neighbour angles from triplets
# dist to rbf
edge_index = radius_graph(coords, self.cutoff_val, batch_node_vec)
num_nodes = atomic_ns.size(0)
dists, angles, node_is, node_js, kj, ji = xyz_to_dg(
coords, edge_index, num_nodes)
dist_embs, angle_embs = self.emb(dists, angles, kj)
# init edge, node, graph feats
#edge_attr = self.
return
def forward(self, z, pos, batch=None):
""""""
edge_index = radius_graph(pos, r=self.cutoff, batch=batch,
max_num_neighbors=self.max_num_neighbors)
i, j, idx_i, idx_j, idx_k, idx_kj, idx_ji = self.triplets(
edge_index, num_nodes=z.size(0))
# Calculate distances.
dist = (pos[i] - pos[j]).pow(2).sum(dim=-1).sqrt()
# Calculate angles.
pos_i = pos[idx_i]
pos_ji, pos_ki = pos[idx_j] - pos_i, pos[idx_k] - pos_i
a = (pos_ji * pos_ki).sum(dim=-1)
b = torch.cross(pos_ji, pos_ki).norm(dim=-1)
angle = torch.atan2(b, a)
rbf = self.rbf(dist)
sbf = self.sbf(dist, angle, idx_kj)
# Embedding block.
x = self.emb(z, rbf, i, j)
P = self.output_blocks[0](x, rbf, i, num_nodes=pos.size(0))
# Interaction blocks.
for interaction_block, output_block in zip(self.interaction_blocks,
self.output_blocks[1:]):
x = interaction_block(x, rbf, sbf, idx_kj, idx_ji)
P += output_block(x, rbf, i)
return P.sum(dim=0) if batch is None else scatter(P, batch, dim=0)
def gen_graph(
node_feature_path: str,
node_feature_cols: List[str],
label: int,
max_neighbours: int = 8,
radius: float = 50,
) -> Data:
""" Generates graph from node features as pytorch object
Arguments:
node_feature_path {str} -- Path to node features for tile
node_feature_cols {List[str]} -- list of features to use
label {int} -- 1, or 0 for cancer/no-cancer
Keyword Arguments:
max_neighbours {int} -- k-max neighbors for graph edge (default: {8})
radius {float} -- max pixel radius for graph edge (default: {50})
Returns:
Data object for use with pytorch geometric graph convolution
"""
features = pd.read_csv(
node_feature_path,
converters={
"diagnostics_Mask-original_CenterOfMass": ast.literal_eval
}
)
# Normalize Features
f_df = features[node_feature_cols]
f_norm_df = (f_df - f_df.mean()) / (f_df.max() - f_df.min())
f_norm_nafilled = f_norm_df.fillna(0)
# Set centroid coordinates from node features
coordinates = torch.tensor(
features['diagnostics_Mask-original_CenterOfMass'].tolist()
)
# Set node features from normalized features
node_features = torch.tensor(
f_norm_nafilled.astype(float).values,
dtype=torch.float32
)
# Initialize data for pytorch
y = torch.tensor([label], dtype=torch.long)
data = Data(
x=node_features,
pos=coordinates,
y=y,
num_classes=2
)
# Create Graph
data.edge_index = radius_graph(
data.pos,
radius,
None,
True,
max_neighbours
)
return data
def pt_to_gexf(numpyfile,
savepath,
sample=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, 1]):
# data = torch.load(torchfile)
# feature = data.x.numpy()
coordinates = np.load(numpyfile)
coordinates = coordinates / 1500.
