add the results
This commit is contained in:
@@ -70,7 +70,7 @@ class DataModule(AbstractDataModule):
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# base_path = pathlib.Path(os.path.realpath(__file__)).parents[2]
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# except NameError:
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# base_path = pathlib.Path(os.getcwd()).parent[2]
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base_path = '/home/stud/hanzhang/nasbenchDiT'
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base_path = '/nfs/data3/hanzhang/nasbenchDiT'
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root_path = os.path.join(base_path, self.datadir)
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self.root_path = root_path
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@@ -408,6 +408,7 @@ def new_graphs_to_json(graphs, filename):
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adj = graph[0]
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n_node = len(ops)
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print(n_node)
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n_edge = len(ops)
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n_node_list.append(n_node)
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n_edge_list.append(n_edge)
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@@ -489,7 +490,7 @@ def new_graphs_to_json(graphs, filename):
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'transition_E': transition_E.tolist(),
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}
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with open(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-meta.json', 'w') as f:
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json.dump(meta_dict, f)
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return meta_dict
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@@ -655,7 +656,7 @@ def graphs_to_json(graphs, filename):
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class Dataset(InMemoryDataset):
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def __init__(self, source, root, target_prop=None, transform=None, pre_transform=None, pre_filter=None):
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self.target_prop = target_prop
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source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.source = source
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# self.api = API(source) # Initialize NAS-Bench-201 API
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# print('API loaded')
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@@ -676,8 +677,8 @@ class Dataset(InMemoryDataset):
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return [f'{self.source}.pt']
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def process(self):
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source = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.api = API(source)
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source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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# self.api = API(source)
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data_list = []
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# len_data = len(self.api)
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@@ -710,14 +711,24 @@ class Dataset(InMemoryDataset):
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return True
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def generate_flex_adj_mat(ori_nodes, ori_edges, max_nodes=12, min_nodes=8,random_ratio=0.5):
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# print(ori_nodes)
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# print(ori_edges)
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ori_edges = np.array(ori_edges)
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# ori_nodes = np.array(ori_nodes)
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nasbench_201_node_num = 8
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# random.seed(random_seed)
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nodes_num = random.randint(min_nodes, max_nodes)
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# 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}')
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add_num = nodes_num - nasbench_201_node_num
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# ori_nodes, ori_edges = parse_architecture_string(arch_str)
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add_nodes = [op for op in random.choices(num_to_op[1:-1], k=add_num)]
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add_nodes = []
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print(f'add_num: {add_num}')
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for i in range(add_num):
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add_nodes.append(random.choice(num_to_op[1:-1]))
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# print(add_nodes)
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print(f'ori_nodes[:-1]: {ori_nodes[:-1]}, add_nodes: {add_nodes}')
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print(f'len(ori_nodes[:-1]): {len(ori_nodes[:-1])}, len(add_nodes): {len(add_nodes)}')
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nodes = ori_nodes[:-1] + add_nodes + ['output']
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edges = np.zeros((nodes_num , nodes_num))
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edges[:6, :6] = ori_edges[:6, :6]
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@@ -727,12 +738,18 @@ class Dataset(InMemoryDataset):
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rand = random.random()
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if rand < random_ratio:
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edges[i, j] = 1
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return nodes, edges
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if nodes_num < max_nodes:
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edges = np.pad(edges, ((0, max_nodes - nodes_num), (0, max_nodes - nodes_num)), 'constant',constant_values=0)
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while len(nodes) < max_nodes:
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nodes.append('none')
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print(f'edges size: {edges.shape}, nodes size: {len(nodes)}')
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return edges,nodes
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def get_nasbench_201_val(idx):
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pass
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def graph_to_graph_data(graph, idx):
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# def graph_to_graph_data(graph, idx):
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def graph_to_graph_data(graph):
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ops = graph[1]
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adj = graph[0]
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nodes = []
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@@ -753,58 +770,95 @@ class Dataset(InMemoryDataset):
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edge_index = torch.tensor(edges_list, dtype=torch.long).t()
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edge_type = torch.tensor(edge_type, dtype=torch.long)
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edge_attr = edge_type
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# y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
