**Abstract**: To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have revealed their tremendous representation capacities in numerous generation tasks, showing potential for stochastic trajectory prediction. However, expensive time consumption prevents diffusion models from real-time prediction, since a large number of denoising steps are required to assure sufficient representation ability. To resolve the dilemma, we present **LEapfrog Diffusion model (LED)**, a novel diffusion-based trajectory prediction model, which provides real-time, precise, and diverse predictions. The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed. Moreover, the leapfrog initializer is trained to appropriately allocate correlated samples to provide a diversity of predicted future trajectories, significantly improving prediction performances. Extensive experiments on four real-world datasets, including NBA/NFL/SDD/ETH-UCY, show that LED consistently improves performance and achieves **23.7\%/21.9\%** ADE/FDE improvement on NFL. The proposed LED also speeds up the inference **19.3/30.8/24.3/25.1** times compared to the standard diffusion model on NBA/NFL/SDD/ETH-UCY, satisfying real-time inference needs.
Here, we present an example (above) to illustrate the mean and variance estimation in the leapfrog initializer under four scenes on the NBA dataset. We see that the variance estimation can well describe the scene complexity for the current agent by the learned variance, showing the rationality of our variance estimation.
## 2. Code Guidance
Overall project structure:
```text
----LED\
|----README.md
|----requirements.txt # packages to install
|----main_led_nba.py # [CORE] main file
|----trainer\ # [CORE] main training files, we define the denoising process HERE!
| |----train_led_trajectory_augment_input.py
|----models\ # [CORE] define models under this file
| |----model_led_initializer.py
| |----model_diffusion.py
| |----layers.py
|----utils\
| |----utils.py
| |----config.py
|----data\ # preprocessed data (~200MB) and dataloader
| |----files\
| | |----nba_test.npy
| | |----nba_train.npy
| |----dataloader_nba.py
|----cfg\ # config files
| |----nba\
| | |----led_augment.yml
|----results\ # store the results and checkpoints (~100MB)
|----visualization\ # some visualization codes
```
Please download the data and results from [Google Drive](https://drive.google.com/drive/folders/1Uy8-WvlCp7n3zJKiEX0uONlEcx2u3Nnx?usp=sharing).
**TODO list**:
- [ ] add training/evaluation for diffusion models (in two weeks).
- [ ] more detailed descripition in trainers (in one month).
- [ ] transfer the parameters in models into yaml.
- [ ] other fast sampling methods (DDIM and PD).
### 2.1. Environment
We train and evaluate our model on `Ubuntu=18.04` with `RTX 3090-24G`.
Create a new python environment (`led`) using `conda`:
```
conda create -n led python=3.7
conda activate led
```
Install required packages using Command 1 or 2:
```bash
# Command 1 (recommend):
pip install -r requirements.txt
# Command 2:
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install easydict
pip install glob2
```
### 2.2. Training
You can use the following command to start training the initializer.
```bash
python main_led_nba.py --cfg <-config_file_name_here-> --gpu <-gpu_index_here-> --train 1 --info <-experiment_information_here->
# e.g.
python main_led_nba.py --cfg led_augment --gpu 5 --train 1 --info try1
```
And the results are stored under the `./results` folder.
### 2.3. Evaluation
We provide pretrained models under the `./checkpoints` folder.
**Reproduce**. Using the command `python main_led_nba.py --cfg led_augment --gpu 5 --train 0 --info reproduce` and you will get the following results:
```text
[Core Denoising Model] Trainable/Total: 6568720/6568720
[Initialization Model] Trainable/Total: 4634721/4634721
./checkpoints/led_new.p
--ADE(1s): 0.1766 --FDE(1s): 0.2694
--ADE(2s): 0.3693 --FDE(2s): 0.5642
--ADE(3s): 0.5817 --FDE(3s): 0.8366
--ADE(4s): 0.8095 --FDE(4s): 1.0960
```
## 3. Citation
If you find this code useful for your research, please cite our paper:
```bibtex
@inproceedings{mao2023leapfrog,
title={Leapfrog Diffusion Model for Stochastic Trajectory Prediction},
author={Mao, Weibo and Xu, Chenxin and Zhu, Qi and Chen, Siheng and Wang, Yanfeng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5517--5526},
year={2023}
}
```
## 4. Acknowledgement
Some code is borrowed from [MID](https://github.com/Gutianpei/MID), [NPSN](https://github.com/InhwanBae/NPSN) and [GroupNet](https://github.com/MediaBrain-SJTU/GroupNet). We thank the authors for releasing their code.
