def tf_map(self, batch_pc, batch_label, batch_pc_idx, batch_cloud_idx):
features = batch_pc
input_points = []
input_neighbors = []
input_pools = []
input_up_samples = []
for i in range(cfg.num_layers): #4
neighbour_idx = DP.knn_search(batch_pc, batch_pc,
cfg.k_n) #16 return index[B,N,K]
#取前1/4N 个点
sub_points = batch_pc[:, :batch_pc.shape[1] //
cfg.sub_sampling_ratio[i], :]
pool_i = neighbour_idx[:, :batch_pc.shape[1] //
cfg.sub_sampling_ratio[i], :]
#[B,N,1] 原始点集对应sub点集的idx
up_i = DP.knn_search(sub_points, batch_pc, 1)
input_points.append(batch_pc) #[N, N/4, N/16,N/64]
input_neighbors.append(neighbour_idx) #[N, N/4, N/16,N/64],k
input_pools.append(pool_i) #[N/4,N/16,N/64,N/256],k
input_up_samples.append(up_i) #[N, N/4, N/16,N/64],1
batch_pc = sub_points
input_list = input_points + input_neighbors + input_pools + input_up_samples
input_list += [features, batch_label, batch_pc_idx, batch_cloud_idx]
return input_list
def np_map(self, batch_xyz, batch_features, batch_labels, batch_pc_idx,
batch_cloud_idx):
batch_features = np.concatenate([batch_xyz, batch_features], axis=-1)
input_points = []
input_neighbors = []
input_pools = []
input_up_samples = []
for i in range(cfg.num_layers):
neighbour_idx = DP.knn_search(batch_xyz, batch_xyz, cfg.k_n)
sub_points = batch_xyz[:, :batch_xyz.shape[1] //
cfg.sub_sampling_ratio[i], :]
pool_i = neighbour_idx[:, :batch_xyz.shape[1] //
cfg.sub_sampling_ratio[i], :]
up_i = DP.knn_search(sub_points, batch_xyz, 1)
input_points.append(batch_xyz)
input_neighbors.append(neighbour_idx)
input_pools.append(pool_i)
input_up_samples.append(up_i)
batch_xyz = sub_points
input_list = input_points + input_neighbors + input_pools + input_up_samples
input_list += [
batch_features, batch_labels, batch_pc_idx, batch_cloud_idx
]
return input_list
def np_map(self, batch_xyz, batch_features, batch_labels, batch_pc_idx,
batch_cloud_idx):
# erase tf
batch_features = self.np_augment_input([batch_xyz, batch_features])
input_points = []
input_neighbors = []
input_pools = []
input_up_samples = []
# erase tf
for i in range(cfg.num_layers):
neigh_idx = DP.knn_search(batch_xyz, batch_xyz, cfg.k_n)
sub_points = batch_xyz[:, :batch_xyz.shape[1] //
cfg.sub_sampling_ratio[i], :]
pool_i = neigh_idx[:, :batch_pc.shape[1] //
cfg.sub_sampling_ratio[i], :]
up_i = DP.knn_search(sub_points, batch_xyz, 1)
input_points.append(batch_xyz)
input_neighbors.append(neigh_idx)
input_pools.append(pool_i)
input_up_samples.append(up_i)
batch_xyz = sub_points
input_list = input_points + input_neighbors + input_pools + input_up_samples
input_list += [
batch_features, batch_labels, batch_pc_idx, batch_cloud_idx
]
return input_list
def main():
cfg = ConfigTest
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = Network(cfg).to(device)
print("model parameters:", sum(param.numel() for param in net.parameters()))
for i in tqdm(range(10)):
npts = cfg.num_points
pcld = np.random.rand(1, npts, 3)
feat = np.random.rand(1, 6, npts)
n_layers = 4
sub_s_r = [16, 1, 4, 1]
inputs = {}
for i in range(n_layers):
nei_idx = DP.knn_search(pcld, pcld, 16)
sub_pts = pcld[:, :pcld.shape[1] // sub_s_r[i], :]
pool_i = nei_idx[:, :pcld.shape[1] // sub_s_r[i], :]
up_i = torch.LongTensor(DP.knn_search(sub_pts, pcld, 1))
inputs['xyz'] = inputs.get('xyz', []) + [torch.from_numpy(pcld).float().to(device)]
inputs['neigh_idx'] = inputs.get('neigh_idx', []) + [torch.LongTensor(nei_idx).to(device)]
