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improvememory requierement of cache

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luni
2023-02-09 14:33:05 +00:00
parent db5cb35718
commit 2f4dc477bd
1 changed files with 21 additions and 11 deletions
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data/curvatures.py
+21 -11
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@@ -96,28 +96,34 @@ class Data:
class Cache: class Cache:
def __init__(self, folder, neighbourhood, frames, include_neighbours=True): def __init__(self, folder, neighbourhood, frames, include_neighbours=True):
self.cache = dict() self.cache = dict()
self.folder = folder
self.neighbourhood = neighbourhood
self.frames = frames
self.include_neighbours = include_neighbours
for frame in tqdm.tqdm(frames):
def read(self, main_id):
frames = [frame for frame in self.frames if frame.split()[0] == main_id]
for frame in frames:
id = frame.split()[0] id = frame.split()[0]
frame_nr = int(frame.split()[1])
garment = frame.split()[2] garment = frame.split()[2]
if id in self.cache.keys(): if id in self.cache.keys():
continue continue
self.cache[id] = Data() self.cache[id] = Data()
mask = read_garment_mask(folder, id, garment) #np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool) mask = read_garment_mask(self.folder, id, garment)
if include_neighbours: if self.include_neighbours:
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood) k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :self.neighbourhood]
self.cache[id].k_neighbors = k_neighbors self.cache[id].k_neighbors = k_neighbors
G = tm.graph.vertex_adjacency_graph(m) G = tm.graph.vertex_adjacency_graph(m)
for i in range(v.shape[0]): for i in range(v.shape[0]):
for k in range(25): for k in range(25):
assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}' assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}'
_, faces = _maskFaces(np.zeros((14475,3)), F, mask) #np.load(os.path.join(folder, 'faces', f'{id}.npy')) _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
v = read_posed_garment_frame(folder, id, garment, frame_nr)[mask,:] v = read_posed_garment_frame(folder, id, garment, 0)[mask,:]
m = tm.Trimesh(v, faces, process=False, validate=False) m = tm.Trimesh(v, faces, process=False, validate=False)
outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0 outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
outliers = np.nonzero(outliers)[0] outliers = np.nonzero(outliers)[0]
@@ -130,6 +136,10 @@ class Cache:
self.cache[id].faces = faces self.cache[id].faces = faces
def __getitem__(self, id): def __getitem__(self, id):
if id not in self.cache.keys():
del self.cache
self.cache = dict()
self.read(id)
return self.cache[id] return self.cache[id]
@@ -383,13 +393,13 @@ if __name__ == '__main__':
start = time.perf_counter() start = time.perf_counter()
# generate_curvatures(folder, neighbourhood, frames) # generate_curvatures(folder, neighbourhood, frames)
# curvatures = read_raw_curvatures(folder, frames, neighbourhood) curvatures = read_raw_curvatures(folder, frames, neighbourhood)
# bins = get_bins(curvatures, nr_of_labels) # bins = get_bins(curvatures, nr_of_labels)
# np.save(f'custom_data/{type}_bins{skirt_modifier}_{neighbourhood}.npy', bins) # np.save(f'custom_data/{type}_bins{skirt_modifier}_{neighbourhood}.npy', bins)
# print(bins) # print(bins)
# bins = np.load(f'custom_data/{type}_bins{skirt_modifier}_{neighbourhood}.npy') bins = np.load(f'custom_data/{type}_bins{skirt_modifier}_{neighbourhood}.npy')
# generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood) generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
visualize_curvature_labels(folder, frames) # visualize_curvature_labels(folder, frames)
print(time.perf_counter() - start) print(time.perf_counter() - start)