[](https://star-history.com/#MediaBrain-SJTU/LED&Date)
================================================
FILE: cfg/nba/led_augment.yml
================================================
# ------------------- General Options -------------------------
description : LED
results_root_dir : results
dataset : nba
# ------------------- Dataset -------------------------
past_frames : 10
future_frames : 20
min_past_frames : 10
min_future_frames : 20
motion_dim : 2
forecast_dim : 2
traj_mean : [14, 7.5]
traj_scale : 5
# ------------------- Model -------------------------
pretrained_core_denoising_model: './results/checkpoints/base_diffusion_model.p'
debug : False # set to True for early stop in each epoch.
diffusion : {
steps : 100,
beta_start : 1.e-4,
beta_end : 5.e-2,
beta_schedule : 'linear'
}
# ------------------- Training Parameters -------------------------
lr : 1.e-3
train_batch_size : 10
test_batch_size : 500
num_epochs : 100
lr_scheduler : 'step'
decay_step : 8
decay_gamma : 0.5
================================================
FILE: cfg/nba/led_augment_debug.yml
================================================
# ------------------- General Options -------------------------
description : LED
results_root_dir : results
dataset : nba
# ------------------- Dataset -------------------------
past_frames : 10
future_frames : 20
min_past_frames : 10
min_future_frames : 20
motion_dim : 2
forecast_dim : 2
traj_mean : [14, 7.5]
traj_scale : 5
# ------------------- Model -------------------------
pretrained_core_denoising_model: './results/checkpoints/base_diffusion_model.p'
debug : True # set to True for early stop in each epoch.
diffusion : {
steps : 100,
beta_start : 1.e-4,
beta_end : 5.e-2,
beta_schedule : 'linear'
}
# ------------------- Training Parameters -------------------------
lr : 1.e-3
train_batch_size : 2
test_batch_size : 5
num_epochs : 100
test_interval : 2
lr_scheduler : 'step'
decay_step : 8
decay_gamma : 0.5
================================================
FILE: main_led_nba.py
================================================
import argparse
from trainer import train_led_trajectory_augment_input as led
def parse_config():
parser = argparse.ArgumentParser()
parser.add_argument("--cuda", default=True)
parser.add_argument("--learning_rate", type=int, default=0.002)
parser.add_argument("--max_epochs", type=int, default=128)
parser.add_argument('--cfg', default='led_augment')
parser.add_argument('--gpu', type=int, default=0, help='Specify which GPU to use.')
parser.add_argument('--train', type=int, default=1, help='Whether train or evaluate.')
parser.add_argument("--info", type=str, default='', help='Name of the experiment. '
'It will be used in file creation.')
return parser.parse_args()
def main(config):
t = led.Trainer(config)
if config.train==1:
t.fit()
else:
# t.save_data()
t.test_single_model()
if __name__ == "__main__":
config = parse_config()
main(config)
================================================
FILE: models/layers.py
================================================
import math
import torch
import torch.nn as nn
from torch.nn import Module, Linear
class PositionalEncoding(nn.Module):
def __init__(self, d_model, dropout=0.1, max_len=5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[: x.size(0), :]
return self.dropout(x)
class ConcatSquashLinear(Module):
def __init__(self, dim_in, dim_out, dim_ctx):
super(ConcatSquashLinear, self).__init__()
self._layer = Linear(dim_in, dim_out)
self._hyper_bias = Linear(dim_ctx, dim_out, bias=False)
self._hyper_gate = Linear(dim_ctx, dim_out)
def forward(self, ctx, x):
# ctx: (B, 1, F+3)
# x: (B, T, 2)
gate = torch.sigmoid(self._hyper_gate(ctx))
bias = self._hyper_bias(ctx)
# if x.dim() == 3:
# gate = gate.unsqueeze(1)
# bias = bias.unsqueeze(1)
ret = self._layer(x) * gate + bias
return ret
def batch_generate(self, ctx, x):
# ctx: (B, n, 1, F+3)
# x: (B, n, T, 2)
gate = torch.sigmoid(self._hyper_gate(ctx))
bias = self._hyper_bias(ctx)
# if x.dim() == 3:
# gate = gate.unsqueeze(1)
# bias = bias.unsqueeze(1)
ret = self._layer(x) * gate + bias
return ret
class GAT(nn.Module):
def __init__(self, in_feat=2, out_feat=64, n_head=4, dropout=0.1, skip=True):
