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Running
on
Zero
File size: 5,521 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path \n",
"import os\n",
"import sys\n",
"sys.path.append(str(Path(os.path.abspath('')).parent))\n",
"\n",
"import torch\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as animation\n",
"\n",
"agent_path = Path(os.path.abspath('')).parent / 'models' / 'genrl_stickman_500k_2.pt'\n",
"print(\"Model path\", agent_path)\n",
"\n",
"agent = torch.load(agent_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tools.genrl_utils import ViCLIPGlobalInstance, DOMAIN2PREDICATES\n",
"model_name = getattr(agent.cfg, 'viclip_model', 'viclip')\n",
"# Get ViCLIP\n",
"if 'viclip_global_instance' not in locals() or model_name != viclip_global_instance._model:\n",
" viclip_global_instance = ViCLIPGlobalInstance(model_name)\n",
" if not viclip_global_instance._instantiated:\n",
" print(\"Instantiating\")\n",
" viclip_global_instance.instantiate()\n",
" clip = viclip_global_instance.viclip\n",
" tokenizer = viclip_global_instance.viclip_tokenizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SAVE = True\n",
"DENOISE = True\n",
"REVERSE = False\n",
"REPEAT_TIME = 2 # standard is n_frames for = 1 \n",
"TEXT_OVERLAY = True\n",
"\n",
"domain = agent.cfg.task.split('_')\n",
"\n",
"labels_list = ['high kick', 'stand up straight', 'doing splits']\n",
"\n",
"with torch.no_grad():\n",
" wm = world_model = agent.wm\n",
" connector = agent.wm.connector\n",
" decoder = world_model.heads['decoder']\n",
" n_frames = connector.n_frames\n",
" \n",
" # Get text(video) embed\n",
" text_feat = []\n",
" for text in labels_list:\n",
" with torch.no_grad():\n",
" text_feat.append(clip.get_txt_feat(text,))\n",
" text_feat = torch.stack(text_feat, dim=0).to(clip.device)\n",
"\n",
" video_embed = text_feat\n",
"\n",
" B = video_embed.shape[0]\n",
" T = 1\n",
"\n",
" # Get initial state\n",
" init = connector.initial(B, init_embed=video_embed)\n",
"\n",
" # Get actions\n",
" video_embed = video_embed.repeat(1,n_frames, 1)\n",
" action = wm.connector.get_action(video_embed)\n",
"\n",
" with torch.no_grad():\n",
" # Imagine\n",
" prior = wm.connector.video_imagine(video_embed, None, sample=False, reset_every_n_frames=False, denoise=DENOISE)\n",
" # Decode\n",
" prior_recon = decoder(wm.decoder_input_fn(prior))['observation'].mean + 0.5\n",
"\n",
" # Plotting video\n",
" R = int(np.sqrt(B))\n",
" C = min((B + (R-1)) // R, B) \n",
"\n",
" fig, axes = plt.subplots(R, C, figsize=(3.5 * C, 4 * R))\n",
" fig.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
" fig.set_size_inches(4,4)\n",
" \n",
" if B == 1:\n",
" axes = [[axes]]\n",
" elif R == 1:\n",
" axes = [axes] \n",
" axes = [ a for row in axes for a in row]\n",
"\n",
" file_path = f'temp_text2video.gif'\n",
"\n",
" if SAVE:\n",
" ims = []\n",
" for t in range(prior_recon.shape[1]):\n",
" if t == 0 :\n",
" continue\n",
" toadd = []\n",
" for b in range(prior_recon.shape[0]):\n",
" ax = axes[b]\n",
" ax.set_axis_off()\n",
" img = np.clip(prior_recon[b, t if not REVERSE else -t].cpu().permute(1,2,0), 0, 1)\n",
" frame = ax.imshow(img)\n",
" if TEXT_OVERLAY: \n",
" test = ax.text(0,5, labels_list[b], color='white')\n",
" toadd.append(frame) # add both the image and the text to the list of artists \n",
" ims.append(toadd)\n",
"\n",
" # Save GIFs\n",
" anim = animation.ArtistAnimation(fig, ims, interval=700, blit=True, repeat_delay=700)\n",
" writer = animation.PillowWriter(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
" domain = agent.cfg.task.split('_')[0]\n",
" os.makedirs(f'videos/{domain}/text2video', exist_ok=True)\n",
" file_path = f'videos/{domain}/text2video/{\"_\".join(labels_list).replace(\" \",\"_\")}.gif'\n",
" print(\"GIF path: \", Path(os.path.abspath('')) / file_path)\n",
" anim.save(file_path, writer=writer)"
]
}
],
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"kernelspec": {
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"language": "python",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
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"vscode": {
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