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y = get_nasbench_201_val(idx)
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y = torch.tensor([0, 0], dtype=torch.float).view(1, -1)
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# y = get_nasbench_201_val(idx)
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data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, idx=i)
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return data
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graph_list = []
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with tqdm(total = len_data) as pbar:
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active_nodes = set()
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for i in range(len_data):
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arch_info = self.api.query_meta_info_by_index(i)
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results = self.api.query_by_index(i, 'cifar100')
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file_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json'
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with open(file_path, 'r') as f:
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graph_list = json.load(f)
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i = 0
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flex_graph_list = []
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flex_graph_path = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json'
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for graph in graph_list:
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# arch_info = self.api.query_meta_info_by_index(i)
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# results = self.api.query_by_index(i, 'cifar100')
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arch_info = graph['arch_str']
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# results =
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# nodes, edges = parse_architecture_string(arch_info.arch_str)
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ops, adj_matrix = parse_architecture_string(arch_info.arch_str)
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# ops, adj_matrix = parse_architecture_string(arch_info.arch_str, padding=4)
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ops, adj_matrix, ori_nodes, ori_adj = parse_architecture_string(arch_info, padding=4)
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# adj_matrix, ops = create_adj_matrix_and_ops(nodes, edges)
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for op in ops:
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if op not in active_nodes:
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active_nodes.add(op)
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graph_list.append({
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"adj_matrix": adj_matrix,
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"ops": ops,
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"idx": i,
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"train": [{
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"iepoch": result.get_train()['iepoch'],
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"loss": result.get_train()['loss'],
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"accuracy": result.get_train()['accuracy'],
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"cur_time": result.get_train()['cur_time'],
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"all_time": result.get_train()['all_time'],
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"seed": seed,
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}for seed, result in results.items()],
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"valid": [{
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"iepoch": result.get_eval('x-valid')['iepoch'],
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"loss": result.get_eval('x-valid')['loss'],
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"accuracy": result.get_eval('x-valid')['accuracy'],
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"cur_time": result.get_eval('x-valid')['cur_time'],
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"all_time": result.get_eval('x-valid')['all_time'],
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"seed": seed,
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}for seed, result in results.items()],
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"test": [{
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"iepoch": result.get_eval('x-test')['iepoch'],
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"loss": result.get_eval('x-test')['loss'],
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"accuracy": result.get_eval('x-test')['accuracy'],
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"cur_time": result.get_eval('x-test')['cur_time'],
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"all_time": result.get_eval('x-test')['all_time'],
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"seed": seed,
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}for seed, result in results.items()]
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})
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data = graph_to_graph_data((adj_matrix, ops))
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# with open(flex_graph_path, 'a') as f:
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# flex_graph = {
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# 'adj_matrix': adj_matrix,
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# 'ops': ops,
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# }
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# json.dump(flex_graph, f)
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flex_graph_list.append({
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'adj_matrix':adj_matrix,
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'ops': ops,
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})
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if i < 3:
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print(f"i={i}, data={data}")
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with open(f'{i}.json', 'w') as f:
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f.write(str(data.x))
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f.write(str(data.edge_index))
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f.write(str(data.edge_attr))
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data_list.append(data)
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# 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)
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# data_list.append(graph_to_graph_data((new_adj, new_ops)))
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new_adj, new_ops = generate_flex_adj_mat(ori_nodes=ori_nodes, ori_edges=ori_adj, max_nodes=12, min_nodes=9, random_ratio=0.5)
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flex_graph_list.append({
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'adj_matrix':new_adj.tolist(),
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'ops': new_ops,
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})
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# with open(flex_graph_path, 'w') as f:
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# flex_graph = {
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# 'adj_matrix': new_adj.tolist(),
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# 'ops': new_ops,
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# }
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# json.dump(flex_graph, f)
