starter_notebook_to_train_on_colab.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oL1qWA50hiW4",
        "outputId": "cb00dd7e-39df-4892-af36-dcbbc106e8b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-10-23 21:00:26--  https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh\n",
            "Resolving repo.continuum.io (repo.continuum.io)... 104.18.200.79, 104.18.201.79, 2606:4700::6812:c84f, ...\n",
            "Connecting to repo.continuum.io (repo.continuum.io)|104.18.200.79|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://repo.anaconda.com/archive/Anaconda3-5.1.0-Linux-x86_64.sh [following]\n",
            "--2022-10-23 21:00:26--  https://repo.anaconda.com/archive/Anaconda3-5.1.0-Linux-x86_64.sh\n",
            "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8303, ...\n",
            "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 577996269 (551M) [application/x-sh]\n",
            "Saving to: ‘Anaconda3-5.1.0-Linux-x86_64.sh’\n",
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            "Anaconda3-5.1.0-Lin 100%[===================>] 551.22M  89.0MB/s    in 8.5s    \n",
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            "2022-10-23 21:00:34 (64.7 MB/s) - ‘Anaconda3-5.1.0-Linux-x86_64.sh’ saved [577996269/577996269]\n",
            "\n",
            "PREFIX=/usr/local\n",
            "installing: python-3.6.4-hc3d631a_1 ...\n",
            "Python 3.6.4 :: Anaconda, Inc.\n",
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            "installing: nbconvert-5.3.1-py36hb41ffb7_0 ...\n",
            "installing: seaborn-0.8.1-py36hfad7ec4_0 ...\n",
            "installing: urllib3-1.22-py36hbe7ace6_0 ...\n",
            "installing: ipykernel-4.8.0-py36_0 ...\n",
            "installing: odo-0.5.1-py36h90ed295_0 ...\n",
            "installing: requests-2.18.4-py36he2e5f8d_1 ...\n",
            "installing: scikit-image-0.13.1-py36h14c3975_1 ...\n",
            "installing: anaconda-client-1.6.9-py36_0 ...\n",
            "installing: blaze-0.11.3-py36h4e06776_0 ...\n",
            "installing: jupyter_console-5.2.0-py36he59e554_1 ...\n",
            "installing: notebook-5.4.0-py36_0 ...\n",
            "installing: qtconsole-4.3.1-py36h8f73b5b_0 ...\n",
            "installing: sphinx-1.6.6-py36_0 ...\n",
            "installing: anaconda-project-0.8.2-py36h44fb852_0 ...\n",
            "installing: jupyterlab_launcher-0.10.2-py36_0 ...\n",
            "installing: numpydoc-0.7.0-py36h18f165f_0 ...\n",
            "installing: widgetsnbextension-3.1.0-py36_0 ...\n",
            "installing: anaconda-navigator-1.7.0-py36_0 ...\n",
            "installing: ipywidgets-7.1.1-py36_0 ...\n",
            "installing: jupyterlab-0.31.5-py36_0 ...\n",
            "installing: spyder-3.2.6-py36_0 ...\n",
            "installing: _ipyw_jlab_nb_ext_conf-0.1.0-py36he11e457_0 ...\n",
            "installing: jupyter-1.0.0-py36_4 ...\n",
            "installing: anaconda-5.1.0-py36_2 ...\n",
            "installing: conda-4.4.10-py36_0 ...\n",
            "installing: conda-build-3.4.1-py36_0 ...\n",
            "installation finished.\n",
            "WARNING:\n",
            "    You currently have a PYTHONPATH environment variable set. This may cause\n",
            "    unexpected behavior when running the Python interpreter in Anaconda3.\n",
            "    For best results, please verify that your PYTHONPATH only points to\n",
            "    directories of packages that are compatible with the Python interpreter\n",
            "    in Anaconda3: /usr/local\n"
          ]
        }
      ],
      "source": [
        "!wget -c https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh\n",
        "\n",
        "!chmod +x Anaconda3-5.1.0-Linux-x86_64.sh\n",
        "\n",
        "!bash ./Anaconda3-5.1.0-Linux-x86_64.sh -b -f -p /usr/local"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rbEubRQTipkE"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.10/site-packages')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOuqTKT0ivcs",
        "outputId": "10e9282b-de6d-456b-f9c0-8ee22faf35e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/usr/local/bin/conda\n",
            "conda 4.4.10\n"
          ]
        }
      ],
      "source": [
