# Copyright 2021 Yan Yan # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time from pathlib import Path import numpy as np import torch from torch import nn from cumm import tensorview as tv from spconv.core import ConvAlgo import spconv.pytorch as spconv from spconv.utils import Point2VoxelCPU3d # torch.backends.cudnn.enabled = False def waymo_data(batch_size=1, num_features=-1): gen = Point2VoxelCPU3d([0.1, 0.1, 0.1], [-80, -80, -2, 80, 80, 6], 3, 150000, 1) # gen = VoxelGeneratorV2([0.1, 0.1, 0.1], [-80, -80, -2, 80, 80, 6], 1, # 150000) data = np.load(Path(__file__).parent / "data" / "benchmark-pc.npz") pc = np.ascontiguousarray(data["pc"]) voxels_tv, indices_tv, _ = gen.point_to_voxel(tv.from_numpy(pc)) voxels = voxels_tv.numpy().reshape(-1, 3) if num_features > 0: voxels = np.zeros((voxels.shape[0], num_features), dtype=voxels.dtype) coors = indices_tv.numpy() N = coors.shape[0] coors = np.concatenate([np.full([N, 1], 0, coors.dtype), coors], axis=1) return voxels, coors, gen.grid_size def waymo_data_large(batch_size=1): gen = Point2VoxelCPU3d([0.1, 0.1, 0.1], [-80, -80, -2, 80, 80, 6], 3, 1200000, 1) # gen = VoxelGeneratorV2([0.1, 0.1, 0.1], [-80, -80, -2, 80, 80, 6], 1, # 150000) data = np.load(Path(__file__).parent / "data" / "benchmark-pc.npz") pc = np.ascontiguousarray(data["pc"]) pc2 = pc.copy() pc2[:, 1] += 1 pc3 = pc.copy() pc3[:, 1] += 2 pc4 = pc.copy() pc4[:, 1] += 3 pc5 = pc.copy() pc5[:, 1] += 4 pc = np.concatenate([pc, pc2, pc3, pc4, pc5]) print(pc.shape) voxels_tv, indices_tv, _ = gen.point_to_voxel(tv.from_numpy(pc)) voxels = voxels_tv.numpy().reshape(-1, 3) coors = indices_tv.numpy() N = coors.shape[0] print("num voxels", N) coors = np.concatenate([np.full([N, 1], 0, coors.dtype), coors], axis=1) return voxels, coors, gen.grid_size class Net(nn.Module): def __init__(self, shape, algo): super().__init__() pool_algo = algo # pool_algo = ConvAlgo.Native self.net = spconv.SparseSequential( spconv.SubMConv3d(3, 64, 3, bias=False, indice_key="c0", algo=algo), # spconv.SubMConv3d(32, # 32, # 3, # bias=False, # indice_key="c0", # algo=algo), # # nn.BatchNorm1d(32), # # nn.ReLU(), # # spconv.SparseConv3d(64, 64, 2, 2, bias=False, # # algo=algo), # spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0", # algo=algo), # spconv.SubMConv3d(64, 64, 3, bias=False, indice_key="c0", # algo=algo), # spconv.SubMConv3d(32, # 32, # 3, # bias=False, # indice_key="c0", # algo=algo), # # nn.BatchNorm1d(32), # # nn.ReLU(), # # spconv.SparseConv3d(64, 64, 2, 2, bias=False, # # algo=algo), # spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0", # algo=algo), spconv.SubMConv3d(64, 64, 3, bias=False, indice_key="c0", algo=algo), # nn.BatchNorm1d(32), # nn.ReLU(), # spconv.SparseConv3d(64, 64, 2, 2, bias=False, indice_key="m0"), spconv.SparseMaxPool3d(2, 2, algo=pool_algo), spconv.SubMConv3d(64, 96, 3, bias=False, indice_key="c1", algo=algo), spconv.SubMConv3d(96, 96, 3, bias=False, indice_key="c1", algo=algo), # nn.BatchNorm1d(64), # nn.ReLU(), # spconv.SparseConv3d(96, 96, 2, 2, bias=False, indice_key="m1"), spconv.SparseMaxPool3d(2, 2, algo=pool_algo), spconv.SubMConv3d(96, 128, 3, bias=False, indice_key="c2", algo=algo), spconv.SubMConv3d(128, 128, 3, bias=False, indice_key="c2", algo=algo), # nn.BatchNorm1d(128), # nn.ReLU(), # spconv.SparseConv3d(128, 128, 2, 2, bias=False, indice_key="m2"), spconv.SparseMaxPool3d(2, 2, algo=pool_algo), spconv.SubMConv3d(128, 160, 3, bias=False, indice_key="c3", algo=algo), spconv.SubMConv3d(160, 