diff --git "a/semanticallysegmentdeezglaciers.ipynb" "b/semanticallysegmentdeezglaciers.ipynb" deleted file mode 100644--- "a/semanticallysegmentdeezglaciers.ipynb" +++ /dev/null @@ -1,5073 +0,0 @@ -{ - "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 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" - ] - }, - { - "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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"efe65fcf78ff497380fe312da5ba776e", - "34208c842fde4d2a9049c7a0988bd86e", - "4c2cdc31a0d94027b4c1448b1d655d3f" - ] - }, - "id": "kOiKU_-vvDb1", - "outputId": "531092ef-a3b9-4156-9d9c-a1835feece0a" - }, - "outputs": [], - "source": [ - "ds = load_dataset(\"glacierscopessegmentation/scopes\",keep_in_memory=True,)\n", - "ds = datasets.concatenate_datasets((ds[\"test\"], ds[\"train\"]))\n", - "\n", - "ds = ds.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": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds = load_dataset(\"glacierscopessegmentation/scopes\",keep_in_memory=True,)\n", - "# ds = datasets.concatenate_datasets((ds[\"test\"], ds[\"train\"]))\n", - "\n", - "# ds = ds.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": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 121, - "referenced_widgets": [ - "bcdeda226ac041a0bf84de22c56a467d", - "1fa4122bc91e418a983925dd36252b2c", - "46a6f57b0cee48babaaf86e35d994889", - "f26e7d204b734d7fb06ef6b3c4e21ea5", - "687793a67c8449bf9aee88d87a33dc34", - "e0e54967e38c4dfab422e594ef687702", - "56fa52c4d8934887a3b460835c8e58ff", - "40704dc6e8674da2b9012e8b0203c338", - "e70a99897bcb4a1d94addda063f0fa8a", - "ee4eaa97724d4f0dbf26e5521c8c7853", - "ca6c9e77735843e29cef89321565df19" - ] - }, - "id": "PAvIJWo1vDb3", - "outputId": "06c909f3-8500-49f6-bca7-b475b1d86885" - }, - "outputs": [], - "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", - "\n", - "# The line below initializes a blank model, while the new line initializes the model from the huggingface hub\n", - "# test_model = SegformerForSemanticSegmentation(test_config).to(\"cuda:0\")\n", - "test_model = SegformerForSemanticSegmentation.from_pretrained(hf_model_name,id2label = id2label, label2id = label2id).to(\"cuda:0\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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": null, - "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", - " \n", - " logits_tensor = logits_tensor.argmax(dim=1)\n", - " logits_tensor = logits_tensor.unsqueeze(1).to(float)\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", - "\n", - " # logits_tensor = logits_tensor.argmax(dim=1)\n", - " logits_tensor = torch.squeeze(logits_tensor,dim = 1)\n", - "\n", - " # Take the argmax of the logits tensor along dimension 1 to get the predicted labels\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=6, # 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=1, # 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": [] - }, - { - "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(\"glacformer1\", clone_from=hf_model_name)\n", - "\n", - "! cp -r glacformer/* glacformer1/\n", - "\n", - "repo.git_pull()\n", - "repo.push_to_hub()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "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)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "032abac7315f4925b32b07a1dd8e8db3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map: 0%| | 0/1848 [00:00Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. 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