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compare: iicd:83f9345028c296344bee141c3f5d065e7b48cd5b
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trainer ... 83f9345028

Author SHA1 Message Date
mhz
83f9345028 add paddings 2024-08-08 00:20:19 +02:00
1 changed files with 89 additions and 44 deletions
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graph_dit/datasets/dataset.py
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@@ -677,7 +677,7 @@ class Dataset(InMemoryDataset):
def process(self): def process(self):
source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth' source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
self.api = API(source) # self.api = API(source)
data_list = [] data_list = []
# len_data = len(self.api) # len_data = len(self.api)
@@ -710,6 +710,10 @@ class Dataset(InMemoryDataset):
return True return True
def generate_flex_adj_mat(ori_nodes, ori_edges, max_nodes=12, min_nodes=8,random_ratio=0.5): def generate_flex_adj_mat(ori_nodes, ori_edges, max_nodes=12, min_nodes=8,random_ratio=0.5):
# print(ori_nodes)
# print(ori_edges)
ori_edges = np.array(ori_edges)
# ori_nodes = np.array(ori_nodes)
nasbench_201_node_num = 8 nasbench_201_node_num = 8
# random.seed(random_seed) # random.seed(random_seed)
nodes_num = random.randint(min_nodes, max_nodes) nodes_num = random.randint(min_nodes, max_nodes)
@@ -727,12 +731,13 @@ class Dataset(InMemoryDataset):
rand = random.random() rand = random.random()
if rand < random_ratio: if rand < random_ratio:
edges[i, j] = 1 edges[i, j] = 1
return nodes, edges return edges,nodes
def get_nasbench_201_val(idx): def get_nasbench_201_val(idx):
pass pass
def graph_to_graph_data(graph, idx): # def graph_to_graph_data(graph, idx):
def graph_to_graph_data(graph):
ops = graph[1] ops = graph[1]
adj = graph[0] adj = graph[0]
nodes = [] nodes = []
@@ -753,58 +758,73 @@ class Dataset(InMemoryDataset):
edge_index = torch.tensor(edges_list, dtype=torch.long).t() edge_index = torch.tensor(edges_list, dtype=torch.long).t()
edge_type = torch.tensor(edge_type, dtype=torch.long) edge_type = torch.tensor(edge_type, dtype=torch.long)
edge_attr = edge_type edge_attr = edge_type
# y = torch.tensor([0, 0], dtype=torch.float).view(1, -1) y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
y = get_nasbench_201_val(idx) # y = get_nasbench_201_val(idx)
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i) data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
return data return data
graph_list = [] graph_list = []
with tqdm(total = len_data) as pbar: with tqdm(total = len_data) as pbar:
active_nodes = set() active_nodes = set()
for i in range(len_data): file_path = '/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
arch_info = self.api.query_meta_info_by_index(i) with open(file_path, 'r') as f:
results = self.api.query_by_index(i, 'cifar100') graph_list = json.load(f)
i = 0
for graph in graph_list:
# arch_info = self.api.query_meta_info_by_index(i)
# results = self.api.query_by_index(i, 'cifar100')
arch_info = graph['arch_str']
# results =
# nodes, edges = parse_architecture_string(arch_info.arch_str) # nodes, edges = parse_architecture_string(arch_info.arch_str)
ops, adj_matrix = parse_architecture_string(arch_info.arch_str) # ops, adj_matrix = parse_architecture_string(arch_info.arch_str, padding=4)
ops, adj_matrix, ori_nodes, ori_adj = parse_architecture_string(arch_info, padding=4)
# adj_matrix, ops = create_adj_matrix_and_ops(nodes, edges) # adj_matrix, ops = create_adj_matrix_and_ops(nodes, edges)
for op in ops: for op in ops:
if op not in active_nodes: if op not in active_nodes:
active_nodes.add(op) active_nodes.add(op)
graph_list.append({
"adj_matrix": adj_matrix,
"ops": ops,
"idx": i,
"train": [{
"iepoch": result.get_train()['iepoch'],
"loss": result.get_train()['loss'],
"accuracy": result.get_train()['accuracy'],
"cur_time": result.get_train()['cur_time'],
"all_time": result.get_train()['all_time'],
"seed": seed,
}for seed, result in results.items()],
"valid": [{
"iepoch": result.get_eval('x-valid')['iepoch'],
"loss": result.get_eval('x-valid')['loss'],
"accuracy": result.get_eval('x-valid')['accuracy'],