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data_list.append(graph_to_graph_data((new_adj, new_ops)))
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# graph_list.append({
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# "adj_matrix": adj_matrix,
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# "ops": ops,
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# "arch_str": arch_info.arch_str,
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# "idx": i,
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# "train": [{
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# "iepoch": result.get_train()['iepoch'],
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# "loss": result.get_train()['loss'],
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# "accuracy": result.get_train()['accuracy'],
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# "cur_time": result.get_train()['cur_time'],
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# "all_time": result.get_train()['all_time'],
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# "seed": seed,
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# }for seed, result in results.items()],
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# "valid": [{
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# "iepoch": result.get_eval('x-valid')['iepoch'],
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# "loss": result.get_eval('x-valid')['loss'],
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# "accuracy": result.get_eval('x-valid')['accuracy'],
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# "cur_time": result.get_eval('x-valid')['cur_time'],
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# "all_time": result.get_eval('x-valid')['all_time'],
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# "seed": seed,
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# }for seed, result in results.items()],
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# "test": [{
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# "iepoch": result.get_eval('x-test')['iepoch'],
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# "loss": result.get_eval('x-test')['loss'],
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# "accuracy": result.get_eval('x-test')['accuracy'],
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# "cur_time": result.get_eval('x-test')['cur_time'],
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# "all_time": result.get_eval('x-test')['all_time'],
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# "seed": seed,
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# }for seed, result in results.items()]
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# })
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pbar.update(1)
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for graph in graph_list:
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@@ -818,6 +872,8 @@ class Dataset(InMemoryDataset):
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graph['ops'] = ops
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with open(f'nasbench-201-graph.json', 'w') as f:
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json.dump(graph_list, f)
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with open(flex_graph_path, 'w') as f:
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json.dump(flex_graph_list, f)
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torch.save(self.collate(data_list), self.processed_paths[0])
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@@ -981,18 +1037,29 @@ class Dataset_origin(InMemoryDataset):
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torch.save(self.collate(data_list), self.processed_paths[0])
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def parse_architecture_string(arch_str):
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def parse_architecture_string(arch_str, padding=0):
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# print(arch_str)
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steps = arch_str.split('+')
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nodes = ['input'] # Start with input node
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adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
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ori_adj_mat = [[0, 1, 1, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 1 ,0 ,0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 0]])
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# [0, 0, 0, 0, 0, 0, 0, 0]])
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[0, 0, 0, 0, 0, 0, 0, 0]]
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# adj_mat = np.array([[0, 1, 1, 0, 1, 0, 0, 0],
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adj_mat = [[0, 1, 1, 0, 1, 0, 0, 0],
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[0, 0, 0, 1, 0, 1 ,0 ,0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 0, 0, 0, 1],
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# [0, 0, 0, 0, 0, 0, 0, 0]])
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[0, 0, 0, 0, 0, 0, 0, 0]]
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steps = arch_str.split('+')
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steps_coding = ['0', '0', '1', '0', '1', '2']
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cont = 0
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@@ -1004,7 +1071,21 @@ def parse_architecture_string(arch_str):
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cont += 1
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nodes.append(n)
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nodes.append('output') # Add output node
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return nodes, adj_mat
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ori_nodes = nodes.copy()
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if padding > 0:
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for i in range(padding):
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nodes.append('none')
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for adj_row in adj_mat:
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for i in range(padding):
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adj_row.append(0)
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# adj_mat = np.append(adj_mat, np.zeros((padding, len(nodes))))
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for i in range(padding):
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adj_mat.append([0] * len(nodes))
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# print(nodes)
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# print(adj_mat)
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# print(len(adj_mat))
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# print(f'len(ori_nodes): {len(ori_nodes)}, len(nodes): {len(nodes)}')
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return nodes, adj_mat, ori_nodes, ori_adj_mat
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def create_adj_matrix_and_ops(nodes, edges):
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num_nodes = len(nodes)
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@@ -1046,6 +1127,7 @@ class DataInfos(AbstractDatasetInfos):
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adj_ops_pairs = []
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for item in data:
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print(item)
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adj_matrix = np.array(item['adj_matrix'])
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ops = item['ops']
