def test_hact_viz(self):
"""Test hierarchical visualization."""
# 1. load the corresponding image
image = np.array(
Image.open(os.path.join(self.image_path, self.image_name)))
# 2. load tissue graph
tissue_graph, _ = load_graphs(
os.path.join(self.tissue_graph_path, self.graph_name))
tissue_graph = tissue_graph[0]
# 3. load cell graph
cell_graph, _ = load_graphs(
os.path.join(self.cell_graph_path, self.graph_name))
cell_graph = cell_graph[0]
# 6. run the visualization
visualizer = HACTVisualization()
out = visualizer.process(
image,
cell_graph=cell_graph,
tissue_graph=tissue_graph,
)
# 5. save output image
out.save(os.path.join(
self.out_path,
self.image_name.replace(".png", "") + "_hierarchical_overlay.png",
),
quality=95)
def pre_process(self):
processed_dir = os.path.join(self.root, 'processed')
pre_processed_file_path = os.path.join(processed_dir, 'dgl_data_processed')
if os.path.exists(pre_processed_file_path):
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
self.ids = label_dict['ids']
else:
url = self.meta_info[self.name]["dgl url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(osp.join(self.original_root, self.download_name), self.root)
else:
print("Stop download.")
exit(-1)
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
self.ids = label_dict['ids']
def load(self):
""" step 5 """
graph_path = f'{self.save_path}/dgl_graph.bin'
line_graph_path = f'{self.save_path}/dgl_line_graph.bin'
self.graphs, label_dict = load_graphs(graph_path)
self.line_graphs, _ = load_graphs(line_graph_path)
self.label = torch.stack([label_dict[key] for key in self.label_keys],
dim=1)
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
self.graph, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
else:
### check download
if not osp.exists(osp.join(self.root, "raw", "edge.csv.gz")):
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(osp.join(self.original_root, self.download_name), self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
### pre-process and save
add_inverse_edge = self.meta_info[self.name]["add_inverse_edge"] == "True"
if self.meta_info[self.name]["additional node files"] == 'None':
additional_node_files = []
else:
additional_node_files = self.meta_info[self.name]["additional node files"].split(',')
if self.meta_info[self.name]["additional edge files"] == 'None':
additional_edge_files = []
else:
additional_edge_files = self.meta_info[self.name]["additional edge files"].split(',')
graph = read_csv_graph_dgl(raw_dir, add_inverse_edge = add_inverse_edge, additional_node_files = additional_node_files, additional_edge_files = additional_edge_files)[0]
### adding prediction target
node_label = pd.read_csv(osp.join(raw_dir, 'node-label.csv.gz'), compression="gzip", header = None).values
if "classification" in self.task_type:
node_label = torch.tensor(node_label, dtype = torch.long)
else:
node_label = torch.tensor(node_label, dtype = torch.float32)
label_dict = {"labels": node_label}
print('Saving...')
save_graphs(pre_processed_file_path, graph, label_dict)
self.graph, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
self.graph, _ = load_graphs(pre_processed_file_path)
else:
### check download
if not osp.exists(osp.join(self.root, "raw", "edge.csv.gz")):
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(
osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
### pre-process and save
if self.meta_info[self.name]["additional node files"] == 'None':
additional_node_files = []
else:
additional_node_files = self.meta_info[
self.name]["additional node files"].split(',')
if self.meta_info[self.name]["additional edge files"] == 'None':
additional_edge_files = []
else:
additional_edge_files = self.meta_info[
self.name]["additional edge files"].split(',')
graph = read_csv_graph_dgl(
raw_dir,
add_inverse_edge=add_inverse_edge,
additional_node_files=additional_node_files,
additional_edge_files=additional_edge_files)[0]
print('Saving...')