        "!which conda\n",
        "!conda --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmbxbwONhVZr",
        "outputId": "f52d0b95-58f7-4381-8704-02e68ffa624b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'sd-video-gen'...\n",
            "remote: Enumerating objects: 2625, done.\u001b[K\n",
            "remote: Counting objects: 100% (2006/2006), done.\u001b[K\n",
            "remote: Compressing objects: 100% (1983/1983), done.\u001b[K\n",
            "remote: Total 2625 (delta 41), reused 1960 (delta 18), pack-reused 619\u001b[K\n",
            "Receiving objects: 100% (2625/2625), 328.96 MiB | 34.15 MiB/s, done.\n",
            "Resolving deltas: 100% (45/45), done.\n",
            "Checking out files: 100% (2161/2161), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/jeremy-collins/sd-video-gen.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tE_dK_yAjpDk",
        "outputId": "b3ebe03f-3ed6-4a86-81bf-b3f1fe372040"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/sd-video-gen\n"
          ]
        }
      ],
      "source": [
        "%cd sd-video-gen/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0N__2RyiwRj",
        "outputId": "f9fd4477-8a62-44d7-e0b9-9f635faca910"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.10.4 ('ls_project')' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: 'conda install -n ls_project ipykernel --update-deps --force-reinstall'"
          ]
        }
      ],
      "source": [
        "!conda env create --name ls_project --file environment.yml"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lXFhLc38-OyF"
      },
      "outputs": [],
      "source": [
        "!source activate ls_project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bYVRij0EjuZM",
        "outputId": "803c11af-1703-4413-f62d-2e105e7ff9f4"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.10.4 ('ls_project')' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: 'conda install -n ls_project ipykernel --update-deps --force-reinstall'"
          ]
        }
      ],
      "source": [
        "!unzip /content/sample_data/bouncing_ball_small1.zip -d /content/sd-video-gen/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSCiYQNow9kq",
        "outputId": "015db9f1-d1d2-4893-8420-c3820df05391"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/envs/ls_project/lib/python3.10/site-packages (0.8.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/envs/ls_project/lib/python3.10/site-packages (from huggingface_hub) (4.64.0)\n",
            "Requirement already satisfied: filelock in /usr/local/envs/ls_project/lib/python3.10/site-packages (from huggingface_hub) (3.7.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from huggingface_hub) (4.1.1)\n",
            "Requirement already satisfied: requests in /usr/local/envs/ls_project/lib/python3.10/site-packages (from huggingface_hub) (2.27.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from huggingface_hub) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from requests->huggingface_hub) (1.26.9)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from requests->huggingface_hub) (3.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from requests->huggingface_hub) (2022.9.24)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/envs/ls_project/lib/python3.10/site-packages (from requests->huggingface_hub) (2.0.4)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!source activate ls_project && python -m pip install huggingface_hub\n",
        "!source activate ls_project && huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_4SAqWn-ST4",
        "outputId": "b57ee169-ff39-4455-c9b2-13cc310387c4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "device:  cpu\n",
            "loading VAE...\n",
            "loading VAE...\n",
            "loading VAE...\n",
            "------------------------- Epoch 1 -------------------------\n",
            "Training and validating model\n",
            "  0% 0/3 [00:00<?, ?it/s]/content/sd-video-gen/trainer.py:100: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  X = torch.tensor(X).to(self.device)\n",
            "/content/sd-video-gen/trainer.py:101: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  y = torch.tensor(y).to(self.device)\n",