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ops = [op_type[op] for op in ops]
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@@ -1066,12 +1148,12 @@ class DataInfos(AbstractDatasetInfos):
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# ops_type[op] = len(ops_type)
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# len_ops.add(len(ops))
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# graphs.append((adj_matrix, ops))
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graphs = read_adj_ops_from_json(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench-201-graph.json')
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graphs = read_adj_ops_from_json(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/flex-nasbench201-graph.json')
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# check first five graphs
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for i in range(5):
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print(f'graph {i} : {graphs[i]}')
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print(f'ops_type: {ops_type}')
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# print(f'ops_type: {ops_type}')
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meta_dict = new_graphs_to_json(graphs, 'nasbench-201')
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self.base_path = base_path
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@@ -1280,11 +1362,11 @@ def compute_meta(root, source_name, train_index, test_index):
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'transition_E': tansition_E.tolist(),
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}
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with open(f'/home/stud/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
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with open(f'/nfs/data3/hanzhang/nasbenchDiT/graph_dit/nasbench201.meta.json', "w") as f:
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json.dump(meta_dict, f)
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return meta_dict
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if __name__ == "__main__":
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dataset = Dataset(source='nasbench', root='/home/stud/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
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dataset = Dataset(source='nasbench', root='/nfs/data3/hanzhang/nasbenchDiT/graph-dit', target_prop='Class', transform=None)
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@@ -3,9 +3,9 @@ import torch.nn.functional as F
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import pytorch_lightning as pl
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import time
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import os
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from naswot.score_networks import get_nasbench201_nodes_score
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from naswot import nasspace
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from naswot import datasets
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# from naswot.score_networks import get_nasbench201_nodes_score
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# from naswot import nasspace
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# from naswot import datasets
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from models.transformer import Denoiser
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from diffusion.noise_schedule import PredefinedNoiseScheduleDiscrete, MarginalTransition
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@@ -41,7 +41,7 @@ class Graph_DiT(pl.LightningModule):
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self.args.batch_size = 128
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self.args.GPU = '0'
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self.args.dataset = 'cifar10-valid'
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self.args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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self.args.data_loc = '../cifardata/'
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self.args.seed = 777
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self.args.init = ''
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@@ -59,10 +59,10 @@ class Graph_DiT(pl.LightningModule):
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if 'valid' in self.args.dataset:
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self.args.dataset = self.args.dataset.replace('-valid', '')
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print('graph_dit starts to get searchspace of nasbench201')
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self.searchspace = nasspace.get_search_space(self.args)
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# self.searchspace = nasspace.get_search_space(self.args)
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print('searchspace of nasbench201 is obtained')
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print('graphdit starts to get train_loader')
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self.train_loader = datasets.get_data(self.args.dataset, self.args.data_loc, self.args.trainval, self.args.batch_size, self.args.augtype, self.args.repeat, self.args)
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# self.train_loader = datasets.get_data(self.args.dataset, self.args.data_loc, self.args.trainval, self.args.batch_size, self.args.augtype, self.args.repeat, self.args)
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print('train_loader is obtained')
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self.cfg = cfg
|
||||
@@ -162,7 +162,7 @@ class Graph_DiT(pl.LightningModule):
|
||||
return pred
|
||||
|
||||
def training_step(self, data, i):
|
||||
data_x = F.one_hot(data.x, num_classes=8).float()[:, self.active_index]
|
||||
data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
|
||||
data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
|
||||
|
||||
dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
|
||||
@@ -220,9 +220,9 @@ class Graph_DiT(pl.LightningModule):
|
||||
# self.sampling_metrics.reset()
|
||||
self.val_y_collection = []
|
||||
|
||||
# @torch.no_grad()
|
||||
@torch.no_grad()
|
||||
def validation_step(self, data, i):
|
||||
data_x = F.one_hot(data.x, num_classes=8).float()[:, self.active_index]
|
||||
data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
|
||||
data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
|
||||
dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
|
||||
dense_data = dense_data.mask(node_mask, collapse=False)
|
||||
@@ -313,9 +313,9 @@ class Graph_DiT(pl.LightningModule):
|
||||
self.test_E_logp.reset()
|
||||
self.test_y_collection = []
|
||||
|
||||
# @torch.no_grad()
|
||||
@torch.no_grad()
|
||||
def test_step(self, data, i):
|
||||
data_x = F.one_hot(data.x, num_classes=8).float()[:, self.active_index]
|
||||
data_x = F.one_hot(data.x, num_classes=12).float()[:, self.active_index]
|
||||
data_edge_attr = F.one_hot(data.edge_attr, num_classes=2).float()
|
||||
|
||||
dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
|
||||
@@ -573,7 +573,7 @@ class Graph_DiT(pl.LightningModule):
|
||||
|
||||
return nll
|
||||
|
||||
# @torch.no_grad()
|
||||
@torch.no_grad()
|
||||
def sample_batch(self, batch_id, batch_size, y, keep_chain, number_chain_steps, save_final, num_nodes=None):
|
||||
"""
|
||||
:param batch_id: int
|
||||
@@ -686,130 +686,120 @@ class Graph_DiT(pl.LightningModule):
|
||||
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())
|
||||
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...
