yolov7_segmentation.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1qesZErKEzubK-sd9f68WL5aTY7lsMG0N",
      "authorship_tag": "ABX9TyOk1qvBlvmSdoB5ABu1+v6w",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chelsea456/detectron2_motor/blob/main/yolov7_segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGnsWrk0FM23",
        "outputId": "1e4588f8-cc71-480a-d1ce-7b81349d6528"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov7_mask'...\n",
            "remote: Enumerating objects: 118, done.\u001b[K\n",
            "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
            "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
            "remote: Total 118 (delta 0), reused 0 (delta 0), pack-reused 112\u001b[K\n",
            "Receiving objects: 100% (118/118), 843.26 KiB | 20.57 MiB/s, done.\n",
            "Resolving deltas: 100% (16/16), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/chelsea456/yolov7_mask.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cd yolov7_mask"
      ],
      "metadata": {
        "id": "uWFuHTYeGQgY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cd yolov7_mask/"
      ],
      "metadata": {
        "id": "U7uC7tDpGUt4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r /content/yolov7_mask/yolov7_mask/requirements.txt"
      ],
      "metadata": {
        "id": "UKpsjoWEGXV2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python /content/yolov7_mask/yolov7_mask/train.py --data /content/yolov7_mask/yolov7_mask/data/coco.yaml --batch 16  --cfg yolov7-seg.yaml --epochs 30 --name yolov7-seg --img 640 --hyp hyp.scratch-high.yaml "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXsCM7UfGcur",
        "outputId": "ed1787b1-71c1-4fc7-f558-f2b5ec691ff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov7_mask/yolov5s-seg.pt, cfg=yolov7-seg.yaml, data=/content/yolov7_mask/yolov7_mask/data/coco.yaml, hyp=hyp.scratch-high.yaml, epochs=30, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov7_mask/runs/train-seg, name=yolov7-seg, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n",
            "YOLOv5 🚀 2022-9-4 Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.1, copy_paste=0.1\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov7_mask/runs/train-seg', view at http://localhost:6006/\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 29.1MB/s]\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-seg.pt to yolov7_mask/yolov5s-seg.pt...\n",
            "100% 14.9M/14.9M [00:00<00:00, 39.0MB/s]\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=1\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
            "  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               \n",
            "  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            "  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 12                -1  1         0  models.common.MP                        []                            \n",
            " 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 16          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              \n",
            " 25                -1  1         0  models.common.MP                        []                            \n",
            " 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 29          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
            " 38                -1  1         0  models.common.MP                        []                            \n",
            " 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              \n",
            " 42          [-1, -3]  1         0  models.common.Concat                    [1]                           \n",
            " 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            \n",
            " 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                \n",
            " 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 55          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
            " 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
            " 67          [-1, -2]  1         0  models.common.Concat                    [1]                           \n",
            " 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               \n",
            " 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                \n",
            " 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              \n",
            " 76                -1  1         0  models.common.MP                        []                            \n",
            " 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
            " 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              \n",
            " 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           \n",
            " 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              \n",
            " 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              \n",
            " 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            " 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             \n",
            " 89                -1  1         0  models.common.MP                        []                            \n",
            " 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              \n",
            " 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           \n",
            " 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              \n",
            " 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            " 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              \n",
            "100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           \n",
            "101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             \n",
            "102                75  1    295424  models.common.Conv                      [128, 256, 3, 1]              \n",
            "103                88  1   1180672  models.common.Conv                      [256, 512, 3, 1]              \n",
            "104               101  1   4720640  models.common.Conv                      [512, 1024, 3, 1]             \n",
            "105   [102, 103, 104]  1   1395404  models.yolo.ISegment                    [1, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], 32, 256, [256, 512, 1024]]\n",
            "yolov7-seg summary: 417 layers, 37866092 parameters, 37866092 gradients, 142.6 GFLOPs\n",
            "\n",
