def _forward_encoder(self, seq, adjs):
x = seq
for i in range(self.n_layers):
x = getattr(self, f"encoder_pre_{i}")(x)
x_seq_half = x[:, :x.shape[1] // 2, :]
x_adj_half = x[:, x.shape[1] // 2:, :]
x_adj_half = getattr(self, f"encoder_0_{i}")(x_adj_half, i, adjs)
x = torch.cat([x_seq_half, x_adj_half], 1)
x = getattr(self, f"encoder_1_{i}")(x, i, adjs)
x = getattr(self, f"encoder_post_{i}")(x)
# logger.debug(f"Encoder layer: {i}, input shape: {x.shape}")
# ...
x = x.maxpool()
if self.bottleneck_size > 0:
pad_amount = padding_amount(x, 2048) # 4 x 512
if pad_amount:
x = F.pad(x, (0, pad_amount))
x = reshape_internal_dim(x, 1, 512)
x = self.conv_in(x)
# x = self.linear_in(x.transpose(1, 2).contiguous())
return x
def forward_bak(self, seq, adjs):
x = seq
for i in range(self.n_layers):
x = getattr(self, f"encoder_pre_{i}")(x)
x_seq = x[:, :x.shape[1] // 2, :]
x_seq = getattr(self, f"encoder_seq_{i}")(x_seq, i, adjs)
x_adj = x[:, x.shape[1] // 2:, :]
x_adj = getattr(self, f"encoder_adj_{i}")(x_adj, i, adjs)
x = torch.cat([x_seq, x_adj], 1)
x = getattr(self, f"encoder_post_{i}")(x)
# logger.debug(f"Encoder layer: {i}, input shape: {x.shape}")
if self.bottleneck_size == 0:
x = x.max(2, keepdim=True)[0]
x = self.linear_in(x.squeeze()).unsqueeze(-1)
else:
raise NotImplementedError
pad_amount = padding_amount(x, 2048) # 4 x 512
if pad_amount:
x = F.pad(x, (0, pad_amount))
x = reshape_internal_dim(x, 1, 512)
x = self.conv_in(x)
return x
def forward(self, seq, adjs):
x = seq
# Encode
for i in range(self.n_layers):
x = getattr(self, f"encoder_pre_{i}")(x)
x_seq = x[:, :x.shape[1] // 2, :]
x_adj = x[:, x.shape[1] // 2:, :]
x_seq = getattr(self, f"encoder_downsample_seq_{i}")(x_seq, i,
adjs)
x_adj = getattr(self, f"encoder_downsample_adj_{i}")(x_adj, i,
adjs)
x = torch.cat([x_seq, x_adj], 1)
x = getattr(self, f"encoder_post_{i}")(x)
logger.debug(f"{i}, {x.shape}")
# === Linear ===
if self.bottleneck_size > 0:
pad_amount = padding_amount(x, 2048) # 4 x 512
if pad_amount:
x = F.pad(x, (0, pad_amount))
n_features = x.shape[1]
x = reshape_internal_dim(x, 1, 512)
x = self.conv_in(x)
# x = self.linear_in(x.transpose(1, 2).contiguous())
assert 0.9 < (np.prod(x.shape) /
(seq.shape[2] / 64 * self.bottleneck_size)) <= 1.1, (
x.shape[1:],
seq.shape[2] / 64 * self.bottleneck_size,
)
# x = self.linear_out(x).transpose(2, 1).contiguous()
x = self.conv_out(x)
x = reshape_internal_dim(x, 1, n_features)
if pad_amount:
x = x[:, :, :-pad_amount]
# Decode
for i in range(self.n_layers - 1, -1, -1):
# x = getattr(self, f'decoder_pre_{i}')(x)
x_seq = x[:, :x.shape[1] // 2, :]
x_adj = x[:, x.shape[1] // 2:, :]
x_seq = getattr(self, f"decoder_upsample_seq_{i}")(x_seq, i, adjs)
x_adj = getattr(self, f"decoder_upsample_adj_{i}")(x_adj, i, adjs)
x = torch.cat([x_seq, x_adj], 1)
x = getattr(self, f"decoder_post_{i}")(x)
logger.debug(f"{i}, {x.shape}")
return x
def forward(self, seq, adjs):
x = seq
# Encode
for i in range(self.n_layers):