|
||||
|
||||
num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
|
||||
op_type = {
|
||||
'input': 0,
|
||||
'nor_conv_1x1': 1,
|
||||
'nor_conv_3x3': 2,
|
||||
'avg_pool_3x3': 3,
|
||||
'skip_connect': 4,
|
||||
'none': 5,
|
||||
'output': 6,
|
||||
}
|
||||
def check_valid_graph(nodes, edges):
|
||||
nodes = [num_to_op[i] for i in nodes]
|
||||
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
|
||||
# num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
|
||||
# op_type = {
|
||||
# 'input': 0,
|
||||
# 'nor_conv_1x1': 1,
|
||||
# 'nor_conv_3x3': 2,
|
||||
# 'avg_pool_3x3': 3,
|
||||
# 'skip_connect': 4,
|
||||
# 'none': 5,
|
||||
# 'output': 6,
|
||||
# }
|
||||
# def check_valid_graph(nodes, edges):
|
||||
# nodes = [num_to_op[i] for i in nodes]
|
||||
# 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
|
||||
|
||||
class Args:
|
||||
pass
|
||||
# class Args:
|
||||
# pass
|
||||
|
||||
def get_score(sampled_s):
|
||||
x_list = sampled_s.X.unbind(dim=0)
|
||||
e_list = sampled_s.E.unbind(dim=0)
|
||||
valid_rlt = [check_valid_graph(x_list[i].cpu().numpy(), e_list[i].cpu().numpy()) for i in range(len(x_list))]
|
||||
from graph_dit.naswot.naswot.score_networks import get_nasbench201_nodes_score
|
||||
score = []
|
||||
# def get_score(sampled_s):
|
||||
# x_list = sampled_s.X.unbind(dim=0)
|
||||
# e_list = sampled_s.E.unbind(dim=0)
|
||||
# valid_rlt = [check_valid_graph(x_list[i].cpu().numpy(), e_list[i].cpu().numpy()) for i in range(len(x_list))]
|
||||
# from graph_dit.naswot.naswot.score_networks import get_nasbench201_nodes_score
|
||||
# score = []
|
||||
|
||||
for i in range(len(x_list)):
|
||||
if valid_rlt[i]:
|
||||
nodes = [num_to_op[j] for j in x_list[i].cpu().numpy()]
|
||||
# edges = e_list[i].cpu().numpy()
|
||||
score.append(get_nasbench201_nodes_score(nodes,train_loader=self.train_loader,searchspace=self.searchspace,device=sampled_s.X.device , args=self.args))
|
||||
else:
|
||||
score.append(-1)
|
||||
# return torch.tensor(score, dtype=torch.float32, requires_grad=True).to(x_list[0].device)
|
||||
target_score = torch.ones(100, dtype=torch.float32, device=sampled_s.X.device, requires_grad=True) * 2000.0
|
||||
# target_score_list = [2000 for i in range(100)]
|
||||
# return torch.tensor(score, device=sampled_s.X.device ,dtype=torch.float32, requires_grad=True), torch.tensor(target_score_list, device=sampled_s.X.device, dtype=torch.float32, requires_grad=True)
|
||||
return torch.tensor(score, device=sampled_s.X.device ,dtype=torch.float32, requires_grad=True), target_score
|
||||
# for i in range(len(x_list)):
|
||||
# if valid_rlt[i]:
|
||||
# nodes = [num_to_op[j] for j in x_list[i].cpu().numpy()]
|
||||
# # edges = e_list[i].cpu().numpy()
|
||||
# score.append(get_nasbench201_nodes_score(nodes,train_loader=self.train_loader,searchspace=self.searchspace,device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu") , args=self.args))
|
||||
# else:
|
||||
# score.append(-1)
|
||||
# return torch.tensor(score, dtype=torch.float32, requires_grad=True).to(x_list[0].device)
|
||||
|
||||
sample_num = 10
|
||||
best_arch = None
|
||||
best_score_int = -1e8
|
||||
score = torch.ones(100, dtype=torch.float32, requires_grad=True) * -1e8
|
||||