inputs['sub_idx'] = inputs.get('sub_idx', []) + [torch.LongTensor(pool_i).to(device)]
inputs['interp_idx'] = inputs.get('interp_idx', []) + [torch.LongTensor(up_i).to(device)]
pcld = sub_pts
inputs['features'] = torch.from_numpy(feat).float().to(device)
end_points = net(inputs)
for k, v in end_points.items():
if type(v) == list:
for ii, item in enumerate(v):
print(k+'%d'%ii, item.size())
else:
print(k, v.size())
def tf_map(self, batch_pc, batch_label, batch_pc_idx, batch_cloud_idx):
features = batch_pc
input_points = []
input_neighbors = []
input_pools = []
input_up_samples = []
for i in range(cfg.num_layers):
neighbour_idx = DP.knn_search(batch_pc, batch_pc, cfg.k_n)
sub_points = batch_pc[:, :batch_pc.shape[1] // cfg.sub_sampling_ratio[i], :]
pool_i = neighbour_idx[:, :batch_pc.shape[1] // cfg.sub_sampling_ratio[i], :]
up_i = DP.knn_search(sub_points, batch_pc, 1)
input_points.append(batch_pc)
input_neighbors.append(neighbour_idx)
input_pools.append(pool_i)
input_up_samples.append(up_i)
batch_pc = sub_points
input_list = input_points + input_neighbors + input_pools + input_up_samples
input_list += [features, batch_label, batch_pc_idx, batch_cloud_idx]
return input_list
def gen_one_sample(point_cloud):
point_cloud_input = tf.random.shuffle(point_cloud, seed=None, name=None)
batch_xyz = tf.expand_dims(point_cloud_input,
axis=0) # [Npts,3] => [1,Npts,3]
point_cloud = point_cloud.reshape(1, *point_cloud.shape)
batch_feature_input = point_cloud
# batch_xyz = tf.expand_dims(batch_xyz, axis=2)
print("batch_xyz.shape", batch_xyz.shape)
input_points = []
input_neighbors = []
input_pools = []
input_up_samples = []
# batch_feature = tf.concat([batch_xyz, batch_xyz], axis=-1)
batch_feature = batch_xyz
# with open("./BATCH_FEATURE.txt", 'w') as f:
# for p in range(batch_feature.shape[1]):
# f.write(str(batch_feature_input[0,p,0]) + " ")
# f.write(str(batch_feature_input[0,p,1]) + " ")
# f.write(str(batch_feature_input[0,p,2]) + " ")
# if(p < batch_feature.shape[1] - 1):
# f.write('\n')
tf_holder_batch_feature = tf.compat.v1.placeholder(tf.float32,
shape=batch_xyz.shape,
name='Batch_Feature')
# tf_holder_batch_feature = tf.compat.v1.placeholder(tf.float32, shape=(batch_xyz.shape[0], batch_xyz.shape[1] , batch_xyz.shape[2] ), name='Batch_Feature')
# tf_batch_feature = tf.compat.v1.placeholder(tf.float32, shape=(batch_xyz.shape[0], batch_xyz.shape[1] , batch_xyz.shape[2] ), name='Batch_Feature')
# tf_batch_feature_map = Semantic3D.tf_augment_input([tf.squeeze(tf_batch_feature,axis=0)])
# tf_holder_batch_feature = tf.expand_dims(tf_batch_feature_map, axis=0)
# print("tf_batch_feature.size: ", tf_batch_feature.shape)
# print("tf_holder_batch_size: ", tf_holder_batch_feature.shape)
tf_holder_input_points = []
tf_holder_input_neighbors = []
tf_holder_input_pools = []
tf_holder_input_up_samples = []
for i in range(cfg.num_layers):
# neigh_idx = self.knn_search(batch_xyz, batch_xyz, cfg.k_n)
# neigh_idx = tf.py_func(DP.knn_search, [batch_xyz, batch_xyz, cfg.k_n], tf.int32)
# neigh_idx = tf.compat.v1.placeholder(tf.int64, shape=(batch_xyz.shape[0], batch_xyz.shape[1], cfg.k_n), name='Neighbor_Index_' + str(i))
# # sub_points = tf.compat.v1.placeholder(tf.float32, shape=(batch_xyz.shape[0], batch_xyz.shape[1] // cfg.sub_sampling_ratio[i], batch_xyz.shape[2]), name='Subpoints_' + str(i))
# pool_i = tf.compat.v1.placeholder(tf.int64, shape=(batch_xyz.shape[0], batch_xyz.shape[1] // cfg.sub_sampling_ratio[i], cfg.k_n), name='Pool_I_' + str(i))
# up_i = tf.compat.v1.placeholder(tf.int64, shape=(batch_xyz.shape[0], batch_xyz.shape[1], 1), name='Up_I_' + str(i))
neigh_idx = tf.compat.v1.placeholder(tf.int64,