save_graphs(pre_processed_file_path, graph, {})
self.graph, _ = load_graphs(pre_processed_file_path)
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if osp.exists(pre_processed_file_path):
self.graph, _ = load_graphs(pre_processed_file_path)
else:
### check download
if not osp.exists(osp.join(self.root, "raw", "edge.csv.gz")):
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(
osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
raw_dir = osp.join(self.root, "raw")
file_names = ["edge"]
if self.meta_info[self.name]["has_node_attr"] == "True":
file_names.append("node-feat")
if self.meta_info[self.name]["has_edge_attr"] == "True":
file_names.append("edge-feat")
raw_file_names = [
file_name + ".csv.gz" for file_name in file_names
]
### pre-process and save
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
graph = read_csv_graph_dgl(raw_dir,
raw_file_names,
add_inverse_edge=add_inverse_edge)
save_graphs(pre_processed_file_path, graph, {})
self.graph, _ = load_graphs(pre_processed_file_path)
def __init__(self,
root=None,
structures=[None],
targets=[None],
cutoff=5.,
transform=None):
self.transform = transform
self.cutoff = cutoff
assert len(structures) == len(
targets), "Number of structures unequal to y"
self.structures = structures
self.targets = targets
self.root = root
if self.root == None:
self._load()
else:
if os.path.isdir(root) is False:
os.mkdir(root)
cache_path = os.path.join(root, "processed.bin")
if os.path.isfile(cache_path):
try:
graphs, labels = load_graphs(cache_path)
self.graphs = graphs
self.labels = labels['labels']
print(len(self.graphs), "loded!")
except:
self._load()
labels_dict = {"labels": self.labels}
save_graphs(cache_path, self.graphs, labels_dict)
print(len(self.graphs), "saved!")
else:
self._load()
labels_dict = {"labels": self.labels}
save_graphs(cache_path, self.graphs, labels_dict)
print(len(self.graphs), "saved!")
def train(args):
set_random_seed(args.seed)
g = load_graphs(os.path.join(args.data_path, 'neighbor_graph.bin'))[0][0]
feats = load_info(os.path.join(args.data_path, 'in_feats.pkl'))
model = HetGNN(feats['author'].shape[-1], args.num_hidden, g.ntypes)
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
neg_sampler = RatioNegativeSampler()
for epoch in range(args.epochs):
model.train()
embeds = model(g, feats)
score = model.calc_score(g, embeds)
neg_g = construct_neg_graph(g, neg_sampler)
neg_score = model.calc_score(neg_g, embeds)
logits = torch.cat([score, neg_score]) # (2A*E,)
labels = torch.cat(
[torch.ones(score.shape[0]),
torch.zeros(neg_score.shape[0])])
loss = F.binary_cross_entropy_with_logits(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Epoch {:d} | Loss {:.4f}'.format(epoch, loss.item()))
with torch.no_grad():
final_embeds = model(g, feats)
with open(args.save_node_embed_path, 'wb') as f:
pickle.dump(final_embeds, f)
print('Final node embeddings saved to', args.save_node_embed_path)
def __getitem__(self, item) -> Tuple[DGLGraph, torch.Tensor]:
graph_filename, label_filename, start_index, end_index = self.batch_description[
item]
if label_filename != self.current_label_file:
self.current_label_file = label_filename
with open(label_filename, 'rb') as pkl_file:
pkl_data = load(pkl_file)
self.labels = torch.tensor(pkl_data['labels'].T,
device=self.device).detach()
graphs, _ = load_graphs(graph_filename,
list(range(start_index, end_index)))
graphs, mask = zip(*[(g, i) for i, g in enumerate(graphs)
if self._is_tree_suitable(g)])
if self.invert_edges:
graphs = [g.reverse(share_ndata=True) for g in graphs]
graph = batch(graphs)
graph.ndata['token'] = graph.ndata['token'].to(self.device).detach()
graph.ndata['type'] = graph.ndata['type'].to(self.device).detach()
# [sequence len, batch size]
labels = self.labels[:, start_index:end_index][:, list(mask)]
return graph, labels
def read_graph(graph_path):
"""
Read graph data from path.
"""
graph_list, _ = load_graphs(graph_path)
graph = graph_list[0]
return graph
def _process( # type: ignore[override]
self, path: Union[str, Path]) -> dgl.DGLGraph:
graph_path = str(path) # DGL cannot handle pathlib.Path
graphs, _ = load_graphs(graph_path)
if len(graphs) == 1:
return graphs[0]
return graphs
def read_ast(self, id_):
try:
graph, _ = load_graphs(os.path.join(self.functions_path, str(id_)))
except Exception as ex:
return None
return graph[0]
def test_graph_serialize_without_feature():
num_graphs = 100
g_list = [generate_rand_graph(30) for _ in range(num_graphs)]