coordinates = torch.from_numpy(coordinates).to(torch.float)
num_nodes = coordinates.shape[0]
for sampling_ratio in sample:
num_sample = int(num_nodes * sampling_ratio)
choice = np.random.choice(num_nodes, num_sample, replace=True)
coordinates_new = coordinates[choice]
edge_index = radius_graph(coordinates_new, 100, None, True, 8)
adj = sparse_to_dense(edge_index)
adj = adj.numpy()
coordinates_new = coordinates_new.numpy()
G = nx.from_numpy_matrix(adj)
x = dict(enumerate(coordinates_new[:, 0].tolist(), 0))
y = dict(enumerate(coordinates_new[:, 1].tolist(), 0))
nx.set_node_attributes(G, x, 'x')
nx.set_node_attributes(G, y, 'y')
nx.write_gexf(
G,
os.path.join(
savepath,
str(sampling_ratio) +
numpyfile.split('/')[-1].replace('.npy', '.gexf')))
def get(self, idx):
epoch = self.epoch if self.split == 'train' else self.val_epoch
if self.dynamic_graph:
data = torch.load(osp.join(self.processed_root, self.idxlist[idx]))
else:
data = torch.load(
osp.join(self.processed_fix_data_root, str(epoch),
self.idxlist[idx]))
if self.feature_type == 'c':
data.x = data.x[:, -2:]
elif self.feature_type == 'a':
data.x = data.x[:, :-2]
if self.dynamic_graph:
num_nodes = data.num_nodes
dist_path = os.path.join(self.root, 'proto', 'distance',
self.setting.dataset, self.idxlist[idx])
choice, _ = self._sampling(num_nodes, self.sampling_ratio,
dist_path)
for key, item in data:
if torch.is_tensor(item) and item.size(0) == num_nodes:
data[key] = item[choice]
if self.graph_sampler == 'knn':
edge_index = radius_graph(data.pos, self.max_edge_distance, None,
True, self.max_neighbours)
data.edge_index = edge_index
else:
raise NotImplementedError
data.patch_idx = torch.tensor([idx])
data.x = (data.x - self.mean) / self.std
return data
def __call__(self, data):
edge_index = radius_graph(data.pos,
self.radius,
None,
max_num_neighbors=64)
edge_index = to_undirected(edge_index, num_nodes=data.pos.size(0))
data.edge_index = edge_index
return data
def forward(self, data):
self.device = data.pos.device
self.num_atoms = len(data.batch)
self.batch_size = len(data.natoms)
atomic_numbers = data.atomic_numbers.long()
pos = data.pos
if self.regress_forces:
pos = pos.requires_grad_(True)
if self.otf_graph:
edge_index, cell_offsets, neighbors = radius_graph_pbc(
data, self.cutoff, 100)
data.edge_index = edge_index
data.cell_offsets = cell_offsets
data.neighbors = neighbors
if self.use_pbc:
assert (atomic_numbers.dim() == 1
and atomic_numbers.dtype == torch.long)
out = get_pbc_distances(
pos,
data.edge_index,
data.cell,
data.cell_offsets,
data.neighbors,
return_distance_vec=True,
)
edge_index = out["edge_index"]
edge_distance = out["distances"]
edge_distance_vec = out["distance_vec"]
else:
edge_index = radius_graph(pos, r=self.cutoff, batch=data.batch)
j, i = edge_index
edge_distance_vec = pos[j] - pos[i]
edge_distance = edge_distance_vec.norm(dim=-1)
edge_index, edge_distance, edge_distance_vec = self._filter_edges(
edge_index,
edge_distance,
edge_distance_vec,
self.max_num_neighbors,
)
outputs = self._forward_helper(data, edge_index, edge_distance,
edge_distance_vec)
if self.show_timing_info is True:
torch.cuda.synchronize()
print("Memory: {}\t{}\t{}".format(
len(edge_index[0]),
torch.cuda.memory_allocated() / (1000 * len(edge_index[0])),
torch.cuda.max_memory_allocated() / 1000000,
))
return outputs
def __call__(self, data):
data.edge_attr = None
batch = data.batch if 'batch' in data else None
edge_index = radius_graph(data.pos, self.r, batch, self.loop,
self.max_num_neighbors)
data.edge_index = edge_index
return data
def generate_random_graph(n, radius, max_num_neighbors, batch_size, device):
# arrange for points to fit more inside the unit square
batch = None
num_nodes = th.tensor([n], device=device, dtype=th.long)
x = 0.90 * (th.rand(n, 2, device=device) + 0.05)
edge_index = radius_graph(
x, radius, batch=batch, loop=True,
max_num_neighbors=max_num_neighbors) # COO format
return x, edge_index, num_nodes, batch
def forward(self, data):
# Get node features
if self.embedding.device != data.atomic_numbers.device:
self.embedding = self.embedding.to(data.atomic_numbers.device)
data.x = self.embedding[data.atomic_numbers.long() - 1]
pos = data.pos
if self.regress_forces:
