From 5db344411821b654ce8246fc55983fafc9e6dc7b Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Wed, 1 May 2024 18:10:12 -0400 Subject: [PATCH 1/9] Changing the loss function and the way the predictions are extracted and passed to the loss function. Before we were calling argmax on the predictions, but now just squeezing out the extraneous dimension. Also, the loss function is no longer a custom function wrapping `nn.CrossEntropyLoss`, but just using `nn.CrossEntropyLoss`directly. --- spectrogram_segmentation.ipynb | 323 ++++++++++++++++++++++----------- 1 file changed, 218 insertions(+), 105 deletions(-) diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index c6ce459..2fa4d93 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -6,7 +6,7 @@ "source": [ "# Spectrogram Segmentation\n", "\n", - "In this example, we use [PyTorch](https://pytorch.org/) and [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/) to train deep learning models to differentiate between 5G NR and 4G LTE signals within wideband spectrograms." + "In this example, we use [PyTorch](https://pytorch.org/) and [Lightning](https://lightning.ai/docs/pytorch/stable/) to train deep learning models to differentiate between 5G NR and 4G LTE signals within wideband spectrograms." ] }, { @@ -17,7 +17,7 @@ "\n", "**[Background](#Background):** Delve into the problem background and learn more about the machine learning frameworks, tools, and datasets used in this example.\n", "\n", - "**[Set-up](#Set-Up):** Install the necessary libraries and set up the variables required to run the code in this notebook.\n", + "**[Set-up](#Set-Up):** Install the libraries and initialize the variables necessary to run the code in this notebook.\n", "\n", "**[Data Preprocessing](#Data-Preprocessing):** Load and analyze the Spectrum Sensing 5G dataset.\n", "\n", @@ -65,7 +65,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this section, we install the necessary dependencies required to run the code in this notebook. These dependencies include libraries and packages for tasks such as data manipulation, visualization, and machine learning." + "In this section, we will install the dependencies required to run the code in this notebook. These dependencies include libraries and packages for tasks such as data manipulation, visualization, and machine learning." ] }, { @@ -91,7 +91,7 @@ "from matplotlib.colors import ListedColormap\n", "import numpy as np\n", "import pandas as pd\n", - "import pytorch_lightning as pl\n", + "import lightning as L\n", "import torch\n", "import torchmetrics\n", "import torchvision\n", @@ -108,6 +108,13 @@ "from torchvision.transforms import v2" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we will initialize a few variables." + ] + }, { "cell_type": "code", "execution_count": null, @@ -125,7 +132,7 @@ "\n", "In semantic segmentation, the input data typically consists of images (in this case, spectrograms), while the output data consists of pixel-wise labels (masks) where each pixel is assigned a category label (in this case, either 'LTE', 'NR', or 'Noise'). \n", "\n", - "In this example, we use [supervised learning](https://www.ibm.com/topics/supervised-learning) techniques, which require both input spectrograms and the corresponding target masks for training. These are read from two separate files. For each frame in the dataset, we have:\n", + "In this example, we use [supervised learning](https://www.ibm.com/topics/supervised-learning) techniques to train our model. These techniques require both input spectrograms and the corresponding target masks for training. Spectrograms and masks are read from two separate files. For each frame in the dataset, we have:\n", "\n", "- A `.png` file containing the spectrogram image to use as input to the model.\n", "\n", @@ -148,10 +155,10 @@ "outputs": [], "source": [ "class SpectrumSensing(VisionDataset):\n", - " \"\"\"5G Spectrum Sensing dataset, by MathWorks.\"\"\"\n", + " \"\"\"5G Spectrum Sensing dataset, by MathWorks.\"\"\"\n", "\n", " def __init__(self, root: str, transform: Optional[callable] = None, target_transform: Optional[callable] = None):\n", - " \"\"\"Initialize the dataset, specifying the root directory where the dataset files are located.\"\"\"\n", + " \"\"\"Initialize the dataset, specifying the root directory where the dataset files are located. \"\"\"\n", " super().__init__(root)\n", "\n", " self.root = root\n", @@ -188,10 +195,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Our `SpectrumSensing` datasets takes as input two functions/transforms: `transform`, which is applied to the spectrogram, \n", + "Notice our `SpectrumSensing` class accepts two functions/transforms: `transform`, which is applied to the spectrogram, \n", "and `target_transform`, which is applied to the mask.\n", "\n", - "To prepare our spectrograms for training, we convert them from a PIL Image into a machine learning tensor and scale the data to a standard range. This scaling process, known as normalization, can enhance training speed and improve model performance. We'll perform normalization using the same mean and standard deviation as used by MathWorks in their spectrum sensing example. To prepare our masks for training, we just need to convert to tensor." + "Both the spectrograms and masks are 256 x 256 pixels. However, as we'll soon see, the spectrograms are three channeled, while the masks are single-channeled. This is because the spectrograms are full RGB images, whereas the masks are ternary-valued images, where each pixel takes one of three discrete values:\n", + "- `0`: Represents noise.\n", + "- `127`: Representing 5G NR signal.\n", + "- `255`: Representing 4G LTE signal.\n", + "\n", + "To prepare our spectrograms for training, we will convert them from PIL Images to Tensor objects and normalize the data using the same mean and standard deviation as used by MathWorks in their spectrum sensing example. To prepare our masks for training, we will convert them to Tensor objects, remove the extraneous channel dimension, and update the pixel values such that `0` respresents noise (no change), `1` represents 5G NR signal, and `2` represents 4G LTE signal. The resulting transforms will put the spectrograms and masks into the format expected by the PyTorch utilities used in this example.\n", + "[0,C)" ] }, { @@ -203,7 +216,8 @@ "project_root = os.getcwd()\n", "data_root = os.path.join(project_root, \"SpectrumSensingDataset\", \"TrainingData\")\n", "\n", - "# We want to normalize the red channel to have a mean of 0.485 and a standard deviation of 0.229, etc.\n", + "# Following MathWorks, we want to normalize the red channel to have a mean of 0.485 and a standard \n", + "# deviation of 0.229, the green channel to have a mean of 0.456 and a standard deviatino of 0.224, etc.\n", "mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", "\n", "transform = v2.Compose(\n", @@ -216,14 +230,38 @@ " ]\n", ")\n", "\n", - "target_transform = v2.PILToTensor()" + "class Squeeze(torch.nn.Module):\n", + " def forward(self, target: Tensor):\n", + " return torch.squeeze(target)\n", + " \n", + "class DivideBy127(torch.nn.Module):\n", + " def forward(self, target: Tensor):\n", + " return torch.div(target, 127, rounding_mode='floor')\n", + "\n", + "target_transform = v2.Compose(\n", + " [\n", + " v2.PILToTensor(),\n", + " Squeeze(),\n", + " DivideBy127(),\n", + " v2.ToDtype(torch.long)\n", + " ]\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "With that done, let's initialize the dataset, and take a closer look at a random training example and the corresponding mask. Due to our expect, we anticipate that the image-mask pair will be returned as tensors." + "With that done, let's initialize the dataset, and take a closer look at a random training example and the corresponding mask. Due to our transforms, we expect that the image-mask pair will be returned as Tensor objects." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = SpectrumSensing(root=data_root, transform=transform, target_transform=target_transform)" ] }, { @@ -232,28 +270,21 @@ "metadata": {}, "outputs": [], "source": [ - "dataset = SpectrumSensing(root=data_root, transform=transform, target_transform=target_transform)\n", - "\n", "random_index = np.random.randint(len(dataset))\n", "training_example, corresponding_mask = dataset[random_index]\n", "\n", "print(f\"The full dataset has {len(dataset)} examples. Loading example at index {random_index}:\")\n", - "print(f\"Spectrogram: {type(training_example)}, {training_example.size()}\")\n", - "print(f\"Mask: {type(corresponding_mask)}, {corresponding_mask.size()}\")" + "print(f\"Spectrogram: {type(training_example)}, {training_example.dtype}, {training_example.size()}\")\n", + "print(f\"Mask: {type(corresponding_mask)}, {corresponding_mask.dtype}, {corresponding_mask.size()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The dataset should contain 1,800 examples: 900 NR fames and 900 LTE frames. \n", - "\n", - "Both the spectrograms and masks are 256 x 256 pixels, however, you'll notice that the spectrograms are three channeled, while the masks are single-channeled. This is because the spectrograms are full RGB images, whereas the masks are ternary-valued images, where each pixel takes one of three discrete values:\n", - "- `0`: representing noise\n", - "- `127`: representing 5G NR signal\n", - "- `255`: representing 4G LTE signal\n", + "The dataset should contain 1,800 samples: 900 NR fames and 900 LTE frames. \n", "\n", - "For ease of viewing, let's convert this image-mask pair back to PIL Images. And, let's build a custom colourmap for the masks, with noise represented as cyan, 5G NR signal as blue, and 4G LTE signal as purple.image)\n" + "For ease of viewing, let's convert this image-mask pair back to PIL Images. And, let's build a custom colourmap for the masks, with noise represented as cyan, 5G NR signal as blue, and 4G LTE signal as purple." ] }, { @@ -262,11 +293,10 @@ "metadata": {}, "outputs": [], "source": [ - "tensor_to_image = v2.ToPILImage()\n", - "training_example = tensor_to_image(training_example)\n", - "corresponding_mask = tensor_to_image(corresponding_mask)\n", + "training_example = v2.functional.to_pil_image(training_example)\n", + "corresponding_mask = v2.functional.to_pil_image(v2.functional.to_dtype(corresponding_mask, dtype=torch.short))\n", "\n", - "values, labels, colors = [0, 127, 255], [\"Noise\", \"NR\", \"LTE\"], [\"cyan\", \"blue\", \"purple\"]\n", + "values, labels, colors = [0, 1, 2], [\"Noise\", \"NR\", \"LTE\"], [\"cyan\", \"blue\", \"purple\"]\n", "mask_cmap = ListedColormap(colors)\n", "\n", "print(f\"Spectrogram: {training_example}\")\n", @@ -288,11 +318,11 @@ "ax1.set_xlabel(\"Freq. [arb. units]\", fontsize=label_font_size)\n", "ax2.set_xlabel(\"Freq. [arb. units]\", fontsize=label_font_size)\n", "\n", - "spect = ax1.imshow(np.true_divide(training_example, 255, dtype=np.float32))\n", + "spect = ax1.imshow(training_example)\n", "fig.colorbar(spect, ax=ax1, fraction=0.04)\n", "\n", - "mask = ax2.imshow(corresponding_mask, cmap=mask_cmap, vmin=0, vmax=255)\n", - "mask_cbar = fig.colorbar(mask, ax=ax2, cmap=mask_cmap, fraction=0.04, ticks=[42.5, 127, 212.5])\n", + "mask = ax2.imshow(corresponding_mask, cmap=mask_cmap, vmin=0, vmax=2)\n", + "mask_cbar = fig.colorbar(mask, ax=ax2, cmap=mask_cmap, fraction=0.04, ticks=[0.33, 1, 1.67])\n", "mask_cbar.ax.set_yticklabels(labels)" ] }, @@ -300,14 +330,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Note:** You can view different examples from the dataset by rerunning the previous few cells." + "**Note:** You can view different examples from the dataset by rerunning the previous three code cells." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's analyze the dataset statistics. This step is critical for identifying data imbalance. Please note that the following code block might take a minute to run." + "Let's analyze the relative frequencies of the different class labels. This step is critical for identifying any class imbalances in our dataset. Please note that the following code block might take a few seconds to run." ] }, { @@ -320,12 +350,12 @@ "\n", "for _, mask in dataset:\n", " arr = np.asarray(mask)\n", - " for i, label in enumerate(freq_counts.keys()):\n", + " for i, label in enumerate(labels):\n", " class_counts[label] += np.sum(arr == values[i])\n", "\n", "normalized_counts = np.array(list(class_counts.values())) / sum(list(class_counts.values()))\n", "\n", - "plt.bar(class_counts.keys(), normalized_counts, tick_label=labels, color=colours)\n", + "plt.bar(class_counts.keys(), normalized_counts, tick_label=labels, color=colors)\n", "plt.title(\"Distribution of Pixel Counts by Class\", fontsize=title_font_size)\n", "plt.xlabel(\"Class\", fontsize=label_font_size)\n", "plt.ylabel(\"Counts (Normalized)\", fontsize=label_font_size)" @@ -335,11 +365,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It looks like most of our data is noise. A classification data set like this—with skewed class proportions—is called imbalanced.\n", + "It looks like most of our data is noise! A classification data set like this—with skewed class proportions—is called imbalanced.\n", "\n", - "An imbalanced dataset can result in biased and poorly performing models, as models trained on imbalanced data tends to focus more on the majority class and may not learn enough about the minority classes. In our case, the majority class is 'noise', while the minority classes are the signals we want to identify and classify. To ensure the development of a fair, accurate, and robust model, we'll need to address this class imbalance. \n", + "An imbalanced dataset can result in biased and poorly performing models. Models trained on imbalanced data tends to focus more on the majority classes and may not learn enough about the minority classes. In our case, the majority class is 'noise', while the minority classes are the signals we want to identify and classify. To ensure the development of a fair, accurate, and robust model, we will need to address this class imbalance. \n", "\n", - "But first, let's split the dataset into separate training and validation sets. The training dataset is the portion of the dataset that will be used to train the model, while the validation dataset is held in reserve and will be used to evaluate the performance of the trained model. Let's start with a simple 80/20 split, where 80% of the dataset is used for training and 20% for validation." + "But first, let's split the dataset into separate training and validation sets. The training dataset is the portion of the dataset that will be used to train the model, while the validation dataset will be held in reserve and used to evaluate the performance of the trained model. Let's start with a simple 80/20 split, where 80% of the dataset is used for training and 20% for validation." ] }, { @@ -353,21 +383,20 @@ "n_val_examples = len(dataset) - n_train_examples\n", "\n", "train_set, val_set = torch.utils.data.random_split(\n", - " dataset, [n_train_examples, n_val_examples], generator=torch.Generator().manual_seed(42)\n", + " dataset=dataset, lengths=[n_train_examples, n_val_examples], generator=torch.Generator().manual_seed(42)\n", ")\n", "\n", "print(f\"The training split contains {len(train_set)} examples.\")\n", - "print(f\"The validation split contains {len(val_set)} examples.\")\n", - "print(train_set)" + "print(f\"The validation split contains {len(val_set)} examples.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In PyTorch, the `DataLoader` class wraps an iterable around our dataset. DataLoaders facilitate easy access to examples, efficiently load and batch data, and offer numerous other features to streamline data preprocessing, management, and integration within the training loop. Let's create loaders for both the training and validation datasets.\n", + "In machine learning, data loaders facilitate easy access to samples, efficiently load and batch data, and offer numerous other features to streamline data preprocessing, management, and integration within the training loop. Let's create data loaders for both the training and validation datasets.