playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from lgtm.model.motion_diffusion import MotionDiffusion as LGTM\n",
    "from lgtm.dataset.HumanML3D import HumanML3D, recover_rearranged_humanml3d_features\n",
    "from lgtm.utils.body_part_annotation_augmentation import BodyPartAnnotationTool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/x/haowen_motion/lgtm/.env/lib/python3.10/site-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.activation_relu_or_gelu was not True\n",
      "  warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n",
      "/x/haowen_motion/lgtm/.env/lib/python3.10/site-packages/pytorch_lightning/core/saving.py:184: Found keys that are in the model state dict but not in the checkpoint: ['tmr_encoders.whole_body.tmr_model.motion_encoder.tokens', 'tmr_encoders.whole_body.tmr_model.motion_encoder.projection.weight', 'tmr_encoders.whole_body.tmr_model.motion_encoder.projection.bias', 'tmr_encoders.whole_body.tmr_model.motion_encoder.seqTransEncoder.layers.0.self_attn.in_proj_weight', 'tmr_encoders.whole_body.tmr_model.motion_encoder.seqTransEncoder.layers.0.self_attn.in_proj_bias', 'tmr_encoders.whole_body.tmr_model.motion_encoder.seqTransEncoder.layers.0.self_attn.out_proj.weight', 'tmr_encoders.whole_body.tmr_model.motion_encoder.seqTransEncoder.layers.0.self_attn.out_proj.bias', 'tmr_encoders.whole_body.tmr_model.motion_encoder.seqTransEncoder.layers.0.linear1.weight', 'tmr_encoders.whole_body.tmr_model.motion_encoder.seqTransEncoder.layers.0.linear1.bias', 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'whole_body_attention.global_text_encoder.0.tmr_model.motion_decoder.final_layer.bias']\n"
     ]
    }
   ],
   "source": [
    "from lgtm.dataset.HumanML3D import BodyPart_HumanML3D\n",
    "\n",
    "# This tool is for decomposition of full body motion description. You need to get an API key from OpenAI\n",
    "tool = BodyPartAnnotationTool(\n",
    "    api_key=\"\",\n",
    "    base_url=\"https://api.openai.com/v1\",\n",
    ")\n",
    "\n",
    "# Load LGTM\n",
    "model = LGTM.load_from_checkpoint(Path(\"./checkpoints/lgtm/checkpoints/epoch=196-val_loss=0.3769.ckpt\"))\n",
    "model.freeze()\n",
    "\n",
    "# This part-level dataset is for de-normalized data. But you can use it for generation\n",
    "dataset = BodyPart_HumanML3D(\n",
    "    HumanML3D(Path(\"third_packages/HumanML3D\"), Path(\"data/glove\"), \"all\"),\n",
    "    Path(\"./third_packages/TMR/datasets/annotations/humanml3d/annotations.json\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The input of LGTM\n",
    "full_body_text = \"a man walks forward sits in a chair then with his right hand.\"\n",
    "part_level_texts = (await tool.augment(\"xxx\", full_body_text))[\"xxx\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [00:51<00:00, 19.44it/s]\n"
     ]
    }
   ],
   "source": [
    "# It Generate 180 frames of motion. You can modify the part-level input as you want\n",
    "part_level_normalized_motion = model.sample(\n",
    "    whole_texts=[full_body_text],\n",
    "    part_texts={\n",
    "        \"head\": [part_level_texts.head.text],\n",
    "        \"torso\": [part_level_texts.torso.text],\n",
    "        \"left_arm\": [part_level_texts.left_arm.text],\n",
    "        \"right_arm\": [part_level_texts.right_arm.text],\n",
    "        \"left_leg\": [part_level_texts.left_leg.text],\n",
    "        \"right_leg\": [part_level_texts.right_leg.text],\n",
    "    },\n",
    "    lengths=[180],\n",
    "    num_inference_steps=1000,\n",
    ")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the network output is part-level representation, you need to recover it to HumanML3D representation\n",
    "normalized_motion = recover_rearranged_humanml3d_features(part_level_normalized_motion)\n",
    "\n",
    "# De-normalize with HumanML3D dataset instance\n",
    "motion = dataset.humanml3d.de_normalize(normalized_motion.cpu().detach().numpy())\n",
    "\n",
    "# Extract position from it\n",
    "positions = HumanML3D.recover(motion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2002c84050674a64bb8c559fe160600a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HTML(value='<video width=\"640\" height=\"480\" controls autoplay loop>\\n  <source type=\"video/mp4\" src=\"data:vide…"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We offer a simple visualization tool using matplotlib\n",
    "\n",
    "from lgtm.utils.visualization import animate\n",
    "from lgtm.utils.transform import exchange_yz\n",
    "from ipywidgets import HTML\n",
    "\n",
    "HTML(animate(exchange_yz(positions), HumanML3D.parents, 20).to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we also provide a simple and faster tool that converts the positions into bvh mocap files\n",
    "# which also provides an interactive way for better visualization\n",
    "\n",
    "from lgtm.utils.visualization import pos_to_bvh\n",
    "from third_packages.fmbvh.visualization.utils import show_bvh, bvh_to_video\n",
    "\n",
    "\n",
    "# recover joint rotations from positions and save them to bvh mocap file with a predefined T pose\n",
    "pos_to_bvh(positions, \"output.bvh\")\n",
    "\n",
    "# render the saved bvh file to video\n",
    "# NOTE: please make sure the opencv-python package is successfully installed\n",
    "bvh_to_video(\"output.bvh\", \"output.mp4\", \"mp4v\")\n",
    "\n",
    "# show the bvh file in an interactive way \n",
    "# NOTE: please make sure the display is valid\n",
    "\"\"\"\n",
    "    A S D W Z X: camera control\n",
    "    Q: quit\n",
    "\"\"\"\n",
    "show_bvh(\"output.bvh\", backend_cv=True)"
   ]
  }
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