{
  "cells": [
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0C76YvrvDbu",
        "outputId": "526c8200-e257-45d7-89ec-6c4d6f30d5d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "5.1\n",
            "5.2\n",
            "5.3\n",
            "6\n",
            "7\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import transformers\n",
        "import datasets\n",
        "import evaluate\n",
        "import datasets\n",
        "import huggingface_hub\n",
        "import albumentations as A\n",
        "from datasets import load_dataset\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from huggingface_hub import notebook_login\n",
        "import accelerate\n",
        "from accelerate import Accelerator\n",
        "from transformers import pipeline\n",
        "from PIL import Image\n",
        "from glob import glob\n",
        "from transformers import SegformerImageProcessor, SegformerModel, SegformerConfig, AutoImageProcessor, SegformerForSemanticSegmentation\n",
        "torch.backends.cuda.matmul.allow_tf32 = True\n",
        "# This sets the model's huggingface URL for uploading and downloading the model\n",
        "hf_model_name = \"glacierscopessegmentation/glacier_segmentation_transformer\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Call the notebook_login function to log in to Hugging Face's hub\n",
        "# notebook_login()\n",
        "\n",
        "# make sure to login, or use the huggingface-cli to login, that way you can actually sync the model\n",
        "# the login for the dataset only works for Aashray and Brian soo... yeah! Email me to get access to the dataset and model at aashraychegu@hotmail.com"
      ]
    },
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        "outputId": "531092ef-a3b9-4156-9d9c-a1835feece0a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Found cached dataset parquet (C:/Users/aashr/.cache/huggingface/datasets/glacierscopessegmentation___parquet/glacierscopessegmentation--secondleg-718284968c2f234c/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7)\n"
          ]
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              "model_id": "593aa8d59e094d338a2fc5cf0121e1db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "(8033, 423)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ds = load_dataset(\"glacierscopessegmentation/secondleg\")\n",
        "\n",
        "ds = ds[\"train\"].train_test_split(.05)\n",
        "train_ds = ds[\"train\"]\n",
        "test_ds = ds[\"test\"]\n",
        "\n",
        "id2label = {\n",
        "    \"0\": \"sky\", # This is given by the rgb value of 00 00 00 for the mask\n",
        "    \"1\": \"surface-to-bed\", # This is given by the rgb value of 01 01 01 for the mask\n",
        "    \"2\": \"bed-to-bottom\", # This is given by the rgb value of 02 02 02 for the mask\n",
        "}\n",
        "\n",
        "id2label = {int(k): v for k, v in id2label.items()}\n",
        "label2id = {v: k for k, v in id2label.items()}\n",
        "num_labels = len(id2label)\n",
        "\n",
        "len(train_ds), len(test_ds)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Program Files\\Python310\\lib\\site-packages\\transformers\\models\\segformer\\image_processing_segformer.py:99: FutureWarning: The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use `do_reduce_labels` instead.\n",
            "  warnings.warn(\n",
            "C:\\Users\\aashr\\AppData\\Roaming\\Python\\Python310\\site-packages\\torch\\storage.py:315: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.\n",
            "  warnings.warn(message, UserWarning)\n",
            "Some weights of SegformerForSemanticSegmentation were not initialized from the model checkpoint at nvidia/MiT-b0 and are newly initialized: ['decode_head.linear_c.3.proj.weight', 'decode_head.linear_fuse.weight', 'decode_head.classifier.weight', 'decode_head.batch_norm.bias', 'decode_head.linear_c.1.proj.bias', 'decode_head.linear_c.0.proj.bias', 'decode_head.linear_c.1.proj.weight', 'decode_head.linear_c.0.proj.weight', 'decode_head.batch_norm.running_mean', 'decode_head.linear_c.3.proj.bias', 'decode_head.linear_c.2.proj.weight', 'decode_head.batch_norm.num_batches_tracked', 'decode_head.batch_norm.running_var', 'decode_head.classifier.bias', 'decode_head.linear_c.2.proj.bias', 'decode_head.batch_norm.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# Define the checkpoint from which to load the pre-trained model preprocessor\n",
        "checkpoint = \"nvidia/MiT-b0\" # We need to use this processor for resizing the images from the dataset to the size expected by the model; the main problem with this is the output scaling for training and testing, so using the right prepreocessor is important\n",
        "\n",
        "# These two models are for testing purposes and have a pretty bad preformance because they think that there ae 150 categories even though our task only has 3.\n",
        "# Load the image processor from the pre-trained checkpoint\n",
        "# image_processor = SegformerImageProcessor.from_pretrained(checkpoint)\n",
        "\n",
        "# Load the Segformer model for semantic segmentation from the pre-trained checkpoint and move it to the GPU\n",