\n", "\n", - "Dataloaders allow us to pass examples in mini-batches, which improves efficiency, stabilizes training dynamics, and enables scalable training on large datasets. Choosing an appropriate mini-batch size in machine learning depends on several factors, including the available memory on your hardware, training efficiency, and generalization. However, as with everything else in machine learning, we ultimately rely on empirical testing to determine the optimal configuration that maximizes model performance for each specific task and dataset. In this example, we'll start with mini-batches containing 4 examples each, which will fit on your CPU/GPU no problem." + "In PyTorch, the `DataLoader` class allows us pass a `batch_size`, which controls the number of samples used in each pass through the network. Using a small number training examples in each pass is called mini-batching, which improves efficiency, stabilizes training dynamics, and enables scalable training on large datasets. Choosing an appropriate mini-batch size in machine learning depends on several factors, including the available memory on your hardware, training efficiency constraints, and generalization requirements. However, as with everything else in machine learning, we ultimately rely on empirical testing to determine the optimal configuration that maximizes model performance for each specific task and dataset. In this example, we'll start with mini-batches containing 4 samples each, which will easily fit on your CPU/GPU without any issues." ] }, { @@ -378,22 +407,20 @@ "source": [ "mini_batch_size = 4\n", "\n", - "train_loader = DataLoader(train_set, batch_size=mini_batch_size, shuffle=True, num_workers=5)\n", - "val_loader = DataLoader(val_set, batch_size=mini_batch_size, shuffle=False, num_workers=2)\n", + "train_loader = DataLoader(train_set, batch_size=mini_batch_size, shuffle=True)\n", + "val_loader = DataLoader(val_set, batch_size=mini_batch_size, shuffle=False)\n", "\n", - "image_batch, target_batch = next(iter(train_loader))\n", + "batch_of_spects, batch_of_masks = next(iter(train_loader))\n", "\n", - "print(\"Shape of image batch tensor: \", image_batch.shape, image_batch.dtype)\n", - "print(\"Shape of mask batch tensor: \", target_batch.shape, target_batch.dtype)" + "print(f\"Batch of spectrograms: {type(batch_of_spects)}, {batch_of_spects.dtype}, {batch_of_spects.size()}\")\n", + "print(f\"Batch of masks: {type(batch_of_masks)}, {batch_of_masks.dtype}, {batch_of_masks.size()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Model Training\n", - "\n", - "Let's start by downloading the model. In this example, you have the option to choose between DeepLabV3 models with ResNet-50 or MobileNetV3 backbones. ResNet-50, which is deeper and more complex, generally offers better model performance, whereas MobileNetV3 is designed to be lightweight and efficient. DeepLabV3 also offers a ResNet-101 model, which we suggest is overkill for the task at hand, but you're welcome to experiment with it if you're interested." + "Let's examine a batch of spectrograms along with their corresponding masks. Note that the following plotting code is optimized for small batch sizes and may not render as effectively with larger batch sizes." ] }, { @@ -402,21 +429,44 @@ "metadata": {}, "outputs": [], "source": [ - "n_classes = 3 # We are dealing with three classes: Noise, NR, and LTE.\n", + "spects = [v2.functional.to_pil_image(i) for i in batch_of_spects]\n", + "masks = [v2.functional.to_pil_image(v2.functional.to_dtype(i, dtype=torch.short)).convert('L') for i in batch_of_masks]\n", "\n", - "model = torchvision.models.segmentation.deeplabv3_mobilenet_v3_large(num_classes=n_classes)\n", - "# model = torchvision.models.segmentation.deeplabv3_resnet50(num_classes=n_classes)\n", - "# model = torchvision.models.segmentation.deeplabv3_resnet101(num_classes=n_classes)" + "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "fig.text(0.5, 0.12, 'Freq. [arb. units]', fontsize=label_font_size, ha='center')\n", + "\n", + "for i, ax in enumerate(axes):\n", + " ax.set_title(\"Spect \" + str(i + 1))\n", + " im = ax.imshow(np.true_divide(spects[i], 255, dtype=np.float32))\n", + "\n", + "fig.subplots_adjust(right=0.85)\n", + "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", + "fig.colorbar(im, cax=cbar_ax)\n", + "\n", + "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "fig.text(0.5, 0.12, 'Freq. [arb. units]', fontsize=label_font_size, ha='center')\n", + "\n", + "for i, ax in enumerate(axes):\n", + " ax.set_title(\"Mask \" + str(i+ 1))\n", + " im = ax.imshow(masks[i], vmin=0, vmax=2, cmap=mask_cmap)\n", + "\n", + "fig.subplots_adjust(right=0.85)\n", + "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", + "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", + "cbar.ax.set_yticklabels(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need a loss function. A loss function, also known as a cost function or objective function, measures how well a machine learning \n", - "model's predictions match the actual target values. This quantifies the error between predicted outputs and ground truth labels, providing\n", - "feedback that guides the model's optimization process. For classification problems, we commonly use the [Cross-Entropy Loss](https://machinelearningmastery.com/cross-entropy-for-machine-learning/), especially for \n", - "multi-class classification problems." + "# Model Training\n", + "\n", + "Let's start by choosing a model. For this example, we suggest choosing between DeepLabV3 models with ResNet-50 or MobileNetV3 backbones. ResNet-50 is the deeper and more complex, and generally offers better model performance, whereas MobileNetV3 is designed to be lightweight and efficient. \n", + "\n", + "Note: DeepLabV3 also provides a deeper ResNet-101 model. Feel free to experiment with it if you're interested, but we suggest 101 layers is overkill for the task at hand and likely requires a larger dataset to train effectively." ] }, { @@ -425,22 +475,22 @@ "metadata": {}, "outputs": [], "source": [ - "def criterion(results: dict[str, Tensor], target: Tensor, weights: Optional[Tensor] = None) -> Tensor:\n", - " \"\"\"Compute the cross-entropy loss for each item in the results dictionary.\"\"\"\n", - " losses = {}\n", - " loss = nn.CrossEntropyLoss(weight=weight)\n", - "\n", - " for i, result in results.items():\n", - " losses[i] = loss(result, target)\n", + "n_classes = 3 # We are dealing with three classes: Noise, NR, and LTE.\n", "\n", - " return losses[\"out\"]" + "# model = torchvision.models.segmentation.deeplabv3_resnet50(num_classes=n_classes)\n", + "model = torchvision.models.segmentation.deeplabv3_mobilenet_v3_large(num_classes=n_classes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Notice we're using the [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) class from PyTorch, which allows us to assign different weights to individual classes during the computation of the loss. We'll use weights inversly propotional to the relative pixel count for each class. That way, we assign lower weights to overrepresented classes, like 'noise', and larger weights to underrepresented classes, like 'LTE'. This reduces the impact of overrepresented classes and allows the model to prioritize samples from underrepresented classes during the training process. Class weights are not the only way to address data imblance, but it is one of the more straightforward methods." + "Next, we need a loss function. A loss function, also known as a cost function or objective function, measures how well a machine learning \n", + "model's predictions match the actual target values. This quantifies the error between predicted outputs and ground truth labels, providing\n", + "feedback that guides the model's training process. For classification problems, we commonly use the [Cross-Entropy Loss](https://machinelearningmastery.com/cross-entropy-for-machine-learning/), especially for \n", + "multi-class classification problems. Let's use the [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) class from PyTorch, which allows us to assign different weights to individual classes during the computation of the loss. \n", + "\n", + "We'll use weights inversly propotional to the relative pixel count for each class. That way, we assign lower weights to overrepresented classes, like noise, and larger weights to underrepresented classes, like LTE signal. This reduces the impact of noise and allows the model to prioritize learning from LTE and NR samples. Class weighting is not the only way to address data imblance, but it is one of the more straightforward methods." ] }, { @@ -450,17 +500,19 @@ "outputs": [], "source": [ "median_count = statistics.median(list(class_counts.values()))\n", - "weights = [median_count / class_counts[k] for k in class_counts.keys()]\n", - "print(\"Class weights\", {k: round(weights[i], 2) for i, k in enumerate(class_counts.keys())})" + "weight = [median_count / class_counts[k] for k in class_counts.keys()]\n", + "loss_function = nn.CrossEntropyLoss(weight=torch.tensor(weight, dtype=torch.float))\n", + "\n", + "print(\"Class weights: \", {k: round(weight[i], 2) for i, k in enumerate(class_counts.keys())})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In this example, we'll use the stochastic gradient descent (SGD) optimizer. SGD is a variant of the standard gradient descent optimizer where the loss function is computed on mini-batches of data rather than the entire dataset. This helps improve computational efficiency and scalability, particularly for large datasets, by updating model parameters based on the gradients computed from these mini-batches.\n", + "In this example, we will train out model using stochastic gradient descent (SGD). SGD is a variant of the standard [gradient descent](https://builtin.com/data-science/gradient-descent) optimizer where the loss function is computed on mini-batches of data rather than the entire dataset. This helps improve computational efficiency and scalability, particularly for large datasets, by updating model parameters based on the gradients computed on our mini-batches.\n", "\n", - "We'll define the training and validation process of our segmentation model using PyTorch Lightning. " + "We'll define the training and validation process of our segmentation model in a [`LightningModule`](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#lightningmodule). " ] }, { @@ -469,64 +521,67 @@ "metadata": {}, "outputs": [], "source": [ - "class SegmentationModelSGD(pl.LightningModule):\n", - " \"\"\"LightningModule for training and evaluating a segmentation model using the SGD optimizer.\"\"\"\n", + "class SegmentationModelSGD(L.LightningModule):\n", + " \"\"\"LightningModule for training and evaluating a segmentation model using the SGD optimizer. \"\"\"\n", "\n", " def __init__(\n", " self,\n", " model: nn.Module,\n", + " loss_function: nn.Module,\n", " n_classes: int,\n", " learning_rate: float,\n", " momentum: float,\n", " weight_decay: float,\n", " step_size: int,\n", " gamma: float,\n", - " optimizer_name: str = \"SGD\",\n", " ):\n", " \"\"\"Initializes the SegmentationModelSGD module.\"\"\"\n", " super().__init__()\n", " self.model = model\n", + " self.loss_function = loss_function\n", " self.n_classes = n_classes\n", - " self.train_acc = MulticlassAccuracy(num_classes=self.n_classes)\n", - " self.val_acc = MulticlassAccuracy(num_classes=self.n_classes)\n", + "\n", " self.learning_rate = learning_rate\n", - " self.optimizer = getattr(torch.optim, optimizer_name)\n", " self.momentum = momentum\n", " self.weight_decay = weight_decay\n", + "\n", " self.step_size = step_size\n", " self.gamma = gamma\n", "\n", + " self.train_accuracy = MulticlassAccuracy(num_classes=self.n_classes)\n", + " self.val_accuracy = MulticlassAccuracy(num_classes=self.n_classes)\n", + "\n", " def forward(self, x: Tensor) -> Tensor:\n", - " \"\"\"Defines a forward pass of the model.\"\"\"\n", + " \"\"\"Defines a forward pass through the model.\"\"\"\n", " return self.model(x)\n", "\n", - " def training_step(self, batch, batch_idx: int) -> Tensor:\n", + " def training_step(self, batch: Tensor, batch_idx: int) -> Tensor:\n", " \"\"\"Defines a single training step.\"\"\"\n", " image, target = batch\n", - " preds = self(image) # Dictionary\n", - " loss = criterion(preds, target)\n", - " preds = preds[\"out\"].argmax(dim=1) # Our prediction\n", - " self.train_acc.update(preds, target)\n", - " self.log(\"train_loss\", loss, on_step=False, on_epoch=True, prog_bar=True)\n", + " preds = self(image)['out']\n", + " loss = self.loss_function(preds, target)\n", + " self.train_accuracy(preds, target)\n", " return loss\n", "\n", " def on_train_epoch_end(self):\n", - " self.log(\"train_accuracy\", self.train_acc.compute(), prog_bar=True)\n", + " self.log(name=\"train_accuracy\", value=self.train_accuracy, prog_bar=True)\n", + " self.log(name=\"train_loss\", value=loss, on_epoch=True, prog_bar=True)\n", "\n", - " def validation_step(self, batch, batch_idx: int) -> Tensor:\n", + " def validation_step(self, batch: Tensor, batch_idx: int) -> Tensor:\n", " \"\"\"Defines a single validation step.\"\"\"\n", " image, target = batch\n", - " preds = self(image)\n", - " loss = criterion(preds, target)\n", - " preds = preds[\"out\"].argmax(dim=1)\n", - " self.val_acc.update(preds, target)\n", - " self.log(\"val_loss\", loss, on_step=False, on_epoch=True, prog_bar=True)\n", - " self.log(\"val_accuracy\", self.val_acc.compute(), on_step=False, on_epoch=True, prog_bar=True)\n", + " preds = self(image)['out']\n", + " loss = self.loss_function(preds, target)\n", + " self.val_accuracy(preds, target)\n", " return loss\n", "\n", + " def on_validation_epoch_end(self):\n", + " self.log(name=\"val_accuracy\", value=self.val_accuracy, prog_bar=True)\n", + " self.log(name=\"val_loss\", value=loss, on_epoch=True, prog_bar=True)\n", + "\n", " def configure_optimizers(self) -> dict[str, Any]:\n", - " \"\"\"Configures the optimizer and learning rate scheduler.\"\"\"\n", - " optimizer = self.optimizer(\n", + " \"\"\"Configure the optimizer and learning rate scheduler.\"\"\"\n", + " optimizer = torch.optim.SGD(\n", " self.parameters(), lr=self.learning_rate, momentum=self.momentum, weight_decay=self.weight_decay\n", " )\n", " lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=self.step_size, gamma=self.gamma)\n", @@ -541,14 +596,14 @@ "Our `SegmentationModelSGD` is initialized with several configuration settings that influence the behavior and performance of the machine learning algorithm or model. These parameters are called hyperparameters, and unlike model parameters, which are learned from the data during training, hyperparameters are set prior to training and influence the learning process.\n", "\n", "The following hyperparameters are used to configure the optimizer:\n", - "- Momentum: A parameter that accelerates SGD in the relevant direction and dampens oscillations.\n", - "- Learning rate: The rate at which the model parameters are updated during optimization.\n", - "- Weight decay: A regularization term added to the loss function to penalize large weights in the model to prevent overfitting\n", + "- **Momentum:** A parameter that accelerates SGD in the relevant direction and dampens oscillations.\n", + "- **Learning Rate:** The rate at which the model parameters are updated during optimization.\n", + "- **Weight Decay:** A regularization term added to the loss function to penalize large weights in the model to prevent overfitting\n", "\n", "By gradually reducing the learning rate over epochs, the scheduler can help improve the convergence and stability of the optimization process\n", "We need to provide the following two parameters, which the learning rate scheduler uses to dynamically adjust the learning rate during training:\n", - "- Step size: The number of epochs after which the learning rate is reduced.\n", - "- Gamma: The factor by which the learning rate is reduced after every step-size epochs.\n", + "- **Step Size:** The number of epochs after which the learning rate is reduced.\n", + "- **Gamma:** The factor by which the learning rate is reduced after every step-size epochs.\n", "\n", "Adjusting these hyperparameters can significantly impact the training process and the final performance of the model, for better or for worse!" ] @@ -560,7 +615,8 @@ "outputs": [], "source": [ "segmentation_module = SegmentationModelSGD(\n", - " model,\n", + " model=model,\n", + " loss_function=loss_function,\n", " n_classes=n_classes,\n", " learning_rate=0.02, # Represents the initial learning rate.\n", " momentum=0.9,\n", @@ -570,11 +626,67 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# TODO: This is just a testing block, to be removed once the tutorial is complete\n", + "model.eval() # Set the model to evaluation mode\n", + "inputs, targets = next(iter(train_loader))\n", + "\n", + "with torch.no_grad():\n", + " # Forward pass with the input data\n", + " preds = model(inputs)['out']\n", + "\n", + "print(\"Initial model predictions:\")\n", + "print(preds.size())\n", + "print(\"\\tPrediction max: \", torch.max(preds))\n", + "print(\"\\tPrediction min: \", torch.min(preds))\n", + "\n", + "print(torch.max(targets.long()))\n", + "print(torch.min(targets.long()))\n", + "loss = loss_function(preds, targets.long())\n", + "print(\"Loss: \", loss)\n", + "\n", + "\n", + "\n", + "# print(\"\\nThe way it was being done before:\")\n", + "# modified_pred = (preds.argmax(dim=1))\n", + "# print(modified_pred.size())\n", + "# loss = loss_function(modified_pred, targets.long())\n", + "# print(\"Loss: \", loss)\n", + "\n", + "\n", + "\n", + "# Convert preds to Image for viewing\n", + "print(\"After converting back to images for viewing:\")\n", + "preds = [v2.functional.to_pil_image(i).convert('L') for i in preds]\n", + "print(preds)\n", + "print()\n", + "\n", + "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "fig.text(0.5, 0.12, 'Freq. [arb. units]', fontsize=label_font_size, ha='center')\n", + "\n", + "for i, ax in enumerate(axes):\n", + " ax.set_title(\"Mask \" + str(i+ 1))\n", + " im = ax.imshow(preds[i], vmin=0, vmax=2, cmap=mask_cmap)\n", + "\n", + "fig.subplots_adjust(right=0.85)\n", + "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", + "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", + "cbar.ax.set_yticklabels(labels)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have our model, weighted loss function, and Lightning Module, we're finally ready to train our model! If possible, we will leverage GPU acceleration. Otherwsie, training will default to using the CPU. Please be patient; model training time may vary depending on your hardware and could take a few minutes.