# create a temporary file and immediately release it so DGL can open it.
f = tempfile.NamedTemporaryFile(delete=False)
path = f.name
f.close()
save_graphs(path, g_list)
idx_list = np.random.permutation(np.arange(num_graphs)).tolist()
loadg_list, _ = load_graphs(path, idx_list)
idx = idx_list[0]
load_g = loadg_list[0]
assert F.allclose(load_g.nodes(), g_list[idx].nodes())
load_edges = load_g.all_edges('uv', 'eid')
g_edges = g_list[idx].all_edges('uv', 'eid')
assert F.allclose(load_edges[0], g_edges[0])
assert F.allclose(load_edges[1], g_edges[1])
os.unlink(path)
def make_dataset(vocab, input_prefix, output_prefix, lang, num_workers=1):
if args['preprocess']['dataset_impl'] == "raw":
in_file = file_name(input_prefix, lang)
out_dir = args['preprocess']['destdir']
os.makedirs(out_dir, exist_ok=True)
LOGGER.info('Copying {} into {}'.format(in_file, out_dir))
shutil.copy(src=in_file, dst=args['preprocess']['destdir'])
else:
in_file = file_name(input_prefix, lang)
out_file = dest_path(output_prefix, lang)
os.makedirs(os.path.dirname(out_file), exist_ok=True)
offsets = find_offsets(in_file, num_workers)
with Pool(num_workers) as mpool:
results = [
mpool.apply_async(
build_dgl_graph,
(vocab, in_file, f'{out_file}{worker_id}.mmap',
offsets[worker_id], offsets[worker_id + 1]),
) for worker_id in range(num_workers)
]
results = [res.get() for res in results]
graph_batch = []
for worker_id in range(num_workers):
sub_file = f'{out_file}{worker_id}.mmap'
glist, _ = load_graphs(sub_file)
graph_batch.extend(glist)
os.remove(sub_file)
save_graphs(f'{out_file}.mmap', graph_batch)
def test_pretrained_bracs_tggnn_3_classes_gin(self):
"""Test bracs_tggnn_3_classes_gin model."""
# 1. Load a tissue graph
graph, _ = load_graphs(os.path.join(self.graph_path, self.graph_name))
graph = graph[0]
graph = set_graph_on_cuda(graph) if IS_CUDA else graph
node_dim = graph.ndata['feat'].shape[1]
# 2. Load model with pre-trained weights
config_fname = os.path.join(self.current_path, 'config',
'tg_bracs_tggnn_3_classes_gin.yml')
with open(config_fname, 'r') as file:
config = yaml.safe_load(file)
model = TissueGraphModel(
gnn_params=config['gnn_params'],
classification_params=config['classification_params'],
node_dim=node_dim,
num_classes=3,
pretrained=True).to(DEVICE)
# 4. forward pass
logits = model(graph)
self.assertIsInstance(logits, torch.Tensor)
self.assertEqual(logits.shape[0], 1)
self.assertEqual(logits.shape[1], 3)
def read_input(input_folder):
X = pd.read_csv(f'{input_folder}/X.csv')
y = pd.read_csv(f'{input_folder}/y.csv')
categorical_columns = []
if os.path.exists(f'{input_folder}/cat_features.txt'):
with open(f'{input_folder}/cat_features.txt') as f:
for line in f:
if line.strip():
categorical_columns.append(line.strip())
cat_features = None
if categorical_columns:
columns = X.columns
cat_features = np.where(columns.isin(categorical_columns))[0]
for col in list(columns[cat_features]):
X[col] = X[col].astype(str)
gs, _ = load_graphs(f'{input_folder}/graph.dgl')
graph = gs[0]
with open(f'{input_folder}/masks.json') as f:
masks = json.load(f)
return graph, X, y, cat_features, masks
def test_graphlrp(self):
"""
Test Graph LRP.
"""
# 1. load a graph
graph, _ = load_graphs(os.path.join(self.graph_path, self.graph_name))
graph = graph[0]
graph = set_graph_on_cuda(graph) if IS_CUDA else graph
node_dim = graph.ndata['feat'].shape[1]
# 2. create model
config_fname = os.path.join(self.current_path, 'config',
'cg_bracs_cggnn_3_classes_gin.yml')
with open(config_fname, 'r') as file:
config = yaml.safe_load(file)
model = CellGraphModel(
gnn_params=config['gnn_params'],
classification_params=config['classification_params'],
node_dim=node_dim,
num_classes=3)
# 2. run the explainer
explainer = GraphLRPExplainer(model=model)
importance_scores, logits = explainer.process(graph)
# 3. tests
self.assertIsInstance(importance_scores, np.ndarray)
self.assertIsInstance(logits, np.ndarray)
self.assertEqual(graph.number_of_nodes(), importance_scores.shape[0])
def test_tissue_graph_model_with_batch(self):
"""Test tissue graph model with batch."""