super(GAT, self).__init__()
self.in_feat = in_feat
self.out_feat = out_feat
self.n_head = n_head
self.skip = skip
self.w = nn.Parameter(torch.Tensor(n_head, in_feat, out_feat))
self.a_src = nn.Parameter(torch.Tensor(n_head, out_feat, 1))
self.a_dst = nn.Parameter(torch.Tensor(n_head, out_feat, 1))
self.bias = nn.Parameter(torch.Tensor(out_feat))
self.leaky_relu = nn.LeakyReLU(negative_slope=0.2)
self.softmax = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(dropout)
nn.init.xavier_uniform_(self.w, gain=1.414)
nn.init.xavier_uniform_(self.a_src, gain=1.414)
nn.init.xavier_uniform_(self.a_dst, gain=1.414)
nn.init.constant_(self.bias, 0)
def forward(self, h, mask):
h_prime = h.unsqueeze(1) @ self.w
attn_src = h_prime @ self.a_src
attn_dst = h_prime @ self.a_dst
attn = attn_src @ attn_dst.permute(0, 1, 3, 2)
attn = self.leaky_relu(attn)
attn = self.softmax(attn)
attn = self.dropout(attn)
attn = attn * mask if mask is not None else attn
out = (attn @ h_prime).sum(dim=1) + self.bias
if self.skip:
out += h_prime.sum(dim=1)
return out, attn
class MLP(nn.Module):
def __init__(self, in_feat, out_feat, hid_feat=(1024, 512), activation=None, dropout=-1):
super(MLP, self).__init__()
dims = (in_feat, ) + hid_feat + (out_feat, )
self.layers = nn.ModuleList()
for i in range(len(dims) - 1):
self.layers.append(nn.Linear(dims[i], dims[i + 1]))
self.activation = activation if activation is not None else lambda x: x
self.dropout = nn.Dropout(dropout) if dropout != -1 else lambda x: x
def forward(self, x):
for i in range(len(self.layers)):
x = self.activation(x)
x = self.dropout(x)
x = self.layers[i](x)
return x
class social_transformer(nn.Module):
def __init__(self, past_len):
super(social_transformer, self).__init__()
self.encode_past = nn.Linear(past_len*6, 256, bias=False)
self.layer = nn.TransformerEncoderLayer(d_model=256, nhead=2, dim_feedforward=256)
self.transformer_encoder = nn.TransformerEncoder(self.layer, num_layers=2)
def forward(self, h, mask):
'''
h: batch_size, t, 2
'''
h_feat = self.encode_past(h.reshape(h.size(0), -1)).unsqueeze(1)
# print(h_feat.shape)
# n_samples, 1, 64
h_feat_ = self.transformer_encoder(h_feat, mask)
h_feat = h_feat + h_feat_
return h_feat
class st_encoder(nn.Module):
def __init__(self):
super().__init__()
channel_in = 6
channel_out = 32
dim_kernel = 3
self.dim_embedding_key = 256
self.spatial_conv = nn.Conv1d(channel_in, channel_out, dim_kernel, stride=1, padding=1)
self.temporal_encoder = nn.GRU(channel_out, self.dim_embedding_key, 1, batch_first=True)
self.relu = nn.ReLU()
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_normal_(self.spatial_conv.weight)
nn.init.kaiming_normal_(self.temporal_encoder.weight_ih_l0)
nn.init.kaiming_normal_(self.temporal_encoder.weight_hh_l0)
nn.init.zeros_(self.spatial_conv.bias)
nn.init.zeros_(self.temporal_encoder.bias_ih_l0)
nn.init.zeros_(self.temporal_encoder.bias_hh_l0)
def forward(self, X):
'''
X: b, T, 2
return: b, F
'''
X_t = torch.transpose(X, 1, 2)
X_after_spatial = self.relu(self.spatial_conv(X_t))
X_embed = torch.transpose(X_after_spatial, 1, 2)
output_x, state_x = self.temporal_encoder(X_embed)
state_x = state_x.squeeze(0)
return state_x
================================================
FILE: models/model_diffusion.py
================================================
import math
import torch
import torch.nn as nn
from torch.nn import Module, Linear
from models.layers import PositionalEncoding, ConcatSquashLinear
class st_encoder(nn.Module):
def __init__(self):
super().__init__()
channel_in = 2
channel_out = 32
dim_kernel = 3
self.dim_embedding_key = 256
self.spatial_conv = nn.Conv1d(channel_in, channel_out, dim_kernel, stride=1, padding=1)
self.temporal_encoder = nn.GRU(channel_out, self.dim_embedding_key, 1, batch_first=True)
self.relu = nn.ReLU()
self.reset_parameters()
def reset_parameters(self):
nn.init.kaiming_normal_(self.spatial_conv.weight)
nn.init.kaiming_normal_(self.temporal_encoder.weight_ih_l0)
nn.init.kaiming_normal_(self.temporal_encoder.weight_hh_l0)
nn.init.zeros_(self.spatial_conv.bias)
nn.init.zeros_(self.temporal_encoder.bias_ih_l0)
nn.init.zeros_(self.temporal_encoder.bias_hh_l0)
def forward(self, X):
'''
X: b, T, 2
return: b, F
'''