\n", + "Now that we have our model, weighted loss function, and Lightning Module, we are prepared to train our model. If available, we will leverage GPU acceleration for training. Otherwise, the training process will default to using the CPU. Please be patient; model training time may vary depending on the current hardware configuration and could take a few minutes.\n", "\n", "The number of epochs determines how many times the entire dataset will be used to train the model. We will begin training with 20 epochs." ] @@ -585,18 +697,19 @@ "metadata": {}, "outputs": [], "source": [ - "n_epochs = 20\n", + "n_epochs = 10\n", "\n", "if torch.cuda.is_available():\n", " print(\"Training model on GPU.\")\n", - " trainer = pl.Trainer(accelerator=\"gpu\", max_epochs=n_epochs, logger=True)\n", + " trainer = L.Trainer(accelerator=\"gpu\", max_epochs=n_epochs, logger=True)\n", " device = \"cuda\"\n", "else:\n", " print(\"Training model on CPU.\")\n", - " trainer = pl.Trainer(max_epochs=n_epochs, logger=True)\n", + " trainer = L.Trainer(max_epochs=n_epochs, logger=True)\n", " device = \"cpu\"\n", "\n", - "trainer.fit(segmentation_module, train_loader, val_loader)" + "print(len(train_loader))\n", + "trainer.fit(model=segmentation_module, train_dataloaders=train_loader, val_dataloaders=val_loader)" ] }, { From 72c34ed036ed9d26c1c0f7604121012ac4416665 Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Thu, 2 May 2024 14:26:26 -0400 Subject: [PATCH 2/9] Model working, and added plotting code to compare predictions and masks. --- spectrogram_segmentation.ipynb | 289 ++++++++++++++++++++++----------- 1 file changed, 192 insertions(+), 97 deletions(-) diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index 2fa4d93..b9a2a78 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -19,11 +19,11 @@ "\n", "**[Set-up](#Set-Up):** Install the libraries and initialize the variables necessary to run the code in this notebook.\n", "\n", - "**[Data Preprocessing](#Data-Preprocessing):** Load and analyze the Spectrum Sensing 5G dataset.\n", + "**[Data Preprocessing](#Data-Preprocessing):** Load and analyze the Spectrum Sensing dataset.\n", "\n", - "**[Model Training](#Model-Training):** Train a machine learning model on the dataset.\n", + "**[Model Training](#Model-Training):** Select and train a deep learning model.\n", "\n", - "**[Model Verification](#Model-Verification):** Assess the performance of your model performance using a suite of common machine learning metrics\n", + "**[Model Verification](#Model-Verification):** Assess the performance of the model using a suite of common machine learning metrics\n", "\n", "**[Challenge Data](#Challange-Data):** Challenge the model on combined frames containing both LTE and NR signal.\n", "\n", @@ -47,25 +47,20 @@ "following labels to each pixel in the spectrogram: 'LTE', 'NR', or 'Noise'. ('Noise' refers to the absence of signal, representing \n", "a vacant or empty spectrum, also known as whitespace.)\n", "\n", - "The machine learning models utilized in this example are DeepLabV3 models featuring ResNet-50 or MobileNet-V3 backbones. The DeepLabv3 framework was originally introduced by Chen _et al._ in their 2017 paper titled '[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)'. For an accessible introduction to the DeepLabV3 framework, please check out Isaac Berrios' article: [DeepLabv3: Building Blocks for Robust Segmentation Models](https://medium.com/@itberrios6/deeplabv3-c0c8c93d25a4).\n", + "The machine learning models utilized in this example are DeepLabV3 models. The DeepLabv3 framework was originally introduced by Chen _et al._ in their 2017 paper titled '[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)'. For an accessible introduction to the DeepLabV3 framework, please check out Isaac Berrios' article: [DeepLabv3: Building Blocks for Robust Segmentation Models](https://medium.com/@itberrios6/deeplabv3-c0c8c93d25a4).\n", "\n", - "The dataset used in this example is the Spectrum Sensing 5G dataset, provided by MathWorks. This dataset contains 900 LTE frames, 900 NR frames, and 900 combined frames with both LTE and NR signal. In this example, we train exclusively on the individual LTE and NR examples, excluding the combined frames.\n", + "The dataset used in this example is the Spectrum Sensing dataset, provided by MathWorks. This dataset contains 900 LTE frames, 900 NR frames, and 900 combined frames with both LTE and NR signal. In this example, we train exclusively on the individual LTE and NR examples, excluding the combined frames.\n", "\n", - "To ensure comparability with results obtained using MathWorks' AI-based network, we use the hyperparameter configuration from MathWorks' spectrum sensing example: [Deep Learning Toolbox](https://www.mathworks.com/products/deep-learning.html): [Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals](https://www.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-lte-signals.html)." + "To ensure comparability with results obtained using MathWorks' AI-based network, we use the hyperparameter configuration from MathWorks' spectrum sensing example: [Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals](https://www.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-lte-signals.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Set-Up" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we will install the dependencies required to run the code in this notebook. These dependencies include libraries and packages for tasks such as data manipulation, visualization, and machine learning." + "# Set-Up\n", + "\n", + "In this section, we will install the dependencies required to run the code in this notebook. These dependencies include libraries and packages for tasks such as data manipulation, visualization, and machine learning. Additionally, we will initialize a few variables." ] }, { @@ -105,14 +100,8 @@ "from torchmetrics.classification import MulticlassAccuracy, MulticlassConfusionMatrix\n", "from torchvision.datasets import VisionDataset\n", "from torchvision.io import read_image\n", - "from torchvision.transforms import v2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, we will initialize a few variables." + "from torchvision.transforms.v2 import Compose, PILToTensor, ToDtype, Normalize, ToPILImage, ToTensor\n", + "from torchvision.models.segmentation import deeplabv3_resnet50, deeplabv3_mobilenet_v3_large" ] }, { @@ -132,13 +121,13 @@ "\n", "In semantic segmentation, the input data typically consists of images (in this case, spectrograms), while the output data consists of pixel-wise labels (masks) where each pixel is assigned a category label (in this case, either 'LTE', 'NR', or 'Noise'). \n", "\n", - "In this example, we use [supervised learning](https://www.ibm.com/topics/supervised-learning) techniques to train our model. These techniques require both input spectrograms and the corresponding target masks for training. Spectrograms and masks are read from two separate files. For each frame in the dataset, we have:\n", + "In this example, we use [supervised learning](https://www.ibm.com/topics/supervised-learning) techniques to train our model. These techniques require both input spectrograms and the corresponding target masks for training. For each frame in the dataset, we have two separate files:\n", "\n", "- A `.png` file containing the spectrogram image to use as input to the model.\n", "\n", "- A `.hdf` ([HDF4](https://www.hdfgroup.org/solutions/hdf4/)) file containing the target mask to use for training.\n", "\n", - "The Spectrum Sensing dataset also includes `.mat` files containing metadata, such as the signal sample rate and the number of DFT points used in spectrogram computation. However, none of this metadata is necessary for this example, so we can safely ignore these files." + "The Spectrum Sensing dataset also includes `.mat` files containing metadata such as the signal sample rate. However, none of this metadata is necessary for this example, so we can safely ignore these files." ] }, { @@ -155,10 +144,9 @@ "outputs": [], "source": [ "class SpectrumSensing(VisionDataset):\n", - " \"\"\"5G Spectrum Sensing dataset, by MathWorks.\"\"\"\n", "\n", " def __init__(self, root: str, transform: Optional[callable] = None, target_transform: Optional[callable] = None):\n", - " \"\"\"Initialize the dataset, specifying the root directory where the dataset files are located. \"\"\"\n", + " \"\"\"Initialize the dataset, specifying the root directory where the dataset files are located.\"\"\"\n", " super().__init__(root)\n", "\n", " self.root = root\n", @@ -198,13 +186,12 @@ "Notice our `SpectrumSensing` class accepts two functions/transforms: `transform`, which is applied to the spectrogram, \n", "and `target_transform`, which is applied to the mask.\n", "\n", - "Both the spectrograms and masks are 256 x 256 pixels. However, as we'll soon see, the spectrograms are three channeled, while the masks are single-channeled. This is because the spectrograms are full RGB images, whereas the masks are ternary-valued images, where each pixel takes one of three discrete values:\n", + "Both the spectrograms and masks are 256 x 256 pixel images. However, the spectrograms are three channeled, while the masks are single-channeled. This is because the spectrograms are full RGB images, whereas the masks are ternary-valued images, where each pixel takes one of three discrete values:\n", "- `0`: Represents noise.\n", "- `127`: Representing 5G NR signal.\n", "- `255`: Representing 4G LTE signal.\n", "\n", - "To prepare our spectrograms for training, we will convert them from PIL Images to Tensor objects and normalize the data using the same mean and standard deviation as used by MathWorks in their spectrum sensing example. To prepare our masks for training, we will convert them to Tensor objects, remove the extraneous channel dimension, and update the pixel values such that `0` respresents noise (no change), `1` represents 5G NR signal, and `2` represents 4G LTE signal. The resulting transforms will put the spectrograms and masks into the format expected by the PyTorch utilities used in this example.\n", - "[0,C)" + "To prepare our spectrograms for training, we will convert them from PIL Images to Tensor objects. As required by our models, the images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]` and `std = [0.229, 0.224, 0.225]`. To prepare our masks for training, we will convert them to Tensor objects, remove the extraneous channel dimension, and update the pixel values so that `0` respresents noise, `1` represents NR signal, and `2` represents LTE signal." ] }, { @@ -216,34 +203,33 @@ "project_root = os.getcwd()\n", "data_root = os.path.join(project_root, \"SpectrumSensingDataset\", \"TrainingData\")\n", "\n", - "# Following MathWorks, we want to normalize the red channel to have a mean of 0.485 and a standard \n", - "# deviation of 0.229, the green channel to have a mean of 0.456 and a standard deviatino of 0.224, etc.\n", "mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", "\n", - "transform = v2.Compose(\n", - " [\n", - " v2.PILToTensor(),\n", - " v2.ToDtype(\n", - " torch.float, scale=True\n", - " ), # Scale is set in case you want to test with the normalization transform commented out.\n", - " # v2.Normalize(mean=mean, std=std),\n", - " ]\n", - ")\n", - "\n", "class Squeeze(torch.nn.Module):\n", " def forward(self, target: Tensor):\n", " return torch.squeeze(target)\n", - " \n", + "\n", + "\n", "class DivideBy127(torch.nn.Module):\n", " def forward(self, target: Tensor):\n", - " return torch.div(target, 127, rounding_mode='floor')\n", + " return torch.div(target, 127, rounding_mode=\"floor\")\n", + " \n", + "transform = Compose(\n", + " [\n", + " PILToTensor(),\n", + " ToDtype(\n", + " torch.float, scale=True\n", + " ), \n", + " Normalize(mean=mean, std=std),\n", + " ]\n", + ")\n", "\n", - "target_transform = v2.Compose(\n", + "target_transform = Compose(\n", " [\n", - " v2.PILToTensor(),\n", - " Squeeze(),\n", - " DivideBy127(),\n", - " v2.ToDtype(torch.long)\n", + " PILToTensor(), \n", + " Squeeze(), \n", + " DivideBy127(), # Mapping 0 -> 0, 127 -> 1, and 255 -> 2.\n", + " ToDtype(torch.long)\n", " ]\n", ")" ] @@ -267,7 +253,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "random_index = np.random.randint(len(dataset))\n", @@ -284,7 +272,7 @@ "source": [ "The dataset should contain 1,800 samples: 900 NR fames and 900 LTE frames. \n", "\n", - "For ease of viewing, let's convert this image-mask pair back to PIL Images. And, let's build a custom colourmap for the masks, with noise represented as cyan, 5G NR signal as blue, and 4G LTE signal as purple." + "To get a better idea of what's going on, let's write some tranforms undo the previous normalization and prepare this image-mask pair for viewing. And, let's build a custom colormap for the masks, with noise as cyan, NR signal as blue, and LTE signal as purple." ] }, { @@ -293,8 +281,23 @@ "metadata": {}, "outputs": [], "source": [ - "training_example = v2.functional.to_pil_image(training_example)\n", - "corresponding_mask = v2.functional.to_pil_image(v2.functional.to_dtype(corresponding_mask, dtype=torch.short))\n", + "inv_transform = Compose(\n", + " [\n", + " Normalize(mean=[0.0, 0.0, 0.0], std=[1 / x for x in std]),\n", + " Normalize(mean=[-x for x in mean], std=[1.0, 1.0, 1.0]),\n", + " ToPILImage(),\n", + " ]\n", + ")\n", + "\n", + "inv_target_transform = Compose(\n", + " [\n", + " ToDtype(dtype=torch.uint8),\n", + " ToPILImage()\n", + " ]\n", + ")\n", + "\n", + "training_example = inv_transform(training_example)\n", + "corresponding_mask = inv_target_transform(corresponding_mask)\n", "\n", "values, labels, colors = [0, 1, 2], [\"Noise\", \"NR\", \"LTE\"], [\"cyan\", \"blue\", \"purple\"]\n", "mask_cmap = ListedColormap(colors)\n", @@ -318,8 +321,8 @@ "ax1.set_xlabel(\"Freq. [arb. units]\", fontsize=label_font_size)\n", "ax2.set_xlabel(\"Freq. [arb. units]\", fontsize=label_font_size)\n", "\n", - "spect = ax1.imshow(training_example)\n", - "fig.colorbar(spect, ax=ax1, fraction=0.04)\n", + "spect = ax1.imshow(training_example, vmin=0, vmax=255)\n", + "fig.colorbar(spect, ax=ax1, fraction=0.04, ticks=[0, 255])\n", "\n", "mask = ax2.imshow(corresponding_mask, cmap=mask_cmap, vmin=0, vmax=2)\n", "mask_cbar = fig.colorbar(mask, ax=ax2, cmap=mask_cmap, fraction=0.04, ticks=[0.33, 1, 1.67])\n", @@ -330,14 +333,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Note:** You can view different examples from the dataset by rerunning the previous three code cells." + "**Note:** You can view different examples from the dataset by rerunning the previous few code cells." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's analyze the relative frequencies of the different class labels. This step is critical for identifying any class imbalances in our dataset. Please note that the following code block might take a few seconds to run." + "Let's analyze the relative frequencies of the different class labels. This step is critical for identifying imbalance in our dataset. Please note that the following code block might take a few seconds to run." ] }, { @@ -365,9 +368,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It looks like most of our data is noise! A classification data set like this—with skewed class proportions—is called imbalanced.\n", + "It looks like most of our data is noise! A classification dataset like this—with skewed class proportions—is called imbalanced.\n", "\n", - "An imbalanced dataset can result in biased and poorly performing models. Models trained on imbalanced data tends to focus more on the majority classes and may not learn enough about the minority classes. In our case, the majority class is 'noise', while the minority classes are the signals we want to identify and classify. To ensure the development of a fair, accurate, and robust model, we will need to address this class imbalance. \n", + "An imbalanced dataset can result in biased and poorly performing models. Models trained on imbalanced data tends to focus more on the majority classes and may not learn enough about the minority classes. In our case, the majority class is noise, while the minority classes are the NR and LTE signals we want to identify and classify. To ensure the development of a fair, accurate, and robust model, we will need to address this class imbalance. \n", "\n", "But first, let's split the dataset into separate training and validation sets. The training dataset is the portion of the dataset that will be used to train the model, while the validation dataset will be held in reserve and used to evaluate the performance of the trained model. Let's start with a simple 80/20 split, where 80% of the dataset is used for training and 20% for validation." ] @@ -396,7 +399,7 @@ "source": [ "In machine learning, data loaders facilitate easy access to samples, efficiently load and batch data, and offer numerous other features to streamline data preprocessing, management, and integration within the training loop. Let's create data loaders for both the training and validation datasets.