        "# model = SegformerForSemanticSegmentation.from_pretrained(\n",
        "    # checkpoint).to(\"cuda:0\")\n",
        "\n",
        "# Define the configuration for the test model, specifying the number of channels, labels, label-to-ID mapping, ID-to-label mapping, depths, hidden sizes, and decoder hidden size\n",
        "test_config = SegformerConfig(num_channels=3, num_labels=num_labels, label2id=label2id,\n",
        "                              id2label=id2label, depths=[2, 3, 4, 3], hidden_sizes=[64, 128, 320, 512], decoder_hidden_size=256*3)\n",
        "\n",
        "# Load the image processor for the test model from the pre-trained checkpoint\n",
        "test_image_processor = SegformerImageProcessor.from_pretrained(checkpoint)\n",
        "\n",
        "# Create a Segformer model for semantic segmentation using the test configuration and move it to the GPU\n",
        "test_model = SegformerForSemanticSegmentation(test_config).to(\"cuda:0\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "transform = A.Compose(\n",
        "    [\n",
        "        A.ElasticTransform(p=0.3),\n",
        "        A.GridDistortion(p=0.3),\n",
        "        A.Perspective(p=0.3),\n",
        "    ],\n",
        "    additional_targets={\"mask\": \"mask\"},\n",
        ")\n",
        "\n",
        "# creates transforms for data augumentation, and using albumentations allows me apply the same transform to the image and the mask at the same time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "L-Eojv9VvDb3"
      },
      "outputs": [],
      "source": [
        "# Define a function to apply transformations to a batch of training examples\n",
        "def train_transforms(example_batch):\n",
        "    imagesandmasks = [transform(image = np.array(image.convert(\"RGB\")),mask = np.array(mask)) for image,mask in zip(example_batch[\"image\"],example_batch[\"label\"])]\n",
        "    # applies the transform to the image and mask, but the data is stored as a list of dictionaries, so the next lines separate out the dicts into 2 different lists\n",
        "    images = [i[\"image\"] for i in imagesandmasks]\n",
        "    masks = [i[\"mask\"] for i in imagesandmasks]\n",
        "    inputs = test_image_processor(images, masks)\n",
        "    return inputs\n",
        "\n",
        "# Define a function to apply transformations to a batch of validation examples\n",
        "def val_transforms(example_batch):\n",
        "    # Convert each image in the batch to RGB\n",
        "    images = [x.convert(\"RGB\") for x in example_batch[\"image\"]]\n",
        "    labels = [x for x in example_batch[\"label\"]]\n",
        "    inputs = test_image_processor(images, labels)\n",
        "    return inputs\n",
        "\n",
        "# this makes the transforms happen when a batch is loaded\n",
        "train_ds.set_transform(train_transforms)\n",
        "test_ds.set_transform(val_transforms)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Load the \"mean_iou\" metric for evaluating semantic segmentation models\n",
        "metric = evaluate.load(\"mean_iou\")\n",
        "\n",
        "# Define a function to compute metrics for evaluation predictions\n",
        "# Here, the metric is mean intersection over union\n",
        "def compute_metrics(eval_pred):\n",
        "    # Ensure that gradient computation is turned off, as it is not needed for evaluation\n",
        "    with torch.no_grad():\n",
        "        # This computes the final logits tensor by interpolating the output logits to the size of the labels tensor from an input of size (batch_size, num_labels, height, width)\n",
        "        # This is input that has gone through the model's forward pass\n",
        "        logits, labels = eval_pred\n",
        "        logits_tensor = torch.from_numpy(logits)\n",
        "        # this can lead to very high ram usage for the upscaling\n",
        "        logits_tensor = nn.functional.interpolate(\n",
        "            logits_tensor,\n",
        "            size=labels.shape[-2:],\n",
        "            mode=\"bilinear\",\n",
        "            align_corners=False,\n",
        "        )\n",
        "        # Take the argmax of the logits tensor along dimension 1 to get the predicted labels\n",
        "        logits_tensor = logits_tensor.argmax(dim=1)\n",
        "        # Detach the predicted labels from the computation graph and move them to the CPU \n",
        "        # (although they are already on the CPU) to save memory and to use numpy features like the metrics module\n",
        "        pred_labels = logits_tensor.detach().cpu().numpy()\n",
        "        # Computes metrics\n",
        "        metrics = metric.compute(\n",
        "            predictions=pred_labels,\n",
        "            references=labels,\n",
        "            num_labels=num_labels,\n",
        "            reduce_labels=False,\n",
        "            ignore_index = 255\n",
        "        )\n",
        "        for key, value in metrics.items():\n",
        "            if type(value) is np.ndarray:\n",
        "                metrics[key] = value.tolist()\n",
        "        # Return the computed metrics\n",