# 1. Load a cell graph
graph, _ = load_graphs(os.path.join(self.graph_path, self.graph_name))
graph = graph[0]
graph = set_graph_on_cuda(graph) if IS_CUDA else graph
node_dim = graph.ndata['feat'].shape[1]
# 2. load config
config_fname = os.path.join(self.current_path, 'config',
'tg_model.yml')
with open(config_fname, 'r') as file:
config = yaml.safe_load(file)
model = TissueGraphModel(
gnn_params=config['gnn_params'],
classification_params=config['classification_params'],
node_dim=node_dim,
num_classes=3).to(DEVICE)
# 4. forward pass
logits = model(dgl.batch([graph, graph]))
self.assertIsInstance(logits, torch.Tensor)
self.assertEqual(logits.shape[0], 2)
self.assertEqual(logits.shape[1], 3)
def load(self):
graphs, _ = load_graphs(os.path.join(self.save_path, self.name + '_dgl_graph.bin'))
self.g = graphs[0]
info = load_info(join(self.save_path, self.name + '_info.pkl'))
self.author_names = info['author_names']
self.paper_titles = info['paper_titles']
self.conf_names = info['conf_names']
def test_serialize_heterograph():
f = tempfile.NamedTemporaryFile(delete=False)
path = f.name
f.close()
g_list0 = create_heterographs2("int64") + create_heterographs2("int32")
save_graphs(path, g_list0)
g_list, _ = load_graphs(path)
assert g_list[0].idtype == F.int64
assert len(g_list[0].canonical_etypes) == 3
for i in range(len(g_list0)):
for j, etypes in enumerate(g_list0[i].canonical_etypes):
assert g_list[i].canonical_etypes[j] == etypes
assert g_list[1].restrict_format() == 'any'
assert g_list[2].restrict_format() == 'csr'
assert g_list[3].idtype == F.int32
assert np.allclose(F.asnumpy(g_list[2].nodes['user'].data['hh']),
np.ones((4, 5)))
assert np.allclose(F.asnumpy(g_list[5].nodes['user'].data['hh']),
np.ones((4, 5)))
edges = g_list[0]['follows'].edges()
assert np.allclose(F.asnumpy(edges[0]), np.array([0, 1, 2]))
assert np.allclose(F.asnumpy(edges[1]), np.array([1, 2, 3]))
for i in range(len(g_list)):
assert g_list[i].ntypes == g_list0[i].ntypes
assert g_list[i].etypes == g_list0[i].etypes
os.unlink(path)
def test_graph_serialize_with_labels(is_hetero):
num_graphs = 100
g_list = [generate_rand_graph(30, is_hetero) for _ in range(num_graphs)]
labels = {"label": F.zeros((num_graphs, 1))}
# create a temporary file and immediately release it so DGL can open it.
f = tempfile.NamedTemporaryFile(delete=False)
path = f.name
f.close()
save_graphs(path, g_list, labels)
idx_list = np.random.permutation(np.arange(num_graphs)).tolist()
loadg_list, l_labels0 = load_graphs(path, idx_list)
l_labels = load_labels(path)
assert F.allclose(l_labels['label'], labels['label'])
assert F.allclose(l_labels0['label'], labels['label'])
idx = idx_list[0]
load_g = loadg_list[0]
assert F.allclose(load_g.nodes(), g_list[idx].nodes())
load_edges = load_g.all_edges('uv', 'eid')
g_edges = g_list[idx].all_edges('uv', 'eid')
assert F.allclose(load_edges[0], g_edges[0])
assert F.allclose(load_edges[1], g_edges[1])
os.unlink(path)
def load(self):
graphs, _ = load_graphs(
os.path.join(self.save_path, self.name + '_dgl_graph.bin'))
self.g = graphs[0]
for k in ('train_mask', 'val_mask', 'test_mask'):
self.g.nodes['movie'].data[k] = self.g.nodes['movie'].data[k].bool(
)
def _process_and_save( # type: ignore[override]
self,
instance_map: np.ndarray,
features: torch.Tensor,
annotation: Optional[np.ndarray] = None,
output_name: str = None,
) -> dgl.DGLGraph:
"""Process and save in provided directory
Args:
output_name (str): Name of output file
instance_map (np.ndarray): Instance map depicting tissue components
(eg nuclei, tissue superpixels)
features (torch.Tensor): Features of each node. Shape (nr_nodes, nr_features)
annotation (Optional[np.ndarray], optional): Optional node level to include.
