{
"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",
"# 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",
"\n",
"# This sets the model's huggingface URL\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"
]
},
{
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"colab": {
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},
"id": "kOiKU_-vvDb1",
"outputId": "531092ef-a3b9-4156-9d9c-a1835feece0a"
},
"outputs": [],
"source": [
"# Load a dataset from Hugging Face's hub using the specified repository name\n",
"ds = load_dataset(\"glacierscopessegmentation/secondleg\")\n",
"\n",
"# Split the \"train\" subset of the dataset into a training set and a test set, with 5% of the data going to the test set\n",
"ds = ds[\"train\"].train_test_split(.05)\n",
"\n",
"# Assign the \"train\" subset of the split dataset to the variable train_ds\n",
"train_ds = ds[\"train\"]\n",
"\n",
"# Assign the \"test\" subset of the split dataset to the variable test_ds\n",
"test_ds = ds[\"test\"]\n",
"\n",
"# Define a dictionary mapping label IDs to their corresponding names\n",
"id2label = {\n",
" \"0\": \"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,
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"source": [
"# Define the checkpoint from which to load the pre-trained model\n",
"checkpoint = \"nvidia/MiT-b0\"\n",
"\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",
"# 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 = 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, # 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=hf_model_name)\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=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",
"# 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)"
]
}
],
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