X_t = torch.transpose(X, 1, 2)
X_after_spatial = self.relu(self.spatial_conv(X_t))
X_embed = torch.transpose(X_after_spatial, 1, 2)
output_x, state_x = self.temporal_encoder(X_embed)
state_x = state_x.squeeze(0)
return state_x
class social_transformer(nn.Module):
def __init__(self):
super(social_transformer, self).__init__()
self.encode_past = nn.Linear(60, 256, bias=False)
# self.encode_past = nn.Linear(48, 256, bias=False)
self.layer = nn.TransformerEncoderLayer(d_model=256, nhead=2, dim_feedforward=256)
self.transformer_encoder = nn.TransformerEncoder(self.layer, num_layers=2)
def forward(self, h, mask):
'''
h: batch_size, t, 2
'''
# print(h.shape)
h_feat = self.encode_past(h.reshape(h.size(0), -1)).unsqueeze(1)
# print(h_feat.shape)
# n_samples, 1, 64
h_feat_ = self.transformer_encoder(h_feat, mask)
h_feat = h_feat + h_feat_
return h_feat
class TransformerDenoisingModel(Module):
def __init__(self, context_dim=256, tf_layer=2):
super().__init__()
self.encoder_context = social_transformer()
self.pos_emb = PositionalEncoding(d_model=2*context_dim, dropout=0.1, max_len=24)
self.concat1 = ConcatSquashLinear(2, 2*context_dim, context_dim+3)
self.layer = nn.TransformerEncoderLayer(d_model=2*context_dim, nhead=2, dim_feedforward=2*context_dim)
self.transformer_encoder = nn.TransformerEncoder(self.layer, num_layers=tf_layer)
self.concat3 = ConcatSquashLinear(2*context_dim,context_dim,context_dim+3)
self.concat4 = ConcatSquashLinear(context_dim,context_dim//2,context_dim+3)
self.linear = ConcatSquashLinear(context_dim//2, 2, context_dim+3)
def forward(self, x, beta, context, mask):
batch_size = x.size(0)
beta = beta.view(batch_size, 1, 1) # (B, 1, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
context = self.encoder_context(context, mask)
# context = context.view(batch_size, 1, -1) # (B, 1, F)
time_emb = torch.cat([beta, torch.sin(beta), torch.cos(beta)], dim=-1) # (B, 1, 3)
ctx_emb = torch.cat([time_emb, context], dim=-1) # (B, 1, F+3)
x = self.concat1(ctx_emb, x)
final_emb = x.permute(1,0,2)
final_emb = self.pos_emb(final_emb)
trans = self.transformer_encoder(final_emb).permute(1,0,2)
trans = self.concat3(ctx_emb, trans)
trans = self.concat4(ctx_emb, trans)
return self.linear(ctx_emb, trans)
def generate_accelerate(self, x, beta, context, mask):
batch_size = x.size(0)
beta = beta.view(beta.size(0), 1, 1) # (B, 1, 1)
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
context = self.encoder_context(context, mask)
# context = context.view(batch_size, 1, -1) # (B, 1, F)
time_emb = torch.cat([beta, torch.sin(beta), torch.cos(beta)], dim=-1) # (B, 1, 3)
# time_emb: [11, 1, 3]
# context: [11, 1, 256]
ctx_emb = torch.cat([time_emb, context], dim=-1).repeat(1, 10, 1).unsqueeze(2)
# x: 11, 10, 20, 2
# ctx_emb: 11, 10, 1, 259
x = self.concat1.batch_generate(ctx_emb, x).contiguous().view(-1, 20, 512)
# x: 110, 20, 512
final_emb = x.permute(1, 0, 2)
final_emb = self.pos_emb(final_emb)
trans = self.transformer_encoder(final_emb).permute(1, 0, 2).contiguous().view(-1, 10, 20, 512)
# trans: 11, 10, 20, 512
trans = self.concat3.batch_generate(ctx_emb, trans)
trans = self.concat4.batch_generate(ctx_emb, trans)
return self.linear.batch_generate(ctx_emb, trans)
================================================
FILE: models/model_led_initializer.py
================================================
import torch
import torch.nn as nn
from models.layers import MLP, social_transformer, st_encoder
class LEDInitializer(nn.Module):
def __init__(self, t_h: int=8, d_h: int=6, t_f: int=40, d_f: int=2, k_pred: int=20):
'''
Parameters
----
t_h: history timestamps,
d_h: dimension of each historical timestamp,
t_f: future timestamps,
d_f: dimension of each future timestamp,
k_pred: number of predictions.
'''
super(LEDInitializer, self).__init__()
self.n = k_pred
self.input_dim = t_h * d_h
self.output_dim = t_f * d_f * k_pred
self.fut_len = t_f
self.social_encoder = social_transformer(t_h)
self.ego_var_encoder = st_encoder()
self.ego_mean_encoder = st_encoder()
self.ego_scale_encoder = st_encoder()
self.scale_encoder = MLP(1, 32, hid_feat=(4, 16), activation=nn.ReLU())
self.var_decoder = MLP(256*2+32, self.output_dim, hid_feat=(1024, 1024), activation=nn.ReLU())
