diff --git "a/semanticallysegmentdeezglaciers.ipynb" "b/semanticallysegmentdeezglaciers.ipynb" new file mode 100644--- /dev/null +++ "b/semanticallysegmentdeezglaciers.ipynb" @@ -0,0 +1,4787 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c0C76YvrvDbu", + "outputId": "526c8200-e257-45d7-89ec-6c4d6f30d5d0" + }, + "outputs": [], + "source": [ + "# Import the torch module for tensor computation and deep learning\n", + "import torch\n", + "\n", + "# Import the matplotlib.pyplot module for creating static, animated, and interactive visualizations in Python\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Import the numpy module for numerical operations in Python\n", + "import numpy as np\n", + "\n", + "# Import the nn module from torch for building neural networks\n", + "import torch.nn as nn\n", + "\n", + "# Import the transformers, datasets, evaluate, datasets, huggingface_hub modules for working with transformer models, datasets, evaluation metrics, and Hugging Face's hub\n", + "import transformers\n", + "import datasets\n", + "import evaluate\n", + "import datasets\n", + "import huggingface_hub\n", + "\n", + "# Import the ColorJitter class from torchvision.transforms for randomly changing the brightness, contrast, and saturation of an image\n", + "from torchvision.transforms import ColorJitter\n", + "\n", + "# Import the load_dataset function from the datasets module for loading datasets\n", + "from datasets import load_dataset\n", + "\n", + "# Import the TrainingArguments and Trainer classes from the transformers module for setting training arguments and training transformer models\n", + "from transformers import TrainingArguments, Trainer\n", + "\n", + "# Import the notebook_login function from the huggingface_hub module for logging in to Hugging Face's hub\n", + "from huggingface_hub import notebook_login\n", + "\n", + "# Import the accelerate module for accelerating PyTorch code with mixed precision and distributed training\n", + "import accelerate\n", + "\n", + "# Import the Accelerator class from the accelerate module for accelerating PyTorch code\n", + "from accelerate import Accelerator\n", + "\n", + "# Import the pipeline function from the transformers module for creating a pipeline that processes and returns the model's output\n", + "from transformers import pipeline\n", + "\n", + "# Import the Image class from the PIL module for opening, manipulating, and saving many different image file formats\n", + "from PIL import Image\n", + "\n", + "# Import the glob function from the glob module for finding all the pathnames matching a specified pattern\n", + "from glob import glob\n", + "\n", + "# Import the SegformerImageProcessor, SegformerModel, SegformerConfig, AutoImageProcessor, SegformerForSemanticSegmentation classes from the transformers module for working with Segformer models\n", + "from transformers import SegformerImageProcessor, SegformerModel, SegformerConfig, AutoImageProcessor, SegformerForSemanticSegmentation\n", + "\n", + "# Set the allow_tf32 attribute of torch.backends.cuda.matmul to True to allow TensorFloat-32 (TF32) on Ampere devices\n", + "torch.backends.cuda.matmul.allow_tf32 = True\n", + "\n", + "# Call the notebook_login function to log in to Hugging Face's hub\n", + "notebook_login()\n", + "\n", + "# This code imports necessary modules and functions for a machine learning task. It sets up for tensor computations, deep learning, data visualization, working with transformer models, datasets, image manipulations, and more. It also logs into Hugging Face's hub.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 264, + "referenced_widgets": [ + "3f531f5f76a6432887863642118b75ba", + "ef6367887f794b1fa997f04b61315f4d", + "728887808eec47e2817d971dc38f1dfd", + "3d937ff17bf24e9fa6c9b9fa4ff5608d", + "a2ae6e500f534aa5b3e1cf2080b785d6", + "64c7315bf59141b7a95071b823161c2f", + "a1740bb380ab4dfca9d6928b7144d380", + "ec6dc5cbf99f43179c9a94ccdd835916", + "0f90a30741f440ada2531e70f25075b9", + "dff92071db58499e94474bff25d2b670", + "3bdaea98e1d84e76a949385bcc46c88d", + "31c90d88248b4315abaddf3d939a44c3", + "d88b9c6014794b5cb04ca725a92447b7", + "2f1d9f3c7ea949babc03078bf787cb55", + "edd06a006b24453f8493bac04575eb6f", + "89abb3c7572a4ac9a5cfac24be88f878", + "942066064dcc4ce89aa3e46ccc1e2ef6", + "648aa9b77d2c4984ad7ee22daadf2ba0", + "0408dbcd1b114dd4a2ddbeeeb83c6e6c", + "ebc47d746b0a4d8c8b8ca64b79b690d7", + 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\"sky\",\n", + " \"1\": \"surface-to-bed\",\n", + " \"2\": \"bed-to-bottom\",\n", + "}\n", + "\n", + "# Convert the keys of the id2label dictionary from strings to integers\n", + "id2label = {int(k): v for k, v in id2label.items()}\n", + "\n", + "# Create a reverse mapping from label names to their corresponding IDs\n", + "label2id = {v: k for k, v in id2label.items()}\n", + "\n", + "# Get the number of unique labels