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# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
# of the code.
import os
import argparse
import numpy as np
from urllib.request import urlretrieve
try:
import open3d as o3d
except ImportError:
raise ImportError("Please install open3d with `pip install open3d`.")
import torch
import MinkowskiEngine as ME
from MinkowskiCommon import convert_to_int_list
import examples.minkunet as UNets
from tests.python.common import data_loader, load_file, batched_coordinates
from examples.common import Timer
# Check if the weights and file exist and download
if not os.path.isfile("weights.pth"):
print("Downloading weights and a room ply file...")
urlretrieve(
"http://cvgl.stanford.edu/data2/minkowskiengine/weights.pth", "weights.pth"
)
urlretrieve("http://cvgl.stanford.edu/data2/minkowskiengine/1.ply", "1.ply")
parser = argparse.ArgumentParser()
parser.add_argument("--file_name", type=str, default="1.ply")
parser.add_argument("--weights", type=str, default="weights.pth")
parser.add_argument("--use_cpu", action="store_true")
parser.add_argument("--backward", action="store_true")
parser.add_argument("--max_batch", type=int, default=12)
def quantize(coordinates):
D = coordinates.size(1) - 1
coordinate_manager = ME.CoordinateManager(
D=D, coordinate_map_type=ME.CoordinateMapType.CPU
)
coordinate_map_key = ME.CoordinateMapKey(convert_to_int_list(1, D), "")
key, (unique_map, inverse_map) = coordinate_manager.insert_and_map(
coordinates, *coordinate_map_key.get_key()
)
return unique_map, inverse_map
def load_file(file_name, voxel_size):
pcd = o3d.io.read_point_cloud(file_name)
coords = torch.from_numpy(np.array(pcd.points))
feats = torch.from_numpy(np.array(pcd.colors)).float()
quantized_coords = torch.floor(coords / voxel_size).int()
inds, inverse_inds = quantize(quantized_coords)
return quantized_coords[inds], feats[inds], pcd
def forward(coords, colors, model):
# Measure time
timer = Timer()
for i in range(5):
# Feed-forward pass and get the prediction
timer.tic()
sinput = ME.SparseTensor(
features=colors,
coordinates=coords,
device=device,
allocator_type=ME.GPUMemoryAllocatorType.PYTORCH,
)
logits = model(sinput)
timer.toc()
return timer.min_time, len(logits)
def train(coords, colors, model):
# Measure time
timer = Timer()
for i in range(5):
# Feed-forward pass and get the prediction
timer.tic()
sinput = ME.SparseTensor(
colors,
coords,
device=device,
allocator_type=ME.GPUMemoryAllocatorType.PYTORCH,
)
logits = model(sinput)
logits.F.sum().backward()
timer.toc()
return timer.min_time, len(logits)
def test_network(coords, feats, model, batch_sizes, forward_only=True):
for batch_size in batch_sizes:
bcoords = batched_coordinates([coords for i in range(batch_size)])
bfeats = torch.cat([feats for i in range(batch_size)], 0)
if forward_only:
with torch.no_grad():
time, length = forward(bcoords, bfeats, model)
else:
time, length = train(bcoords, bfeats, model)
print(f"{net.__name__}\t{voxel_size}\t{batch_size}\t{length}\t{time}")
torch.cuda.empty_cache()
if __name__ == "__main__":
config = parser.parse_args()
device = torch.device(
"cuda" if (torch.cuda.is_available() and not config.use_cpu) else "cpu"
)
print(f"Using {device}")
print(f"Using backward {config.backward}")
# Define a model and load the weights
batch_sizes = [i for i in range(2, config.max_batch + 1, 2)]
batch_sizes = [1, *batch_sizes]
for net in [UNets.MinkUNet14, UNets.MinkUNet18, UNets.MinkUNet34, UNets.MinkUNet50]:
model = net(3, 20).to(device)
model.eval()
for voxel_size in [0.02]:
print(voxel_size)
coords, feats, _ = load_file(config.file_name, voxel_size)
test_network(coords, feats, model, batch_sizes, not config.backward)
torch.cuda.empty_cache()
del model
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