\n", "\n", - "In PyTorch, the `DataLoader` class allows us pass a `batch_size`, which controls the number of samples used in each pass through the network. Using a small number training examples in each pass is called mini-batching, which improves efficiency, stabilizes training dynamics, and enables scalable training on large datasets. Choosing an appropriate mini-batch size in machine learning depends on several factors, including the available memory on your hardware, training efficiency constraints, and generalization requirements. However, as with everything else in machine learning, we ultimately rely on empirical testing to determine the optimal configuration that maximizes model performance for each specific task and dataset. In this example, we'll start with mini-batches containing 4 samples each, which will easily fit on your CPU/GPU without any issues." + "In PyTorch, the `DataLoader` class allows us to pass a `batch_size` argument, which controls the number of samples used in each pass through the network. Using a small number of training examples each pass is called mini-batching, and can improve efficiency, stabilize training dynamics, and enable scalable training on large datasets. Choosing an appropriate mini-batch size depends on several factors, including the available memory on your hardware, training efficiency constraints, and generalization requirements. However, as with everything in machine learning, we ultimately rely on empirical testing to determine the optimal configuration that maximizes model performance for each specific task and dataset. In this example, we'll start with mini-batches containing 4 samples each, which will easily fit on any CPU/GPU without issue." ] }, { @@ -410,17 +413,27 @@ "train_loader = DataLoader(train_set, batch_size=mini_batch_size, shuffle=True)\n", "val_loader = DataLoader(val_set, batch_size=mini_batch_size, shuffle=False)\n", "\n", - "batch_of_spects, batch_of_masks = next(iter(train_loader))\n", + "spects, masks = next(iter(train_loader))\n", "\n", - "print(f\"Batch of spectrograms: {type(batch_of_spects)}, {batch_of_spects.dtype}, {batch_of_spects.size()}\")\n", - "print(f\"Batch of masks: {type(batch_of_masks)}, {batch_of_masks.dtype}, {batch_of_masks.size()}\")" + "print(f\"Batch of spectrograms: {type(spects)}, {spects.dtype}, {spects.size()}\")\n", + "print(f\"Batch of masks: {type(masks)}, {masks.dtype}, {masks.size()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's examine a batch of spectrograms along with their corresponding masks. Note that the following plotting code is optimized for small batch sizes and may not render as effectively with larger batch sizes." + "Let's examine a batch of spectrograms along with their corresponding masks. Note that the following plotting code is optimized for small batch sizes and may not render as nicely with larger batch sizes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spects = [inv_transform(i) for i in spects]\n", + "masks = [inv_target_transform(i) for i in masks]" ] }, { @@ -429,27 +442,24 @@ "metadata": {}, "outputs": [], "source": [ - "spects = [v2.functional.to_pil_image(i) for i in batch_of_spects]\n", - "masks = [v2.functional.to_pil_image(v2.functional.to_dtype(i, dtype=torch.short)).convert('L') for i in batch_of_masks]\n", - "\n", "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, 'Freq. [arb. units]', fontsize=label_font_size, ha='center')\n", + "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", "\n", "for i, ax in enumerate(axes):\n", " ax.set_title(\"Spect \" + str(i + 1))\n", - " im = ax.imshow(np.true_divide(spects[i], 255, dtype=np.float32))\n", + " im = ax.imshow(spects[i], vmin=0, vmax=255)\n", "\n", "fig.subplots_adjust(right=0.85)\n", "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "fig.colorbar(im, cax=cbar_ax)\n", + "fig.colorbar(im, cax=cbar_ax, ticks=[0, 255])\n", "\n", "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, 'Freq. [arb. units]', fontsize=label_font_size, ha='center')\n", + "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", "\n", "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Mask \" + str(i+ 1))\n", + " ax.set_title(\"Mask \" + str(i + 1))\n", " im = ax.imshow(masks[i], vmin=0, vmax=2, cmap=mask_cmap)\n", "\n", "fig.subplots_adjust(right=0.85)\n", @@ -464,7 +474,7 @@ "source": [ "# Model Training\n", "\n", - "Let's start by choosing a model. For this example, we suggest choosing between DeepLabV3 models with ResNet-50 or MobileNetV3 backbones. ResNet-50 is the deeper and more complex, and generally offers better model performance, whereas MobileNetV3 is designed to be lightweight and efficient. \n", + "Let's start by choosing a model. For this example, we suggest choosing between DeepLabV3 models with ResNet-50 or MobileNetV3 backbones. ResNet-50 is the deeper and more complex, and generally offers better model performance, whereas MobileNetV3 is designed to be lightweight and efficient. Because both models provide the same interface, and either will work with this example.\n", "\n", "Note: DeepLabV3 also provides a deeper ResNet-101 model. Feel free to experiment with it if you're interested, but we suggest 101 layers is overkill for the task at hand and likely requires a larger dataset to train effectively." ] @@ -477,20 +487,20 @@ "source": [ "n_classes = 3 # We are dealing with three classes: Noise, NR, and LTE.\n", "\n", - "# model = torchvision.models.segmentation.deeplabv3_resnet50(num_classes=n_classes)\n", - "model = torchvision.models.segmentation.deeplabv3_mobilenet_v3_large(num_classes=n_classes)" + "# model = deeplabv3_resnet50(num_classes=n_classes)\n", + "model = deeplabv3_mobilenet_v3_large(num_classes=n_classes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need a loss function. A loss function, also known as a cost function or objective function, measures how well a machine learning \n", + "Next, we need a loss function. A loss function, also known as a cost or objective function, measures how well a machine learning \n", "model's predictions match the actual target values. This quantifies the error between predicted outputs and ground truth labels, providing\n", "feedback that guides the model's training process. For classification problems, we commonly use the [Cross-Entropy Loss](https://machinelearningmastery.com/cross-entropy-for-machine-learning/), especially for \n", "multi-class classification problems. Let's use the [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) class from PyTorch, which allows us to assign different weights to individual classes during the computation of the loss. \n", "\n", - "We'll use weights inversly propotional to the relative pixel count for each class. That way, we assign lower weights to overrepresented classes, like noise, and larger weights to underrepresented classes, like LTE signal. This reduces the impact of noise and allows the model to prioritize learning from LTE and NR samples. Class weighting is not the only way to address data imblance, but it is one of the more straightforward methods." + "We'll use weights inversly propotional to the relative pixel count for each class. That way, we assign lower weights to overrepresented classes, like noise, and larger weights to underrepresented classes, like LTE signal. This reduces the impact of noise and allows the model to prioritize learning from NR and especially LTE samples. Class weighting is not the only way to address data imblance, but it is one of the more straightforward methods." ] }, { @@ -521,7 +531,7 @@ "metadata": {}, "outputs": [], "source": [ - "class SegmentationModelSGD(L.LightningModule):\n", + "class SegmentationSGD(L.LightningModule):\n", " \"\"\"LightningModule for training and evaluating a segmentation model using the SGD optimizer. \"\"\"\n", "\n", " def __init__(\n", @@ -535,7 +545,7 @@ " step_size: int,\n", " gamma: float,\n", " ):\n", - " \"\"\"Initializes the SegmentationModelSGD module.\"\"\"\n", + " \"\"\"Initializes the SegmentationSGD module.\"\"\"\n", " super().__init__()\n", " self.model = model\n", " self.loss_function = loss_function\n", @@ -564,6 +574,7 @@ " return loss\n", "\n", " def on_train_epoch_end(self):\n", + " \"\"\"Logs the training accuracy and loss metrics at the end of each training epoch.\"\"\"\n", " self.log(name=\"train_accuracy\", value=self.train_accuracy, prog_bar=True)\n", " self.log(name=\"train_loss\", value=loss, on_epoch=True, prog_bar=True)\n", "\n", @@ -576,6 +587,7 @@ " return loss\n", "\n", " def on_validation_epoch_end(self):\n", + " \"\"\"Logs the training accuracy and loss metrics at the end of each validation epoch.\"\"\"\n", " self.log(name=\"val_accuracy\", value=self.val_accuracy, prog_bar=True)\n", " self.log(name=\"val_loss\", value=loss, on_epoch=True, prog_bar=True)\n", "\n", @@ -586,7 +598,10 @@ " )\n", " lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=self.step_size, gamma=self.gamma)\n", "\n", - " return {\"optimizer\": optimizer, \"lr_scheduler\": lr_scheduler}" + " return {\n", + " \"optimizer\": optimizer, \n", + " \"lr_scheduler\": lr_scheduler\n", + " }" ] }, { @@ -614,7 +629,7 @@ "metadata": {}, "outputs": [], "source": [ - "segmentation_module = SegmentationModelSGD(\n", + "segmentation_module = SegmentationSGD(\n", " model=model,\n", " loss_function=loss_function,\n", " n_classes=n_classes,\n", @@ -640,41 +655,48 @@ "\n", "with torch.no_grad():\n", " # Forward pass with the input data\n", - " preds = model(inputs)['out']\n", + " preds = model(inputs)[\"out\"]\n", "\n", "print(\"Initial model predictions:\")\n", "print(preds.size())\n", "print(\"\\tPrediction max: \", torch.max(preds))\n", "print(\"\\tPrediction min: \", torch.min(preds))\n", "\n", - "print(torch.max(targets.long()))\n", - "print(torch.min(targets.long()))\n", + "print(\"\\tMax value in target: \", torch.max(targets.long()))\n", + "print(\"\\tMin value in target: \",torch.min(targets.long()))\n", "loss = loss_function(preds, targets.long())\n", "print(\"Loss: \", loss)\n", "\n", + "# Convert preds to Image for viewing\n", + "print(\"After converting back to images for viewing:\")\n", + "print(\"Predictions: \", preds.size())\n", "\n", + "# Need to find the classes with the largest probability.\n", + "# Take the maximum value along the class axis.\n", + "output = preds.argmax(1)\n", "\n", - "# print(\"\\nThe way it was being done before:\")\n", - "# modified_pred = (preds.argmax(dim=1))\n", - "# print(modified_pred.size())\n", - "# loss = loss_function(modified_pred, targets.long())\n", - "# print(\"Loss: \", loss)\n", + "print(\"Output: \", output.size())\n", "\n", + "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", "\n", + "for i, ax in enumerate(axes):\n", + " ax.set_title(\"Prediction \" + str(i + 1))\n", + " im = ax.imshow(output[i], vmin=0, vmax=2, cmap=mask_cmap)\n", "\n", - "# Convert preds to Image for viewing\n", - "print(\"After converting back to images for viewing:\")\n", - "preds = [v2.functional.to_pil_image(i).convert('L') for i in preds]\n", - "print(preds)\n", - "print()\n", + "fig.subplots_adjust(right=0.85)\n", + "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", + "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", + "cbar.ax.set_yticklabels(labels)\n", "\n", "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, 'Freq. [arb. units]', fontsize=label_font_size, ha='center')\n", + "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", "\n", "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Mask \" + str(i+ 1))\n", - " im = ax.imshow(preds[i], vmin=0, vmax=2, cmap=mask_cmap)\n", + " ax.set_title(\"Mask \" + str(i + 1))\n", + " im = ax.imshow(targets[i], vmin=0, vmax=2, cmap=mask_cmap)\n", "\n", "fig.subplots_adjust(right=0.85)\n", "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", @@ -718,7 +740,80 @@ "source": [ "# Model Verification\n", "\n", - "Having trained our model, the next step is to evaluate its performance. To accomplish this, we'll use a suite of standard machine learning metrics. Let's start with the confusion matrix, which provides a comprehensive overview of the model's ability. The diagonal elements represent the correct predictions and off-diagonal elements indicate prediction errors." + "Having trained our model, the next step is to evaluate its performance. To accomplish this, we'll use a suite of standard machine learning metrics. But first, let's take quick look at a random batch of predictions and true labels.\n", + "\n", + "Because the model returns the unnormalized probabilities corresponding to the predictions of each class. We need to use `argmax()` to get the maximum prediction of each class. The result is a ternary-valued image for each example in the batch." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.eval()\n", + "model.to(device)\n", + "\n", + "spects, masks = next(iter(train_loader))\n", + "spects = spects.to(device)\n", + "\n", + "with torch.no_grad():\n", + " preds = (model(spects)[\"out\"]).argmax(1)\n", + "\n", + "preds, spects = preds.cpu(), [inv_transform(i.cpu()) for i in spects]\n", + "print(\"Predictions:\", preds.size())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", + "\n", + "for i, ax in enumerate(axes):\n", + " ax.set_title(\"Spect \" + str(i + 1))\n", + " im = ax.imshow(spects[i], vmin=0, vmax=255)\n", + "\n", + "fig.subplots_adjust(right=0.85)\n", + "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", + "fig.colorbar(im, cax=cbar_ax, ticks=[0, 255])\n", + "\n", + "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", + "\n", + "for i, ax in enumerate(axes):\n", + " ax.set_title(\"Mask \" + str(i + 1))\n", + " im = ax.imshow(masks[i], vmin=0, vmax=2, cmap=mask_cmap)\n", + "\n", + "fig.subplots_adjust(right=0.85)\n", + "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", + "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", + "cbar.ax.set_yticklabels(labels)\n", + "\n", + "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", + "\n", + "for i, ax in enumerate(axes):\n", + " ax.set_title(\"Prediction \" + str(i + 1))\n", + " im = ax.imshow(preds[i], vmin=0, vmax=2, cmap=mask_cmap)\n", + "\n", + "fig.subplots_adjust(right=0.85)\n", + "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", + "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", + "cbar.ax.set_yticklabels(labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with the confusion matrix, which provides a comprehensive overview of the model's ability. The diagonal elements represent the correct predictions and off-diagonal elements indicate prediction errors." ] }, { From 1852c37c03e54b3581af40359b6253a3477f9181 Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Thu, 2 May 2024 14:52:45 -0400 Subject: [PATCH 3/9] Pulling the batch plotting code out into reusable functions. --- spectrogram_segmentation.ipynb | 262 ++++++++++++++++++++++----------- 1 file changed, 172 insertions(+), 90 deletions(-) diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index b9a2a78..943a531 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -252,11 +252,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 151, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The full dataset has 1800 examples. Loading example at index 497:\n", + "Spectrogram: , torch.float32, torch.Size([3, 256, 256])\n", + "Mask: , torch.int64, torch.Size([256, 256])\n" + ] + } + ], "source": [ "random_index = np.random.randint(len(dataset))\n", "training_example, corresponding_mask = dataset[random_index]\n", @@ -277,9 +287,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Spectrogram: \n", + "Mask: \n" + ] + } + ], "source": [ "inv_transform = Compose(\n", " [\n", @@ -308,24 +327,45 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(1, 0.33, 'Noise'), Text(1, 1.0, 'NR'), Text(1, 1.67, 'LTE')]" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, (ax1, ax2) = plt.subplots(figsize=[8, 3.5], nrows=1, ncols=2)\n", "plt.subplots_adjust(wspace=0.5)\n", "ax1.set_title(\"\\nRandom Spectrogram\", fontsize=title_font_size)\n", "ax2.set_title(\"Corresponding Mask\", fontsize=title_font_size)\n", - "ax1.set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "ax2.set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", + "ax1.set_ylabel(\"Time [arb. units]\", fontsize=label_font_size)\n", + "ax2.set_ylabel(\"Time [arb. units]\", fontsize=label_font_size)\n", "ax1.set_xlabel(\"Freq. [arb. units]\", fontsize=label_font_size)\n", "ax2.set_xlabel(\"Freq. [arb. units]\", fontsize=label_font_size)\n", "\n", "spect = ax1.imshow(training_example, vmin=0, vmax=255)\n", - "fig.colorbar(spect, ax=ax1, fraction=0.04, ticks=[0, 255])\n", + "fig.colorbar(spect, ax=ax1, fraction=0.045, ticks=[0, 255])\n", "\n", "mask = ax2.imshow(corresponding_mask, cmap=mask_cmap, vmin=0, vmax=2)\n", - "mask_cbar = fig.colorbar(mask, ax=ax2, cmap=mask_cmap, fraction=0.04, ticks=[0.33, 1, 1.67])\n", + "mask_cbar = fig.colorbar(mask, ax=ax2, cmap=mask_cmap, fraction=0.045, ticks=[0.33, 1, 1.67])\n", "mask_cbar.ax.set_yticklabels(labels)" ] }, @@ -404,9 +444,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch of spectrograms: , torch.float32, torch.Size([4, 3, 256, 256])\n", + "Batch of masks: , torch.int64, torch.Size([4, 256, 256])\n" + ] + } + ], "source": [ "mini_batch_size = 4\n", "\n", @@ -428,44 +477,71 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "spects = [inv_transform(i) for i in spects]\n", - "masks = [inv_target_transform(i) for i in masks]" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 157, "metadata": {}, "outputs": [], "source": [ - "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", - "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", + "def plot_spects(spects: list[Image.Image]) -> None:\n", + " fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + " fig.text(0.5, 0.75, \"Spectrograms\", fontsize=title_font_size, ha=\"center\")\n", + " axes[0].set_ylabel(\"Time [arb. units]\", fontsize=label_font_size)\n", + " fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", "\n", - "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Spect \" + str(i + 1))\n", - " im = ax.imshow(spects[i], vmin=0, vmax=255)\n", + " for i, ax in enumerate(axes):\n", + " im = ax.imshow(spects[i], vmin=0, vmax=255)\n", "\n", - "fig.subplots_adjust(right=0.85)\n", - "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "fig.colorbar(im, cax=cbar_ax, ticks=[0, 255])\n", + " fig.subplots_adjust(right=0.90)\n", + " cbar_ax = fig.add_axes(rect=[0.93, 0.24, 0.02, 0.5])\n", + " fig.colorbar(im, cax=cbar_ax, ticks=[0, 255])\n", "\n", - "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", - "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", "\n", - "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Mask \" + str(i + 1))\n", - " im = ax.imshow(masks[i], vmin=0, vmax=2, cmap=mask_cmap)\n", + "def plot_masks(masks: list[Image.Image], prediction: bool = False) -> None:\n", + " fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + " if prediction:\n", + " fig.text(0.5, 0.75, \"Model Predictions\", fontsize=title_font_size, ha=\"center\")\n", + " else:\n", + " fig.text(0.5, 0.75, \"Masks\", fontsize=title_font_size, ha=\"center\")\n", + " axes[0].set_ylabel(\"Time [arb. units]\", fontsize=label_font_size)\n", + " fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", "\n", - "fig.subplots_adjust(right=0.85)\n", - "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", - "cbar.ax.set_yticklabels(labels)" + " for i, ax in enumerate(axes):\n", + " im = ax.imshow(masks[i], vmin=0, vmax=2, cmap=mask_cmap)\n", + "\n", + " fig.subplots_adjust(right=0.90)\n", + " cbar_ax = fig.add_axes(rect=[0.93, 0.24, 0.02, 0.5])\n", + " cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", + " cbar.ax.set_yticklabels(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_spects(spects=[inv_transform(i) for i in spects])\n", + "plot_masks(masks=[inv_target_transform(i) for i in masks])" ] }, { @@ -747,9 +823,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: torch.Size([4, 256, 256])\n" + ] + } + ], "source": [ "model.eval()\n", "model.to(device)\n", @@ -760,59 +844,57 @@ "with torch.no_grad():\n", " preds = (model(spects)[\"out\"]).argmax(1)\n", "\n", - "preds, spects = preds.cpu(), [inv_transform(i.cpu()) for i in spects]\n", "print(\"Predictions:\", preds.size())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", - "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", - "\n", - "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Spect \" + str(i + 1))\n", - " im = ax.imshow(spects[i], vmin=0, vmax=255)\n", - "\n", - "fig.subplots_adjust(right=0.85)\n", - "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "fig.colorbar(im, cax=cbar_ax, ticks=[0, 255])\n", - "\n", - "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", - "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", - "\n", - "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Mask \" + str(i + 1))\n", - " im = ax.imshow(masks[i], vmin=0, vmax=2, cmap=mask_cmap)\n", - "\n", - "fig.subplots_adjust(right=0.85)\n", - "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", - "cbar.ax.set_yticklabels(labels)\n", - "\n", - "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", - "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", - "\n", - "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Prediction \" + str(i + 1))\n", - " im = ax.imshow(preds[i], vmin=0, vmax=2, cmap=mask_cmap)\n", - "\n", - "fig.subplots_adjust(right=0.85)\n", - "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", - "cbar.ax.set_yticklabels(labels)" + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_spects(spects=[inv_transform(i.cpu()) for i in spects])\n", + "plot_masks(masks=[inv_target_transform(i) for i in masks])\n", + "plot_masks(masks=preds.cpu(), prediction=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "Looks pretty good! To get a more ojective sense, \n", + "\n", "Let's start with the confusion matrix, which provides a comprehensive overview of the model's ability. The diagonal elements represent the correct predictions and off-diagonal elements indicate prediction errors." ] }, From 9ae18b014127e8135d0da36e0c08246b7fcc9579 Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Thu, 2 May 2024 16:43:32 -0400 Subject: [PATCH 4/9] Completed the model validation section. --- spectrogram_segmentation.ipynb | 451 ++++++--------------------------- 1 file changed, 73 insertions(+), 378 deletions(-) diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index 943a531..e27633e 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -23,7 +23,7 @@ "\n", "**[Model Training](#Model-Training):** Select and train a deep learning model.\n", "\n", - "**[Model Verification](#Model-Verification):** Assess the performance of the model using a suite of common machine learning metrics\n", + "**[Model Validation](#Model-Validation):** Assess the performance of the model using a suite of common machine learning metrics\n", "\n", "**[Challenge Data](#Challange-Data):** Challenge the model on combined frames containing both LTE and NR signal.\n", "\n", @@ -80,12 +80,12 @@ "source": [ "import glob\n", "import os\n", + "from pprint import pprint\n", "\n", "import statistics\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "import numpy as np\n", - "import pandas as pd\n", "import lightning as L\n", "import torch\n", "import torchmetrics\n", @@ -97,7 +97,7 @@ "from torch import nn, Tensor\n", "from torch.utils.data import DataLoader\n", "from torchmetrics import JaccardIndex as jac_ind\n", - "from torchmetrics.classification import MulticlassAccuracy, MulticlassConfusionMatrix\n", + "from torchmetrics.classification import MulticlassAccuracy, MulticlassConfusionMatrix, MulticlassRecall, MulticlassPrecision, MulticlassF1Score, MulticlassJaccardIndex\n", "from torchvision.datasets import VisionDataset\n", "from torchvision.io import read_image\n", "from torchvision.transforms.v2 import Compose, PILToTensor, ToDtype, Normalize, ToPILImage, ToTensor\n", @@ -252,21 +252,11 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The full dataset has 1800 examples. Loading example at index 497:\n", - "Spectrogram: , torch.float32, torch.Size([3, 256, 256])\n", - "Mask: , torch.int64, torch.Size([256, 256])\n" - ] - } - ], + "outputs": [], "source": [ "random_index = np.random.randint(len(dataset))\n", "training_example, corresponding_mask = dataset[random_index]\n", @@ -287,18 +277,9 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Spectrogram: \n", - "Mask: \n" - ] - } - ], + "outputs": [], "source": [ "inv_transform = Compose(\n", " [\n", @@ -327,30 +308,9 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Text(1, 0.33, 'Noise'), Text(1, 1.0, 'NR'), Text(1, 1.67, 'LTE')]" - ] - }, - "execution_count": 155, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(figsize=[8, 3.5], nrows=1, ncols=2)\n", "plt.subplots_adjust(wspace=0.5)\n", @@ -444,18 +404,9 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch of spectrograms: , torch.float32, torch.Size([4, 3, 256, 256])\n", - "Batch of masks: , torch.int64, torch.Size([4, 256, 256])\n" - ] - } - ], + "outputs": [], "source": [ "mini_batch_size = 4\n", "\n", @@ -477,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -515,30 +466,9 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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TAAAAADdmuoj4+uuvtWrVKgoIAAAA4BZlemF1bGysLly4UBGxAAAAAPAApouI8ePH64033tClS5cqIh4AAAAAbs705UxffvmlsrOzdfvtt6tz584KCwtz2G+xWDR37lyXBQgAAADAvZguIubPn2//evny5SX2U0QAAAAA3s10EVFcXFwRcQAAAADwEDyxGgAAAIApFBEAAAAATDF9OZOPj89Nn0pdVFRU5oAAAAAAuDfTRcQrr7xSooj44Ycf9NFHH6moqEgDBw50WXAAAAAA3I/pIiI1NbXU9sLCQiUnJ/MkawAAAMDLuWxNREBAgIYPH65Zs2a5akgAAAAAbsilC6uDgoJ06tQpVw4JAAAAwM24rIj44Ycf9Prrr+uOO+5w1ZAAAAAA3JDpNRHx8fElFlYXFBQoOztbPj4+Wrt2rcuCAwAAACrbNL0oKaQMR+ZKmubiaNyT6SKiY8eOJYqIwMBAxcXFqV+/foqLi3NVbAAAAADckOkiIi0trQLCAAAAAOApeGI1AAAAAFMoIgAAAACYUuVFxKeffqoePXooOjpaFotFa9ascdhvGIZSU1MVHR2toKAgderUSQcOHHDoU1BQoOHDhys8PFzBwcHq2bOnvv3220qcBQAAAHDrqPIi4uLFi2rWrJnmz59f6v4ZM2Zo1qxZmj9/vnbs2CGbzaYHH3xQ58+ft/cZOXKkVq9erRUrVuizzz7ThQsX1L17dxUVFVXWNAAAAIBbhumF1a728MMP6+GHHy51n2EYmjNnjiZMmKDHHntMkrR06VJFRUXpvffe07PPPqucnBwtWrRIy5YtU2JioiTp3XffVUxMjDZu3Kjk5ORKmwsAAABwK6jyMxE3cvToUWVlZSkpKcneZrVa1bFjR23btk2StHPnTl2+fNmhT3R0tBISEux9SlNQUKDc3FyHF1DZyENUNXIQ7oA8BDyPWxcRWVlZkqSoqCiH9qioKPu+rKwsBQQEqFatWtftU5qpU6cqNDTU/oqJiXFx9MDNkYeoauQg3AF5CHgelxYR/v7+8vNz/RVSP3+4nWEYJdp+7mZ9XnzxReXk5NhfmZmZLokVMIM8RFUjB+EOyEPA87j0N/4OHTqouLjYZePZbDZJV8821KlTx96enZ1tPzths9lUWFioc+fOOZyNyM7O1n333Xfdsa1Wq6xWq8tiBcqCPERVIwfhDshDwPO49EzExx9/rE2bNrlsvPj4eNlsNqWnp9vbCgsLtWXLFnuB0LJlS/n7+zv0OXXqlPbv33/DIgIAAABA2VT53ZkuXLigI0eO2LePHj2q3bt3q3bt2qpfv75GjhypKVOmqGHDhmrYsKGmTJmiatWqacCAAZKk0NBQ/eIXv9Do0aMVFham2rVra8yYMWratKn9bk0AAAAAXKfMRcQXX3yhTZs26cyZMwoLC1OnTp3K9Jf/f//73+rcubN9e9SoUZKkQYMGKS0tTWPHjlVeXp6GDBmic+fOqU2bNvroo49Uo0YN+zGzZ8+Wn5+f+vbtq7y8PHXt2lVpaWny9fUt6/QAAAAAXIfpIiIvL0/9+/fX3//+dxmGYW+3WCzq1q2bPvjgAwUFBTk9XqdOnRzG+TmLxaLU1FSlpqZet09gYKDmzZunefPmOf2+AAAAAMrG9JqIsWPH6sMPP9Rrr72mo0ePKi8vT0ePHtXkyZO1YcMGjR07tiLiBAAAAOAmTJ+JeP/99/Xyyy/rxRdftLfFxsZq/Pjxunz5subPn88ZAQAAAMCLmT4TcenSpeuufWjfvr3y8vLKHRQAAAAA92W6iGjbtq127NhR6r4dO3aodevW5Q4KAAAAgPsyXUT8/ve/1x//+Ee9+eabOnfunCTp3Llzmj9/vv70pz9xKRMAAADghJSUFFksFk2bNs2hfc2aNbJYLJKkzZs3y2Kx2F9hYWHq0qWLPv/886oI2c6pIqJGjRoKCQlRSEiI2rZtq1OnTun5559XeHi4rFarwsPDNWLECJ06dYoHvAEAAABOCgwM1PTp0+1/nL+ew4cP69SpU9q8ebMiIiL0yCOPKDs7u5KiLMmphdV9+vSxV0MAAAAAXCMxMVFHjhzR1KlTNWPGjOv2i4yMVM2aNWWz2fTSSy/pgw8+0Pbt29WjR49KjPZ/nCoi0tLSKjgMAAAA4Nbj6+urKVOmaMCAAXr++edVr169G/a/dOmSlixZIkny9/evjBBLZWpNRF5enurWrat169ZVVDwAAACAR8vNzXV4FRQU3LB/7969dc8992jixInX7VOvXj1Vr15d1atX1+zZs9WyZUt17drV1aE7zVQRERQUpLy8PAUHB1dUPAAAAIBHi4mJUWhoqP01derUmx4zffp0LV26VAcPHix1/9atW7Vr1y4tX75csbGxSktLq9IzEaYfNte1a1dt3LhRXbp0qYh4AAAAAI+WmZmpkJAQ+7bVar3pMR06dFBycrLGjx+vlJSUEvvj4+NVs2ZNNWrUSPn5+erdu7f279/v1NgVwfQtXsePH68VK1bo1Vdf1f79+3XmzBmdPXvW4QUAAADcqq7d1fTay9lf9KdNm6Z169Zp27ZtN+z39NNPq7i4WG+99ZYrwi0T00VEy5YtdezYMaWmpqpZs2aKjIxURESEwwsAAACAOU2bNtWTTz550+eu+fj4aOTIkZo2bZouXbpUSdE5Mn050yuvvMLtXgEAAIAKMHnyZH3wwQc37ffMM89o4sSJmj9/vsaOHVsJkTkyXUSkpqZWQBgAAADAraW0xyjExsYqPz/fvt2pUycZhlGiX3BwcJUuIzB9ORMAAACAW5vpMxGSVFRUpA8//FBfffWV8vLyHPZZLBa9/PLLLgkOAAAAgPsxXUScOXNGDzzwgA4dOiSLxWI/vfLTdRIUEQAAAID3Mn0504QJExQYGKjjx4/LMAxt375dX3/9tUaNGqVGjRrpxIkTFREnAAAAADdhuoj4+OOPNWrUKEVHR18dwMdHDRo00Ouvv67ExESNGTPG5UECAAAAcB+mi4hvv/1WcXFx8vX1lY+Pjy5evGjf16NHD6Wnp7s0QAAAAADuxXQRER4erpycHElSdHS09u/fb9939uxZXblyxXXRAQAAAHA7phdWt2zZUgcOHNAjjzyibt266dVXX1VISIgCAgI0fvx4tW3btiLiBAAAAOAmTBcRw4YN0zfffCPp6hP1/vWvf2ngwIGSpAYNGmju3LmujRAAAACAWzFdRCQmJioxMVGSFBERoYyMDO3fv18Wi0WNGzeWn1+ZHj0BAAAAwEOU+zd+i8Wipk2buiIWAAAAAB7AqYXVZ8+eVXFxsamBy3IMAAAAAPfnVBERERGhf//7304PWlRUpIiICO3evbuscQEAAABwU05dzmQYhrKyspx+GvWVK1dkGEa5AgMAAADgnpxeE9G7d29TA1ssFtPBAAAAAHB/ThURS5YsKdPg8fHxZToOAAAAgPtyqogYNGhQRccBAAAAwEM4tbAaAAAAAK6hiAAAAABgCkUEAAAA8FM5kowyvHKqItiqQREBAAAAwBSKCAAAAACmlKuIyMvL08mTJ3XlyhVXxQMAAADAzZWpiNi0aZPatWunGjVqKDY2Vnv37pUkDR06VKtWrXJpgAAAAADci+ki4pNPPlFSUpLy8/M1ZswYFRcX2/eFh4crLS3NlfEBAAAAcDOmi4hXXnlF3bp1U0ZGhl577TWHfc2aNdPu3btdFRsAAAAAN2S6iMjIyNCzzz4rSbJYLA77IiIilJ2dbWq8qVOnqlWrVqpRo4YiIyP16KOP6vDhww59DMNQamqqoqOjFRQUpE6dOunAgQMOfQoKCjR8+HCFh4crODhYPXv21Lfffmt2egAAAABuwnQR4efnp8uXL5e6Lzs7WzVq1DA13pYtWzR06FD961//Unp6uq5cuaKkpCRdvHjR3mfGjBmaNWuW5s+frx07dshms+nBBx/U+fPn7X1Gjhyp1atXa8WKFfrss8904cIFde/eXUVFRWanCAAAAOAGTBcRrVq10rJly0rd99e//lXt2rUzNd769euVkpKiu+66S82aNdOSJUt04sQJ7dy5U9LVsxBz5szRhAkT9NhjjykhIUFLly7VpUuX9N5770mScnJytGjRIs2cOVOJiYlq3ry53n33Xe3bt08bN240O0UAAAAAN2C6iBg3bpxWr16t3r17a+3atbJYLNq+fbuGDRumv/71rxo7dmy5AsrJufqov9q1a0uSjh49qqysLCUlJdn7WK1WdezYUdu2bZMk7dy5U5cvX3boEx0drYSEBHsfAAAAAK7hZ/aAxMRELV26VCNHjtTf/vY3SVdv7VqzZk2lpaXp/vvvL3MwhmFo1KhRuv/++5WQkCBJysrKkiRFRUU59I2KitLx48ftfQICAlSrVq0Sfa4d/3MFBQUqKCiwb+fm5pY5bqCsyENUNXIQ7oA8BDxPmZ4T8dRTTykzM1Pp6el69913tX79emVmZurJJ58sVzDDhg3T3r17tXz58hL7fr6I2zCMEm0/d6M+U6dOVWhoqP0VExNT9sCBMiIPUdXIQbgD8hDwPGV+YnVQUJC6du2qAQMGKCkpScHBweUKZPjw4Vq7dq02bdqkevXq2dttNpsklTijkJ2dbT87YbPZVFhYqHPnzl23z8+9+OKLysnJsb8yMzPLFT9QFuQhqho5CHdAHgKex/TlTNccOHBAx48fV35+fol9jz32mNPjGIah4cOHa/Xq1dq8ebPi4+Md9sfHx8tmsyk9PV3NmzeXJBUWFmrLli2aPn26JKlly5by9/dXenq6+vbtK0k6deqU9u/frxkzZpT6vlarVVar1ek4gYpAHqKqkYNwB+Qh4HlMFxHffPONHn/8ce3du1fS1SLgpywWi6nbqg4dOlTvvfee/va3v6lGjRr2Mw6hoaEKCgqSxWLRyJEjNWXKFDVs2FANGzbUlClTVK1aNQ0YMMDe9xe/+IVGjx6tsLAw1a5dW2PGjFHTpk2VmJhodooAAAAAbsB0EfHrX/9aWVlZmj17tpo0aaKAgIByBbBgwQJJUqdOnRzalyxZopSUFEnS2LFjlZeXpyFDhujcuXNq06aNPvroI4dnUsyePVt+fn7q27ev8vLy1LVrV6WlpcnX17dc8QEAAABwZLqI+PLLL7Vw4UL179/fJQH8/ExGaSwWi1JTU5WamnrdPoGBgZo3b57mzZvnkrgAAAAAlM70wuqIiAiFhoZWRCwAAAAAPIDpIuK5557TwoULKyIWAAAAAB7A9OVMv/3tbzV69Gi1bNlSDz/8sP3J0tdYLBa98MILLgsQAAAAwPXFxcVp5MiRGjlyZKW9p+kiYvv27Vq6dKnOnj2rjIyMEvspIgAAAICbS0lJ0dKlSzV16lSNGzfO3r5mzRr17t3bqbXDkrRjx45yP7PNLNNFxLBhwxQeHq7Fixe75O5MAAAAwK0qMDBQ06dP17PPPqtatWqVaYyIiAgXR3VzptdEHDhwQDNmzFDPnj3VsGFDxcbGlngBAAAAuLnExETZbDZNnTr1un1Wrlypu+66S1arVXFxcZo5c6bD/ri4OM2ZM8e+nZqaqvr168tqtSo6OlrPP/+8fV9hYaHGjh2runXrKjg4WG3atNHmzZtNx236TET9+vWdPrUCAAAA3Gpyc3Mdtm/0VHZfX19NmTJFAwYM0PPPP6969eo57N+5c6f69u2r1NRU9evXT9u2bdOQIUMUFhZmf6baT/31r3/V7NmztWLFCt11113KysrSnj177PsHDx6sY8eOacWKFYqOjtbq1av10EMPad++fWrYsKHTczR9JmLcuHF64403lJ+fb/ZQAAAAwOvFxMQoNDTU/rrRWQZJ6t27t+655x5NnDixxL5Zs2apa9euevnll9WoUSOlpKRo2LBhev3110sd68SJE7LZbEpMTFT9+vXVunVr/epXv5IkffPNN1q+fLn+8pe/6IEHHlCDBg00ZswY3X///VqyZImpOZo+E7Fr1y6dPHlSDRo0UOfOnUu9O9PcuXPNDgsAAAB4hczMTIWEhNi3r3cW4qemT5+uLl26aPTo0Q7tX331lXr16uXQ1r59e82ZM0dFRUXy9fV12Pd///d/mjNnjm677TY99NBD6tatm3r06CE/Pz/t2rVLhmGoUaNGDscUFBQoLCzM1BxNFxHz58+3f/3ee++V2E8RAQAAgFtZSEiIQxHhjA4dOig5OVnjx493uEzJMAxZLBaHvjdaWhATE6PDhw8rPT1dGzdu1JAhQ/T6669ry5YtKi4ulq+vr3bu3Fmi+KhevbqpeE0XEcXFxWYPAQAAAHAT06ZN0z333ONwpuDOO+/UZ5995tBv27ZtatSoUYlC4JqgoCD17NlTPXv21NChQ9W4cWPt27dPzZs3V1FRkbKzs/XAAw+UK1bTRQQAAAAA12vatKmefPJJzZs3z942evRotWrVSpMnT1a/fv30xRdfaP78+XrrrbdKHSMtLU1FRUVq06aNqlWrpmXLlikoKEixsbEKCwvTk08+qYEDB2rmzJlq3ry5Tp8+rU8++URNmzZVt27dnI7V9MJqAAAAABVj8uTJDpcrtWjRQh988IFWrFihhIQEvfLKK3r11VdLvTOTJNWsWVMLFy5U+/btdffdd+vjjz/WunXr7GselixZooEDB2r06NG644471LNnT23fvl0xMTGm4rQYTtyv9bbbbtPq1avVrFkzxcfHl7guy2FAi0XffPONqSDcQW5urkJDQ6WcHMnkNWwe4/of263F7B2Kc3Ol0FDl5OSYvr7RrErPQ3KiYrnqbtjenIOezCKlapJTXVM10XX5UFXIw/Lh5+1V5f13UMF5WO7cq8R/J1XNqcuZOnbsaP9GdOzY8YZFBAAAAADv5lQRMXjwYPvjtNPS0ioyHgAAAABuzqk1EZ07d9bBgwcrOhYAAAAAHsCpIsKJZRMAAAAAbhHcnQkAAACAKU4XESymBgAAACCZeNhc586d5eNz85rDYrEoJyenXEEBAAAAcF9OFxGdOnWy36EJAAAAwK3L6SLilVdeUevWrSsyliplXzyem1u1gaDimf2I/5sTlXGDAfLQy7jqYyQH3Va+8p3smeu6fKgq5CFcobwfaSXmIW7M6SLC2505c+bqFyYf+Q0PFFq2w86fP3/1KZYViDz0Mi5Ol8rIwfPnz1/9ghx0yjQzPSv2o6s0/CxEubgodSojD3FjFBH/Vbt2bUnSiRMnvCYpc3NzFRMTo8zMTK949HpVzccwDJ0/f17R0dEV/l7kofurivlUZg5GR0fr4MGDuvPOO/nM3Ji35yE/Cz2Dt+chbowi4r+uLRoPDQ31mn/c14SEhHjVnKpiPpX1PzHy0HNU9nwqMwfr1q0ric/ME3hzHl57Pz4z9+eteYgbc6qIKC4urug4AAAAAHgIHjYHAAAAwBSKiP+yWq2aOHGirFZrVYfiMt42J2+bT2m8cY7eNidvm09pvG2O3jYfyTvn9FPeOD/mBG9jMbhHFgAAAKDc3Nyray5ycqSyrPPIzZVCQ5WTk+N1a19+jjMRAAAAAEzh7kwAAADAT5X1QYe30AMSKSIAAAAASQEBAbLZbMoqx4MObTabAgICXBiVe2JNBAAAAPBf+fn5KiwsLPPxAQEBCgwMdGFE7ok1Ef/11ltvKT4+XoGBgWrZsqW2bt1a1SGV6tNPP1WPHj0UHR0ti8WiNWvWOOw3DEOpqamKjo5WUFCQOnXqpAMHDjj0KSgo0PDhwxUeHq7g4GD17NlT3377bSXO4n+mTp2qVq1aqUaNGoqMjNSjjz6qw4cPO/TxtDmVlafkoEQeesKcyoo8JA/dgafkITno/nMqi8DAQPsD9MryuhUKCEmSAWPFihWGv7+/sXDhQuPgwYPGiBEjjODgYOP48eNVHVoJ//znP40JEyYYK1euNCQZq1evdtg/bdo0o0aNGsbKlSuNffv2Gf369TPq1Klj5Obm2vv85je/MerWrWukp6cbu3btMjp37mw0a9bMuHLlSiXPxjCSk5ONJUuWGPv37zd2795tPPLII0b9+vWNCxcueOycysKTctAwyENPmFNZkIfkoTvwpDwkB91/Tqg4FBGGYbRu3dr4zW9+49DWuHFjY9y4cVUUkXN+/gOruLjYsNlsxrRp0+xt+fn5RmhoqPGHP/zBMAzD+PHHHw1/f39jxYoV9j4nT540fHx8jPXr11da7NeTnZ1tSDK2bNliGIZ3zMkZnpqDhkEeesqcnEEeutdnRh7+jyfkITnoGXOC69zylzMVFhZq586dSkpKcmhPSkrStm3bqiiqsjl69KiysrIc5mK1WtWxY0f7XHbu3KnLly879ImOjlZCQoJbzDcnJ0eSVLt2bUneMaeb8aYclLzjMyMP/4c8rDrk4f94Yh56w+d1K+YgnHfLFxGnT59WUVGRoqKiHNqjoqKUlZVVRVGVzbV4bzSXrKwsBQQEqFatWtftU1UMw9CoUaN0//33KyEhQZLnz8kZ3pSDkud/ZuQheegOcyYPPT8PPf3zulVzEM7jFq//ZbFYHLYNwyjR5inKMhd3mO+wYcO0d+9effbZZyX2eeqczPCmHJQ89zMjD8lDd5gzeeg9eeipn9etnoO4uVv+TER4eLh8fX1LVMfZ2dklKm13Z7PZJOmGc7HZbCosLNS5c+eu26cqDB8+XGvXrtWmTZtUr149e7snz8lZ3pSDkmd/ZuQhefjzPlWBPPSOPPTkz+tWzkE475YvIgICAtSyZUulp6c7tKenp+u+++6roqjKJj4+XjabzWEuhYWF2rJli30uLVu2lL+/v0OfU6dOaf/+/VUyX8MwNGzYMK1atUqffPKJ4uPjHfZ74pzM8qYclDzzMyMPycNryMOq5U156ImfFzkIUypn/bZ7u3Y7uUWLFhkHDx40Ro4caQQHBxvHjh2r6tBKOH/+vJGRkWFkZGQYkoxZs2YZGRkZ9lvfTZs2zQgNDTVWrVpl7Nu3z3jiiSdKvfVavXr1jI0bNxq7du0yunTpUmW3XnvuueeM0NBQY/PmzcapU6fsr0uXLtn7eNqcysKTctAwyENPmFNZkIfkoTvwpDwkB91/Tqg4FBH/9eabbxqxsbFGQECA0aJFC/vtzNzNpk2bDEklXoMGDTIM4+rt1yZOnGjYbDbDarUaHTp0MPbt2+cwRl5enjFs2DCjdu3aRlBQkNG9e3fjxIkTVTAbo9S5SDKWLFli7+NpcyorT8lBwyAPPWFOZUUekofuwFPykBx0/zmh4lgMwzBcf34DAAAAgLe65ddEAAAAADCHIgIAAACAKRQRAAAAAEyhiAAAAABgCkUEAAAAAFMoIgAAAACYQhEBAAAAwBSKCAAAAACmUEQAAAAAMIUiAgAAAIApFBEAAAAATKGIAOAR0tLSZLFYSn2NGTOmqsO7rpSUFHucCQkJLh8/Li5O3bt3d/m4rpaSkqK4uDiHtilTpmjNmjVlGm/OnDkOOXD69OnyBwkAcJpfVQcAAGYsWbJEjRs3dmiLjo6uomicY7PZtHr1alWrVq2qQ6kyL7/8skaMGOHQNmXKFD3++ON69NFHTY/Xv39/tW3bVm+//bYWLVrkoigBAM6iiADgURISEnTvvfc61ffy5cuyWCzy86vaH3VWq1Vt27Z16ZiXLl3yqKKkQYMGLh3PZrPJZrNp/fr1Lh0XAOAcLmcC4BU2b94si8WiZcuWafTo0apbt66sVquOHDkiSdq4caO6du2qkJAQVatWTe3bt9fHH39cYpx//OMfuueee2S1WhUfH6833nhDqampslgsLo/5zTffVIcOHRQZGang4GA1bdpUM2bM0OXLlx36derUSQkJCfr000913333qVq1anrmmWcc+qxevVp33323AgMDddttt+n3v/99meOKi4tTSkpKifZOnTqpU6dO9u1r3/Ply5drwoQJio6OVkhIiBITE3X48GGHY39+OZPFYtHFixe1dOlS+yVJ18a+dOmSxowZo/j4eAUGBqp27dq69957tXz58jLPCQDgWpyJAOBRioqKdOXKFYe2n55pePHFF9WuXTv94Q9/kI+PjyIjI/Xuu+9q4MCB6tWrl5YuXSp/f3/98Y9/VHJysjZs2KCuXbtKkj7++GP16tVL7dq104oVK1RUVKQZM2bo+++/r5C5fPPNNxowYIDi4+MVEBCgPXv26He/+50OHTqkxYsXO/Q9deqUnnrqKY0dO1ZTpkyRj8///ga0e/dujRw5UqmpqbLZbPrzn/+sESNGqLCwsFLWi4wfP17t27fX22+/rdzcXP2///f/1KNHD3311Vfy9fUt9ZgvvvhCXbp0UefOnfXyyy9LkkJCQiRJo0aN0rJly/Taa6+pefPmunjxovbv368zZ85U+FwAAM6hiADgUUq7LOinf7lv0KCB/vKXv9i3L126pBEjRqh79+5avXq1vb1bt25q0aKFxo8fr+3bt0uSJkyYoKioKKWnpyswMFCSlJycXGJBsKvMmjXL/nVxcbEeeOABhYWFafDgwZo5c6Zq1apl33/27Fn95S9/UZcuXUqM89133ykjI0PNmjWTJD388MPKzs7W5MmTNWTIkAq/7OnOO+/Uu+++a9/29fVV3759tWPHjutextW2bVv5+PgoIiKiRJ/PP/9cSUlJeuGFF+xtjzzySMUEDwAoEy5nAuBR3nnnHe3YscPh9dMzEX369HHov23bNp09e1aDBg3SlStX7K/i4mI99NBD2rFjhy5evKiLFy9qx44deuyxx+wFhCTVqFFDPXr0qJC5ZGRkqGfPngoLC5Ovr6/8/f01cOBAFRUV6T//+Y9D31q1apVaQEjSXXfdZS8grhkwYIByc3O1a9euCon9p3r27Omwfffdd0uSjh8/XqbxWrdurQ8//FDjxo3T5s2blZeXV+4YAQCuxZkIAB6lSZMmN1xYXadOHYfta5ciPf7449c95uzZs7JYLCouLpbNZiuxv7S28jpx4oQeeOAB3XHHHZo7d67i4uIUGBioL7/8UkOHDi3xi/PP53Wz+K61VcYlQGFhYQ7bVqtVksr8y//vf/971atXT++//76mT5+uwMBAJScn6/XXX1fDhg3LHS8AoPwoIgB4lZ8vgA4PD5ckzZs377qX1kRFRdnv5JSVlVVif2lt5bVmzRpdvHhRq1atUmxsrL199+7dpfa/0cLuG8X881/wnREYGKiCgoIS7adPn7Z/PytScHCwJk2apEmTJun777+3n5Xo0aOHDh06VOHvDwC4OS5nAuDV2rdvr5o1a+rgwYO69957S30FBAQoODhYrVu31qpVq5Sfn28//vz581q3bp3L47pWFFz7q70kGYahhQsXmh7rwIED2rNnj0Pbe++9pxo1aqhFixamx4uLi9PevXsd2v7zn/+UuONSeVmt1puerYiKilJKSoqeeOIJHT58WJcuXXJpDACAsuFMBACvVr16dc2bN0+DBg3S2bNn9fjjjysyMlI//PCD9uzZox9++EELFiyQJE2ePFkPPfSQHnzwQY0ePVpFRUWaPn26goODdfbsWYdxu3btqi1btpS4U5SzHnzwQQUEBOiJJ57Q2LFjlZ+frwULFujcuXOmx4qOjlbPnj2VmpqqOnXq6N1331V6erqmT5/usKjaz89PHTt2LPXWtj/19NNP66mnntKQIUPUp08fHT9+XDNmzFBERITp2G6kadOm2rx5s9atW6c6deqoRo0auuOOO9SmTRt1795dd999t2rVqqWvvvpKy5YtU7t27Tzq2RgA4M04EwHA6z311FPatGmTLly4oGeffVaJiYkaMWKEdu3aZb+9q3T1F/s1a9YoNzdX/fr106hRo9SnT58Sz2SQrt5qtqioqMwxNW7cWCtXrtS5c+f02GOPafjw4brnnnvK9HyHe+65R7NmzdLMmTPVq1cvff7555o1a5bGjh1bppgHDBigGTNmaMOGDerevbsWLFigBQsWqFGjRqZju5G5c+eqYcOG6t+/v1q1aqVnn31WktSlSxetXbtWgwcPVlJSkmbMmKGBAwdWyBkhAEDZWAzDMKo6CABwZ6mpqZo0aZLK8uMyJSVFmzdv1pEjR2SxWK773ASYYxiGioqK9Oqrr2ry5Mn64YcfKmW9BgDgKs5EAEAFO378uPz9/UvchhVlN3fuXPn7+2vy5MlVHQoA3JJYEwEAFSg1NVXDhg2TJAUFBVVxNN5jwIABuv/+++3bNWvWrLpgAOAWxOVMAAAAAEzhciYAAAAAplBEAAAAADCFIgIAAACAKRQRAAAAAEyhiAAAAABgCkUEAAAAAFMoIgAAAACYQhEBAAAAwBSKCAAAAACm/H+qfydVMai0GwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_spects(spects=[inv_transform(i) for