first commit

.gitignore

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+# experiment folders
+experiments/
+experiments_temp/
+
+# installation folders
+apex/
+hmr_data/
+posetrack_data/
+data/
+_DATA/
+hmr/
+_TEMP/
+_BACKUP/
+_BACKUP2/
+out/
+save/
+deep_sort/
+*.npy
+*.pickle
+*.avi
+*.pth
+_BACKUP/
+tmap_data/
+hmr/external/
+pyopengl/
+video_out/
+save/
+wandb/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.npz
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

HMAR_tracker.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision.utils import save_image, make_grid
+
+import os
+import pyrender
+import trimesh
+import warnings
+
+
+import cv2
+import math
+import time
+import numpy as np
+import wandb
+import random
+import copy
+import argparse
+from tqdm import tqdm
+
+
+from models.hmar import HMAR
+from models.relational_model_apk import RelationTransformerModel_APK
+
+
+
+def positionalencoding1d(d_model, length):
+ if d_model % 2 != 0:
+ raise ValueError("Cannot use sin/cos positional encoding with odd dim (got dim={:d})".format(d_model))
+ pe = torch.zeros(length, d_model)
+ position = torch.arange(0, length).unsqueeze(1)
+ div_term = torch.exp((torch.arange(0, d_model, 2, dtype=torch.float) *
+ -(math.log(10000.0) / d_model)))
+ pe[:, 0::2] = torch.sin(position.float() * div_term)
+ pe[:, 1::2] = torch.cos(position.float() * div_term)
+
+ return pe
+
+class keypoint_encoder(nn.Module):
+ def __init__(self, embedding_size_in, embedding_size_out):
+ super(keypoint_encoder, self).__init__()
+ self.layer1 = nn.Linear(embedding_size_in, embedding_size_out)
+
+ def forward(self, keypoints):
+ x_ = self.layer1(keypoints)
+ return x_
+
+
+class HMAR_tracker(nn.Module):
+
+ def __init__(self, mode='A', betas=[1.0,1.0,1.0]):
+ super(HMAR_tracker, self).__init__()
+
+ self.device=torch.device('cuda')
+ self.mode = mode
+
+ self.A_size = 512
+ self.P_size = 2048
+ self.K_size = 15*4
+ self.betas = betas
+
+
+ self.keypoint_encoder = keypoint_encoder(self.K_size, self.K_size)
+
+ ####################### Single Attibute Model ##############################################################
+ self.total_size = self.A_size + self.P_size + self.K_size*2 
+ self.relation_transformer = RelationTransformerModel_APK([self.total_size, self.A_size, self.P_size, self.K_size*2], 
+ depth = 1, 
+ heads = 1,
+ dim_head = self.total_size, 
+ mlp_dim = self.total_size,
+ dropout = 0.,
+ betas = self.betas)
+ ##############################################################################################################
+
+ def forward(self, BS, T, P, embeddings, ids, bboxs, keypoints):
+
+ if ("K" in self.mode):
+ temporal_embedding = positionalencoding1d(self.K_size, T)
+ temporal_embedding = temporal_embedding.unsqueeze(0) 
+ temporal_embedding = temporal_embedding.unsqueeze(2) 
+ temporal_embedding = temporal_embedding.repeat(BS, 1, P, 1) 
+ temporal_embedding = temporal_embedding.cuda()
+
+ keypoints_3d = torch.cat((keypoints[:, :, :, :, 0], keypoints[:, :, :, :, 1], keypoints[:, :, :, :, 2], keypoints[:, :, :, :, 2]), -1) 
+ keypoints_3d = keypoints_3d.cuda()
+ keypoints_3d = keypoints_3d.view(BS*T*P, -1) 
+
+ embedding_key_3d = self.keypoint_encoder(keypoints_3d) 
+ embedding_key_3d = embedding_key_3d.view(BS, T, P, self.K_size)
+ keypoints_3d = keypoints_3d.view(BS, T, P, self.K_size)
+ embedding_key_3d = torch.cat((embedding_key_3d, temporal_embedding), 3) 
+
+
+ ids_x = ids.view(BS,T*P)
+ mask_ids = torch.where(ids_x==-1)
+ mask_x = torch.ones_like(ids_x)
+ mask_x[mask_ids] = 0.0
+ mask_x = mask_x.cuda()
+
+ mask_a = torch.zeros((T*P, T*P))
+ mask_a[:, :] = 1.0
+ mask_a = mask_a.cuda() 
+
+
+
+ input_embeddings = torch.cat((embeddings[1]*self.betas[0], embeddings[0]*self.betas[1], embedding_key_3d*self.betas[2]), 3) 
+ input_embeddings = input_embeddings.view(BS, T*P, -1)
+ output_embeddings = self.relation_transformer(input_embeddings, [mask_x, mask_a])
+ output_embeddings = torch.cat((output_embeddings[:, :, :self.A_size]*self.betas[0], 
+ output_embeddings[:, :, self.A_size:self.A_size+self.P_size].view(BS, T*P, -1)*self.betas[1], 
+ output_embeddings[:, :, self.A_size+self.P_size:].view(BS, T*P, -1)*self.betas[2]), -1) 
+
+ output = {}
+ output["output_embeddings"] = output_embeddings
+ output["ids"] = ids_x
+ output["mask"] = mask_x
+
+
+ return output, 0
+
+
+ def forward_edge_loss(self, emb1, emb2, target):
+ output = self.edge_classifier(emb1, emb2)
+ return F.binary_cross_entropy_with_logits(output, target.view(-1, 1))
+
+
+ def normalize_embeddings(self, x):
+ norm = x.norm(p=2, dim=-1, keepdim=True)
+ x_normalized = x.div(norm.expand_as(x))
+ return x_normalized
+