3.1_multi_train_pytorch_colab.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Drfengze/ANN-CBIR/blob/main/3.1_multi_train_pytorch_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "t4vkspe7xdDA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b42e532-151a-4a42-80cd-79125b02475a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "OunVTa3wqpiQ"
      },
      "outputs": [],
      "source": [
        "!cp /content/drive/MyDrive/RDA/model_and_env/data_loader.py .\n",
        "!cp /content/drive/MyDrive/RDA/model_and_env/att2d_unet.py ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "tzpml74S0Pc9"
      },
      "outputs": [],
      "source": [
        "!pip install segmentation_models_pytorch -q\n",
        "!pip install affine -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "wen4SPu9xNe2"
      },
      "outputs": [],
      "source": [
        "import datetime\n",
        "import segmentation_models_pytorch as smp\n",
        "import numpy as np\n",
        "import torch\n",
        "from glob import glob\n",
        "import torch.utils.data\n",
        "from data_loader import data_load\n",
        "import tensorflow as tf\n",
        "from att2d_unet import build_unet\n",
        "import torch.nn as nn\n",
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from copy import deepcopy\n",
        "import pandas as pd\n",
        "\n",
        "writer = SummaryWriter()\n",
        "\n",
        "\n",
        "def dataset(bands, description, batch, folder,targets,train=True,nclass = 2, buffer_size=6000):\n",
        "    if train:\n",
        "        sub_folder = \"train\"\n",
        "    else:\n",
        "        sub_folder = \"valid\"\n",
        "    tf_files = glob(\n",
        "        folder + \"/{}/*.gz\".format(sub_folder)\n",
        "    )\n",
        "\n",
        "    print(tf_files)\n",
        "    data = data_load(\n",
        "        tf_files,\n",
        "        bands,\n",
        "        description,\n",
        "        nclass=nclass,\n",
        "        batch_size=batch,\n",
        "        response=targets,\n",
        "        buffer_size=buffer_size,\n",
        "    ).get_training_dataset()\n",
        "    return data\n",
        "\n",
        "\n",
        "class StepRunner:\n",
        "    def __init__(self, net, loss_fn, stage=\"train\", metrics_dict=None, optimizer=None):\n",
        "        self.net, self.loss_fn, self.metrics_dict, self.stage = (\n",
        "            net,\n",
        "            loss_fn,\n",
        "            metrics_dict,\n",
        "            stage,\n",
        "        )\n",
        "        self.optimizer = optimizer\n",
        "\n",
        "    def step(self, features, labels):\n",
        "        # loss\n",
        "        preds = self.net(features)\n",
        "        loss = self.loss_fn(preds, labels)\n",
        "\n",
        "        # backward()\n",
        "        if self.optimizer is not None and self.stage == \"train\":\n",
        "            loss.backward()\n",
        "            self.optimizer.step()\n",
        "            self.optimizer.zero_grad()\n",
        "\n",
        "        # metrics\n",
        "        step_metrics = {\n",
        "            self.stage + \"_\" + name: metric_fn(preds, labels).item()\n",
        "            for name, metric_fn in self.metrics_dict.items()\n",
        "        }\n",
        "        return loss.item(), step_metrics\n",
        "\n",
        "    def train_step(self, features, labels):\n",
        "        self.net.train()  # 训练模式, dropout层发生作用\n",
        "        return self.step(features, labels)\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def eval_step(self, features, labels):\n",
        "        self.net.eval()  # 预测模式, dropout层不发生作用\n",
        "        return self.step(features, labels)\n",
        "\n",
        "    def __call__(self, features, labels):\n",
        "        if self.stage == \"train\":\n",
        "            return self.train_step(features, labels)\n",
        "        else:\n",
        "            return self.eval_step(features, labels)\n",
        "\n",
        "\n",
        "class EpochRunner:\n",
        "    def __init__(self, steprunner):\n",
        "        self.steprunner = steprunner\n",
        "        self.stage = steprunner.stage\n",
        "\n",
        "    def __call__(self, dataset):\n",
        "        total_loss, step = 0, 1\n",
        "        loop = enumerate(dataset.as_numpy_iterator())\n",
        "\n",
        "        for _, batch in loop:\n",
        "            features = torch.tensor(batch[0]).to(device)\n",
        "            labels = torch.tensor(batch[1]).to(torch.int64).to(device)\n",
        "            loss, step_metrics = self.steprunner(features, labels)\n",
        "            step_log = dict({self.stage + \"_loss\": loss}, **step_metrics)\n",
        "            total_loss += loss\n",
        "            step += 1\n",
        "            print(step_log, flush=True)\n",
        "\n",
        "        epoch_loss = total_loss / step\n",
        "        epoch_metrics = {\n",
        "            self.stage + \"_\" + name: metric_fn.compute().item()\n",