i in spects])\n", "plot_masks(masks=[inv_target_transform(i) for i in masks])" @@ -814,7 +744,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Model Verification\n", + "# Model Validation\n", "\n", "Having trained our model, the next step is to evaluate its performance. To accomplish this, we'll use a suite of standard machine learning metrics. But first, let's take quick look at a random batch of predictions and true labels.\n", "\n", @@ -823,17 +753,9 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predictions: torch.Size([4, 256, 256])\n" - ] - } - ], + "outputs": [], "source": [ "model.eval()\n", "model.to(device)\n", @@ -849,40 +771,9 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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TeHh4acQEAAAAwI2ZLiJ+/PFHLV++nAICAAAAqKRML6yOjo7W6dOnSyMWAAAAAB7AdBExbtw4Pf/88zp79mxpxAMAAADAzZm+nOmbb75RZmamrr32WnXq1EkhISEO+y0Wi+bMmeOyAAEAAAC4F9NFxLx58+xfL126tMh+iggAAACgYjNdRBQWFpZGHAAAAAA8BE+sBgAAAGAKRQQAAAAAU0xfzuTl5XXFp1IXFBSUOCAAAAAA7s10EfH0008XKSJ+++03ffLJJyooKNCAAQNcFhwAAAAA92O6iEhOTi62PT8/X4mJiTzJGgAAAKjgXLYmws/PT8OGDdPMmTNdNSQAAAAAN+TShdUBAQE6evSoK4cEAAAA4GZcVkT89ttveu6559SwYUNXDQkAAADADZleExEbG1tkYXVeXp4yMzPl5eWlVatWuSw4AAAAoKxN1ROSgkpwZLakqS6Oxj2ZLiI6dOhQpIjw9/dXTEyM+vbtq5iYGFfFBgAAAMANmS4iUlJSSiEMAAAAAJ6CJ1YDAAAAMIUiAgAAAIAp5V5EfP755+revbsiIyNlsVi0cuVKh/2GYSg5OVmRkZEKCAhQx44dtWfPHoc+eXl5GjZsmEJDQxUYGKgePXro559/LsNZAAAAAJVHuRcRZ86cUdOmTTVv3rxi90+fPl0zZ87UvHnztHXrVtlsNt122206deqUvc+IESO0YsUKLVu2TF988YVOnz6tbt26qaCgoKymAQAAAFQaphdWu9odd9yhO+64o9h9hmFo9uzZGj9+vO666y5J0uLFixUREaE333xTDz/8sLKysrRgwQItWbJE8fHxkqQ33nhDUVFRWrdunRITE8tsLgAAAEBlUO5nIi7nwIEDysjIUEJCgr3NarWqQ4cO2rx5syRp27ZtOnfunEOfyMhIxcXF2fsUJy8vT9nZ2Q4voKyRhyhv5CDcAXkIeB63LiIyMjIkSREREQ7tERER9n0ZGRny8/NTjRo1LtmnOFOmTFFwcLD9FRUV5eLogSsjD1HeyEG4A/IQ8DwuLSJ8fX3l4+P6K6T++nA7wzCKtP3Vlfo88cQTysrKsr/S09NdEitgBnmI8kYOwh2Qh4Dncelv/O3bt1dhYaHLxrPZbJIunG2oVauWvT0zM9N+dsJmsyk/P18nT550OBuRmZmpm2+++ZJjW61WWa1Wl8UKlAR5iPJGDsIdkIeA53HpmYhPP/1U69evd9l4sbGxstlsSk1Ntbfl5+dr48aN9gKhRYsW8vX1dehz9OhR7d69+7JFBAAAAICSKfe7M50+fVr79++3bx84cEA7duxQzZo1VbduXY0YMUKTJ09W/fr1Vb9+fU2ePFlVqlRR//79JUnBwcF66KGHNGrUKIWEhKhmzZoaPXq0mjRpYr9bEwAAAADXKXER8dVXX2n9+vU6fvy4QkJC1LFjxxL95f/bb79Vp06d7NsjR46UJA0cOFApKSkaM2aMcnJyNHjwYJ08eVKtW7fWJ598omrVqtmPmTVrlnx8fHTPPfcoJydHXbp0UUpKiry9vUs6PQAAAACXYLqIyMnJUb9+/fTBBx/IMAx7u8ViUdeuXfX2228rICDA6fE6duzoMM5fWSwWJScnKzk5+ZJ9/P39NXfuXM2dO9fp9wUAAABQMqbXRIwZM0Yff/yxnn32WR04cEA5OTk6cOCAJk2apLVr12rMmDGlEScAAAAAN2H6TMRbb72lp556Sk888YS9LTo6WuPGjdO5c+c0b948zggAAAAAFZjpMxFnz5695NqHdu3aKScn56qDAgAAAOC+TBcRbdq00datW4vdt3XrVrVq1eqqgwIAAADgvkwXES+88IJefvllvfjiizp58qQk6eTJk5o3b55eeeUVLmUCAAAAnJCUlCSLxaKpU6c6tK9cuVIWi0WStGHDBlksFvsrJCREnTt31pdfflkeIds5VURUq1ZNQUFBCgoKUps2bXT06FE99thjCg0NldVqVWhoqIYPH66jR4/ygDcAAADASf7+/po2bZr9j/OXsm/fPh09elQbNmxQWFiY7rzzTmVmZpZRlEU5tbC6T58+9moIAAAAgGvEx8dr//79mjJliqZPn37JfuHh4apevbpsNpuefPJJvf3229qyZYu6d+9ehtH+j1NFREpKSimHAQAAAFQ+3t7emjx5svr376/HHntMderUuWz/s2fPatGiRZIkX1/fsgixWKbWROTk5Kh27dpavXp1acUDAAAAeLTs7GyHV15e3mX79+7dWzfeeKMmTJhwyT516tRR1apVVbVqVc2aNUstWrRQly5dXB2600wVEQEBAcrJyVFgYGBpxQMAAAB4tKioKAUHB9tfU6ZMueIx06ZN0+LFi7V3795i92/atEnbt2/X0qVLFR0drZSUlHI9E2H6YXNdunTRunXr1Llz59KIBwAAAPBo6enpCgoKsm9brdYrHtO+fXslJiZq3LhxSkpKKrI/NjZW1atXV4MGDZSbm6vevXtr9+7dTo1dGkzf4nXcuHFatmyZnnnmGe3evVvHjx/XiRMnHF4AAABAZXXxrqYXX87+oj916lStXr1amzdvvmy/Bx54QIWFhXrppZdcEW6JmC4iWrRooYMHDyo5OVlNmzZVeHi4wsLCHF4AAAAAzGnSpInuu+++Kz53zcvLSyNGjNDUqVN19uzZMorOkenLmZ5++mlu9woAAACUgkmTJuntt9++Yr8HH3xQEyZM0Lx58zRmzJgyiMyR6SIiOTm5FMIAAAAAKpfiHqMQHR2t3Nxc+3bHjh1lGEaRfoGBgeW6jMD05UwAAAAAKjfTZyIkqaCgQB9//LG+//575eTkOOyzWCx66qmnXBIcAAAAAPdjuog4fvy4br31Vv3www+yWCz20yt/XidBEQEAAABUXKYvZxo/frz8/f116NAhGYahLVu26Mcff9TIkSPVoEEDHT58uDTiBAAAAOAmTBcRn376qUaOHKnIyMgLA3h5qV69enruuecUHx+v0aNHuzxIAAAAAO7DdBHx888/KyYmRt7e3vLy8tKZM2fs+7p3767U1FSXBggAAADAvZguIkJDQ5WVlSVJioyM1O7du+37Tpw4ofPnz7suOgAAAABux/TC6hYtWmjPnj2688471bVrVz3zzDMKCgqSn5+fxo0bpzZt2pRGnAAAAADchOkiYujQofrpp58kXXii3tdff60BAwZIkurVq6c5c+a4NkIAAAAAbsV0EREfH6/4+HhJUlhYmNLS0rR7925ZLBY1atRIPj4levQEAAAAAA9x1b/xWywWNWnSxBWxAAAAAPAATi2sPnHihAoLC00NXJJjAAAAALg/p4qIsLAwffvtt04PWlBQoLCwMO3YsaOkcQEAAABwU05dzmQYhjIyMpx+GvX58+dlGMZVBQYAAADAPTm9JqJ3796mBrZYLKaDAQAAAOD+nCoiFi1aVKLBY2NjS3QcAAAAAPflVBExcODA0o4DAAAAgIdwamE1AAAAAFxEEQEAAADAFIoIAAAA4M+yJBkleGWVR7DlgyICAAAAgCkUEQAAAABMuaoiIicnR0eOHNH58+ddFQ8AAAAAN1eiImL9+vVq27atqlWrpujoaO3cuVOSNGTIEC1fvtylAQIAAABwL6aLiM8++0wJCQnKzc3V6NGjVVhYaN8XGhqqlJQUV8YHAAAAwM2YLiKefvppde3aVWlpaXr22Wcd9jVt2lQ7duxwVWwAAAAA3JDpIiItLU0PP/ywJMlisTjsCwsLU2ZmpqnxpkyZopYtW6patWoKDw9Xr169tG/fPoc+hmEoOTlZkZGRCggIUMeOHbVnzx6HPnl5eRo2bJhCQ0MVGBioHj166OeffzY7PQAAAABXYLqI8PHx0blz54rdl5mZqWrVqpkab+PGjRoyZIi+/vprpaam6vz580pISNCZM2fsfaZPn66ZM2dq3rx52rp1q2w2m2677TadOnXK3mfEiBFasWKFli1bpi+++EKnT59Wt27dVFBQYHaKAAAAAC7DdBHRsmVLLVmypNh97777rtq2bWtqvDVr1igpKUnXX3+9mjZtqkWLFunw4cPatm2bpAtnIWbPnq3x48frrrvuUlxcnBYvXqyzZ8/qzTfflCRlZWVpwYIFmjFjhuLj49WsWTO98cYb2rVrl9atW2d2igAAAAAuw3QRMXbsWK1YsUK9e/fWqlWrZLFYtGXLFg0dOlTvvvuuxowZc1UBZWVdeNRfzZo1JUkHDhxQRkaGEhIS7H2sVqs6dOigzZs3S5K2bdumc+fOOfSJjIxUXFycvQ8AAAAA1/Axe0B8fLwWL16sESNG6P3335d04dau1atXV0pKim655ZYSB2MYhkaOHKlbbrlFcXFxkqSMjAxJUkREhEPfiIgIHTp0yN7Hz89PNWrUKNLn4vF/lZeXp7y8PPt2dnZ2ieMGSoo8RHkjB+EOyEPA85ToORH333+/0tPTlZqaqjfeeENr1qxRenq67rvvvqsKZujQodq5c6eWLl1aZN9fF3EbhlGk7a8u12fKlCkKDg62v6KiokoeOFBC5CHKGzkId0AeAp6nxE+sDggIUJcuXdS/f38lJCQoMDDwqgIZNmyYVq1apfXr16tOnTr2dpvNJklFzihkZmbaz07YbDbl5+fr5MmTl+zzV0888YSysrLsr/T09KuKHygJ8hDljRyEOyAPAc9j+nKmi/bs2aNDhw4pNze3yL677rrL6XEMw9CwYcO0YsUKbdiwQbGxsQ77Y2NjZbPZlJqaqmbNmkmS8vPztXHjRk2bNk2S1KJFC/n6+io1NVX33HOPJOno0aPavXu3pk+fXuz7Wq1WWa1Wp+MESgN5iPJGDsIdkIeA5zFdRPz000+6++67tXPnTkkXioA/s1gspm6rOmTIEL355pt6//33Va1aNfsZh+DgYAUEBMhisWjEiBGaPHmy6tevr/r162vy5MmqUqWK+vfvb+/70EMPadSoUQoJCVHNmjU1evRoNWnSRPHx8WanCAAAAOAyTBcR//jHP5SRkaFZs2apcePG8vPzu6oA5s+fL0nq2LGjQ/uiRYuUlJQkSRozZoxycnI0ePBgnTx5Uq1bt9Ynn3zi8EyKWbNmycfHR/fcc49ycnLUpUsXpaSkyNvb+6riAwAAAODIdBHxzTff6NVXX1W/fv1cEsBfz2QUx2KxKDk5WcnJyZfs4+/vr7lz52ru3LkuiQsAAABA8UwvrA4LC1NwcHBpxAIAAADAA5guIh599FG9+uqrpRELAAAAAA9g+nKmf/7znxo1apRatGihO+64w/5k6YssFosef/xxlwUIAAAA4NJiYmI0YsQIjRgxosze03QRsWXLFi1evFgnTpxQWlpakf0UEQAAAMCVJSUlafHixZoyZYrGjh1rb1+5cqV69+7t1NphSdq6detVP7PNLNNFxNChQxUaGqqFCxe65O5MAAAAQGXl7++vadOm6eGHH1aNGjVKNEZYWJiLo7oy02si9uzZo+nTp6tHjx6qX7++oqOji7wAAAAAXFl8fLxsNpumTJlyyT7vvfeerr/+elmtVsXExGjGjBkO+2NiYjR79mz7dnJysurWrSur1arIyEg99thj9n35+fkaM2aMateurcDAQLVu3VobNmwwHbfpMxF169Z1+tQKAAAAUNlkZ2c7bF/uqeze3t6aPHmy+vfvr8cee0x16tRx2L9t2zbdc889Sk5OVt++fbV582YNHjxYISEh9meq/dm7776rWbNmadmyZbr++uuVkZGh7777zr5/0KBBOnjwoJYtW6bIyEitWLFCt99+u3bt2qX69es7PUfTZyLGjh2r559/Xrm5uWYPBQAAACq8qKgoBQcH21+XO8sgSb1799aNN96oCRMmFNk3c+ZMdenSRU899ZQaNGigpKQkDR06VM8991yxYx0+fFg2m03x8fGqW7euWrVqpb///e+SpJ9++klLly7VO++8o1tvvVX16tXT6NGjdcstt2jRokWm5mj6TMT27dt15MgR1atXT506dSr27kxz5swxOywAAABQIaSnpysoKMi+famzEH82bdo0de7cWaNGjXJo//7779WzZ0+Htnbt2mn27NkqKCiQt7e3w77/+7//0+zZs3XNNdfo9ttvV9euXdW9e3f5+Pho+/btMgxDDRo0cDgmLy9PISEhpuZouoiYN2+e/es333yzyH6KCAAAAFRmQUFBDkWEM9q3b6/ExESNGzfO4TIlwzBksVgc+l5uaUFUVJT27dun1NRUrVu3ToMHD9Zzzz2njRs3qrCwUN7e3tq2bVuR4qNq1aqm4jVdRBQWFpo9BAAAAMAVTJ06VTfeeKPDmYLrrrtOX3zxhUO/zZs3q0GDBkUKgYsCAgLUo0cP9ejRQ0OGDFGjRo20a9cuNWvWTAUFBcrMzNStt956VbGaLiIAAAAAuF6TJk103333ae7cufa2UaNGqWXLlpo0aZL69u2rr776SvPmzdNLL71U7BgpKSkqKChQ69atVaVKFS1ZskQBAQGKjo5WSEiI7rvvPg0YMEAzZsxQs2bNdOzYMX322Wdq0qSJunbt6nSsphdWAwAAACgdkyZNcrhcqXnz5nr77be1bNkyxcXF6emnn9YzzzxT7J2ZJKl69ep69dVX1a5dO91www369NNPtXr1avuah0WLFmnAgAEaNWqUGjZsqB49emjLli2KiooyFadTZyKuueYarVixQk2bNlVsbGyR67L+zGKx6KeffjIVBAAAAFDZpKSkFGmLjo4uchfUPn36qE+fPpcc5+DBg/ave/XqpV69el2yr6+vryZOnKiJEyeaDdeBU0VEhw4d7ItDOnTocNkiAgAAAEDF5lQRMWjQIPvjtIurmAAAAABUHk6tiejUqZP27t1b2rEAAAAA8ABOFRGXuxctAAAAgMqFuzMBAAAAMMXpIoLF1AAAAAAkEw+b69Spk7y8rlxzWCwWZWVlXVVQAAAAANyX00VEx44d7XdoAgAAAFB5OV1EPP3002rVqlVpxlKu7IvHs7PL7D1zlXvlTu6kDL83buWPeZfFDQbKIw/hCchBt1DZvyXl/LPQPf7NzCYPylsZ5iEuz+kioqI7fvz4hS9MPvL7akwts3dykWCPi9ilTp06peDg4FJ9j/LIQ3iOssjBU6dOXfiCHCyqdL/1HqO8fha6x79AU8kDN1EWeYjLo4j4Q82aNSVJhw8frjBJmZ2draioKKWnp9ufOO7Jyms+hmHo1KlTioyMLPX3Ig/dX3nMpyxzMDIyUnv37tV1113HZ+bGKnoe8rPQM1T0PMTlUUT84eKi8eDg4ArzP/dFQUFBFWpO5TGfsvpHjDz0HGU9n7LMwdq1a0viM/MEFTkPL74fn5n7q6h5iMtzqogoLCws7TgAAAAAeAgeNgcAAADAFIqIP1itVk2YMEFWq7W8Q3GZijanijaf4lTEOVa0OVW0+RSnos2xos1Hqphz+rOKOD/mhIrGYnCPLAAAAEDZ2dkX1lxkZUklWeeRnS0FBysrK6vCrX35K85EAAAAADCFuzMBAAAAf1bSB25Wogd1UkQAAAAAkvz8/GSz2ZRxFQ/ctNls8vPzc2FU7ok1EQAAAMAfcnNzlZ+fX+Lj/fz85O/v78KI3BNrIv7w0ksvKTY2Vv7+/mrRooU2bdpU3iEV6/PPP1f37t0VGRkpi8WilStXOuw3DEPJycmKjIxUQECAOnbsqD179jj0ycvL07BhwxQaGqrAwED16NFDP//8cxnO4n+mTJmili1bqlq1agoPD1evXr20b98+hz6eNqeS8pQclMhDT5hTSZGH5KE78JQ8JAfdf04l4e/vb3+AXklelaGAkCQZMJYtW2b4+voar776qrF3715j+PDhRmBgoHHo0KHyDq2Ijz76yBg/frzx3nvvGZKMFStWOOyfOnWqUa1aNeO9994zdu3aZfTt29eoVauWkZ2dbe/zyCOPGLVr1zZSU1ON7du3G506dTKaNm1qnD9/voxnYxiJiYnGokWLjN27dxs7duww7rzzTqNu3brG6dOnPXZOJeFJOWgY5KEnzKkkyEPy0B14Uh6Sg+4/J5QeigjDMFq1amU88sgjDm2NGjUyxo4dW04ROeevP7AKCwsNm81mTJ061d6Wm5trBAcHG//+978NwzCM33//3fD19TWWLVtm73PkyBHDy8vLWLNmTZnFfimZmZmGJGPjxo2GYVSMOTnDU3PQMMhDT5mTM8hD9/rMyMP/8YQ8JAc9Y05wnUp/OVN+fr62bdumhIQEh/aEhARt3ry5nKIqmQMHDigjI8NhLlarVR06dLDPZdu2bTp37pxDn8jISMXFxbnFfLOysiRJNWvWlFQx5nQlFSkHpYrxmZGH/0Melh/y8H88MQ8rwudVGXMQzqv0RcSxY8dUUFCgiIgIh/aIiAhlZGSUU1QlczHey80lIyNDfn5+qlGjxiX7lBfDMDRy5EjdcsstiouLk+T5c3JGRcpByfM/M/KQPHSHOZOHnp+Hnv55VdYchPO4xesfLBaLw7ZhGEXaPEVJ5uIO8x06dKh27typL774osg+T52TGRUpByXP/czIQ/LQHeZMHlacPPTUz6uy5yCurNKfiQgNDZW3t3eR6jgzM7NIpe3ubDabJF12LjabTfn5+Tp58uQl+5SHYcOGadWqVVq/fr3q1Kljb/fkOTmrIuWg5NmfGXlIHv61T3kgDytGHnry51WZcxDOq/RFhJ+fn1q0aKHU1FSH9tTUVN18883lFFXJxMbGymazOcwlPz9fGzdutM+lRYsW8vX1dehz9OhR7d69u1zmaxiGhg4dquXLl+uzzz5TbGysw35PnJNZFSkHJc/8zMhD8vAi8rB8VaQ89MTPixyEKWWzftu9Xbyd3IIFC4y9e/caI0aMMAIDA42DBw+Wd2hFnDp1ykhLSzPS0tIMScbMmTONtLQ0+63vpk6dagQHBxvLly83du3aZdx7773F3nqtTp06xrp164zt27cbnTt3Lrdbrz366KNGcHCwsWHDBuPo0aP219mzZ+19PG1OJeFJOWgY5KEnzKkkyEPy0B14Uh6Sg+4/J5Qeiog/vPjii0Z0dLTh5+dnNG/e3H47M3ezfv16Q1KR18CBAw3DuHD7tQkTJhg2m82wWq1G+/btjV27djmMkZOTYwwdOtSoWbOmERAQYHTr1s04fPhwOczGKHYukoxFixbZ+3janErKU3LQMMhDT5hTSZGH5KE78JQ8JAfdf04oPRbDMAzXn98AAAAAUFFV+jURAAAAAMyhiAAAAABgCkUEAAAAAFMoIgAAAACYQhEBAAAAwBSKCAAAAACmUEQAAAAAMIUiAgAAAIApFBEAAAAATKGIAAAAAGAKRQQAAAAAUygiAHiElJQUWSyWYl+jR48u7/AuKSkpyR5nXFycy8ePiYlRt27dXD6uqyUlJSkmJsahbfLkyVq5cmWJxps9e7ZDDhw7duzqgwQAOM2nvAMAADMWLVqkRo0aObRFRkaWUzTOsdlsWrFihapUqVLeoZSbp556SsOHD3domzx5su6++2716tXL9Hj9+vVTmzZt9Nprr2nBggUuihIA4CyKCAAeJS4uTjfddJNTfc+dOyeLxSIfn/L9UWe1WtWmTRuXjnn27FmPKkrq1avn0vFsNptsNpvWrFnj0nEBAM7hciYAFcKGDRtksVi0ZMkSjRo1SrVr15bVatX+/fslSevWrVOXLl0UFBSkKlWqqF27dvr000+LjPPhhx/qxhtvlNVqVWxsrJ5//nklJyfLYrG4POYXX3xR7du3V3h4uAIDA9WkSRNNnz5d586dc+jXsWNHxcXF6fPPP9fNN9+sKlWq6MEHH3Tos2LFCt1www3y9/fXNddcoxdeeKHEccXExCgpKalIe8eOHdWxY0f79sXv+dKlSzV+/HhFRkYqKChI8fHx2rdvn8Oxf72cyWKx6MyZM1q8eLH9kqSLY589e1ajR49WbGys/P39VbNmTd10001aunRpiecEAHAtzkQA8CgFBQU6f/68Q9ufzzQ88cQTatu2rf7973/Ly8tL4eHheuONNzRgwAD17NlTixcvlq+vr15++WUlJiZq7dq16tKliyTp008/Vc+ePdW2bVstW7ZMBQUFmj59un799ddSmctPP/2k/v37KzY2Vn5+fvruu+/0r3/9Sz/88IMWLlzo0Pfo0aO6//77NWbMGE2ePFleXv/7G9COHTs0YsQIJScny2az6T//+Y+GDx+u/Pz8MlkvMm7cOLVr106vvfaasrOz9f/+3/9T9+7d9f3338vb27vYY7766it17txZnTp10lNPPSVJCgoKkiSNHDlSS5Ys0bPPPqtmzZrpzJkz2r17t44fP17qcwEAOIciAoBHKe6yoD//5b5evXp655137Ntnz57V8OHD1a1bN61YscLe3rVrVzVv3lzjxo3Tli1bJEnjx49XRESEUlNT5e/vL0lKTEwssiDYVWbOnGn/urCwULfeeqtCQkI0aNAgzZgxQzVq1LDvP3HihN555x117ty5yDi//PKL0tLS1LRpU0nSHXfcoczMTE2aNEmDBw8u9cuerrvuOr3xxhv2bW9vb91zzz3aunXrJS/jatOmjby8vBQWFlakz5dffqmEhAQ9/vjj9rY777yzdIIHAJQIlzMB8Civv/66tm7d6vD685mIPn36OPTfvHmzTpw4oYEDB+r8+fP2V2FhoW6//XZt3bpVZ86c0ZkzZ7R161bddddd9gJCkqpVq6bu3buXylzS0tLUo0cPhYSEyNvbW76+vhowYIAKCgr03//+16FvjRo1ii0gJOn666+3FxAX9e/fX9nZ2dq+fXupxP5nPXr0cNi+4YYbJEmHDh0q0XitWrXSxx9/rLFjx2rDhg3Kycm56hgBAK7FmQgAHqVx48aXXVhdq1Yth+2LlyLdfffdlzzmxIkTslgsKiwslM1mK7K/uLardfjwYd16661q2LCh5syZo5iYGPn7++ubb77RkCFDivzi/Nd5XSm+i21lcQlQSEiIw7bVapWkEv/y/8ILL6hOnTp66623NG3aNPn7+ysxMVHPPfec6tevf9XxAgCuHkUEgArlrwugQ0NDJUlz58695KU1ERER9js5ZWRkFNlfXNvVWrlypc6cOaPly5crOjra3r5jx45i+19uYfflYv7rL/jO8Pf3V15eXpH2Y8eO2b+fpSkwMFATJ07UxIkT9euvv9rPSnTv3l0//PBDqb8/AODKuJwJQIXWrl07Va9eXXv37tVNN91U7MvPz0+BgYFq1aqVli9frtzcXPvxp06d0urVq10e18Wi4OJf7SXJMAy9+uqrpsfas2ePvvvuO4e2N998U9WqVVPz5s1NjxcTE6OdO3c6tP33v/8tcselq2W1Wq94tiIiIkJJSUm69957tW/fPp09e9alMQAASoYzEQAqtKpVq2ru3LkaOHCgTpw4obvvvlvh4eH67bff9N133+m3337T/PnzJUmTJk3S7bffrttuu02jRo1SQUGBpk2bpsDAQJ04ccJh3C5dumjjxo1F7hTlrNtuu01+fn669957NWbMGOXm5mr+/Pk6efKk6bEiIyPVo0cPJScnq1atWnrjjTeUmpqqadOmOSyq9vHxUYcOHYq9te2fPfDAA7r//vs1ePBg9enTR4cOHdL06dMVFhZmOrbLadKkiTZs2KDVq1erVq1aqlatmho2bKjWrVurW7duuuGGG1SjRg19//33WrJkidq2betRz8YAgIqMMxEAKrz7779f69ev1+nTp/Xwww8rPj5ew4cP1/bt2+23d5Uu/GK/cuVKZWdnq2/fvho5cqT69OlT5JkM0oVbzRYUFJQ4pkaNGum9997TyZMnddddd2nYsGG68cYbS/R8hxtvvFEzZ87UjBkz1LNnT3355ZeaOXOmxowZU6KY+/fvr+nTp2vt2rXq1q2b5s+fr/nz56tBgwamY7ucOXPmqH79+urXr59atmyphx9+WJLUuXNnrVq1SoMGDVJCQoKmT5+uAQMGlMoZIQBAyVgMwzDKOwgAcGfJycmaOHGiSvLjMikpSRs2bND+/ftlsVgu+dwEmGMYhgoKCvTMM89o0qRJ+u2338pkvQYA4ALORABAKTt06JB8fX2L3IYVJTdnzhz5+vpq0qRJ5R0KAFRKrIkAgFKUnJysoUOHSpICAgLKOZqKo3///rrlllvs29WrVy+/YACgEuJyJgAAAACmcDkTAAAAAFMoIgAAAACYQhEBAAAAwBSKCAAAAACmUEQAAAAAMIUiAgAAAIApFBEAAAAATKGIAAAAAGAKRQQAAAAAU/4/3XX8COVxXLAAAAAASUVORK5CYII=", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_spects(spects=[inv_transform(i.cpu()) for i in spects])\n", "plot_masks(masks=[inv_target_transform(i) for i in masks])\n", @@ -893,9 +784,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Looks pretty good! To get a more ojective sense, \n", - "\n", - "Let's start with the confusion matrix, which provides a comprehensive overview of the model's ability. The diagonal elements represent the correct predictions and off-diagonal elements indicate prediction errors." + "Looks pretty good! To get a more ojective sense, let's turn to the metrics. Let's start with model accuracy, calculated as the ratio of correctly predicted pixels to the total number of pixels." ] }, { @@ -904,46 +793,16 @@ "metadata": {}, "outputs": [], "source": [ - "def confusion_matrix(\n", - " model: nn.Module, val_loader: DataLoader, n_classes: int, device: str, normalize: Optional[str] = \"true\"\n", - ") -> MulticlassConfusionMatrix:\n", - " \"\"\"Compute the confusion matrix for a given model using the validation dataset.\"\"\"\n", - " conf_matrix = (MulticlassConfusionMatrix(num_classes=num_classes, normalize=normalize)).to(device)\n", - " model.to(device)\n", - " model.eval()\n", - "\n", - " with torch.no_grad():\n", - " for x, y in val_loader:\n", - " x = x.to(device)\n", - " y = y.to(device)\n", - " pred = (model(x)[\"out\"]).argmax(dim=1)\n", - " conf_matrix.update(pred, y)\n", - "\n", - " return conf_matrix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "conf_matrix = confusion_matrix(model=segmentation_module, val_loader=val_loader, n_classes=n_classes, device=device)\n", - "\n", - "fig, ax = plt.subplots(1, figsize=(3, 3))\n", - "ax.set_title(\"Confusion Matrix\", fontsize=title_font_size)\n", - "ax.set_xlabel(\"True label\", fontsize=label_font_size)\n", - "ax.set_ylabel(\"Predicted label\", fontsize=label_font_size)\n", - "\n", - "displ = ConfusionMatrixDisplay(np.array(torch.round(conf_matrix.compute(), decimals=2).to(\"cpu\")))\n", - "displ.plot(ax=ax, colorbar=False)" + "scores = trainer.validate(model=segmentation_module, dataloaders=val_loader)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Next, let's examine the model accuracy, calculated as the ratio of correctly predicted pixels to the total number of pixels. However, it's important to note that accuracy alone doesn't tell the whole story. Due to the imbalance in our dataset, a high accuracy can be achieved by always predicting noise." + "The accuracy can give us a quick sense of the model's overall performance. However, it's important to note that accuracy alone doesn't tell the whole story, especially for imblanaced datasets. In fact, because of the imbalance in our dataset, a reasonably high accuracy could be achieved by always predicting noise. The simple accuracy provided above is an unweighted mean of the accuracies over each class.