            "Transferred 31/565 items from yolov7_mask/yolov5s-seg.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 98 weight(decay=0.0), 95 weight(decay=0.0005), 95 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/drive/MyDrive/image_test/train' images and labels...28 found, 0 missing, 0 empty, 0 corrupt: 100% 28/28 [00:02<00:00, 11.22it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/image_test/train.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/drive/MyDrive/image_test/train.cache' images and labels... 28 found, 0 missing, 0 empty, 0 corrupt: 100% 28/28 [00:00<?, ?it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.14 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to yolov7_mask/runs/train-seg/yolov7-seg/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1myolov7_mask/runs/train-seg/yolov7-seg\u001b[0m\n",
            "Starting training for 30 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       0/29      12.7G     0.1152     0.2547    0.02183          0         24        640: 100% 2/2 [00:35<00:00, 17.82s/it]\n",
            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0% 0/1 [00:00<?, ?it/s]WARNING: NMS time limit 1.900s exceeded\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.95s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       1/29      13.6G     0.1177     0.3072     0.0223          0         40        640: 100% 2/2 [00:18<00:00,  9.26s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0% 0/1 [00:00<?, ?it/s]WARNING: NMS time limit 1.900s exceeded\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.87s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       2/29      13.6G     0.1167     0.2462    0.02179          0         26        640: 100% 2/2 [00:23<00:00, 11.57s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.40s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       3/29      13.6G     0.1133     0.2683    0.02592          0         46        640: 100% 2/2 [00:28<00:00, 14.00s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.43s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       4/29      13.6G     0.1146     0.2751    0.02485          0         42        640: 100% 2/2 [00:20<00:00, 10.26s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.47s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       5/29      13.6G     0.1183     0.2995    0.02212          0         29        640: 100% 2/2 [00:28<00:00, 14.02s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0% 0/1 [00:00<?, ?it/s]WARNING: NMS time limit 1.900s exceeded\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.99s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       6/29      13.6G     0.1131     0.2686     0.0223          0         25        640: 100% 2/2 [00:16<00:00,  8.20s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.37s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       7/29      13.6G      0.115     0.2263    0.02264          0         32        640: 100% 2/2 [00:19<00:00,  9.95s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.65s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       8/29      13.6G     0.1151     0.2567    0.02283          0         29        640: 100% 2/2 [00:21<00:00, 10.60s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.37s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "       9/29      13.6G     0.1176       0.31    0.02312          0         30        640: 100% 2/2 [00:20<00:00, 10.25s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.43s/it]\n",
            "                   all         28         28          0          0          0          0          0          0          0          0\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      10/29      13.6G     0.1173     0.3159    0.02151          0         32        640: 100% 2/2 [00:19<00:00,  9.98s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.45s/it]\n",
            "                   all         28         28    0.00333          1    0.00623    0.00285    0.00333          1    0.00623    0.00284\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      11/29      13.6G     0.1187     0.3118     0.0228          0         33        640: 100% 2/2 [00:20<00:00, 10.34s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.46s/it]\n",
            "                   all         28         28    0.00333          1    0.00624     0.0032    0.00333          1    0.00624    0.00332\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      12/29      13.6G     0.1177     0.2031    0.02349          0         41        640: 100% 2/2 [00:22<00:00, 11.27s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.53s/it]\n",
            "                   all         28         28    0.00333          1    0.00674    0.00344    0.00333          1    0.00674     0.0036\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      13/29      13.6G      0.113      0.237    0.02313          0         35        640: 100% 2/2 [00:18<00:00,  9.28s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.37s/it]\n",
            "                   all         28         28    0.00333          1    0.00688    0.00347    0.00333          1    0.00688    0.00366\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      14/29      13.6G     0.1181     0.2705    0.02254          0         27        640: 100% 2/2 [00:24<00:00, 12.06s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.45s/it]\n",
            "                   all         28         28    0.00333          1    0.00761     0.0035    0.00333          1    0.00761    0.00355\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      15/29      13.6G     0.1128     0.1986    0.02443          0         41        640: 100% 2/2 [00:19<00:00,  9.58s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.50s/it]\n",
            "                   all         28         28    0.00333          1    0.00686    0.00337    0.00333          1    0.00686    0.00352\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      16/29      13.6G      0.114     0.2134     0.0222          0         32        640: 100% 2/2 [00:19<00:00,  9.86s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.69s/it]\n",
            "                   all         28         28    0.00333          1    0.00728     0.0031    0.00333          1    0.00728    0.00312\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      17/29      13.6G     0.1153     0.2587    0.02547          0         37        640: 100% 2/2 [00:22<00:00, 11.46s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.53s/it]\n",
            "                   all         28         28    0.00333          1    0.00718    0.00376    0.00333          1    0.00718    0.00377\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      18/29      13.6G     0.1156     0.2371      0.022          0         24        640: 100% 2/2 [00:19<00:00,  9.58s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.76s/it]\n",