pos = pos.requires_grad_(True)
if self.otf_graph:
edge_index, cell_offsets, neighbors = radius_graph_pbc(
data, self.cutoff, 50, data.pos.device
)
data.edge_index = edge_index
data.cell_offsets = cell_offsets
data.neighbors = neighbors
if self.use_pbc:
out = get_pbc_distances(
pos,
data.edge_index,
data.cell,
data.cell_offsets,
data.neighbors,
)
data.edge_index = out["edge_index"]
distances = out["distances"]
else:
data.edge_index = radius_graph(
data.pos, r=self.cutoff, batch=data.batch
)
row, col = data.edge_index
distances = (pos[row] - pos[col]).norm(dim=-1)
data.edge_attr = self.distance_expansion(distances)
# Forward pass through the network
mol_feats = self._convolve(data)
mol_feats = self.conv_to_fc(mol_feats)
if hasattr(self, "fcs"):
mol_feats = self.fcs(mol_feats)
energy = self.fc_out(mol_feats)
if self.regress_forces:
forces = -1 * (
torch.autograd.grad(
energy,
pos,
grad_outputs=torch.ones_like(energy),
create_graph=True,
)[0]
)
return energy, forces
else:
return energy
def forward(self, data):
# mesh = data[0]
mesh = data
if self.configs['euc_radius'] > 0:
mesh.euc_edge_index = radius_graph(mesh.pos, self.configs['euc_radius'])
else:
mesh.euc_edge_index = None
mesh_feat, global_feat = self.edgeconv_feat(mesh)
skin_logits = self.skinnet(mesh_feat)
return skin_logits
def forward(self, z, pos, batch=None):
assert z.dim() == 1 and z.dtype == torch.long
batch = torch.zeros_like(z) if batch is None else batch
h = self.embedding(z)
edge_index = radius_graph(pos,
r=self.cutoff,
batch=batch,
max_num_neighbors=1000)
row, col = edge_index
edge_vec = pos[row] - pos[col]
edge_sh = o3.spherical_harmonics(self.Rs_sh, edge_vec,
'component') / self.num_neighbors**0.5
edge_len = edge_vec.norm(dim=1)
edge_weight = self.radial(edge_len)
edge_c = (pi * edge_len / self.cutoff).cos().add(1).div(2)
for conv, act, shortcut in self.layers[:-1]:
with torch.autograd.profiler.record_function("Layer"):
if shortcut:
s = shortcut(h)
h = conv(h, edge_index, edge_weight, edge_c,
edge_sh) # convolution
h = act(h) # gate non linearity
if shortcut:
m = shortcut.output_mask
h = 0.5**0.5 * s + (1 + (0.5**0.5 - 1) * m) * h
with torch.autograd.profiler.record_function("Layer"):
h = self.layers[-1](h, edge_index, edge_weight, edge_c, edge_sh)
s = 0
for i, (mul, l, p) in enumerate(self.Rs_out):
assert mul == 1 and l == 0
if p == 1:
s += h[:, i]
if p == -1:
s += h[:, i].pow(2).mul(0.5) # odd^2 = even
h = s.view(-1, 1)
if self.mean is not None and self.std is not None:
h = h * self.std + self.mean
if self.atomref is not None:
h = h + self.atomref(z)
out = scatter(h, batch, dim=0, reduce=self.readout)
if self.scale is not None:
out = self.scale * out
return out
def __call__(self, data):
edge_index = radius_graph(data.pos, r=self.cutoff, batch=data.batch)
edge_index, _ = remove_self_loops(edge_index)
row, col = edge_index[0], edge_index[1]
edge_weight = (data.pos[row] - data.pos[col]).norm(dim=-1)
if hasattr(data, "edge_attr"):
pass
else:
data.edge_attr = edge_weight.reshape(-1, 1)
return data
def forward(self, atomic_ns, edge_index, coords, batch_node_vec):
# create initial node embs from atom charges
node_embs = self.node_emb(atomic_ns)
# create initial edge embs from coordinates
edge_index = radius_graph(coords, self.cutoff, batch_node_vec)
node_is, node_js = edge_index
edge_weights = (coords[node_is] - coords[node_js]).norm(dim = -1)
edge_embs = self.dist_emb(edge_weights) # edge_embs = interatomic dist embs
return node_embs, edge_embs, edge_weights
def forward(self, data, phase):
# pdb.set_trace()
x, edge_index, pseudo, batch = data.x, data.edge_index, data.pos, data.batch
radius = 4
edge_index = radius_graph(pseudo.float(), r=radius, batch=batch)
x = self.lin01(x)
x_g1 = F.relu(self.conv1(x, edge_index))
x = self.graph_norm_1(x_g1, batch)
# pdb.set_trace()
x = global_mean_pool(x, batch)
x = self.lin2(x)
x = self.lin3(x)
# pdb.set_trace()
return {'A2': x}
def forward(self, data):
pos = data.pos
if self.regress_forces:
pos = pos.requires_grad_(True)
batch = data.batch
x = self.embedding(data.atomic_numbers.long())
edge_index = radius_graph(pos, r=self.cutoff, batch=batch)
j, i = edge_index
idx_i, idx_j, idx_k, idx_kj, idx_ji = self.triplets(
edge_index, num_nodes=x.size(0))