\n", + "\n", + "To gain a better understanding of our model's ability to predict specific classes, let's take a look at the confusion matrix, which provides a more comprehensive overview of model capability. The diagonal elements represent the correct predictions and off-diagonal elements indicate prediction errors." ] }, { @@ -952,100 +811,33 @@ "metadata": {}, "outputs": [], "source": [ - "segmentation_module.eval()\n", - "scores = trainer.validate(segmentation_module, val_loader)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### _Histogram of Intersection over Union (IoU) Scores per Image_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's generate a more comprehensive report, complete with the following metrics:\n", - "\n", - "- Recall: The recall (sensitivity) measures the ability of the model to identify all relevant pixels.\n", + "confusion_matrix = MulticlassConfusionMatrix(num_classes=n_classes, normalize='true').to(device)\n", "\n", - "- Precision: The precision assesses the accuracy of positive predictions.\n", - "\n", - "- F1 Score: The F1 score combines both recall and precision into a single value, providing a more balanced measure of the model's performance.\n", + "with torch.no_grad():\n", + " for spect, mask in val_loader:\n", + " spect, mask = spect.to(device), mask.to(device)\n", + " pred = (model(spect)[\"out\"]).argmax(dim=1)\n", + " confusion_matrix.update(pred, mask)\n", "\n", - "- Intersection over Union (IoU): The IoU quantifies the overlap between the predicted bounding box or segmented region and the ground truth bounding box or annotated region from a dataset. A higher IoU value indicates a better alignment between the predicted and actual regions, reflecting a more accurate model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_metric(model, loader, metric_name, num_classes, device):\n", - " metric = getattr(torchmetrics.classification, metric_name)\n", - " metric_per_class = metric(num_classes=num_classes, average=\"none\").to(device)\n", - " average_metric = metric(num_classes=num_classes, average=\"macro\").to(device)\n", - " weighted_metric = metric(num_classes=num_classes, average=\"weighted\").to(device)\n", - " model.to(device)\n", - " model.eval()\n", - "\n", - " with torch.no_grad():\n", - " for x, y in loader:\n", - " x = x.to(device)\n", - " y = y.to(device)\n", - " pred = (model(x)[\"out\"]).argmax(dim=1)\n", - " pred = pred.to(device)\n", - " metric_per_class.update(pred, y)\n", - " average_metric.update(pred, y)\n", - " weighted_metric.update(pred, y)\n", - "\n", - " value_per_class = metric_per_class.compute()\n", - " average_value = average_metric.compute()\n", - " weighted_value = weighted_metric.compute()\n", - " return value_per_class, average_value.unsqueeze(0), weighted_value.unsqueeze(0)" + "confusion_matrix = np.round(confusion_matrix.compute().cpu().numpy(), decimals=3)\n", + "ConfusionMatrixDisplay(confusion_matrix=confusion_matrix, display_labels=labels).plot()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "metric_names = [\"MulticlassRecall\", \"MulticlassPrecision\", \"MulticlassF1Score\", \"MulticlassJaccardIndex\"]\n", + "Let's generate a more comprehensive report, complete with the following metrics:\n", "\n", - "metric_results = {\n", - " metric_name: torch.hstack(compute_metric(segm_sgd_model, val_loader, metric_name, NUM_CLASSES, device))\n", - " for metric_name in metric_names\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "metric_results_cpu = {key: (value.to(\"cpu\")).numpy() for key, value in metric_results.items()}\n", + "- **Recall:** The recall (sensitivity) measures the ability of the model to identify all relevant pixels.\n", "\n", - "classification_report = pd.DataFrame(metric_results_cpu)\n", + "- **Precision:** The precision assesses the accuracy of positive predictions.\n", "\n", - "index = [\"Noise\", \"NR\", \"LTE\", \"macro avg\", \"weighted avg\"]\n", - "columns = [\"Recall\", \"Precision\", \"F1 Score\", \"IoU\"]\n", + "- **F1 Score:** The F1 score combines both recall and precision into a single value, providing a more balanced measure of the model's performance.\n", "\n", - "classification_report.columns = columns\n", - "classification_report.index = index\n", + "- **Intersection over Union (IoU):** The IoU quantifies the overlap between the predicted bounding box or segmented region and the ground truth bounding box or annotated region from a dataset. A higher IoU value indicates a better alignment between the predicted and actual regions, reflecting a more accurate model.\n", "\n", - "print(classification_report)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's look at a histogram of IoU Scores per Image." + "We will calculate these metrics for each class separately. However, test with `average=\"weighted\"` to return the weighted metrics based on the relative class frequencies." ] }, { @@ -1054,24 +846,7 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_hist(model, loader, device):\n", - " iou_scores = []\n", - " model.to(device)\n", - " model.eval()\n", - "\n", - " with torch.no_grad():\n", - " for x, y in loader:\n", - " x = x.to(device)\n", - " y = y.to(device)\n", - " pred = (model(x)[\"out\"]).argmax(dim=1)\n", - " jaccard = jac_ind(task=\"multiclass\", num_classes=3).to(device)\n", - " score = jaccard(pred.to(device), y)\n", - " iou_scores.append(score.item())\n", - "\n", - " plt.hist(iou_scores, color=\"green\", histtype=\"bar\")\n", - " plt.xlabel(\"IoU\", color=\"blue\")\n", - " plt.ylabel(\"Number of Masks\", color=\"blue\")\n", - " plt.title(\"Mean IoU\", color=\"blue\")" + "from tabulate import tabulate" ] }, { @@ -1080,125 +855,45 @@ "metadata": {}, "outputs": [], "source": [ - "new_val_loader = DataLoader(val_subset, batch_size=1, shuffle=False) # Changed the batch_size\n", - "plot_hist(segmentation_module, new_val_loader, device)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Signal Identification in Spectrograms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def signal_label(mask) -> str:\n", - " \"\"\"\n", - " :param mask: The mask image containing signal labels.\n", + "average = None\n", + "# average = \"weighted\"\n", + "accuracy = MulticlassAccuracy(num_classes=n_classes, average=average)\n", + "recall = MulticlassRecall(num_classes=n_classes, average=average)\n", + "precision = MulticlassPrecision(num_classes=n_classes, average=average)\n", + "f1_score = MulticlassF1Score(num_classes=n_classes, average=average)\n", + "jaccard_index = MulticlassJaccardIndex(num_classes=n_classes, average=average)\n", + "metrics = [\n", + " accuracy,\n", + " recall,\n", + " precision,\n", + " f1_score,\n", + " jaccard_index\n", + "]\n", + "metrics = [m.to(device) for m in metrics]\n", "\n", - " :return: The signal label based on unique labels in the mask.\n", - " \"\"\"\n", - " labels = {0: \"Noise\", 1: \"NR\", 2: \"LTE\"}\n", - " unique_labels_in_mask = torch.unique(mask)\n", + "with torch.no_grad():\n", + " for spect, mask in val_loader:\n", + " spect, mask = spect.to(device), mask.to(device)\n", + " pred = (model(spect)[\"out\"]).argmax(dim=1)\n", + " for m in metrics:\n", + " m.update(pred, mask)\n", "\n", - " if len(unique_labels_in_mask) == 2:\n", - " key = torch.unique(mask)[1].item()\n", - " return labels[key]\n", + "metrics = [m.compute().cpu().numpy() for m in metrics]\n", + "metrics = [np.append(m, statistics.mean(m)) for m in metrics]\n", "\n", - " elif len(unique_labels_in_mask) == 3:\n", - " key_1 = torch.unique(mask)[1].item()\n", - " key_2 = torch.unique(mask)[2].item()\n", - " return labels[key_1] + \"_\" + labels[key_2]\n", "\n", - " else:\n", - " key = torch.unique(mask)[0].item()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_spectrogram_mask(image, mask: Image, target: Image = None):\n", - "\n", - " if target is not None:\n", - " fig, ax = plt.subplots(3, 1, figsize=(4, 10))\n", - "\n", - " ax[0].imshow(torch.permute(image, (1, 2, 0)))\n", - " ax[0].set_xlabel(\"Frequency\", fontsize=12, color=\"blue\")\n", - " ax[0].set_ylabel(\"Time\", fontsize=12, color=\"blue\")\n", - " ax[0].set_title(f\"Received Spectrogram ({signal_label(target)})\", color=\"blue\")\n", - "\n", - " ax[1].set_ylabel(\"Time\", fontsize=12, color=\"blue\")\n", - " ax[1].set_xlabel(\"Frequency\", fontsize=12, color=\"blue\")\n", - " ax[1].imshow(target)\n", - " ax[1].set_title(f\"True Signal Label ({signal_label(target)})\", color=\"blue\")\n", - "\n", - " ax[2].imshow(mask)\n", - " # plt.imshow(predicted_image.permute(1,2,0)[:,:,0])\n", - " # or equivalently\n", - " # plt.imshow(pred['out'][0][0].to('cpu').detach())\n", - " ax[2].set_xlabel(\"Frequency\", fontsize=12, color=\"blue\")\n", - " ax[2].set_ylabel(\"Time\", fontsize=12, color=\"blue\")\n", - " ax[2].set_title(f\"Prediction ({signal_label(mask)})\", color=\"blue\")\n", - " plt.tight_layout()\n", "\n", - " else:\n", - " fig, ax = plt.subplots(2, 1, figsize=(4, 10))\n", - "\n", - " ax[0].imshow(torch.permute(image, (1, 2, 0)))\n", - " ax[0].set_xlabel(\"Frequency\", fontsize=12, color=\"blue\")\n", - " ax[0].set_ylabel(\"Time\", fontsize=12, color=\"blue\")\n", - " ax[0].set_title(\"Received Spectrogram\", color=\"blue\")\n", - "\n", - " ax[1].imshow(mask)\n", - " # plt.imshow(predicted_image.permute(1,2,0)[:,:,0])\n", - " # or equivalently\n", - " # plt.imshow(pred['out'][0][0].to('cpu').detach())\n", - " ax[1].set_xlabel(\"Frequency\", fontsize=12, color=\"blue\")\n", - " ax[1].set_ylabel(\"Time\", fontsize=12, color=\"blue\")\n", - " ax[1].set_title(f\"Prediction ({signal_label(mask)})\", color=\"blue\")\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image, target = next(iter(val_loader)) # First batch of spectrograms" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image, target = image.to(device), target.to(device)\n", - "segm_sgd_model.eval()\n", - "with torch.no_grad():\n", - " predicted_masks = segm_sgd_model(image)[\"out\"]\n", - " first_mask_in_batch = predicted_masks[0].argmax(dim=0)\n", "\n", - "first_image_in_batch = image[0]\n", - "first_target_in_batch = target[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_spectrogram_mask(first_image_in_batch.to(\"cpu\"), first_mask_in_batch.to(\"cpu\"), first_target_in_batch.to(\"cpu\"))" + "\n", + "df = pd.DataFrame({\n", + " \"Class\": np.append(np.asarray(labels), \"Mean\"),\n", + " \"Accuracy\": metrics[0],\n", + " \"Recall\": metrics[1],\n", + " \"Precision\": metrics[2],\n", + " \"F1 Score\": metrics[3],\n", + " \"Jaccard Index\": metrics[4]\n", + "})\n", + "print(tabulate(df, headers='keys', tablefmt='psql', showindex=False))" ] }, { From 944f5bd171b6e8d0facf8e9d089edf5ebef19fe3 Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Thu, 2 May 2024 20:55:31 -0400 Subject: [PATCH 5/9] Completed the challenge data section. Tested with the ResNet-50 model, and results seem reasonable. As part of these changes, I added tabulate and pulled the metrics report into a function. --- environment.yml | 1 + spectrogram_segmentation.ipynb | 276 ++++++++++++++------------------- 2 files changed, 121 insertions(+), 156 deletions(-) diff --git a/environment.yml b/environment.yml index a11402f..efac23b 100644 --- a/environment.yml +++ b/environment.yml @@ -18,6 +18,7 @@ dependencies: - lightning=2.0.9 - torchmetrics=1.1.2 - gdal=3.6.2 + - tabulate=0.9.0 - pip: - torchvision==0.18.0 diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index e27633e..4a1b413 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -60,7 +60,7 @@ "source": [ "# Set-Up\n", "\n", - "In this section, we will install the dependencies required to run the code in this notebook. These dependencies include libraries and packages for tasks such as data manipulation, visualization, and machine learning. Additionally, we will initialize a few variables." + "In this section, we will install the dependencies required to run the code in this notebook. These dependencies include libraries and packages for tasks such as data manipulation, visualization, and machine learning." ] }, { @@ -80,28 +80,41 @@ "source": [ "import glob\n", "import os\n", - "from pprint import pprint\n", - "\n", "import statistics\n", + "from typing import Optional\n", + "\n", + "import lightning as L\n", "import matplotlib.pyplot as plt\n", - "from matplotlib.colors import ListedColormap\n", "import numpy as np\n", - "import lightning as L\n", + "import pandas as pd\n", "import torch\n", - "import torchmetrics\n", - "import torchvision\n", + "from matplotlib.colors import ListedColormap\n", "from osgeo import gdal\n", "from PIL import Image\n", "from sklearn.metrics import ConfusionMatrixDisplay\n", - "from typing import Optional, Any\n", - "from torch import nn, Tensor\n", + "from tabulate import tabulate\n", + "from torch import Tensor, nn\n", "from torch.utils.data import DataLoader\n", - "from torchmetrics import JaccardIndex as jac_ind\n", - "from torchmetrics.classification import MulticlassAccuracy, MulticlassConfusionMatrix, MulticlassRecall, MulticlassPrecision, MulticlassF1Score, MulticlassJaccardIndex\n", + "from torchmetrics.classification import (\n", + " MulticlassAccuracy,\n", + " MulticlassConfusionMatrix,\n", + " MulticlassF1Score,\n", + " MulticlassJaccardIndex,\n", + " MulticlassPrecision,\n", + " MulticlassRecall,\n", + ")\n", "from torchvision.datasets import VisionDataset\n", - "from torchvision.io import read_image\n", - "from torchvision.transforms.v2 import Compose, PILToTensor, ToDtype, Normalize, ToPILImage, ToTensor\n", - "from torchvision.models.segmentation import deeplabv3_resnet50, deeplabv3_mobilenet_v3_large" + "from torchvision.models.segmentation import (\n", + " deeplabv3_mobilenet_v3_large,\n", + " deeplabv3_resnet50,\n", + ")\n", + "from torchvision.transforms.v2 import (\n", + " Compose,\n", + " Normalize,\n", + " PILToTensor,\n", + " ToDtype,\n", + " ToPILImage,\n", + ")" ] }, { @@ -191,7 +204,7 @@ "- `127`: Representing 5G NR signal.\n", "- `255`: Representing 4G LTE signal.\n", "\n", - "To prepare our spectrograms for training, we will convert them from PIL Images to Tensor objects. As required by our models, the images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]` and `std = [0.229, 0.224, 0.225]`. To prepare our masks for training, we will convert them to Tensor objects, remove the extraneous channel dimension, and update the pixel values so that `0` respresents noise, `1` represents NR signal, and `2` represents LTE signal." + "To prepare our spectrograms for training, we will convert them from PIL Images to Tensor objects. As required by our models, the images have to be loaded in to a range of `[0, 1]` and then normalized using a mean of `[0.485, 0.456, 0.406]` and a standard deviation of `[0.229, 0.224, 0.225]`. To prepare our masks for training, we will convert them to Tensor objects, remove the extraneous channel dimension, and update the pixel values such that `0` represents noise, `1` represents NR signal, and `2` represents LTE signal." ] }, { @@ -238,7 +251,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "With that done, let's initialize the dataset, and take a closer look at a random training example and the corresponding mask. Due to our transforms, we expect that the image-mask pair will be returned as Tensor objects." + "Perfect. Now let's initialize the dataset, and take a closer look at a random training example and the corresponding mask. Due to our transforms, we expect that the image-mask pair will be returned as Tensor objects." ] }, { @@ -270,9 +283,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The dataset should contain 1,800 samples: 900 NR fames and 900 LTE frames. \n", + "The dataset should contain 1,800 samples: 900 NR frames and 900 LTE frames. \n", "\n", - "To get a better idea of what's going on, let's write some tranforms undo the previous normalization and prepare this image-mask pair for viewing. And, let's build a custom colormap for the masks, with noise as cyan, NR signal as blue, and LTE signal as purple." + "To gain further insight, let's write some transforms to undo the previous normalization and prepare this image-mask pair for viewing. And, let's build a custom colormap for the masks, with noise as cyan, NR signal as blue, and LTE signal as purple." ] }, { @@ -368,7 +381,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It looks like most of our data is noise! A classification dataset like this—with skewed class proportions—is called imbalanced.\n", + "It looks like our data is mostly noise! A classification dataset like this—with skewed class proportions—is called imbalanced.