"cur_time": result.get_eval('x-valid')['cur_time'],
"all_time": result.get_eval('x-valid')['all_time'],
"seed": seed,
}for seed, result in results.items()],
"test": [{
"iepoch": result.get_eval('x-test')['iepoch'],
"loss": result.get_eval('x-test')['loss'],
"accuracy": result.get_eval('x-test')['accuracy'],
"cur_time": result.get_eval('x-test')['cur_time'],
"all_time": result.get_eval('x-test')['all_time'],
"seed": seed,
}for seed, result in results.items()]
})
data = graph_to_graph_data((adj_matrix, ops)) data = graph_to_graph_data((adj_matrix, ops))
if i < 3:
print(f"i={i}, data={data}")
with open(f'{i}.json', 'w') as f:
f.write(str(data.x))
f.write(str(data.edge_index))
f.write(str(data.edge_attr))
data_list.append(data) 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) new_adj, new_ops = generate_flex_adj_mat(ori_nodes=ori_nodes, ori_edges=ori_adj, max_nodes=12, min_nodes=8, random_ratio=0.5)
# data_list.append(graph_to_graph_data((new_adj, new_ops))) data_list.append(graph_to_graph_data((new_adj, new_ops)))
# graph_list.append({
# "adj_matrix": adj_matrix,
# "ops": ops,
# "arch_str": arch_info.arch_str,
# "idx": i,
# "train": [{
# "iepoch": result.get_train()['iepoch'],
# "loss": result.get_train()['loss'],
# "accuracy": result.get_train()['accuracy'],
# "cur_time": result.get_train()['cur_time'],
# "all_time": result.get_train()['all_time'],
# "seed": seed,
# }for seed, result in results.items()],
# "valid": [{
# "iepoch": result.get_eval('x-valid')['iepoch'],
# "loss": result.get_eval('x-valid')['loss'],
# "accuracy": result.get_eval('x-valid')['accuracy'],
# "cur_time": result.get_eval('x-valid')['cur_time'],
# "all_time": result.get_eval('x-valid')['all_time'],
# "seed": seed,
# }for seed, result in results.items()],
# "test": [{
# "iepoch": result.get_eval('x-test')['iepoch'],
# "loss": result.get_eval('x-test')['loss'],
# "accuracy": result.get_eval('x-test')['accuracy'],
# "cur_time": result.get_eval('x-test')['cur_time'],
# "all_time": result.get_eval('x-test')['all_time'],
# "seed": seed,
# }for seed, result in results.items()]
# })
pbar.update(1) pbar.update(1)
for graph in graph_list: for graph in graph_list:
@@ -981,18 +1001,29 @@ class Dataset_origin(InMemoryDataset):
torch.save(self.collate(data_list), self.processed_paths[0]) torch.save(self.collate(data_list), self.processed_paths[0])
def parse_architecture_string(arch_str): def parse_architecture_string(arch_str, padding=0):
# print(arch_str) # print(arch_str)
steps = arch_str.split('+') steps = arch_str.split('+')
nodes = ['input'] # Start with input node nodes = ['input'] # Start with input node
adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0], ori_adj_mat = [[0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 1 ,0 ,0], [0, 0, 0, 1, 0, 1 ,0 ,0],
[0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0]]) # [0, 0, 0, 0, 0, 0, 0, 0]])
[0, 0, 0, 0, 0, 0, 0, 0]]
# adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
adj_mat = [[0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 1 ,0 ,0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1],
# [0, 0, 0, 0, 0, 0, 0, 0]])
[0, 0, 0, 0, 0, 0, 0, 0]]
steps = arch_str.split('+') steps = arch_str.split('+')
steps_coding = ['0', '0', '1', '0', '1', '2'] steps_coding = ['0', '0', '1', '0', '1', '2']
cont = 0 cont = 0
@@ -1003,8 +1034,22 @@ def parse_architecture_string(arch_str):
assert idx == steps_coding[cont] assert idx == steps_coding[cont]
cont += 1 cont += 1
nodes.append(n) nodes.append(n)
ori_nodes = nodes.copy()
nodes.append('output') # Add output node nodes.append('output') # Add output node
return nodes, adj_mat if padding > 0:
for i in range(padding):
nodes.append('none')
for adj_row in adj_mat:
for i in range(padding):
adj_row.append(0)
# adj_mat = np.append(adj_mat, np.zeros((padding, len(nodes))))
for i in range(padding):
adj_mat.append([0] * len(nodes))
# print(nodes)
# print(adj_mat)
# print(len(adj_mat))
return nodes, adj_mat, ori_nodes, ori_adj_mat
def create_adj_matrix_and_ops(nodes, edges): def create_adj_matrix_and_ops(nodes, edges):
num_nodes = len(nodes) num_nodes = len(nodes)