            "pred torch.Size([6, 32, 256]) y_exp torch.Size([5, 32, 256])\n",
            "torch.Size([32, 256]) torch.Size([32, 256])\n",
            "torch.Size([31, 256])\n",
            "torch.Size([32, 255])\n",
            "gdloss tensor(15623.9434, grad_fn=<AddBackward0>)\n",
            " 33% 1/3 [00:48<01:36, 48.09s/it]pred torch.Size([6, 32, 256]) y_exp torch.Size([5, 32, 256])\n",
            "torch.Size([32, 256]) torch.Size([32, 256])\n",
            "torch.Size([31, 256])\n",
            "torch.Size([32, 255])\n",
            "gdloss tensor(11936.3320, grad_fn=<AddBackward0>)\n",
            " 67% 2/3 [01:41<00:51, 51.06s/it]pred torch.Size([6, 16, 256]) y_exp torch.Size([5, 16, 256])\n",
            "torch.Size([16, 256]) torch.Size([16, 256])\n",
            "torch.Size([15, 256])\n",
            "torch.Size([16, 255])\n",
            "gdloss tensor(9097.6738, grad_fn=<AddBackward0>)\n",
            "100% 3/3 [02:05<00:00, 41.71s/it]\n",
            "  0% 0/1 [00:00<?, ?it/s]/content/sd-video-gen/trainer.py:163: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  X = torch.tensor(X).to(self.device)\n",
            "/content/sd-video-gen/trainer.py:164: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  y = torch.tensor(y).to(self.device)\n",
            "torch.Size([20, 256]) torch.Size([20, 256])\n",
            "torch.Size([19, 256])\n",
            "torch.Size([20, 255])\n",
            "gdloss tensor(3925.2661)\n",
            "100% 1/1 [00:28<00:00, 28.98s/it]\n",
            "Training loss: 12219.3164\n",
            "Validation loss: 3925.2661\n",
            "model saved as model_best.pt\n",
            "------------------------- Epoch 2 -------------------------\n",
            "Training and validating model\n",
            "  0% 0/3 [00:00<?, ?it/s]pred torch.Size([6, 32, 256]) y_exp torch.Size([5, 32, 256])\n",
            "torch.Size([32, 256]) torch.Size([32, 256])\n",
            "torch.Size([31, 256])\n",
            "torch.Size([32, 255])\n",
            "gdloss tensor(14173.6387, grad_fn=<AddBackward0>)\n",
            " 33% 1/3 [00:52<01:45, 52.71s/it]pred torch.Size([6, 32, 256]) y_exp torch.Size([5, 32, 256])\n",
            "torch.Size([32, 256]) torch.Size([32, 256])\n",
            "torch.Size([31, 256])\n",
            "torch.Size([32, 255])\n",
            "gdloss tensor(13175.2266, grad_fn=<AddBackward0>)\n",
            " 67% 2/3 [01:40<00:49, 49.63s/it]pred torch.Size([6, 16, 256]) y_exp torch.Size([5, 16, 256])\n",
            "torch.Size([16, 256]) torch.Size([16, 256])\n",
            "torch.Size([15, 256])\n",
            "torch.Size([16, 255])\n",
            "gdloss tensor(6662.5859, grad_fn=<AddBackward0>)\n",
            "100% 3/3 [02:04<00:00, 41.34s/it]\n",
            "  0% 0/1 [00:00<?, ?it/s]torch.Size([20, 256]) torch.Size([20, 256])\n",
            "torch.Size([19, 256])\n",
            "torch.Size([20, 255])\n",
            "gdloss tensor(3620.9565)\n",
            "100% 1/1 [00:28<00:00, 28.91s/it]\n",
            "Training loss: 11337.1504\n",
            "Validation loss: 3620.9565\n",
            "model saved as model_best.pt\n",
            "------------------------- Epoch 3 -------------------------\n",
            "Training and validating model\n",
            "  0% 0/3 [00:00<?, ?it/s]pred torch.Size([6, 32, 256]) y_exp torch.Size([5, 32, 256])\n",
            "torch.Size([32, 256]) torch.Size([32, 256])\n",
            "torch.Size([31, 256])\n",
            "torch.Size([32, 255])\n",
            "gdloss tensor(14426.7334, grad_fn=<AddBackward0>)\n",
            " 33% 1/3 [00:52<01:44, 52.44s/it]pred torch.Size([6, 32, 256]) y_exp torch.Size([5, 32, 256])\n",
            "torch.Size([32, 256]) torch.Size([32, 256])\n",
            "torch.Size([31, 256])\n",
            "torch.Size([32, 255])\n",
            "gdloss tensor(12000.0957, grad_fn=<AddBackward0>)\n",
            " 67% 2/3 [01:39<00:49, 49.55s/it]pred torch.Size([6, 16, 256]) y_exp torch.Size([5, 16, 256])\n",
            "torch.Size([16, 256]) torch.Size([16, 256])\n",
            "torch.Size([15, 256])\n",
            "torch.Size([16, 255])\n",
            "gdloss tensor(7901.1074, grad_fn=<AddBackward0>)\n",
            "100% 3/3 [02:09<00:00, 43.02s/it]\n",