        "            for name, metric_fn in self.steprunner.metrics_dict.items()\n",
        "        }\n",
        "        epoch_log = dict({self.stage + \"_loss\": epoch_loss}, **epoch_metrics)\n",
        "        print(epoch_log)\n",
        "\n",
        "        for name, metric_fn in self.steprunner.metrics_dict.items():\n",
        "            metric_fn.reset()\n",
        "        return epoch_log\n",
        "\n",
        "\n",
        "def printlog(info):\n",
        "    nowtime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
        "    print(\"\\n\" + \"==========\" * 4 + \"%s\" % nowtime + \"==========\" * 4)\n",
        "    print(str(info) + \"\\n\")\n",
        "\n",
        "\n",
        "def train_model(\n",
        "    net,\n",
        "    optimizer,\n",
        "    loss_fn,\n",
        "    metrics_dict,\n",
        "    train_data,\n",
        "    val_data=None,\n",
        "    epochs=30,\n",
        "    ckpt_path=None,\n",
        "    patience=5,\n",
        "    monitor=\"val_loss\",\n",
        "    mode=\"min\",\n",
        "):\n",
        "\n",
        "    history = {}\n",
        "\n",
        "    for epoch in range(1, epochs + 1):\n",
        "        printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n",
        "\n",
        "        # 1，train -------------------------------------------------\n",
        "        train_step_runner = StepRunner(\n",
        "            net=net,\n",
        "            stage=\"train\",\n",
        "            loss_fn=loss_fn,\n",
        "            metrics_dict=deepcopy(metrics_dict),\n",
        "            optimizer=optimizer,\n",
        "        )\n",
        "        train_epoch_runner = EpochRunner(train_step_runner)\n",
        "        train_metrics = train_epoch_runner(train_data)\n",
        "\n",
        "        for name, metric in train_metrics.items():\n",
        "            history[name] = history.get(name, []) + [metric]\n",
        "\n",
        "        writer.add_scalar(\"Loss/train\", history[\"train_loss\"][-1], epoch)\n",
        "        # 2，validate -------------------------------------------------\n",
        "        if val_data:\n",
        "            val_step_runner = StepRunner(\n",
        "                net=net,\n",
        "                stage=\"val\",\n",
        "                loss_fn=loss_fn,\n",
        "                metrics_dict=deepcopy(metrics_dict),\n",
        "            )\n",
        "            val_epoch_runner = EpochRunner(val_step_runner)\n",
        "            with torch.no_grad():\n",
        "                val_metrics = val_epoch_runner(val_data)\n",
        "            val_metrics[\"epoch\"] = epoch\n",
        "            for name, metric in val_metrics.items():\n",
        "                history[name] = history.get(name, []) + [metric]\n",
        "        writer.add_scalar(\"Loss/val\", history[\"val_loss\"][-1], epoch)\n",
        "        # 3，early-stopping -------------------------------------------------\n",
        "        arr_scores = history[monitor]\n",
        "        best_score_idx = (\n",
        "            np.argmax(arr_scores) if mode == \"max\" else np.argmin(arr_scores)\n",
        "        )\n",
        "        if best_score_idx == len(arr_scores) - 1:\n",
        "            torch.save(net.state_dict(), ckpt_path)\n",
        "            print(\n",
        "                \"<<<<<< reach best {0} : {1} >>>>>>\".format(\n",
        "                    monitor, arr_scores[best_score_idx]\n",
        "                )\n",
        "            )\n",
        "        if len(arr_scores) - best_score_idx > patience:\n",
        "            print(\n",
        "                \"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n",
        "                    monitor, patience\n",
        "                )\n",
        "            )\n",
        "            net.load_state_dict(torch.load(ckpt_path))\n",
        "            break\n",
        "\n",
        "\n",
        "    return pd.DataFrame(history)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%load_ext tensorboard\n",
        "%tensorboard --logdir /content/runs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "664QLAYIJOIk",
        "outputId": "5b1c5aab-000e-4ee8-f5cb-71e2a298080b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "ERROR: Failed to launch TensorBoard (exited with 1).\n",
              "Contents of stderr:\n",
              "2023-06-29 10:24:08.732332: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
              "/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.33' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n",
              "/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.34' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n",
              "/usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server: /lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_2.32' not found (required by /usr/local/lib/python3.10/dist-packages/tensorboard_data_server/bin/server)\n",
              "Address already in use\n",
              "Port 6006 is in use by another program. Either identify and stop that program, or start the server with a different port."