# Calculate distances.
dist = (pos[i] - pos[j]).pow(2).sum(dim=-1).sqrt()
# Calculate angles.
pos_i = pos[idx_i].detach()
pos_ji, pos_ki = (
pos[idx_j].detach() - pos_i,
pos[idx_k].detach() - pos_i,
)
a = (pos_ji * pos_ki).sum(dim=-1)
b = torch.cross(pos_ji, pos_ki).norm(dim=-1)
angle = torch.atan2(b, a)
rbf = self.rbf(dist)
sbf = self.sbf(dist, angle, idx_kj)
# Embedding block.
x = self.emb(x, rbf, i, j)
P = self.output_blocks[0](x, rbf, i, num_nodes=pos.size(0))
# Interaction blocks.
for interaction_block, output_block in zip(self.interaction_blocks,
self.output_blocks[1:]):
x = interaction_block(x, rbf, sbf, idx_kj, idx_ji)
P += output_block(x, rbf, i)
energy = P.sum(dim=0) if batch is None else scatter(P, batch, dim=0)
if self.regress_forces:
forces = -1 * (torch.autograd.grad(
energy,
pos,
grad_outputs=torch.ones_like(energy),
create_graph=True,
)[0])
return energy, forces
else:
return energy
def __call__(self, data):
#pdb.set_trace()
nodes = data.x
#global_node = torch.ones([1, data.x.size()[1]])
global_node = nodes.mean(dim = 0).unsqueeze(0)
data.x = torch.cat([global_node, nodes])
pos = data.pos
#pdb.set_trace()
edge_index = radius_graph(pos, r = self.radius)
zero_index = torch.stack((torch.arange(data.x.size()[0]), torch.zeros(data.x.size()[0]).long()))[:,1:]
edge_index = torch.cat((zero_index, edge_index+1), dim = 1)
data.pos = torch.cat((torch.zeros(1, 2).int(), pos+1))
data.edge_index = edge_index
return data
def forward(self, data, phase):
# pdb.set_trace()
x, edge_index, pseudo, batch = data.x, data.edge_index, data.pos, data.batch
# radius = np.random.randint(1,4) if phase == 'train' else 4
radius = 4
edge_index = radius_graph(pseudo.float(), r=radius, batch=batch)
x = self.lin01(x)
x = F.relu(self.conv1(x, edge_index))
x = self.graph_norm_1(x, batch)
# pdb.set_trace()
x = global_mean_pool(x, batch)
x = self.lin2(x)
x = self.lin3(x)
# pdb.set_trace()
return {'A2': x}
def __call__(self, data, sigma=0.01, clip=0.05):
"""
Randomly jitter points. jittering is per point.
:param pc: B X N X 3 array, original batch of point clouds
:param sigma:
:param clip:
:return:
"""
pc = data.pos
jittered_data = torch.from_numpy(
np.clip(sigma * np.random.randn(*pc.shape), -1 * clip, clip))
jittered_data += pc
data.pos = jittered_data
data.edge_index = nn.radius_graph(x=torch.tensor(pc), r=0.05)
return data
def __call__(self, data: Data)->Data:
data.edge_attr = None
nans = torch.isnan(data.pos[:, 0])
pos = data.pos.clone()
pos[nans] = 10_000
batch = data.batch if "batch" in data else None
edge_index = radius_graph(
pos, self.r, batch, self.loop, self.max_num_neighbors,
)
drop = torch.any(edge_index[0] == torch.nonzero(nans), dim=0) | torch.any(
edge_index[1] == torch.nonzero(nans), dim=0
)
data.edge_index = edge_index[:, ~drop]
return data