print(f'score.requires_grad: {score.requires_grad}')
|
||||
# sample_num = 10
|
||||
# best_arch = None
|
||||
# best_score_int = -1e8
|
||||
# score = torch.ones(100, dtype=torch.float32, requires_grad=True) * -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, target_score = get_score(sampled_s)
|
||||
print(f'score: {score}')
|
||||
print(f'score.shape: {score.shape}')
|
||||
print(f'torch.sum(score): {torch.sum(score)}')
|
||||
sum_score = torch.sum(score)
|
||||
print(f'sum_score: {sum_score}')
|
||||
if sum_score > best_score_int:
|
||||
best_score_int = sum_score
|
||||
best_score = score
|
||||
best_arch = sampled_s
|
||||
# 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)
|
||||
# print(f'score: {score}')
|
||||
# print(f'score.shape: {score.shape}')
|
||||
# print(f'torch.sum(score): {torch.sum(score)}')
|
||||
# sum_score = torch.sum(score)
|
||||
# print(f'sum_score: {sum_score}')
|
||||
# if sum_score > best_score_int:
|
||||
# best_score_int = sum_score
|
||||
# best_score = score
|
||||
# best_arch = sampled_s
|
||||
|
||||
# print(f'prob_X: {prob_X.shape}, prob_E: {prob_E.shape}')
|
||||
|
||||
# best_arch = diffusion_utils.sample_discrete_features(prob_X, prob_E, node_mask=node_mask, step=s[0,0].item())
|
||||
|
||||
# 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()
|
||||
print(f'best_arch.X: {best_arch.X.shape}, best_arch.E: {best_arch.E.shape}') # 100 8 8, bs n n, 100 8 8 2, bs n n 2
|
||||
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()
|
||||
# print(f'best_arch.X: {best_arch.X.shape}, best_arch.E: {best_arch.E.shape}') # 100 8 8, bs n n, 100 8 8 2, bs n n 2
|
||||
|
||||
print(f'best_arch.X: {best_arch.X}, best_arch.E: {best_arch.E}')
|
||||
X_s = F.one_hot(best_arch.X, num_classes=self.Xdim_output).float()
|
||||
E_s = F.one_hot(best_arch.E, num_classes=self.Edim_output).float()
|
||||
print(f'X_s: {X_s}, E_s: {E_s}')
|
||||
# print(f'best_arch.X: {best_arch.X}, best_arch.E: {best_arch.E}')
|
||||
# X_s = F.one_hot(best_arch.X, num_classes=self.Xdim_output).float()
|
||||
# E_s = F.one_hot(best_arch.E, num_classes=self.Edim_output).float()
|
||||
# print(f'X_s: {X_s}, E_s: {E_s}')
|
||||
|
||||
# NASWOT score
|
||||
# target_score = torch.ones(100, requires_grad=True, device=X_s.device) * 2000.0
|
||||
# # NASWOT score
|
||||
# target_score = torch.ones(100, requires_grad=True) * 2000.0
|
||||
print(f'best_score: {best_score.shape}, target_score: {target_score.shape}')
|
||||
print(f'best_score.requires_grad: {best_score.requires_grad}, target_score.requires_grad: {target_score.requires_grad}')
|
||||
print(f'best_score.device: {best_score.device}, target_score.device: {target_score.device}')
|
||||
# target_score = target_score.to(X_s.device)
|
||||
|
||||
# # compute loss mse(cur_score - target_score)
|
||||
# mse_loss = torch.nn.MSELoss()
|
||||
# print(f'best_score: {best_score.shape}, target_score: {target_score.shape}')
|
||||
# print(f'best_score.requires_grad: {best_score.requires_grad}, target_score.requires_grad: {target_score.requires_grad}')
|
||||
|
||||
# compute loss mse(cur_score - target_score)
|
||||
mse_loss = torch.nn.MSELoss()
|
||||
loss = mse_loss(target_score, best_score)
|
||||
print(f'loss: {loss.requires_grad}')
|
||||
loss.backward(retain_graph=True)
