save state

examples/real_robot_example/DEBUG_ME.md

@@ -0,0 +1,14 @@
+inp = torch.load('/home/thomwolf/Documents/Github/ACT/tensor_inp.pt')
+conv = torch.load('/home/thomwolf/Documents/Github/ACT/tensor_conv.pt')
+out = torch.nn.functional.conv2d(inp, conv, bias=None, stride=1, padding=1, dilation=1, groups=1)
+d = torch.load('/home/thomwolf/Documents/Github/ACT/tensor_out.pt')
+print((out-d).abs().max())
+tensor(0.0044, device='cuda:0', grad_fn=<MaxBackward1>)
+inp = torch.load('/home/thomwolf/Documents/Github/ACT/tensor_inp.pt').to('cpu')
+conv = torch.load('/home/thomwolf/Documents/Github/ACT/tensor_conv.pt').to('cpu')
+out = torch.nn.functional.conv2d(inp, conv, bias=None, stride=1, padding=1, dilation=1, groups=1)
+d = torch.load('/home/thomwolf/Documents/Github/ACT/tensor_out.pt')
+print((out-d).abs().max())
+tensor(0., grad_fn=<MaxBackward1>)
+out = torch.nn.functional.conv2d(inp, conv, bias=None, stride=1, padding=1, dilation=1, groups=1)
+torch.save(out, '/home/thomwolf/Documents/Github/ACT/tensor_out_lerobot.pt')

examples/real_robot_example/convert_original_act_batch..ipynb

@@ -0,0 +1,92 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from pprint import pprint\n",
+    "import pickle\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "original_batch_file = \"/home/thomwolf/Documents/Github/ACT/batch_save.pt\"\n",
+    "data = torch.load(original_batch_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#orig: image_data, qpos_data, action_data, is_pad\n",
+    "#target: ['observation.images.front', 'observation.images.top', 'observation.state', 'action', 'episode_index', 'frame_index', 'timestamp', 'next.done', 'index', 'action_is_pad']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conv = {}\n",
+    "conv['observation.images.front'] = data[0][:, 0]\n",
+    "conv['observation.images.top'] = data[0][:, 1]\n",
+    "conv['observation.state'] = data[1]\n",
+    "conv['action'] = data[2]\n",
+    "conv['episode_index'] = np.zeros(data[0].shape[0])\n",
+    "conv['frame_index'] = np.zeros(data[0].shape[0])\n",
+    "conv['timestamp'] = np.zeros(data[0].shape[0])\n",
+    "conv['next.done'] = np.zeros(data[0].shape[0])\n",
+    "conv['index'] = np.arange(data[0].shape[0])\n",
+    "conv['action_is_pad'] = data[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(conv, \"/home/thomwolf/Documents/Github/ACT/batch_save_converted.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lerobot",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.1.-1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}