shape=(point_cloud.shape[0],
point_cloud.shape[1],
cfg.k_n),
name='Neighbor_Index_' + str(i))
# sub_points = tf.compat.v1.placeholder(tf.float32, shape=(batch_xyz.shape[0], batch_xyz.shape[1] // cfg.sub_sampling_ratio[i], batch_xyz.shape[2]), name='Subpoints_' + str(i))
pool_i = tf.compat.v1.placeholder(
tf.int64,
shape=(point_cloud.shape[0],
point_cloud.shape[1] // cfg.sub_sampling_ratio[i], cfg.k_n),
name='Pool_I_' + str(i))
up_i = tf.compat.v1.placeholder(tf.int64,
shape=(point_cloud.shape[0],
point_cloud.shape[1], 1),
name='Up_I_' + str(i))
# neigh_idx = DP.tf_knn_search(batch_xyz, batch_xyz, cfg.k_n)
neigh_idx_np = DP.knn_search(point_cloud, point_cloud, cfg.k_n)
# sub_points = batch_xyz[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
sub_points_np = point_cloud[:, :point_cloud.shape[1] //
cfg.sub_sampling_ratio[i], :]
# pool_i = neigh_idx[:, :tf.shape(batch_xyz)[1] // cfg.sub_sampling_ratio[i], :]
pool_i_np = neigh_idx_np[:, :point_cloud.shape[1] //
cfg.sub_sampling_ratio[i], :]
# # up_i = tf.py_func(DP.knn_search, [sub_points, batch_xyz, 1], tf.int32)
# up_i = DP.tf_knn_search(sub_points, batch_xyz, 1)
up_i_np = DP.knn_search(sub_points_np, point_cloud, 1)
# up_i = self.knn_search(sub_points, batch_xyz, 1)
# print("neigh_idx: ", neigh_idx.shape, "sub_points: ", sub_points.shape , "pool_i: ", pool_i.shape, "up_i: ", up_i.shape)
# tf_holder_batch_xyz = tf.compat.v1.placeholder(tf.float32, shape=(batch_xyz.shape[0], batch_xyz.shape[1] , batch_xyz.shape[2]), name='Batch_XYZ_' +str(i))
tf_holder_batch_xyz = tf.compat.v1.placeholder(
tf.float32,
shape=(point_cloud.shape[0], point_cloud.shape[1],
point_cloud.shape[2]),
name='Batch_XYZ_' + str(i))
tf_holder_input_points.append(tf_holder_batch_xyz)
# input_points.append(batch_xyz)
# input_neighbors.append(neigh_idx)
# input_pools.append(pool_i)
# input_up_samples.append(up_i)
input_points.append(point_cloud)
input_neighbors.append(neigh_idx_np)
input_pools.append(pool_i_np)
input_up_samples.append(up_i_np)
point_cloud = sub_points_np
# batch_xyz = sub_points
# print("sub_points.shape", sub_points.shape)
# print("neigh_idx.shape", neigh_idx.shape)
# print("pool_i.shape", pool_i.shape)
# print("up_i.shape", up_i.shape)
# print("sub_points.shape", sub_points.shape)
# print("neigh_idx.shape", neigh_idx.shape)
# print("pool_i.shape", pool_i.shape)
# print("up_i.shape", up_i.shape)
print("neigh_idx_np.shape", neigh_idx_np.shape)
print("sub_points_np.shape", sub_points_np.shape)
print("pool_i_np.shape", pool_i_np.shape)
print("up_i_np.shape", up_i_np.shape)
tf_holder_input_neighbors.append(neigh_idx)
tf_holder_input_pools.append(pool_i)
tf_holder_input_up_samples.append(up_i)
input_list_data = input_points + input_neighbors + input_pools + input_up_samples
input_list_data += [batch_feature_input]
input_list = tf_holder_input_points + tf_holder_input_neighbors + tf_holder_input_pools + tf_holder_input_up_samples
input_list += [tf_holder_batch_feature]
for i, tf_holder in enumerate(input_list):
print("tf_holder ", i, " ", tf_holder.shape)
return input_list, input_list_data
pass