|
||||
# loss = mse_loss(best_score, target_score)
|
||||
# loss.backward(retain_graph=True)
|
||||
|
||||
# loss backward = gradient
|
||||
|
||||
# get prob.X, prob_E gradient
|
||||
x_grad = pred.X.grad
|
||||
e_grad = pred.E.grad
|
||||
# x_grad = pred.X.grad
|
||||
# e_grad = pred.E.grad
|
||||
|
||||
beta_ratio = 0.5
|
||||
# x_current = pred.X - beta_ratio * x_grad
|
||||
# e_current = pred.E - beta_ratio * e_grad
|
||||
X_s = pred.X - beta_ratio * x_grad
|
||||
E_s = pred.E - beta_ratio * e_grad
|
||||
# beta_ratio = 0.5
|
||||
# # x_current = pred.X - beta_ratio * x_grad
|
||||
# # e_current = pred.E - beta_ratio * e_grad
|
||||
# E_s = pred.X - beta_ratio * x_grad
|
||||
# X_s = pred.E - beta_ratio * e_grad
|
||||
|
||||
# update prob.X prob_E with using gradient
|
||||
|
||||
|
||||
File diff suppressed because one or more lines are too long
101
graph_dit/output_graphs/151042.txt
Normal file
101
graph_dit/output_graphs/151042.txt
Normal file
@@ -0,0 +1,101 @@
|
||||
graph, input_nasbench201, output_nasbench201
|
||||
nodes: tensor(0) tensor(4) tensor(5) tensor(3) tensor(3) tensor(1) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.152663734901322
|
||||
nodes: tensor(0) tensor(1) tensor(5) tensor(4) tensor(3) tensor(4) tensor(2) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.3023566097075212
|
||||
nodes: tensor(0) tensor(1) tensor(5) tensor(4) tensor(3) tensor(5) tensor(3) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.062036414714562005
|
||||
nodes: tensor(0) tensor(5) tensor(4) tensor(3) tensor(5) tensor(5) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.4598603416089688
|
||||
nodes: tensor(0) tensor(3) tensor(5) tensor(3) tensor(5) tensor(5) tensor(5) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1550.51611328125, 0.8352533844376283
|
||||
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101
graph_dit/output_graphs/151613.txt
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101
graph_dit/output_graphs/151613.txt
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@@ -0,0 +1,101 @@
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graph, input_nasbench201, output_nasbench201
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1
graph_dit/output_graphs/151613.txt.json
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1
graph_dit/output_graphs/151613.txt.json
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101
graph_dit/output_graphs/161959.txt
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101
graph_dit/output_graphs/161959.txt
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graph, input_nasbench201, output_nasbench201
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||||
2
graph_dit/output_graphs/161959.txt.json
Normal file
2
graph_dit/output_graphs/161959.txt.json
Normal file
@@ -0,0 +1,2 @@
|
||||
1600分以上
|
||||
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|
||||
101
graph_dit/output_graphs/170944-1000>1600.txt
Normal file
101
graph_dit/output_graphs/170944-1000>1600.txt
Normal file
@@ -0,0 +1,101 @@
|
||||
graph, input_nasbench201, output_nasbench201
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||||
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|
||||
2
graph_dit/output_graphs/170944.txt.json