x = getattr(self, f"encoder_pre_{i}")(x)
x = getattr(self, f"encoder_downsample_{i}")(x, i, adjs)
x = getattr(self, f"encoder_post_{i}")(x)
# Linear
if self.bottleneck_size > 0:
pad_amount = padding_amount(x, 2048) # 4 x 512
if pad_amount:
x = F.pad(x, (0, pad_amount))
n_features = x.shape[1]
x = reshape_internal_dim(x, 1, 512)
x = self.conv_in(x)
# x = self.linear_in(x.transpose(1, 2).contiguous())
assert 0.9 < (np.prod(x.shape) /
(seq.shape[2] / 64 * self.bottleneck_size)) <= 1.1, (
x.shape[1:],
seq.shape[2] / 64 * self.bottleneck_size,
)
# x = self.linear_out(x).transpose(2, 1).contiguous()
x = self.conv_out(x)
x = reshape_internal_dim(x, 1, n_features)
if pad_amount:
x = x[:, :, :-pad_amount]
# Decode
for i in range(self.n_layers - 1, -1, -1):
x = getattr(self, f"decoder_pre_{i}")(x)
x = getattr(self, f"decoder_upsample_{i}")(x, i, adjs)
x = getattr(self, f"decoder_post_{i}")(x)
return x
def forward(self, seq, adjs):
x = seq
# Encode
for i in range(self.n_layers):
x_list = []
start = 0
for adj in adjs:
seq_len = adj[i].shape[1]
end = start + seq_len
assert end <= x.shape[2]
xd = x[:, :, start:end]
xd = getattr(self, f"encoder_{i}")(xd, seq_len)
assert xd.shape[2] == adj[i + 1].shape[1]
x_list.append(xd)
start = end
assert end == x.shape[2]
x = torch.cat(x_list, 2)
x = getattr(self, f"encoder_post_{i}")(x)
logger.debug(f"{i}, {x.shape}")
# Linear
# x_list = []
# start = 0
# for adj in adjs:
# seq_len = adj[i + 1]
# end = start + seq_len
# assert end <= x.shape[2]
# xd = x[:, :, start:end]
# pad_amount = padding_amount(xd, 2048)
# if pad_amount:
# xd = F.pad(xd, (0, pad_amount))
# xd = unfold_to(xd, 2048)
# xd = self.linear_in(xd)
# # xd = self.conv(xd)
# assert np.prod(xd.shape) == 64, xd.shape
# # xd = self.convt(xd)
# xd = self.linear_out(xd)
# xd = unfold_from(xd, n_features)
# if pad_amount:
# xd = xd[:, :, :-pad_amount]
# x_list.append(xd)
# start = end
# assert end == x.shape[2]
# x = torch.cat(x_list, 2)
# === Linear ===
if self.bottleneck_size > 0:
pad_amount = padding_amount(x, 2048) # 4 x 512
if pad_amount:
x = F.pad(x, (0, pad_amount))
n_features = x.shape[1]
x = reshape_internal_dim(x, 1, 512)
x = self.conv_in(x)
# x = self.linear_in(x.transpose(1, 2).contiguous())
assert 0.9 < (np.prod(x.shape) / (seq.shape[2] / 64 * self.bottleneck_size)) <= 1.1, (
x.shape[1:],
seq.shape[2] / 64 * self.bottleneck_size,
)
# x = self.linear_out(x).transpose(2, 1).contiguous()
x = self.conv_out(x)
x = reshape_internal_dim(x, 1, n_features)
if pad_amount:
x = x[:, :, :-pad_amount]
# Decode
for i in range(self.n_layers - 1, -1, -1):
x_list = []
start = 0
for adj in adjs:
seq_len = adj[i].shape[1]
conv_seq_len = adj[i + 1].shape[1]
end = start + conv_seq_len
assert end <= x.shape[2]
xd = x[:, :, start:end]
xd = getattr(self, f"decoder_{i}")(xd, seq_len)
x_list.append(xd)
start = end
assert end == x.shape[2]
x = torch.cat(x_list, 2)
x = getattr(self, f"decoder_post_{i}")(x)
logger.debug(f"{i}, {x.shape}")
return x