self.mean_decoder = MLP(256*2, t_f * d_f, hid_feat=(256, 128), activation=nn.ReLU())
self.scale_decoder = MLP(256*2, 1, hid_feat=(256, 128), activation=nn.ReLU())
def forward(self, x, mask=None):
'''
x: batch size, t_p, 6
'''
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
social_embed = self.social_encoder(x, mask)
social_embed = social_embed.squeeze(1)
# B, 256
ego_var_embed = self.ego_var_encoder(x)
ego_mean_embed = self.ego_mean_encoder(x)
ego_scale_embed = self.ego_scale_encoder(x)
# B, 256
mean_total = torch.cat((ego_mean_embed, social_embed), dim=-1)
guess_mean = self.mean_decoder(mean_total).contiguous().view(-1, self.fut_len, 2)
scale_total = torch.cat((ego_scale_embed, social_embed), dim=-1)
guess_scale = self.scale_decoder(scale_total)
guess_scale_feat = self.scale_encoder(guess_scale)
var_total = torch.cat((ego_var_embed, social_embed, guess_scale_feat), dim=-1)
guess_var = self.var_decoder(var_total).reshape(x.size(0), self.n, self.fut_len, 2)
return guess_var, guess_mean, guess_scale
================================================
FILE: requirements.txt
================================================
absl-py==1.4.0
aiohttp==3.8.4
aiosignal==1.3.1
antlr4-python3-runtime==4.8
async-timeout==4.0.2
asynctest==0.13.0
attrs==23.1.0
cachetools==5.3.0
charset-normalizer==3.1.0
colour==0.1.5
cycler==0.11.0
descartes==1.1.0
easydict==1.10
fonttools==4.38.0
frozenlist==1.3.3
fsspec==2023.1.0
future==0.18.3
glob2==0.7
google-auth==2.18.0
google-auth-oauthlib==0.4.6
googledrivedownloader==0.4
grpcio==1.54.0
h5py==3.8.0
hydra-core==1.1.0
imageio==2.27.0
imgaug==0.4.0
importlib-metadata==4.13.0
importlib-resources==5.12.0
isodate==0.6.1
Jinja2==3.1.2
joblib==1.2.0
kiwisolver==1.4.4
lapsolver==1.1.0
llvmlite==0.39.1
Markdown==3.4.3
MarkupSafe==2.1.2
matplotlib==3.5.3
motmetrics==1.1.3
multidict==6.0.4
networkx==2.6.3
numba==0.56.4
numpy==1.19.0
oauthlib==3.2.2
omegaconf==2.1.0
opencv-python==4.7.0.72
packaging==23.1
pandas==1.3.5
Pillow==9.5.0
polars==0.17.12
protobuf==3.20.3
pyasn1==0.5.0
pyasn1-modules==0.3.0
pyDeprecate==0.3.1
pyntcloud==0.3.1
pyparsing==3.0.9
python-dateutil==2.8.2
python-louvain==0.16
pytorch-lightning==1.5.2
pytz==2023.3
PyWavelets==1.3.0
PyYAML==6.0
rdflib==6.3.2
requests-oauthlib==1.3.1
rsa==4.9
scikit-image==0.19.3
scikit-learn==1.0.2
scipy==1.7.3
shapely==2.0.1
spconv==1.2.1
tensorboard==2.11.2
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorboardX==2.6
threadpoolctl==3.1.0
tifffile==2021.11.2
torch==1.8.0+cu111
torchaudio==0.8.0
torchmetrics==0.11.4
torchvision==0.9.0+cu111
tqdm==4.65.0
typing_extensions==4.5.0
Werkzeug==2.2.3
yarl==1.9.2
zipp==3.15.0
================================================
FILE: trainer/train_led_trajectory_augment_input.py
================================================
import os
import time
import torch
import random
import numpy as np
import torch.nn as nn
from utils.config import Config
from utils.utils import print_log
from torch.utils.data import DataLoader
from data.dataloader_nba import NBADataset, seq_collate
from models.model_led_initializer import LEDInitializer as InitializationModel
from models.model_diffusion import TransformerDenoisingModel as CoreDenoisingModel
import pdb
NUM_Tau = 5
class Trainer:
def __init__(self, config):
if torch.cuda.is_available(): torch.cuda.set_device(config.gpu)
self.device = torch.device('cuda') if config.cuda else torch.device('cpu')
self.cfg = Config(config.cfg, config.info)
# ------------------------- prepare train/test data loader -------------------------
train_dset = NBADataset(
obs_len=self.cfg.past_frames,
pred_len=self.cfg.future_frames,
training=True)
self.train_loader = DataLoader(
train_dset,
batch_size=self.cfg.train_batch_size,
shuffle=True,
num_workers=4,
collate_fn=seq_collate,
pin_memory=True)
test_dset = NBADataset(
obs_len=self.cfg.past_frames,
pred_len=self.cfg.future_frames,
training=False)
self.test_loader = DataLoader(
test_dset,
batch_size=self.cfg.test_batch_size,
shuffle=False,
num_workers=4,
collate_fn=seq_collate,
pin_memory=True)
# data normalization parameters
self.traj_mean = torch.FloatTensor(self.cfg.traj_mean).cuda().unsqueeze(0).unsqueeze(0).unsqueeze(0)
