forked from rancheng/S3E_SLAM
/
tainer_me.py
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/
tainer_me.py
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# Copyright 2021 Ran Cheng <ran.cheng2@mail.mcgill.ca>
# 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.
import argparse
import numpy as np
import torch
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import MinkowskiEngine as ME
from module.SparseConvNet import PointNet
from module.ViT import ViT
def plot(C, L):
import matplotlib.pyplot as plt
mask = L == 0
cC = C[mask].t().numpy()
plt.scatter(cC[0], cC[1], c='r', s=0.1)
mask = L == 1
cC = C[mask].t().numpy()
plt.scatter(cC[0], cC[1], c='b', s=0.1)
plt.show()
class RandomLineDataset(Dataset):
# Warning: read using mutable obects for default input arguments in python.
def __init__(
self,
angle_range_rad=[-np.pi, np.pi],
line_params=[
-1, # Start
1, # end
],
is_linear_noise=True,
dataset_size=100,
num_samples=10000,
quantization_size=0.005):
self.angle_range_rad = angle_range_rad
self.is_linear_noise = is_linear_noise
self.line_params = line_params
self.dataset_size = dataset_size
self.rng = np.random.RandomState(0)
self.num_samples = num_samples
self.num_data = int(0.2 * num_samples)
self.num_noise = num_samples - self.num_data
self.quantization_size = quantization_size
def __len__(self):
return self.dataset_size
def _uniform_to_angle(self, u):
return (self.angle_range_rad[1] -
self.angle_range_rad[0]) * u + self.angle_range_rad[0]
def _sample_noise(self, num, noise_params):
noise = noise_params[0] + self.rng.randn(num, 1) * noise_params[1]
return noise
def _sample_xs(self, num):
"""Return random numbers between line_params[0], line_params[1]"""
return (self.line_params[1] - self.line_params[0]) * self.rng.rand(
num, 1) + self.line_params[0]
def __getitem__(self, i):
# Regardless of the input index, return randomized data
angle, intercept = np.tan(self._uniform_to_angle(
self.rng.rand())), self.rng.rand()
# Line as x = cos(theta) * t, y = sin(theta) * t + intercept and random t's
# Drop some samples
xs_data = self._sample_xs(self.num_data)
ys_data = angle * xs_data + intercept + self._sample_noise(
self.num_data, [0, 0.1])
noise = 4 * (self.rng.rand(self.num_noise, 2) - 0.5)
# Concatenate data
input = np.vstack([np.hstack([xs_data, ys_data]), noise])
feats = input
labels = np.vstack(
[np.ones((self.num_data, 1)),
np.zeros((self.num_noise, 1))]).astype(np.int32)
# Quantize the input
discrete_coords, unique_feats, unique_labels = ME.utils.sparse_quantize(
coords=input,
feats=feats,
labels=labels,
quantization_size=self.quantization_size,
ignore_label=-100)
return discrete_coords, unique_feats, unique_labels
def collation_fn(data_labels):
coords, feats, labels = list(zip(*data_labels))
coords_batch, feats_batch, labels_batch = [], [], []
# Generate batched coordinates
coords_batch = ME.utils.batched_coordinates(coords)
# Concatenate all lists
feats_batch = torch.from_numpy(np.concatenate(feats, 0)).float()
labels_batch = torch.from_numpy(np.concatenate(labels, 0))
return coords_batch, feats_batch, labels_batch
def main(config):
# Binary classification
net = PointNet(
2, # in nchannel
2) # out_nchannel
optimizer = optim.SGD(
net.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
criterion = torch.nn.CrossEntropyLoss(ignore_index=-100)
# Dataset, data loader
train_dataset = RandomLineDataset()
train_dataloader = DataLoader(
train_dataset,
batch_size=config.batch_size,
# 1) collate_fn=collation_fn,
# 2) collate_fn=ME.utils.batch_sparse_collate,
# 3) collate_fn=ME.utils.SparseCollation(),
collate_fn=ME.utils.batch_sparse_collate,
num_workers=1)
accum_loss, accum_iter, tot_iter = 0, 0, 0
for epoch in range(config.max_epochs):
train_iter = iter(train_dataloader)
# Training
net.train()
for i, data in enumerate(train_iter):
coords, feats, labels = data
out = net(ME.SparseTensor(feats.float(), coords))
optimizer.zero_grad()
loss = criterion(out.F.squeeze(), labels.long())
loss.backward()
optimizer.step()
accum_loss += loss.item()
accum_iter += 1
tot_iter += 1
if tot_iter % 10 == 0 or tot_iter == 1:
print(
f'Epoch: {epoch} iter: {tot_iter}, Loss: {accum_loss / accum_iter}'
)
accum_loss, accum_iter = 0, 0
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=12, type=int)
parser.add_argument('--max_epochs', default=10, type=int)
parser.add_argument('--lr', default=0.1, type=float)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight_decay', type=float, default=1e-4)
config = parser.parse_args()
main(config)