Defaults to None.
Returns:
dgl.DGLGraph: [description]
"""
assert (
self.save_path is not None
), "Can only save intermediate output if base_path was not None during construction"
output_path = self.output_dir / f"{output_name}.bin"
if output_path.exists():
logging.info(
f"Output of {output_name} already exists, using it instead of recomputing"
)
graphs, _ = load_graphs(str(output_path))
assert len(graphs) == 1
graph = graphs[0]
else:
graph = self._process(instance_map=instance_map,
features=features,
annotation=annotation)
save_graphs(str(output_path), [graph])
return graph
def load(self):
graph_path = os.path.join(self.save_path, 'dgl_graph.bin')
graphs, _ = load_graphs(graph_path)
self._graph = graphs[0]
self._graph.ndata['train_mask'] = generate_mask_tensor(self._graph.ndata['train_mask'].numpy())
self._graph.ndata['val_mask'] = generate_mask_tensor(self._graph.ndata['val_mask'].numpy())
self._graph.ndata['test_mask'] = generate_mask_tensor(self._graph.ndata['test_mask'].numpy())
self._print_info()
def convert_holdout(holdout_path: str, output_path: str, batch_size: int,
token_to_id: Dict, type_to_id: Dict, label_to_id: Dict,
tokens_to_leaves: bool = False, is_split: bool = False,
max_token_len: int = -1, max_label_len: int = -1, wrap_tokens: bool = False,
wrap_labels: bool = False, delimiter: str = '|', shuffle: bool = True, n_jobs: int = -1) -> str:
log_file = os.path.join('logs', f"convert_{datetime.now().strftime('%Y_%m_%d_%H:%M:%S')}.txt")
projects_paths = [os.path.join(holdout_path, project, 'java') for project in os.listdir(holdout_path)]
print("converting projects...")
for project_path in tqdm(projects_paths):
print(f"converting {project_path}")
_convert_project_safe(
project_path, log_file, token_to_id=token_to_id, type_to_id=type_to_id, label_to_id=label_to_id,
token_to_leaves=tokens_to_leaves, is_split=is_split, max_token_len=max_token_len,
max_label_len=max_label_len, wrap_tokens=wrap_tokens, wrap_labels=wrap_labels, delimiter=delimiter,
n_jobs=n_jobs
)
graphs = []
labels = []
source_paths = []
print("load graphs to memory...")
for project_path in tqdm(projects_paths):
graph_path = os.path.join(project_path, 'converted.dgl')
labels_path = os.path.join(project_path, 'converted.pkl')
if not os.path.exists(graph_path) or not os.path.exists(labels_path):
with open(log_file, 'a') as file:
file.write(f"can't load graphs for {project_path} project\n")
continue
cur_graphs, _ = load_graphs(graph_path)
with open(labels_path, 'rb') as pkl_file:
pkl_data = load(pkl_file)
cur_labels, cur_source_paths = pkl_data['labels'], pkl_data['source_paths']
graphs += cur_graphs
labels += cur_labels.tolist()
source_paths += cur_source_paths.tolist()
assert len(graphs) == len(labels), "unequal lengths of graphs and labels"
assert len(graphs) == len(source_paths), "unequal lengths of graphs and source paths"
print(f"total number of graphs: {len(graphs)}")
if shuffle:
order = np.random.permutation(len(graphs))
graphs = [graphs[i] for i in order]
labels = [labels[i] for i in order]
source_paths = [source_paths[i] for i in order]
print(f"save batches...")
n_batches = len(graphs) // batch_size + (1 if len(graphs) % batch_size > 0 else 0)
for batch_num in tqdm(range(n_batches)):
current_slice = slice(batch_num * batch_size, min((batch_num + 1) * batch_size, len(graphs)))
output_graph_path = os.path.join(output_path, f'batch_{batch_num}.dgl')
output_labels_path = os.path.join(output_path, f'batch_{batch_num}.pkl')
save_graphs(output_graph_path, graphs[current_slice])
with open(output_labels_path, 'wb') as pkl_file:
dump({
'labels': np.array(labels[current_slice]), 'source_paths': source_paths[current_slice]
}, pkl_file)
def test_hact_model_bracs_hact_5_classes_pna(self):
"""Test HACT bracs_hact_5_classes_pna model."""