160, 3, bias=False, indice_key="c3", algo=algo), # nn.BatchNorm1d(128), # nn.ReLU(), # spconv.SparseConv3d(160, 160, 2, 2, bias=False, indice_key="m3"), spconv.SparseMaxPool3d(2, 2, algo=pool_algo), spconv.SubMConv3d(160, 192, 3, bias=False, indice_key="c4", algo=algo), spconv.SubMConv3d(192, 192, 3, bias=False, indice_key="c4", algo=algo), # nn.BatchNorm1d(128), # nn.ReLU(), spconv.SparseMaxPool3d(2, 2, indice_key="m4", algo=pool_algo), # spconv.SparseConv3d(192, 192, 2, 2, bias=False, indice_key="m4"), spconv.SubMConv3d(192, 224, 3, bias=False, indice_key="c5", algo=algo), spconv.SubMConv3d(224, 224, 3, bias=False, indice_key="c5", algo=algo), # nn.BatchNorm1d(224), # nn.ReLU(), # spconv.SparseConv3d(224, 224, 2, 2, bias=False, indice_key="m5"), spconv.SparseMaxPool3d(2, 2, indice_key="m5", algo=pool_algo), spconv.SubMConv3d(224, 256, 3, bias=False, indice_key="c6", algo=algo), spconv.SubMConv3d(256, 256, 3, bias=False, indice_key="c6", algo=algo), # nn.BatchNorm1d(256), # nn.ReLU(), # spconv.SparseInverseConv3d(256, 128, 2, indice_key="m5", bias=False, algo=algo), # # # nn.BatchNorm1d(128), # # # nn.ReLU(), # spconv.SparseInverseConv3d(128, 64, 2, indice_key="m4", bias=False, algo=algo), ) max_batch_size = 1 # grid (dense map) is used for indice generation. use pre-allocated grid can run faster. self.grid = torch.full([max_batch_size, *shape], -1, dtype=torch.int32).cuda() # self.grid = None self.shape = shape def forward(self, features, coors, batch_size, enable_timer: bool = False): x = spconv.SparseConvTensor(features, coors, self.shape, batch_size, self.grid, enable_timer=enable_timer) return self.net(x) class Net2(nn.Module): def __init__(self, shape, algo): super().__init__() self.net = spconv.SparseSequential( spconv.SubMConv3d(3, 128, 3, bias=False, indice_key="c0", algo=algo), # spconv.SubMConv3d(32, # 32, # 3, # bias=False, # indice_key="c0", # algo=algo), # # nn.BatchNorm1d(32), # # nn.ReLU(), # # spconv.SparseConv3d(64, 64, 2, 2, bias=False, # # algo=algo), # spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0", # algo=algo), spconv.SubMConv3d(128, 128, 3, bias=False, indice_key="c0", algo=algo), # nn.BatchNorm1d(32), # nn.ReLU(), # spconv.SparseMaxPool3d(2, 2), # spconv.SubMConv3d(256, # 512, # 3, # bias=False, # indice_key="c1", # algo=algo), # spconv.SubMConv3d(512, # 512, # 3, # bias=False, # indice_key="c1", # algo=algo), ) max_batch_size = 1 # grid (dense map) is used for indice generation. use pre-allocated grid can run faster. self.grid = torch.full([max_batch_size, *shape], -1, dtype=torch.int32).cuda() # self.grid = None self.shape = shape def forward(self, features, coors, batch_size): x = spconv.SparseConvTensor(features, coors, self.shape, batch_size, self.grid) return self.net(x) class NetSm(nn.Module): def __init__(self, shape, algo): super().__init__() self.net = spconv.SparseSequential( spconv.SubMConv3d(3, 8, 3, bias=False, indice_key="c0", algo=algo), spconv.SubMConv3d(8, 16, 3, bias=False, indice_key="c0", algo=algo), spconv.SubMConv3d(16, 32, 3, bias=False, indice_key="c0", algo=algo), spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0", algo=algo), ) max_batch_size = 1 # grid (dense map) is used for indice generation. use pre-allocated grid can run faster. self.grid = torch.full([max_batch_size, *shape], -1, dtype=torch.int32).cuda() # self.grid = None self.shape = shape def forward(self, features, coors, batch_size, enable_timer: bool = False): x = spconv.SparseConvTensor(features, coors, self.shape, batch_size, self.grid, enable_timer=enable_timer) return self.net(x) import numpy as np from cumm import tensorview as tv from spconv.core_cc.csrc.sparse.all import SpconvOps import pickle import torch from spconv.pytorch.cppcore import torch_tensor_to_tv def sort_bench(): with open("/home/yy/asd.pkl", "rb") as f: a_th = pickle.load(f) mask_argsort = torch.empty((1, a_th.shape[1]), dtype=torch.int32, device=a_th.device) a = a_th.cpu().numpy()[0] a_tv = torch_tensor_to_tv(a_th) mask_argsort_tv = torch_tensor_to_tv(mask_argsort) for i in range(10): a_tv_1 = a_tv.clone() SpconvOps.sort_1d_by_key(a_tv_1[0], mask_argsort_tv[0]) import json def main(): import pickle np.random.seed(50051) torch.manual_seed(50051) # voxels, coors, spatial_shape = waymo_data(num_features=128) # with open("/home/yy/test_spconv.pkl", "wb") as f: # pickle.dump((voxels, coors, spatial_shape), f) with open(Path(__file__).parent / "data" / "test_spconv.pkl", "rb") as f: (voxels, coors, spatial_shape) = pickle.load(f) # voxels, coors, spatial_shape = waymo_data_large() print(spatial_shape) print(voxels.shape) # voxels = voxels[:100] # coors = coors[:100] dtype = torch.float16 device = torch.device("cuda:0") voxels_th = torch.from_numpy(voxels).to(device).to(dtype) coors_th = torch.from_numpy(coors).to(device).int() voxels_th.requires_grad = True algo = spconv.ConvAlgo.MaskImplicitGemm print("ALGO") # 3080 Laptop # MaskImpGemm: 11.2ms # MaskSplitImpGemm: 12.2ms # Native: 13.7ms # F32 # MaskSplitImpGemm: 22ms # MaskImplicitGemm: 23.5ms # Native: 21.7ms # Pure Gemm # Native: 6.6ms # MaskImpGemm: 4.3ms # MaskSplitImpGemm: 4.0ms # F16 Bwd # MaskSplitImpGemm: 12.2ms # MaskImpGemm: 13.8ms # Native: 25.2ms # F32 Bwd # Native: 41.9ms # MaskImpGemm: 51.0ms # MaskSplitImpGemm: 41.1ms # algo = None net = NetSm(spatial_shape, algo).to(device).eval().to(dtype)# .train() # net.load_state_dict(net.state_dict()) spconv.assign_name_for_sparse_modules(net) print(coors_th.shape) out = net(voxels_th, coors_th, 1) print(out.spatial_shape) print(voxels.mean(), voxels.max(), voxels.min()) dout = np.random.uniform(-0.2, 0.2, out.features.shape).astype(np.float32) dout_t = torch.from_numpy(dout).to(device).to(dtype) print(out.spatial_shape, out.features.sum(1).mean(), out.features.max(), out.features.min()) times = [] show_metrics = False with torch.no_grad(): for i in range(100): # print("------------") with tv.measure_duration() as measure: out_nograd = net(voxels_th, coors_th, 1, show_metrics) times.append(measure.duration) if show_metrics: timer = out_nograd._timer items = list(timer.get_all_pair_time().items()) items.sort(key=lambda x: x[0]) print("SUM TIME:", sum([x[1] for x in items])) print(json.dumps(dict(items), indent=2)) inds_sum = 0 for k, v in items: if "gen_pairs" in k: inds_sum += v print("SUM GEN INDS:", inds_sum) # state = net.state_dict() # state.pop("net.2.max_num_voxels_during_training") # net.load_state_dict(state) # breakpoint() print("spconv time", np.mean(times[10:])) # times = [] # for i in range(10): # out = net(voxels_th, coors_th, 1) # print("------------") # torch.cuda.synchronize() # t = time.time() # out.features.backward(dout_t) # torch.cuda.synchronize() # times.append(time.time() - t) # # # print((net.grid == -1).float().sum(), net.grid.numel()) # # # print("spconv time", time.time() - t) # print("spconv bw time", np.mean(times[5:])) if __name__ == "__main__": main()