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add the idea of guidance

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mhz
2024-07-25 22:09:03 +02:00
parent fcdd8efc4f
commit 55ff19421d
2 changed files with 85 additions and 4 deletions
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53
graph_dit/datasets/dataset.py
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@@ -8,6 +8,7 @@ import os
import os.path as osp
import pathlib
import json
import random
import torch
import torch.nn.functional as F
@@ -49,6 +50,9 @@ op_type = {
'none': 5,
'output': 6,
}
num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
class DataModule(AbstractDataModule):
def __init__(self, cfg):
self.datadir = cfg.dataset.datadir
@@ -676,6 +680,52 @@ class Dataset(InMemoryDataset):
data_list = []
len_data = len(self.api)
def check_valid_graph(nodes, edges):
if len(nodes) != edges.shape[0] or len(nodes) != edges.shape[1]:
return False
if nodes[0] != 'input' or nodes[-1] != 'output':
return False
for i in range(0, len(nodes)):
if edges[i][i] == 1:
return False
for i in range(1, len(nodes) - 1):
if nodes[i] not in op_type or nodes[i] == 'input' or nodes[i] == 'output':
return False
for i in range(0, len(nodes)):
for j in range(i, len(nodes)):
if edges[i, j] == 1 and nodes[j] == 'input':
return False
for i in range(0, len(nodes)):
for j in range(i, len(nodes)):
if edges[i, j] == 1 and nodes[i] == 'output':
return False
flag = 0
for i in range(0,len(nodes)):
if edges[i,-1] == 1:
flag = 1
break
if flag == 0: return False
return True
def generate_flex_adj_mat(ori_nodes, ori_edges, max_nodes=12, min_nodes=8,random_ratio=0.5):
nasbench_201_node_num = 8
# random.seed(random_seed)
nodes_num = random.randint(min_nodes, max_nodes)
# print(f'arch_str: {arch_str}, \nmax_nodes: {max_nodes}, min_nodes: {min_nodes}, nodes_num: {nodes_num},random_seed: {random_seed},random_ratio: {random_ratio}')
add_num = nodes_num - nasbench_201_node_num
# ori_nodes, ori_edges = parse_architecture_string(arch_str)
add_nodes = [op for op in random.choices(num_to_op[1:-1], k=add_num)]
# print(add_nodes)
nodes = ori_nodes[:-1] + add_nodes + ['output']
edges = np.zeros((nodes_num , nodes_num))
edges[:6, :6] = ori_edges[:6, :6]
edges[0:8, -1] = ori_edges[0:8 , -1]
for i in range(0, nodes_num):
for j in range(max(7,i + 1), nodes_num):
rand = random.random()
if rand < random_ratio:
edges[i, j] = 1
return nodes, edges
def graph_to_graph_data(graph):
ops = graph[1]
@@ -746,6 +796,9 @@ class Dataset(InMemoryDataset):
})
data = graph_to_graph_data((adj_matrix, ops))
data_list.append(data)
# new_adj, new_ops = generate_flex_adj_mat(ori_nodes=ops, ori_edges=adj_matrix, max_nodes=12, min_nodes=8, random_ratio=0.5)
# data_list.append(graph_to_graph_data((new_adj, new_ops)))
pbar.update(1)
for graph in graph_list:

36
graph_dit/diffusion_model.py
View File

@@ -134,7 +134,7 @@ class Graph_DiT(pl.LightningModule):
loss = self.train_loss(masked_pred_X=pred.X, masked_pred_E=pred.E, pred_y=pred.y,
true_X=X, true_E=E, true_y=data.y, node_mask=node_mask,
log=i % self.log_every_steps == 0)
# print(f'training loss: {loss}, epoch: {self.current_epoch}, batch: {i}\n, pred type: {type(pred)}, pred.X shape: {type(pred.X)}, {pred.X.shape}, pred.E shape: {type(pred.E)}, {pred.E.shape}')
self.train_metrics(masked_pred_X=pred.X, masked_pred_E=pred.E, true_X=X, true_E=E,
log=i % self.log_every_steps == 0)
self.log(f'loss', loss, batch_size=X.size(0), sync_dist=True)
@@ -601,7 +601,8 @@ class Graph_DiT(pl.LightningModule):
# Normalize predictions
pred_X = F.softmax(pred.X, dim=-1) # bs, n, d0
pred_E = F.softmax(pred.E, dim=-1) # bs, n, n, d0
pred_E = F.softmax(pred.E, dim=-1) # bs, n, n, d0
# gradient
# Retrieve transitions matrix
Qtb = self.transition_model.get_Qt_bar(alpha_t_bar, self.device)
@@ -629,25 +630,52 @@ class Graph_DiT(pl.LightningModule):
prob_E = prob_E.reshape(bs, n, n, pred_E.shape[-1])
return prob_X, prob_E
# diffusion nag: P_t(G_{t-1} |G_t, C) = P_t(G_{t-1} |G_t) + P_t(C | G_{t-1}, G_t)
# with condition = P_t(G_{t-1} |G_t, C)
# with condition = P_t(A_{t-1} |A_t, y)
prob_X, prob_E = get_prob(noisy_data)
### Guidance
if self.guidance_target is not None and self.guide_scale is not None and self.guide_scale != 1:
uncon_prob_X, uncon_prob_E = get_prob(noisy_data, unconditioned=True)
prob_X = uncon_prob_X * (prob_X / uncon_prob_X.clamp_min(1e-10)) ** self.guide_scale
prob_X = uncon_prob_X * (prob_X / uncon_prob_X.clamp_min(1e-10)) ** self.guide_scale
prob_E = uncon_prob_E * (prob_E / uncon_prob_E.clamp_min(1e-10)) ** self.guide_scale
prob_X = prob_X / prob_X.sum(dim=-1, keepdim=True).clamp_min(1e-10)
prob_E = prob_E / prob_E.sum(dim=-1, keepdim=True).clamp_min(1e-10)
assert ((prob_X.sum(dim=-1) - 1).abs() < 1e-4).all()
assert ((prob_E.sum(dim=-1) - 1).abs() < 1e-4).all()
sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
# sample multiple times and get the best score arch...
sample_num = 100
best_arch = None
best_score = -1e8
for i in range(sample_num):
sampled_s = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
score = get_score(sampled_s)
if score > best_score:
best_score = score
best_arch = sampled_s
X_s = F.one_hot(sampled_s.X, num_classes=self.Xdim_output).float()
E_s = F.one_hot(sampled_s.E, num_classes=self.Edim_output).float()
# NASWOT score
target_score = torch.tensor([3000.0])
# compute loss mse(cur_score - target_score)
# loss backward = gradient
# get prob.X, prob_E gradient
# update prob.X prob_E with using gradient
assert (E_s == torch.transpose(E_s, 1, 2)).all()
assert (X_t.shape == X_s.shape) and (E_t.shape == E_s.shape)