# 1. Load a cell graph
cell_graph, _ = load_graphs(
os.path.join(self.cg_graph_path, self.cg_graph_name))
cell_graph = cell_graph[0]
cell_graph = set_graph_on_cuda(cell_graph) if IS_CUDA else cell_graph
cg_node_dim = cell_graph.ndata['feat'].shape[1]
tissue_graph, _ = load_graphs(
os.path.join(self.tg_graph_path, self.tg_graph_name))
tissue_graph = tissue_graph[0]
tissue_graph = set_graph_on_cuda(
tissue_graph) if IS_CUDA else tissue_graph
tg_node_dim = tissue_graph.ndata['feat'].shape[1]
assignment_matrix = torch.randint(
2, (tissue_graph.number_of_nodes(),
cell_graph.number_of_nodes())).float()
assignment_matrix = assignment_matrix.cuda(
) if IS_CUDA else assignment_matrix
assignment_matrix = [assignment_matrix] # ie. batch size is 1.
# 2. load config and build model with pretrained weights
config_fname = os.path.join(self.current_path, 'config',
'bracs_hact_5_classes_pna.yml')
with open(config_fname, 'r') as file:
config = yaml.safe_load(file)
model = HACTModel(
cg_gnn_params=config['cg_gnn_params'],
tg_gnn_params=config['tg_gnn_params'],
classification_params=config['classification_params'],
cg_node_dim=cg_node_dim,
tg_node_dim=tg_node_dim,
num_classes=5,
pretrained=True).to(DEVICE)
# 3. forward pass
logits = model(cell_graph, tissue_graph, assignment_matrix)
self.assertIsInstance(logits, torch.Tensor)
self.assertEqual(logits.shape[0], 1)
self.assertEqual(logits.shape[1], 5)
def pre_process(self):
processed_dir = osp.join(self.root, 'processed')
raw_dir = osp.join(self.root, 'raw')
pre_processed_file_path = osp.join(processed_dir, 'dgl_data_processed')
if os.path.exists(pre_processed_file_path):
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
else:
### download
url = self.meta_info[self.name]["url"]
if decide_download(url):
path = download_url(url, self.original_root)
extract_zip(path, self.original_root)
os.unlink(path)
# delete folder if there exists
try:
shutil.rmtree(self.root)
except:
pass
shutil.move(osp.join(self.original_root, self.download_name),
self.root)
else:
print("Stop download.")
exit(-1)
### preprocess
add_inverse_edge = self.meta_info[
self.name]["add_inverse_edge"] == "True"
graphs = read_csv_graph_dgl(raw_dir,
add_inverse_edge=add_inverse_edge)
labels = torch.tensor(
pd.read_csv(osp.join(raw_dir, "graph-label.csv.gz"),
compression="gzip",
header=None).values)
print('Saving...')
save_graphs(pre_processed_file_path,
graphs,
labels={'labels': labels})
### load preprocessed files
self.graphs, label_dict = load_graphs(pre_processed_file_path)
self.labels = label_dict['labels']
def get_hetgnn_graph(self, length, walks, restart_prob):
fname = './{}_het.bin'.format(self.n_dataset)
if os.path.exists(fname):
g, _ = load_graphs(fname)
return g[0]
else:
g = self.build_hetgnn_graph(length, walks, restart_prob)
save_graphs(fname, g)
return g
def _load(self):
if self.load_mode == 'read':
self.graphs, tour_dict = load_graphs(self.file_name)
self.tours = tour_dict['tsp_tours']
else:
print('Start generating dataset...')
self.graphs, tour_dict = self.generate_data(
self.num_samples, self.num_nodes, self.node_dim,
self.file_name, self.seed)
self.tours = tour_dict['tsp_tours']
def load(self):
graph_path = os.path.join(self.save_path, 'tu_{}.bin'.format(self.name))
info_path = os.path.join(self.save_path, 'tu_{}.pkl'.format(self.name))
graphs, label_dict = load_graphs(str(graph_path))
info_dict = load_info(str(info_path))
self.graph_lists = graphs
self.graph_labels = label_dict['labels']
self.max_num_node = info_dict['max_num_node']
self.num_labels = info_dict['num_labels']