        "        return metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define the training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"glacformer\",  # The output directory for the model predictions and checkpoints\n",
        "    learning_rate=6e-5,  # The initial learning rate for Adam\n",
        "    num_train_epochs=1,  # Total number of training epochs to perform\n",
        "    auto_find_batch_size=True,  # Whether to automatically find an appropriate batch size\n",
        "    save_total_limit=3,  # Limit the total amount of checkpoints and delete the older checkpoints\n",
        "    eval_accumulation_steps=0,  # Number of steps to accumulate gradients before performing a backward/update pass\n",
        "    evaluation_strategy=\"epoch\",  # The evaluation strategy to adopt during training\n",
        "    save_strategy=\"epoch\",  # The checkpoint save strategy to adopt during training\n",
        "    save_steps=1,  # Number of updates steps before two checkpoint saves\n",
        "    eval_steps=1,  # Number of update steps before two evaluations\n",
        "    logging_steps=30,  # Number of update steps before logging learning rate and other metrics\n",
        "    remove_unused_columns=False,  # Whether to remove columns not used by the model when using a dataset\n",
        "    fp16=True,  # Whether to use 16-bit float precision instead of 32-bit for saving memory\n",
        "    tf32=True,  # Whether to use tf32 precision instead of 32-bit for saving memory\n",
        "    gradient_accumulation_steps=4,  # Number of updates steps to accumulate before performing a backward/update pass for saving memory\n",
        "    hub_model_id = hf_model_name # The model ID on the Hugging Face model hub\n",
        ")\n",
        "\n",
        "# Define the trainer\n",
        "trainer = Trainer(\n",
        "    model=test_model,  \n",
        "    args=training_args,  \n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,  \n",
        "    compute_metrics=compute_metrics,  \n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Start the training process\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "trainer.model.save_pretrained(\"glacformer\")\n",
        "\n",
        "# Create a repository object for the specified repository on Hugging Face's hub, cloning from the specified source\n",
        "repo = huggingface_hub.Repository(\"glacformer\", clone_from=hf_model_name)\n",
        "\n",
        "repo.git_pull()\n",
        "repo.push_to_hub()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define a function to combine images\n",
        "def combine_images(images):\n",
        "    # Convert images to HSV mode\n",
        "    hsvimages = [img.convert('HSV') for img in images]\n",
        "\n",
        "    # Define the hues for each image\n",
        "    hues = [120, 200, 360]\n",
        "\n",
        "    # Colorize each image with the corresponding hue\n",
        "    for i, (img,limg) in enumerate(zip(hsvimages,images)):\n",
        "        h, s, v = img.convert(\"HSV\").split()\n",
        "        h = h.point(lambda _: hues[i]) # actually changes the hue\n",
        "        s = s.point(lambda _: 255)\n",
        "        img = Image.merge('HSV', (h, s, v)).convert('RGBA')\n",
        "        img.putalpha(limg)\n",
        "        images[i] = img\n",
        "\n",
        "    # Combine the images\n",
        "    combined_image = Image.alpha_composite(images[0], images[1])\n",
        "    combined_image = Image.alpha_composite(combined_image, images[2])\n",
        "\n",
        "    return combined_image\n",
        "\n",
        "# Define a class for the pipeline and dsiplay; allows me to save and reuse \n",
        "class glacformer():\n",
        "    def __init__(self, pipeline=pipeline(\"image-segmentation\",\n",
        "                                         model=hf_model_name, image_processor=\"nvidia/MiT-b0\"), image_list = glob(\"secondleg/*/cropped_images/*.png\")) -> None:\n",
        "        self.pipeline = pipeline\n",
        "        self.image_list = image_list\n",
        "    def __getitem__(self, index, alpha = 100):\n",
        "        originals = [i[\"mask\"] for i in self.pipeline(self.image_list[index])]\n",
        "        segmap = combine_images(originals)\n",
        "        segmap.putalpha(100)\n",
        "        rgbaorig = Image.open(self.image_list[index]).convert(\"RGBA\")\n",
        "        rgbaorig.putalpha(255-alpha)\n",
        "        return Image.alpha_composite(segmap,rgbaorig)\n",
        "    def __len__(self):\n",
        "        return len(self.image_list)\n",
        "    def __iter__(self):\n",
        "        for i in range(len(self)):\n",
        "            yield self[i]\n",
        "    def display(self, display):\n",
        "        for i in evalmodel:\n",
        "            display(i)\n",
        "            if input(\"press enter to continue, anything else to stop\") == \"\":\n",
        "                continue\n",
        "            else:\n",
        "                break\n",
        "\n",
        "evalmodel = glacformer()\n",
        "\n",
        "from IPython.display import display\n",
        "\n",
        "glacformer.display(display)"
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