Debugging s3 upload.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d045c52-a8da-419a-8acf-b1b7bec854c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/teamspace/studios/this_studio/sam-hq/segment_anything/modeling/tiny_vit_sam.py:662: UserWarning: Overwriting tiny_vit_5m_224 in registry with segment_anything.modeling.tiny_vit_sam.tiny_vit_5m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
      "  return register_model(fn_wrapper)\n",
      "/teamspace/studios/this_studio/sam-hq/segment_anything/modeling/tiny_vit_sam.py:662: UserWarning: Overwriting tiny_vit_11m_224 in registry with segment_anything.modeling.tiny_vit_sam.tiny_vit_11m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
      "  return register_model(fn_wrapper)\n",
      "/teamspace/studios/this_studio/sam-hq/segment_anything/modeling/tiny_vit_sam.py:662: UserWarning: Overwriting tiny_vit_21m_224 in registry with segment_anything.modeling.tiny_vit_sam.tiny_vit_21m_224. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
      "  return register_model(fn_wrapper)\n",
      "/teamspace/studios/this_studio/sam-hq/segment_anything/modeling/tiny_vit_sam.py:662: UserWarning: Overwriting tiny_vit_21m_384 in registry with segment_anything.modeling.tiny_vit_sam.tiny_vit_21m_384. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
      "  return register_model(fn_wrapper)\n",
      "/teamspace/studios/this_studio/sam-hq/segment_anything/modeling/tiny_vit_sam.py:662: UserWarning: Overwriting tiny_vit_21m_512 in registry with segment_anything.modeling.tiny_vit_sam.tiny_vit_21m_512. This is because the name being registered conflicts with an existing name. Please check if this is not expected.\n",
      "  return register_model(fn_wrapper)\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "os.chdir('/teamspace/studios/this_studio/sam-hq')\n",
    "from segment_anything import sam_model_registry, SamPredictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34480da2-6718-4357-9f09-d7ebf897c6a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version: 2.2.1+cu121\n",
      "CUDA is available: True\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "print(\"PyTorch version:\", torch.__version__)\n",
    "print(\"CUDA is available:\", torch.cuda.is_available())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94bcb6d4-4665-4f6e-9223-ef0ac1a92f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<All keys matched successfully>\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "!mkdir pretrained_checkpoint\n",
    "!wget https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_l.pth\n",
    "!mv sam_hq_vit_l.pth pretrained_checkpoint\n",
    "'''\n",
    "\n",
    "def show_mask(mask, ax, random_color=False):\n",
    "    if random_color:\n",
    "        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)\n",
    "    else:\n",
    "        color = np.array([30/255, 144/255, 255/255, 0.6])\n",
    "    h, w = mask.shape[-2:]\n",
    "    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\n",
    "    ax.imshow(mask_image)\n",
    "\n",
    "def show_points(coords, labels, ax, marker_size=375):\n",
    "    pos_points = coords[labels==1]\n",
    "    neg_points = coords[labels==0]\n",
    "    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n",
    "    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)\n",
    "\n",
    "def show_box(box, ax):\n",
    "    x0, y0 = box[0], box[1]\n",
    "    w, h = box[2] - box[0], box[3] - box[1]\n",
    "    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))\n",
    "\n",
    "\n",
    "def show_res(masks, scores, input_point, input_label, input_box, image):\n",
    "    for i, (mask, score) in enumerate(zip(masks, scores)):\n",
    "        plt.figure(figsize=(10,10))\n",
    "        plt.imshow(image)\n",
    "        show_mask(mask, plt.gca())\n",
    "        if input_box is not None:\n",
    "            box = input_box[i]\n",
    "            show_box(box, plt.gca())\n",
    "        if (input_point is not None) and (input_label is not None):\n",
    "            show_points(input_point, input_label, plt.gca())\n",
    "\n",
    "        print(f\"Score: {score:.3f}\")\n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "def show_res_multi(masks, scores, input_point, input_label, input_box, image):\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    plt.imshow(image)\n",
    "    for mask in masks:\n",
    "        show_mask(mask, plt.gca(), random_color=True)\n",
    "    for box in input_box:\n",
    "        show_box(box, plt.gca())\n",
    "    for score in scores:\n",
    "        print(f\"Score: {score:.3f}\")\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "sam_checkpoint = \"pretrained_checkpoint/sam_hq_vit_l.pth\"\n",
    "model_type = \"vit_l\"\n",
    "device = \"cuda\"\n",
    "sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)\n",
    "sam.to(device=device)\n",
    "predictor = SamPredictor(sam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "913fe623-d43d-4296-9ffd-b302374fc8b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/teamspace/studios/this_studio/GroundingDINO')\n",
    "#GroundingDINO\n",
    "\n",
    "from groundingdino.util.inference import load_model, load_image, predict, annotate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71e9bb7e-6400-4f65-be8a-90cd9b9cf4dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/teamspace/studios/this_studio/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\n"
     ]
    }
   ],
   "source": [
    "WEIGHTS_PATH = '/teamspace/studios/this_studio/weights/groundingdino_swint_ogc.pth'\n",
    "CONFIG_PATH = os.path.join('/teamspace/studios/this_studio', \"GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py\")\n",
    "print(CONFIG_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78451080-3da2-4838-b1d9-c4c11c8cab7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3549.)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final text_encoder_type: bert-base-uncased\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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     "metadata": {},
     "output_type": "display_data"
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    {
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      "text/plain": [
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       "version_minor": 0
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      "text/plain": [
       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd4a178bfe4940aaa58bb564598bcfe0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fc41371abcd4f1eb7b184655955e5ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = load_model(CONFIG_PATH, WEIGHTS_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74f69766-4aa9-4d68-a752-12318c9f1a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/teamspace/studios/this_studio')"
   ]
  }
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