Normal file
2
graph_dit/output_graphs/170944.txt.json
Normal file
@@ -0,0 +1,2 @@
|
||||
1600 分以上
|
||||
[[3701, 81.88], [8730, 85.06], [8421, 10.0], [3578, 85.18], [7253, 73.97], [9307, 30.88], [11161, 78.98], [9354, 85.67], [11323, 79.28], [12638, 84.54], [6117, 84.47], [6876, 83.63], [4574, 73.42], [11272, 85.13], [2165, 85.24], [10798, 77.08], [5094, 70.43], [8793, 79.3], [3191, 74.31], [13138, 68.1], [6745, 80.82], [5207, 84.35], [7356, 74.59], [7561, 80.96], [351, 84.33], [2202, 79.85], [316, 79.86], [1509, 10.0], [8891, 81.05], [13653, 83.0], [7552, 78.67], [8050, 84.72], [220, 71.79], [4147, 78.07], [15300, 72.31], [2242, 78.95], [9631, 85.88], [9281, 85.86], [10855, 82.28], [12599, 86.94], [7269, 83.93], [8521, 60.16], [9991, 65.21], [7770, 83.5], [6995, 69.2], [14804, 76.73], [7737, 83.23], [7129, 85.89], [12183, 74.68], [12557, 77.58], [11349, 77.49], [10257, 79.58], [10405, 81.27], [14604, 84.12], [7439, 78.68], [46, 77.81], [4728, 85.11], [14286, 76.99], [5330, 76.07], [12556, 79.12], [4959, 85.47], [14090, 84.16], [13507, 85.03], [383, 68.91], [13362, 80.76], [4626, 70.49], [13617, 81.27], [5843, 10.0], [12256, 84.86], [10789, 78.03], [15455, 61.81], [11898, 76.73], [11073, 78.84], [10907, 80.0], [462, 78.04], [5293, 83.08], [6750, 79.18], [8082, 87.11], [11453, 66.1], [6565, 71.23], [4446, 70.95], [12301, 77.23], [12986, 81.49], [6208, 86.24], [1692, 84.16], [11177, 79.32], [418, 83.87], [14323, 83.36], [4856, 84.65], [14604, 84.12], [15064, 83.22], [14268, 85.39], [835, 80.18], [1668, 84.41], [6431, 85.32], [5191, 77.79], [1713, 73.72]]
|
||||
101
graph_dit/output_graphs/171452-15625>1600.txt
Normal file
101
graph_dit/output_graphs/171452-15625>1600.txt
Normal file
@@ -0,0 +1,101 @@
|
||||
graph, input_nasbench201, output_nasbench201
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nodes: tensor(0) tensor(5) tensor(1) tensor(1) tensor(2) tensor(4) tensor(3) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1689.0260009765625, 0.36474530769828506
|
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nodes: tensor(0) tensor(3) tensor(1) tensor(5) tensor(1) tensor(2) tensor(1) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1645.819091796875, 0.4672992103757777
|
||||
nodes: tensor(0) tensor(1) tensor(3) tensor(1) tensor(5) tensor(1) tensor(3) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1661.2313232421875, 0.8321289859690038
|
||||
nodes: tensor(0) tensor(2) tensor(4) tensor(2) tensor(1) tensor(2) tensor(4) tensor(6) /n edges: /ntensor(0) tensor(1) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(1) tensor(0) tensor(1) tensor(0) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(0) /n tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(1) tensor(0) tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(1) tensor(1) tensor(0) tensor(0) tensor(0) tensor(1) /n tensor(0) tensor(0) tensor(0) tensor(0) tensor(1) tensor(1) tensor(1) tensor(0) /n, 1691.4451904296875, 0.1319178847781063
|
||||
1
graph_dit/output_graphs/171452.txt.json
Normal file
1
graph_dit/output_graphs/171452.txt.json
Normal file
@@ -0,0 +1 @@
|
||||
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|
||||
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Reference in New Issue
Block a user