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comment some output statements

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mhz
2024-07-01 10:04:07 +02:00
parent dd31fda8d5
commit 817ef04c58
1 changed files with 13 additions and 13 deletions
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26
graph_dit/models/transformer.py
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@@ -87,7 +87,7 @@ class Denoiser(nn.Module):
def forward(self, x, e, node_mask, y, t, unconditioned): def forward(self, x, e, node_mask, y, t, unconditioned):
print("Denoiser Forward") print("Denoiser Forward")
print(x.shape, e.shape, y.shape, t.shape, unconditioned) # print(x.shape, e.shape, y.shape, t.shape, unconditioned)
force_drop_id = torch.zeros_like(y.sum(-1)) force_drop_id = torch.zeros_like(y.sum(-1))
# drop the nan values # drop the nan values
force_drop_id[torch.isnan(y.sum(-1))] = 1 force_drop_id[torch.isnan(y.sum(-1))] = 1
@@ -98,32 +98,32 @@ class Denoiser(nn.Module):
# bs = batch size, n = number of nodes # bs = batch size, n = number of nodes
bs, n, _ = x.size() bs, n, _ = x.size()
x = torch.cat([x, e.reshape(bs, n, -1)], dim=-1) x = torch.cat([x, e.reshape(bs, n, -1)], dim=-1)
print("X after concat with E") # print("X after concat with E")
print(x.shape) # print(x.shape)
# self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False) # self.x_embedder = nn.Linear(Xdim + max_n_nodes * Edim, hidden_size, bias=False)
x = self.x_embedder(x) x = self.x_embedder(x)
print("X after x_embedder") # print("X after x_embedder")
print(x.shape) # print(x.shape)
# self.t_embedder = TimestepEmbedder(hidden_size) # self.t_embedder = TimestepEmbedder(hidden_size)
c1 = self.t_embedder(t) c1 = self.t_embedder(t)
print("C1 after t_embedder") # print("C1 after t_embedder")
print(c1.shape) # print(c1.shape)
for i in range(1, self.ydim): for i in range(1, self.ydim):
if i == 1: if i == 1:
c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t) c2 = self.y_embedding_list[i-1](y[:, :2], self.training, force_drop_id, t)
else: else:
c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t) c2 = c2 + self.y_embedding_list[i-1](y[:, i:i+1], self.training, force_drop_id, t)
print("C2 after y_embedding_list") # print("C2 after y_embedding_list")
print(c2.shape) # print(c2.shape)
print("C1 + C2") # print("C1 + C2")
c = c1 + c2 c = c1 + c2
print(c.shape) # print(c.shape)
for i, block in enumerate(self.encoders): for i, block in enumerate(self.encoders):
x = block(x, c, node_mask) x = block(x, c, node_mask)
print("X after block") # print("X after block")
print(x.shape) # print(x.shape)
# X: B * N * dx, E: B * N * N * de # X: B * N * dx, E: B * N * N * de
X, E, y = self.out_layer(x, x_in, e_in, c, t, node_mask) X, E, y = self.out_layer(x, x_in, e_in, c, t, node_mask)