            "                   all         28         28    0.00333          1    0.00696    0.00332    0.00333          1    0.00696    0.00322\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      19/29      13.6G     0.1218     0.2744    0.02213          0         36        640: 100% 2/2 [00:22<00:00, 11.10s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.70s/it]\n",
            "                   all         28         28    0.00333          1    0.00721    0.00386    0.00333          1    0.00721    0.00391\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      20/29      13.6G     0.1157     0.1708    0.02144          0         24        640: 100% 2/2 [00:19<00:00, 10.00s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.72s/it]\n",
            "                   all         28         28    0.00333          1    0.00755     0.0036    0.00333          1    0.00755    0.00349\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      21/29      13.6G     0.1164      0.168    0.02347          0         32        640: 100% 2/2 [00:21<00:00, 10.81s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0% 0/1 [00:00<?, ?it/s]WARNING: NMS time limit 1.900s exceeded\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  3.00s/it]\n",
            "                   all         28         28    0.00333       0.75    0.00607    0.00276    0.00333       0.75    0.00607    0.00268\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      22/29      13.6G     0.1129     0.2091    0.02254          0         25        640: 100% 2/2 [00:23<00:00, 11.52s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.73s/it]\n",
            "                   all         28         28    0.00333          1    0.00733     0.0035    0.00333          1    0.00733    0.00327\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      23/29      13.6G     0.1157     0.2083    0.02288          0         34        640: 100% 2/2 [00:20<00:00, 10.19s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.75s/it]\n",
            "                   all         28         28    0.00333          1    0.00702     0.0034    0.00333          1    0.00702    0.00315\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      24/29      13.6G     0.1144     0.1728    0.02269          0         30        640: 100% 2/2 [00:24<00:00, 12.22s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.73s/it]\n",
            "                   all         28         28    0.00333          1     0.0085    0.00388    0.00333          1     0.0085    0.00369\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      25/29      13.6G      0.116     0.2024    0.02544          0         38        640: 100% 2/2 [00:20<00:00, 10.05s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.74s/it]\n",
            "                   all         28         28    0.00333          1    0.00779     0.0042    0.00333          1    0.00779    0.00418\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      26/29      13.6G     0.1108     0.1788    0.02268          0         25        640: 100% 2/2 [00:21<00:00, 10.66s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.68s/it]\n",
            "                   all         28         28    0.00333          1    0.00741    0.00401    0.00333          1    0.00741      0.004\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      27/29      13.6G     0.1146     0.2103    0.02463          0         31        640: 100% 2/2 [00:22<00:00, 11.20s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.76s/it]\n",
            "                   all         28         28    0.00333          1    0.00741    0.00401    0.00333          1    0.00741      0.004\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      28/29      13.6G      0.114     0.2057    0.02501          0         37        640: 100% 2/2 [00:21<00:00, 10.74s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.94s/it]\n",
            "                   all         28         28    0.00333          1     0.0085     0.0035    0.00333          1     0.0085    0.00326\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   seg_loss   obj_loss   cls_loss  Instances       Size\n",
            "      29/29      13.6G     0.1124     0.1955    0.02437          0         36        640: 100% 2/2 [00:21<00:00, 10.77s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% 1/1 [00:02<00:00,  2.75s/it]\n",
            "                   all         28         28    0.00333          1    0.00858    0.00407    0.00333          1    0.00858    0.00381\n",
            "\n",
            "30 epochs completed in 0.239 hours.\n",
            "Optimizer stripped from yolov7_mask/runs/train-seg/yolov7-seg/weights/last.pt, 76.2MB\n",
            "Optimizer stripped from yolov7_mask/runs/train-seg/yolov7-seg/weights/best.pt, 76.2MB\n",
            "\n",
            "Validating yolov7_mask/runs/train-seg/yolov7-seg/weights/best.pt...\n",
            "Fusing layers... \n",
            "yolov7-seg summary: 325 layers, 37842476 parameters, 0 gradients, 141.9 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95):   0% 0/1 [00:00<?, ?it/s]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/yolov7_mask/yolov7_mask/train.py\", line 681, in <module>\n",
            "    main(opt)\n",
            "  File \"/content/yolov7_mask/yolov7_mask/train.py\", line 577, in main\n",
            "    train(opt.hyp, opt, device, callbacks)\n",
            "  File \"/content/yolov7_mask/yolov7_mask/train.py\", line 457, in train\n",
            "    overlap=overlap)  # val best model with plots\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/content/yolov7_mask/yolov7_mask/segment/val.py\", line 255, in run\n",
            "    out, train_out = model(im)  # if training else model(im, augment=augment, val=True)  # inference, loss\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1130, in _call_impl\n",
            "    return forward_call(*input, **kwargs)\n",
            "  File \"/content/yolov7_mask/yolov7_mask/models/yolo.py\", line 300, in forward\n",
            "    return self._forward_once(x, profile, visualize)  # single-scale inference, train\n",
            "  File \"/content/yolov7_mask/yolov7_mask/models/yolo.py\", line 212, in _forward_once\n",
            "    x = m(x)  # run\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\", line 1130, in _call_impl\n",
            "    return forward_call(*input, **kwargs)\n",
            "  File \"/content/yolov7_mask/yolov7_mask/models/yolo.py\", line 179, in forward\n",
            "    x = self.detect(self, x)\n",
            "  File \"/content/yolov7_mask/yolov7_mask/models/yolo.py\", line 131, in forward\n",
            "    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy\n",
            "RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!\n"
          ]
        }
      ]
    }
  ]
}