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "fdBs6EnVtW0_",
        "colab": {
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          "height": 1000
        },
        "outputId": "3e2634c6-85a2-4c30-b69f-263576cbc4d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-9bb5d12c0bcc>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     46\u001b[0m     )\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     train_model(\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-4-b41e8edd709f>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(net, optimizer, loss_fn, metrics_dict, train_data, val_data, epochs, ckpt_path, patience, monitor, mode)\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0mval_epoch_runner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEpochRunner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_step_runner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m                 \u001b[0mval_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_epoch_runner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m             \u001b[0mval_metrics\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"epoch\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_metrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-4-b41e8edd709f>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_numpy_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m             \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/dataset_ops.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   4660\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4662\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4663\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4664\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    795\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    796\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 797\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    798\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    799\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/iterator_ops.py\u001b[0m in \u001b[0;36m_next_internal\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    778\u001b[0m     \u001b[0;31m# to communicate that there is no more data to iterate over.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecution_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSYNC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 780\u001b[0;31m       ret = gen_dataset_ops.iterator_get_next(\n\u001b[0m\u001b[1;32m    781\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterator_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    782\u001b[0m           \u001b[0moutput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flat_output_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/gen_dataset_ops.py\u001b[0m in \u001b[0;36miterator_get_next\u001b[0;34m(iterator, output_types, output_shapes, name)\u001b[0m\n\u001b[1;32m   3009\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_eager\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3010\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3011\u001b[0;31m       _result = pywrap_tfe.TFE_Py_FastPathExecute(\n\u001b[0m\u001b[1;32m   3012\u001b[0m         \u001b[0m_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"IteratorGetNext\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"output_types\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3013\u001b[0m         \"output_shapes\", output_shapes)\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "if __name__ == \"__main__\":\n",
        "    bands = [\n",
        "        \"blue\",\n",
        "        \"green\",\n",
        "        \"red\",\n",
        "        \"nir\",\n",
        "        \"swir1\",\n",
        "        \"swir2\",\n",
        "        \"ndvi\",\n",
        "        \"nirv\",\n",
        "    ]\n",
        "    targets = [\"cropland\"]\n",
        "    features = bands + targets\n",
        "    ckpt_path = (\n",
        "        \"/content/drive/MyDrive/RDA/model_and_env/America/wheat_multiclass_att.pt\"\n",
        "    )\n",
        "    nclass = 255\n",
        "    batch = 64\n",
        "    # class_names = [\"Others\", \"Wheat\"]\n",
        "    columns = [\n",
        "        tf.io.FixedLenFeature(shape=[128, 128], dtype=tf.float32) for k in features\n",
        "    ]\n",
        "    description = dict(zip(features, columns))\n",
        "    metrics_dict = {}\n",
        "    folder = \"/content/drive/MyDrive/RDA/training_cdl/data\"\n",
        "\n",
        "    training = dataset(bands, description, batch,folder,targets,nclass=nclass, buffer_size=6000)\n",
        "    valid = dataset(bands, description, batch, folder, targets =targets,nclass=nclass, train=False, buffer_size=6000)\n",
        "    device = \"cuda\"\n",
        "    loss_fn = smp.losses.DiceLoss(mode=\"multiclass\")\n",
        "    loss_name = \"DiceLoss\"\n",
        "    model = (\n",
        "        build_unet(len(bands), nclass,'channel_spatial').cuda()\n",
        "        if device == \"cuda\"\n",
        "        else build_unet(len(bands), nclass,'channel_spatial')\n",
        "    )\n",
        "    for name, layer in model.named_modules():\n",
        "        if isinstance(layer, torch.nn.Conv2d):\n",
        "            print(name, layer)\n",
        "    print(model.outputs)\n",
        "    model = nn.DataParallel(model)\n",
        "    optimizer = torch.optim.Adam(\n",
        "        [\n",
        "            dict(params=model.parameters(), lr=0.001),\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    train_model(\n",
        "        model,\n",
        "        optimizer,\n",
        "        loss_fn,\n",
        "        metrics_dict,\n",
        "        training,\n",
        "        val_data=valid,\n",
        "        epochs=10,\n",
        "        ckpt_path=ckpt_path,\n",
        "    )"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "gpuType": "V100",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.10"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}