in the dataset\n", + "num_labels = len(id2label)\n", + "\n", + "len(train_ds), len(test_ds)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 121, + "referenced_widgets": [ + "bcdeda226ac041a0bf84de22c56a467d", + "1fa4122bc91e418a983925dd36252b2c", + "46a6f57b0cee48babaaf86e35d994889", + "f26e7d204b734d7fb06ef6b3c4e21ea5", + "687793a67c8449bf9aee88d87a33dc34", + "e0e54967e38c4dfab422e594ef687702", + "56fa52c4d8934887a3b460835c8e58ff", + "40704dc6e8674da2b9012e8b0203c338", + 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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", + "# This code loads a pre-trained Segformer model and its image processor for semantic segmentation. It also sets up a test model with a specific configuration.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the device to be used for tensor computations to the first CUDA device\n", + "device = \"cuda:0\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L-Eojv9VvDb3" + }, + "outputs": [], + "source": [ + "# Define a ColorJitter object to randomly change the brightness, contrast, saturation, and hue of an image\n", + "jitter = ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)\n", + "\n", + "# Define a function to apply transformations to a batch of training examples\n", + "\n", + "\n", + "def train_transforms(example_batch):\n", + " # Apply the jitter to each image in the batch and convert them to RGB\n", + " images = [jitter(x.convert(\"RGB\")) for x in example_batch[\"image\"]]\n", + " # Extract the labels from the batch\n", + " labels = [x for x in example_batch[\"label\"]]\n", + " # Process the images and labels using the test image processor\n", + " inputs = test_image_processor(images, labels)\n", + " # Return the processed inputs\n", + " return inputs\n", + "\n", + "# Define a function to apply transformations to a batch of validation examples\n", + "\n", + "\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", + " # Extract the labels from the batch\n", + " labels = [x for x in example_batch[\"label\"]]\n", + " # Process the images and labels using the test image processor\n", + " inputs = test_image_processor(images, labels)\n", + " # Return the processed inputs\n", + " return inputs\n", + "\n", + "\n", + "# Set the transform function for the training dataset to be the train_transforms function\n", + "train_ds.set_transform(train_transforms)\n", + "\n", + "# Set the transform function for the test dataset to be the val_transforms function\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", + "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", + " # Extract the logits and labels from the evaluation predictions\n", + " logits, labels = eval_pred\n", + " # Convert the logits to a PyTorch tensor\n", + " logits_tensor = torch.from_numpy(logits)\n", + " # Resize the logits tensor to match the size of the labels\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", + " pred_labels = logits_tensor.detach().cpu().numpy()\n", + " # Compute the \"mean_iou\" metric for the predicted labels and the true labels\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", + " # Convert any numpy arrays in the metrics to lists\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\n", + " tf32=True, # Whether to use tf32 precision instead of 32-bit\n", + " gradient_accumulation_steps=4, # Number of updates steps to accumulate before performing a backward/update pass\n", + " hub_model_id = \"aashraychegu/glacformer\" # The model ID on the Hugging Face model hub\n", + ")\n", + "\n", + "# Define the trainer\n", + "trainer = Trainer(\n", + " model=test_model, # The model to train\n", + " args=training_args, # Training arguments\n", + " train_dataset=train_ds, # The training dataset\n", + " eval_dataset=test_ds, # The evaluation dataset\n", + " compute_metrics=compute_metrics, # The function that computes 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": [ + "# Save the trained model to the specified directory\n", + "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=\"aashraychegu/glacformer\")\n", + "\n", + "# Pull the latest changes from the remote repository\n", + "repo.git_pull()\n", + "\n", + "# Push the local changes to the remote repository\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])\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 model\n", + "class glacformer():\n", + " def __init__(self, pipeline=pipeline(\"image-segmentation\",\n", + " model=\"aashraychegu/glacformer\", 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", + "# Create an instance of the model\n", + "evalmodel = glacformer()\n", + "\n", + "# Import the display function from IPython\n", + "from IPython.display import display\n", + "\n", + "# Display the model\n", + "glacformer.display(display)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + 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