self.traj_scale = self.cfg.traj_scale
# ------------------------- define diffusion parameters -------------------------
self.n_steps = self.cfg.diffusion.steps # define total diffusion steps
# make beta schedule and calculate the parameters used in denoising process.
self.betas = self.make_beta_schedule(
schedule=self.cfg.diffusion.beta_schedule, n_timesteps=self.n_steps,
start=self.cfg.diffusion.beta_start, end=self.cfg.diffusion.beta_end).cuda()
self.alphas = 1 - self.betas
self.alphas_prod = torch.cumprod(self.alphas, 0)
self.alphas_bar_sqrt = torch.sqrt(self.alphas_prod)
self.one_minus_alphas_bar_sqrt = torch.sqrt(1 - self.alphas_prod)
# ------------------------- define models -------------------------
self.model = CoreDenoisingModel().cuda()
# load pretrained models
model_cp = torch.load(self.cfg.pretrained_core_denoising_model, map_location='cpu')
self.model.load_state_dict(model_cp['model_dict'])
self.model_initializer = InitializationModel(t_h=10, d_h=6, t_f=20, d_f=2, k_pred=20).cuda()
self.opt = torch.optim.AdamW(self.model_initializer.parameters(), lr=config.learning_rate)
self.scheduler_model = torch.optim.lr_scheduler.StepLR(self.opt, step_size=self.cfg.decay_step, gamma=self.cfg.decay_gamma)
# ------------------------- prepare logs -------------------------
self.log = open(os.path.join(self.cfg.log_dir, 'log.txt'), 'a+')
self.print_model_param(self.model, name='Core Denoising Model')
self.print_model_param(self.model_initializer, name='Initialization Model')
# temporal reweight in the loss, it is not necessary.
self.temporal_reweight = torch.FloatTensor([21 - i for i in range(1, 21)]).cuda().unsqueeze(0).unsqueeze(0) / 10
def print_model_param(self, model: nn.Module, name: str = 'Model') -> None:
'''
Count the trainable/total parameters in `model`.
'''
total_num = sum(p.numel() for p in model.parameters())
trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
print_log("[{}] Trainable/Total: {}/{}".format(name, trainable_num, total_num), self.log)
return None
def make_beta_schedule(self, schedule: str = 'linear',
n_timesteps: int = 1000,
start: float = 1e-5, end: float = 1e-2) -> torch.Tensor:
'''
Make beta schedule.
Parameters
----
schedule: str, in ['linear', 'quad', 'sigmoid'],
n_timesteps: int, diffusion steps,
start: float, beta start, `start 0 and len(ext) > 0
else: return False
def isfolder(pathname):
'''
if '.' exists in the subfolder, the function still justifies it as a folder. e.g., /mnt/dome/adhoc_0.5x/abc is a folder
if '.' exists after all slashes, the function will not justify is as a folder. e.g., /mnt/dome/adhoc_0.5x is NOT a folder
'''
if is_path_valid(pathname):
pathname = os.path.normpath(pathname)
if pathname == './': return True
name = os.path.splitext(os.path.basename(pathname))[0]
ext = os.path.splitext(pathname)[1]
return len(name) > 0 and len(ext) == 0
else: return False
def mkdir_if_missing(input_path):
folder = input_path if isfolder(input_path) else os.path.dirname(input_path)
os.makedirs(folder, exist_ok=True)
def safe_list(input_data, warning=True, debug=True):
'''
copy a list to the buffer for use
parameters:
input_data: a list
outputs:
safe_data: a copy of input data
'''
if debug: assert islist(input_data), 'the input data is not a list'
safe_data = copy.copy(input_data)
return safe_data
def safe_path(input_path, warning=True, debug=True):
'''
convert path to a valid OS format, e.g., empty string '' to '.', remove redundant '/' at the end from 'aa/' to 'aa'
parameters:
input_path: a string
outputs:
safe_data: a valid path in OS format
'''
if debug: assert isstring(input_path), 'path is not a string: %s' % input_path
safe_data = copy.copy(input_path)
safe_data = os.path.normpath(safe_data)
return safe_data
def prepare_seed(rand_seed):
np.random.seed(rand_seed)
random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed_all(rand_seed)
def initialize_weights(modules):
for m in modules:
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None: nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
if m.bias is not None: nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None: nn.init.constant_(m.bias, 0)
def print_log(print_str, log, same_line=False, display=True):
'''
print a string to a log file
parameters:
print_str: a string to print
log: a opened file to save the log
same_line: True if we want to print the string without a new next line
display: False if we want to disable to print the string onto the terminal
'''
if display:
if same_line: print('{}'.format(print_str), end='')
else: print('{}'.format(print_str))
if same_line: log.write('{}'.format(print_str))
else: log.write('{}\n'.format(print_str))
