python 3.10 update

README.md

@@ -40,34 +40,41 @@ Our model consists of three key components: Generator (G), Pooling Module (PM) a
 </div>
 
 ## Setup
-All code was developed and tested on Ubuntu 16.04 with Python 3.5 and PyTorch 0.4.
+All code was developed and tested on Ubuntu 22.04 with Python 3.10 and torch
 
-You can setup a virtual environment to run the code like this:
+You can setup a virtual conda environment to run the code like this:
 
 ```bash
-python3 -m venv env               # Create a virtual environment
-source env/bin/activate           # Activate virtual environment
-pip install -r requirements.txt   # Install dependencies
-echo $PWD > env/lib/python3.5/site-packages/sgan.pth  # Add current directory to python path
+conda create -n test python=3.10 -y               # Create a virtual environment
+conda activate test                               # Activate virtual environment
 # Work for a while ...
-deactivate  # Exit virtual environment
+conda deactivate  # Exit virtual environment
 ```
 
-## Pretrained Models
-You can download pretrained models by running the script `bash scripts/download_models.sh`. This will download the following models:
+## clone repo and download files
 
-- `sgan-models/<dataset_name>_<pred_len>.pt`: Contains 10 pretrained models for all five datasets. These models correspond to SGAN-20V-20 in Table 1.
-- `sgan-p-models/<dataset_name>_<pred_len>.pt`: Contains 10 pretrained models for all five datasets. These models correspond to SGAN-20VP-20 in Table 1.
+```bash
+git clone https://github.com/bharath5673/Social-GAN.git
+cd Social-GAN
+pip install -r requirements.txt   # Install dependencies
+sh scripts/download_data.sh
+sh scripts/download_models.sh
+```
 
 Please refer to [Model Zoo](MODEL_ZOO.md) for results.
 
 ## Running Models
 You can use the script `scripts/evaluate_model.py` to easily run any of the pretrained models on any of the datsets. For example you can replicate the Table 1 results for all datasets for SGAN-20V-20 like this:
 
 ```bash
-python scripts/evaluate_model.py \
-  --model_path models/sgan-models
+cd scripts
+sh run_eval.sh
 ```
 
 ## Training new models
+
+```bash
+cd scripts
+sh run_traj.sh
+```
 Instructions for training new models can be [found here](TRAINING.md).

requirements.txt

@@ -1,7 +1,6 @@
-attrdict==2.0.0
-numpy==1.14.5
-Pillow==6.2.0
-pkg-resources==0.0.0
-six==1.11.0
-torch==0.4.0
-torchvision==0.2.1
+scripts/attrdict-2.0.1-py2.py3-none-any.whl
+numpy
+Pillow
+six
+torch
+torchvision

scripts/evaluate_model.py

@@ -4,6 +4,9 @@
 
 from attrdict import AttrDict
 
+import sys
+sys.path.append('../')
+
 from sgan.data.loader import data_loader
 from sgan.models import TrajectoryGenerator
 from sgan.losses import displacement_error, final_displacement_error

scripts/run_eval.sh

@@ -0,0 +1 @@
+python3 evaluate_model.py --model_path ../models/sgan-models

scripts/train.py

@@ -11,6 +11,9 @@
 import torch.nn as nn
 import torch.optim as optim
 
+
+import sys
+sys.path.append('../')
 from sgan.data.loader import data_loader
 from sgan.losses import gan_g_loss, gan_d_loss, l2_loss
 from sgan.losses import displacement_error, final_displacement_error
@@ -109,7 +112,7 @@ def get_dtypes(args):
 
 
 def main(args):
-    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
+    # os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
     train_path = get_dset_path(args.dataset_name, 'train')
     val_path = get_dset_path(args.dataset_name, 'val')
 

sgan/models.py

@@ -60,7 +60,7 @@ def forward(self, obs_traj):
         """
         # Encode observed Trajectory
         batch = obs_traj.size(1)
-        obs_traj_embedding = self.spatial_embedding(obs_traj.view(-1, 2))
+        obs_traj_embedding = self.spatial_embedding(obs_traj.reshape(-1, 2))
         obs_traj_embedding = obs_traj_embedding.view(
             -1, batch, self.embedding_dim
         )