            "  0% 0/1 [00:00<?, ?it/s]torch.Size([20, 256]) torch.Size([20, 256])\n",
            "torch.Size([19, 256])\n",
            "torch.Size([20, 255])\n",
            "gdloss tensor(3509.3203)\n",
            "100% 1/1 [00:29<00:00, 29.03s/it]\n",
            "Training loss: 11442.6455\n",
            "Validation loss: 3509.3203\n",
            "model saved as model_best.pt\n",
            "------------------------- Epoch 4 -------------------------\n",
            "Training and validating model\n",
            "  0% 0/3 [00:17<?, ?it/s]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/sd-video-gen/trainer.py\", line 340, in <module>\n",
            "    train_loss_list, validation_loss_list = trainer.fit(model=model, opt=opt, loss_fn=loss_fn, train_dataloader=train_loader, val_dataloader=test_loader, epochs=1, frames_to_predict=frames_to_predict)\n",
            "  File \"/content/sd-video-gen/trainer.py\", line 201, in fit\n",
            "    train_loss = self.train_loop(model, opt, loss_fn, train_dataloader, frames_to_predict)\n",
            "  File \"/content/sd-video-gen/trainer.py\", line 88, in train_loop\n",
            "    for i, (index_list, batch) in enumerate(tqdm(dataloader)):\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/tqdm/std.py\", line 1195, in __iter__\n",
            "    for obj in iterable:\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 530, in __next__\n",
            "    data = self._next_data()\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 570, in _next_data\n",
            "    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 49, in fetch\n",
            "    data = [self.dataset[idx] for idx in possibly_batched_index]\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 49, in <listcomp>\n",
            "    data = [self.dataset[idx] for idx in possibly_batched_index]\n",
            "  File \"/content/sd-video-gen/bouncing_ball_loader.py\", line 38, in __getitem__\n",
            "    frame = self.sd_utils.encode_img(frame)\n",
            "  File \"/content/sd-video-gen/sd_utils.py\", line 132, in encode_img\n",
            "    latent_dists = self.vae.encode(img_arr.to(self.device))\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/diffusers/models/vae.py\", line 433, in encode\n",
            "    h = self.encoder(x)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1110, in _call_impl\n",
            "    return forward_call(*input, **kwargs)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/diffusers/models/vae.py\", line 79, in forward\n",
            "    sample = self.mid_block(sample)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1110, in _call_impl\n",
            "    return forward_call(*input, **kwargs)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/diffusers/models/unet_blocks.py\", line 266, in forward\n",
            "    hidden_states = self.resnets[0](hidden_states, temb)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1110, in _call_impl\n",
            "    return forward_call(*input, **kwargs)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/diffusers/models/resnet.py\", line 358, in forward\n",
            "    h = self.conv2(h)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1110, in _call_impl\n",
            "    return forward_call(*input, **kwargs)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/nn/modules/conv.py\", line 447, in forward\n",
            "    return self._conv_forward(input, self.weight, self.bias)\n",
            "  File \"/usr/local/envs/ls_project/lib/python3.10/site-packages/torch/nn/modules/conv.py\", line 443, in _conv_forward\n",
            "    return F.conv2d(input, weight, bias, self.stride,\n",
            "KeyboardInterrupt\n",
            "^C\n"
          ]
        }
      ],
      "source": [
        "!source activate ls_project && python trainer.py --dataset ball --save_best T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KUQjvGmt-3Le"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.4 ('ls_project')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.4"
    },
    "vscode": {
      "interpreter": {
        "hash": "e44f99785ba64ca8f23cc642be6114293ae76007187604e95501ff1805144a89"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}