log.flush()
def find_unique_common_from_lists(input_list1, input_list2, warning=True, debug=True):
'''
find common items from 2 lists, the returned elements are unique. repetitive items will be ignored
if the common items in two elements are not in the same order, the outputs follows the order in the first list
parameters:
input_list1, input_list2: two input lists
outputs:
list_common: a list of elements existing both in list_src1 and list_src2
index_list1: a list of index that list 1 has common items
index_list2: a list of index that list 2 has common items
'''
input_list1 = safe_list(input_list1, warning=warning, debug=debug)
input_list2 = safe_list(input_list2, warning=warning, debug=debug)
common_list = list(set(input_list1).intersection(input_list2))
# find index
index_list1 = []
for index in range(len(input_list1)):
item = input_list1[index]
if item in common_list:
index_list1.append(index)
index_list2 = []
for index in range(len(input_list2)):
item = input_list2[index]
if item in common_list:
index_list2.append(index)
return common_list, index_list1, index_list2
def load_txt_file(file_path, debug=True):
'''
load data or string from text file
'''
file_path = safe_path(file_path)
if debug: assert is_path_exists(file_path), 'text file is not existing at path: %s!' % file_path
with open(file_path, 'r') as file: data = file.read().splitlines()
num_lines = len(data)
file.close()
return data, num_lines
def load_list_from_folder(folder_path, ext_filter=None, depth=1, recursive=False, sort=True, save_path=None, debug=True):
'''
load a list of files or folders from a system path
parameters:
folder_path: root to search
ext_filter: a string to represent the extension of files interested
depth: maximum depth of folder to search, when it's None, all levels of folders will be searched
recursive: False: only return current level
True: return all levels till to the input depth
outputs:
fulllist: a list of elements
num_elem: number of the elements
'''
folder_path = safe_path(folder_path)
if debug: assert isfolder(folder_path), 'input folder path is not correct: %s' % folder_path
if not is_path_exists(folder_path):
print('the input folder does not exist\n')
return [], 0
if debug:
assert islogical(recursive), 'recursive should be a logical variable: {}'.format(recursive)
assert depth is None or (isinteger(depth) and depth >= 1), 'input depth is not correct {}'.format(depth)
assert ext_filter is None or (islist(ext_filter) and all(isstring(ext_tmp) for ext_tmp in ext_filter)) or isstring(ext_filter), 'extension filter is not correct'
if isstring(ext_filter): ext_filter = [ext_filter] # convert to a list
# zxc
fulllist = list()
if depth is None: # find all files recursively
recursive = True
wildcard_prefix = '**'
if ext_filter is not None:
for ext_tmp in ext_filter:
# wildcard = os.path.join(wildcard_prefix, '*' + string2ext_filter(ext_tmp))
wildcard = os.path.join(wildcard_prefix, '*' + ext_tmp)
curlist = glob2.glob(os.path.join(folder_path, wildcard))
if sort: curlist = sorted(curlist)
fulllist += curlist
else:
wildcard = wildcard_prefix
curlist = glob2.glob(os.path.join(folder_path, wildcard))
if sort: curlist = sorted(curlist)
fulllist += curlist
else: # find files based on depth and recursive flag
wildcard_prefix = '*'
for index in range(depth-1): wildcard_prefix = os.path.join(wildcard_prefix, '*')
if ext_filter is not None:
for ext_tmp in ext_filter:
# wildcard = wildcard_prefix + string2ext_filter(ext_tmp)
wildcard = wildcard_prefix + ext_tmp
curlist = glob.glob(os.path.join(folder_path, wildcard))
if sort: curlist = sorted(curlist)
fulllist += curlist
# zxc
else:
wildcard = wildcard_prefix
curlist = glob.glob(os.path.join(folder_path, wildcard))
# print(curlist)
if sort: curlist = sorted(curlist)
fulllist += curlist
if recursive and depth > 1:
newlist, _ = load_list_from_folder(folder_path=folder_path, ext_filter=ext_filter, depth=depth-1, recursive=True)
fulllist += newlist
fulllist = [os.path.normpath(path_tmp) for path_tmp in fulllist]
num_elem = len(fulllist)
# save list to a path
if save_path is not None:
save_path = safe_path(save_path)
if debug: assert is_path_exists_or_creatable(save_path), 'the file cannot be created'
with open(save_path, 'w') as file:
for item in fulllist: file.write('%s\n' % item)
file.close()
return fulllist, num_elem
================================================
FILE: visualization/draw_mean_variance.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import torch\n",
"import numpy as np\n",
"import time\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"past_traj = torch.load('data/past.pt', map_location=torch.device('cpu'))\n",
"fut_traj = torch.load('data/future.pt', map_location=torch.device('cpu'))\n",
"\n",
"traj = torch.cat((past_traj, fut_traj), dim=2) * 94 / 28\n",
"\n",
"prediction = torch.load('data/prediction.pt', map_location=torch.device('cpu'))\n",
"prediction = (prediction * 5 + past_traj[:, :, -1:].contiguous().view(-1, 1, 2)[:, None]) * 94 / 28\n",
"\n",
"\n",
"mean = torch.load('data/p_mean_denoise.pt', map_location=torch.device('cpu'))[:, 0]\n",
"mean = (mean * 5 + past_traj[:, :, -1:].contiguous().view(-1, 1, 2)) * 94 / 28\n",
"\n",
"sigma = torch.load('data/p_sigma.pt', map_location=torch.device('cpu'))\n",
"sigma = torch.exp(sigma/2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"court = plt.imread(\"court.png\")\n",
"mask = np.zeros_like(court)\n",
"\n",
"class Constant:\n",
" \"\"\"A class for handling constants\"\"\"\n",
" NORMALIZATION_COEF = 7\n",
" PLAYER_CIRCLE_SIZE = 12 / NORMALIZATION_COEF\n",
" INTERVAL = 10\n",
" DIFF = 6\n",
" X_MIN = 0\n",
" X_MAX = 100\n",
" Y_MIN = 0\n",
" Y_MAX = 50\n",
" COL_WIDTH = 0.3\n",
" SCALE = 1.65\n",
" FONTSIZE = 6\n",
" X_CENTER = X_MAX / 2 - DIFF / 1.5 + 0.10\n",
" Y_CENTER = Y_MAX - DIFF / 1.5 - 0.35"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"idx = [[0, 0], [2, 8], [3, 0], [4, 10]]\n",
"\n",
"for i in idx:\n",
" scene = i[0]\n",
" actor_num = i[1]\n",
"\n",
" plt.clf()\n",
"\n",
" ax = plt.axes(xlim=(Constant.X_MIN,\n",
" Constant.X_MAX),\n",
" ylim=(Constant.Y_MIN,\n",
" Constant.Y_MAX))\n",
" ax.axis('off')\n",
" fig = plt.gcf()\n",
" ax.grid(False) # Remove grid\n",
"\n",
"\n",
" idx = scene*11 + actor_num\n",
"\n",
" colorteam1 = 'dodgerblue'\n",
" colorteam2 = 'dodgerblue'\n",
" colorball = 'dodgerblue'\n",
" colorteam1_pre = 'skyblue'\n",
" colorteam2_pre = 'skyblue'\n",
" colorball_pre = 'skyblue'\n",
"\n",
"\n",
"\n",
"\n",
" traj_pred = prediction[idx]\n",
" traj_mean = mean[idx]\n",
" # background players\n",
" for actor_num_other in range(11):\n",
" zorder = 5\n",
" if actor_num_other==actor_num:\n",
" zorder = 105\n",
" traj_curr_ = traj[scene, actor_num_other].numpy()\n",
" if actor_num_other < 5:\n",
" color = colorteam1\n",
" color_pre = colorteam1_pre\n",
" elif actor_num_other < 10:\n",
" color = colorteam2\n",
" color_pre = colorteam2_pre\n",
" else:\n",
" color_pre = colorball_pre\n",
" color = colorball\n",
" for i in range(30):\n",
" points = [(traj_curr_[i,0],traj_curr_[i,1])]\n",
" (x, y) = zip(*points)\n",
" if i < 10:\n",
" plt.scatter(x, y, color=color_pre,s=15,alpha=1, zorder=zorder)\n",
" else:\n",
" if actor_num_other==actor_num and i==29:\n",
" plt.scatter(x, y, color=color,s=60, marker='*', alpha=1, zorder=zorder)\n",
" \n",
" plt.scatter(x, y, color=color, s=15,alpha=1, zorder=zorder)\n",
"\n",
" for i in range(29):\n",
" points = [(traj_curr_[i,0],traj_curr_[i,1]),(traj_curr_[i+1,0],traj_curr_[i+1,1])]\n",
" (x, y) = zip(*points)\n",
" if i < 10:\n",
" plt.plot(x, y, color=color_pre,alpha=0.5,linewidth=2, zorder=zorder-1)\n",
" else:\n",
" plt.plot(x, y, color=color,alpha=1,linewidth=2, zorder=zorder-1)\n",
"\n",
"\n",
" for i in range(20):\n",
" plt.plot(traj_pred[i, :19, 0], traj_pred[i, :19, 1], \n",
" linestyle='-', markersize=2, marker=\"|\", alpha=1,\n",
" color='white',linewidth=1, zorder=101)\n",
" plt.plot(traj_pred[i,18:, 0], traj_pred[i, 18:, 1], \n",
" linestyle='-', markersize=0, marker=\"o\", alpha=1,\n",
" color='white',linewidth=1, zorder=101)\n",
"\n",
"\n",
" plt.scatter(traj_pred[:, -1, 0], traj_pred[:, -1, 1], \n",
" marker='*', \n",
" color='white',s=60, alpha=1, zorder=120)\n",
"\n",
"\n",
"\n",
" plt.plot(traj_mean[:19, 0], traj_mean[:19, 1],\n",
" linestyle='-', markersize=4, marker=\"o\", alpha=1,\n",
" color='violet',linewidth=3, zorder=130)\n",
" plt.plot(traj_mean[18:, 0], traj_mean[18:, 1],\n",
" linestyle='-', markersize=0, marker=\"o\", alpha=1,\n",
" color='violet',linewidth=3, zorder=130) \n",
" plt.scatter(traj_mean[-1, 0], traj_mean[ -1, 1], \n",
" marker='*', \n",
" color='violet',s=60, alpha=1, zorder=130)\n",
" \n",
" \n",
"\n",
" plt.title('$\\sigma$: {:.3f}'.format(sigma[idx, 0]))\n",
" plt.imshow(court, zorder=0, extent=[Constant.X_MIN, Constant.X_MAX - Constant.DIFF,\n",
" Constant.Y_MAX, Constant.Y_MIN],alpha=0.5)\n",
" plt.imshow(mask, zorder=90, extent=[Constant.X_MIN, Constant.X_MAX - Constant.DIFF,\n",
" Constant.Y_MAX, Constant.Y_MIN],alpha=0.7)\n",
" # plt.savefig('fig3/scene_{}_agent_{}.jpg'.format(scene, actor_num), bbox_inches='tight', dpi=300)\n",
"# print(scene)\n",
" plt.show()\n",
" plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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