\n", "\n", "An imbalanced dataset can result in biased and poorly performing models. Models trained on imbalanced data tends to focus more on the majority classes and may not learn enough about the minority classes. In our case, the majority class is noise, while the minority classes are the NR and LTE signals we want to identify and classify. To ensure the development of a fair, accurate, and robust model, we will need to address this class imbalance. \n", "\n", @@ -506,7 +519,7 @@ "feedback that guides the model's training process. For classification problems, we commonly use the [Cross-Entropy Loss](https://machinelearningmastery.com/cross-entropy-for-machine-learning/), especially for \n", "multi-class classification problems. Let's use the [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) class from PyTorch, which allows us to assign different weights to individual classes during the computation of the loss. \n", "\n", - "We'll use weights inversly propotional to the relative pixel count for each class. That way, we assign lower weights to overrepresented classes, like noise, and larger weights to underrepresented classes, like LTE signal. This reduces the impact of noise and allows the model to prioritize learning from NR and especially LTE samples. Class weighting is not the only way to address data imblance, but it is one of the more straightforward methods." + "We'll use weights inversely proportional to the relative pixel count for each class. That way, we assign lower weights to overrepresented classes, like noise, and larger weights to underrepresented classes, like LTE signal. This reduces the impact of noise and allows the model to prioritize learning from NR and especially LTE samples. Class weighting is not the only way to address data imblance, but it is one of the more straightforward methods." ] }, { @@ -577,12 +590,9 @@ " preds = self(image)['out']\n", " loss = self.loss_function(preds, target)\n", " self.train_accuracy(preds, target)\n", - " return loss\n", - "\n", - " def on_train_epoch_end(self):\n", - " \"\"\"Logs the training accuracy and loss metrics at the end of each training epoch.\"\"\"\n", " self.log(name=\"train_accuracy\", value=self.train_accuracy, prog_bar=True)\n", - " self.log(name=\"train_loss\", value=loss, on_epoch=True, prog_bar=True)\n", + " self.log(name=\"train_loss\", value=loss, on_epoch=True, on_step=False, prog_bar=True)\n", + " return loss\n", "\n", " def validation_step(self, batch: Tensor, batch_idx: int) -> Tensor:\n", " \"\"\"Defines a single validation step.\"\"\"\n", @@ -590,12 +600,9 @@ " preds = self(image)['out']\n", " loss = self.loss_function(preds, target)\n", " self.val_accuracy(preds, target)\n", - " return loss\n", - "\n", - " def on_validation_epoch_end(self):\n", - " \"\"\"Logs the training accuracy and loss metrics at the end of each validation epoch.\"\"\"\n", " self.log(name=\"val_accuracy\", value=self.val_accuracy, prog_bar=True)\n", - " self.log(name=\"val_loss\", value=loss, on_epoch=True, prog_bar=True)\n", + " self.log(name=\"val_loss\", value=loss, on_epoch=True, on_step=False, prog_bar=True)\n", + " return loss\n", "\n", " def configure_optimizers(self) -> dict[str, Any]:\n", " \"\"\"Configure the optimizer and learning rate scheduler.\"\"\"\n", @@ -647,76 +654,13 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# TODO: This is just a testing block, to be removed once the tutorial is complete\n", - "model.eval() # Set the model to evaluation mode\n", - "inputs, targets = next(iter(train_loader))\n", - "\n", - "with torch.no_grad():\n", - " # Forward pass with the input data\n", - " preds = model(inputs)[\"out\"]\n", - "\n", - "print(\"Initial model predictions:\")\n", - "print(preds.size())\n", - "print(\"\\tPrediction max: \", torch.max(preds))\n", - "print(\"\\tPrediction min: \", torch.min(preds))\n", - "\n", - "print(\"\\tMax value in target: \", torch.max(targets.long()))\n", - "print(\"\\tMin value in target: \",torch.min(targets.long()))\n", - "loss = loss_function(preds, targets.long())\n", - "print(\"Loss: \", loss)\n", - "\n", - "# Convert preds to Image for viewing\n", - "print(\"After converting back to images for viewing:\")\n", - "print(\"Predictions: \", preds.size())\n", - "\n", - "# Need to find the classes with the largest probability.\n", - "# Take the maximum value along the class axis.\n", - "output = preds.argmax(1)\n", - "\n", - "print(\"Output: \", output.size())\n", - "\n", - "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", - "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", - "\n", - "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Prediction \" + str(i + 1))\n", - " im = ax.imshow(output[i], vmin=0, vmax=2, cmap=mask_cmap)\n", - "\n", - "fig.subplots_adjust(right=0.85)\n", - "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", - "cbar.ax.set_yticklabels(labels)\n", - "\n", - "fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", - "axes[0].set_ylabel(\"Time [s]\", fontsize=label_font_size)\n", - "fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", - "\n", - "for i, ax in enumerate(axes):\n", - " ax.set_title(\"Mask \" + str(i + 1))\n", - " im = ax.imshow(targets[i], vmin=0, vmax=2, cmap=mask_cmap)\n", - "\n", - "fig.subplots_adjust(right=0.85)\n", - "cbar_ax = fig.add_axes(rect=[0.90, 0.25, 0.02, 0.5])\n", - "cbar = fig.colorbar(im, cax=cbar_ax, ticks=[0.33, 1, 1.66])\n", - "cbar.ax.set_yticklabels(labels)" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have our model, weighted loss function, and Lightning Module, we are prepared to train our model. If available, we will leverage GPU acceleration for training. Otherwise, the training process will default to using the CPU. Please be patient; model training time may vary depending on the current hardware configuration and could take a few minutes.\n", + "Now that we have our model, weighted loss function, and Lightning Module, we are prepared to train our model. If available, we will leverage GPU acceleration. Otherwise, the training process will default to using the CPU. Please be patient; model training time may vary depending on the current hardware configuration and could take a few minutes.\n", "\n", - "The number of epochs determines how many times the entire dataset will be used to train the model. We will begin training with 20 epochs." + "The number of epochs determines how many times the entire dataset will be used to train the model. We will begin training with 10 epochs." ] }, { @@ -736,10 +680,16 @@ " trainer = L.Trainer(max_epochs=n_epochs, logger=True)\n", " device = \"cpu\"\n", "\n", - "print(len(train_loader))\n", "trainer.fit(model=segmentation_module, train_dataloaders=train_loader, val_dataloaders=val_loader)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are running this example locally, you can refer to the `metrics.csv` file located in the `lightning_logs` directory for more information regarding training and validation loss and accuracy across training epochs. Please remember to close the `metrics.csv` file before proceeding." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -800,7 +750,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The accuracy can give us a quick sense of the model's overall performance. However, it's important to note that accuracy alone doesn't tell the whole story, especially for imblanaced datasets. In fact, because of the imbalance in our dataset, a reasonably high accuracy could be achieved by always predicting noise. The simple accuracy provided above is an unweighted mean of the accuracies over each class.\n", + "The accuracy can give us a quick sense of the model's overall performance. However, it's important to note that accuracy alone doesn't tell the whole story. In fact, because of the imbalance in our dataset, a reasonably high accuracy could be achieved by always predicting noise. The simple accuracy provided above is an unweighted mean of the accuracies over each class.\n", "\n", "To gain a better understanding of our model's ability to predict specific classes, let's take a look at the confusion matrix, which provides a more comprehensive overview of model capability. The diagonal elements represent the correct predictions and off-diagonal elements indicate prediction errors." ] @@ -819,7 +769,7 @@ " pred = (model(spect)[\"out\"]).argmax(dim=1)\n", " confusion_matrix.update(pred, mask)\n", "\n", - "confusion_matrix = np.round(confusion_matrix.compute().cpu().numpy(), decimals=3)\n", + "confusion_matrix = np.round(confusion_matrix.compute().cpu().numpy(), decimals=2)\n", "ConfusionMatrixDisplay(confusion_matrix=confusion_matrix, display_labels=labels).plot()" ] }, @@ -827,17 +777,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's generate a more comprehensive report, complete with the following metrics:\n", - "\n", - "- **Recall:** The recall (sensitivity) measures the ability of the model to identify all relevant pixels.\n", + "Finally, let's generate a more comprehensive report, complete with the following metrics:\n", "\n", - "- **Precision:** The precision assesses the accuracy of positive predictions.\n", + "- **Recall:** The recall (sensitivity) measures the ability of the model to identify all relevant pixels. A higher recall indicates that the model is better at identifying positive instances.\n", "\n", - "- **F1 Score:** The F1 score combines both recall and precision into a single value, providing a more balanced measure of the model's performance.\n", + "- **Precision:** The precision assesses the accuracy of positive predictions. A higher precision indicates that when the model predicts a positive outcome, it is more likely to be correct.\n", "\n", - "- **Intersection over Union (IoU):** The IoU quantifies the overlap between the predicted bounding box or segmented region and the ground truth bounding box or annotated region from a dataset. A higher IoU value indicates a better alignment between the predicted and actual regions, reflecting a more accurate model.\n", + "- **F1 Score:** The F1 score combines both recall and precision into a single value, providing a more balanced measure of the model's performance. A higher F1 indicates a model with both good precision and recall (fewer false positives and false negatives overall).\n", "\n", - "We will calculate these metrics for each class separately. However, test with `average=\"weighted\"` to return the weighted metrics based on the relative class frequencies." + "- **Intersection over Union (IoU):** The IoU, commonly called Jaccard's Index, quantifies the overlap between the predicted bounding box or segmented region and the ground truth bounding box or annotated region from a dataset. A higher IoU value indicates a better alignment between the predicted and actual regions, reflecting a more accurate model." ] }, { @@ -846,7 +794,36 @@ "metadata": {}, "outputs": [], "source": [ - "from tabulate import tabulate" + "def print_metrics_report(dataloader: DataLoader) -> None:\n", + " \"\"\"Compute accuracy, recall, precision, F1 score, and IoU (Intersection over Union), and print a report containing these metrics. \"\"\"\n", + " metrics = [\n", + " MulticlassAccuracy(num_classes=n_classes, average=None),\n", + " MulticlassRecall(num_classes=n_classes, average=None),\n", + " MulticlassPrecision(num_classes=n_classes, average=None),\n", + " MulticlassF1Score(num_classes=n_classes, average=None),\n", + " MulticlassJaccardIndex(num_classes=n_classes, average=None)\n", + " ]\n", + " metrics = [m.to(device) for m in metrics]\n", + " \n", + " with torch.no_grad():\n", + " for spect, mask in dataloader:\n", + " spect, mask = spect.to(device), mask.to(device)\n", + " pred = (model(spect)[\"out\"]).argmax(dim=1)\n", + " for m in metrics:\n", + " m.update(pred, mask)\n", + " \n", + " metrics = [m.compute().cpu().numpy() for m in metrics]\n", + " metrics = [np.append(m, statistics.mean(m)) for m in metrics]\n", + " \n", + " df = pd.DataFrame({\n", + " \"Class\": np.append(np.asarray(labels), \"Mean\"),\n", + " \"Accuracy\": metrics[0],\n", + " \"Recall\": metrics[1],\n", + " \"Precision\": metrics[2],\n", + " \"F1 Score\": metrics[3],\n", + " \"Jaccard Index\": metrics[4]\n", + " })\n", + " print(tabulate(df, headers='keys', tablefmt='grid', showindex=False, numalign=\"center\", stralign=\"center\", floatfmt=\".2f\"))" ] }, { @@ -855,45 +832,7 @@ "metadata": {}, "outputs": [], "source": [ - "average = None\n", - "# average = \"weighted\"\n", - "accuracy = MulticlassAccuracy(num_classes=n_classes, average=average)\n", - "recall = MulticlassRecall(num_classes=n_classes, average=average)\n", - "precision = MulticlassPrecision(num_classes=n_classes, average=average)\n", - "f1_score = MulticlassF1Score(num_classes=n_classes, average=average)\n", - "jaccard_index = MulticlassJaccardIndex(num_classes=n_classes, average=average)\n", - "metrics = [\n", - " accuracy,\n", - " recall,\n", - " precision,\n", - " f1_score,\n", - " jaccard_index\n", - "]\n", - "metrics = [m.to(device) for m in metrics]\n", - "\n", - "with torch.no_grad():\n", - " for spect, mask in val_loader:\n", - " spect, mask = spect.to(device), mask.to(device)\n", - " pred = (model(spect)[\"out\"]).argmax(dim=1)\n", - " for m in metrics:\n", - " m.update(pred, mask)\n", - "\n", - "metrics = [m.compute().cpu().numpy() for m in metrics]\n", - "metrics = [np.append(m, statistics.mean(m)) for m in metrics]\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "df = pd.DataFrame({\n", - " \"Class\": np.append(np.asarray(labels), \"Mean\"),\n", - " \"Accuracy\": metrics[0],\n", - " \"Recall\": metrics[1],\n", - " \"Precision\": metrics[2],\n", - " \"F1 Score\": metrics[3],\n", - " \"Jaccard Index\": metrics[4]\n", - "})\n", - "print(tabulate(df, headers='keys', tablefmt='psql', showindex=False))" + "print_metrics_report(dataloader=val_loader)" ] }, { @@ -902,9 +841,19 @@ "source": [ "# Challenge Data\n", "\n", - "TODO: Testing Model with Combined (NR_LTE) Signals\n", + "In machine leaning, generalization refers to the ability of a trained model to perform well on new data that it hasn't been trained on. As an easy way to test the generalization of our model, let's test on combined frames with both LTE and NR signal. As a reminder, such frames were excluded from testing." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "challenge_data_root = os.path.join(data_root, \"LTE_NR\")\n", + "challenge_dataset = SpectrumSensing(root=challenge_data_root, transform=transform, target_transform=target_transform)\n", "\n", - "Recall combined frames with both NR and LTE signals were excluded from the training dataset." + "challenge_loader = DataLoader(challenge_dataset, batch_size=mini_batch_size, shuffle=True)" ] }, { @@ -913,16 +862,31 @@ "metadata": {}, "outputs": [], "source": [ - "# Grab the first NR_LTE signal\n", - "PATH_TO_MATLAB5G_TRAINING_DATA = os.getcwd()\n", - "spec_path = os.path.join(PATH_TO_MATLAB5G_TRAINING_DATA, \"LTE_NR\", \"LTE_NR_frame_0.png\")\n", - "spectrogram = read_image(spec_path) # Image has both NR and LTE signal\n", - "spectrogram = spectrogram.to(device)\n", + "spects, masks = next(iter(challenge_loader))\n", + "spects = spects.to(device)\n", "\n", - "segm_sgd_model.eval()\n", "with torch.no_grad():\n", - " pred = segm_sgd_model((spectrogram.to(torch.float)).unsqueeze(0))[\"out\"]\n", - " mask = pred[0].argmax(dim=0)" + " preds = (model(spects)[\"out\"]).argmax(1)\n", + "\n", + "print(\"Predictions:\", preds.size())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_spects(spects=[inv_transform(i.cpu()) for i in spects])\n", + "plot_masks(masks=[inv_target_transform(i) for i in masks])\n", + "plot_masks(masks=preds.cpu(), prediction=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's evaluate the same metrics as we did above in the [Model Validation](#Model-Validation) section, but now for the challenge dataset. Given that these combined frames represent a more challenging problem, we anticipate the model's capabilities to be somewhat diminished, yet we still anticipate reasonable results." ] }, { @@ -931,7 +895,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot_spectrogram_mask(spectrogram.to(\"cpu\"), mask.to(\"cpu\"))" + "print_metrics_report(dataloader=challenge_loader)" ] }, { From ecdad74ea8b6f9c25524a96d28f10855c82a0d21 Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Fri, 3 May 2024 10:22:42 -0400 Subject: [PATCH 6/9] README.md updates. --- README.md | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index c7f7d0b..d121222 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,10 @@ # Spectrogram Segmentation -The successful application of semantic segmentation to radiofrequency (RF) spectrograms holds significant applications +The successful application of [semantic segmentation](https://www.ibm.com/topics/semantic-segmentation) to radiofrequency (RF) spectrograms holds significant applications for [spectrum sensing](https://iopscience.iop.org/article/10.1088/1742-6596/2261/1/012016#:~:text=In%20cognitive%20radio%2C%20spectrum%20sensing,user%20can%20use%20the%20spectrum.) and serves as a foundational example showcasing the near-term feasibility of [intelligent radio](https://www.qoherent.ai/intelligentradio/) technology. -In this example, we use [PyTorch](https://pytorch.org/) and [Lightning](https://lightning.ai/docs/pytorch/stable/) to train DeepLabV3 segmentation models to +In this example, we use [PyTorch](https://pytorch.org/) and [Lightning](https://lightning.ai/docs/pytorch/stable/) to train a segmentation model to identify and differentiate between 5G NR and 4G LTE signals within wideband spectrograms. Qoherent's mission to drive the creation of intelligent radio technology requires a combination of open-source and @@ -25,9 +25,8 @@ open-source project: [RIA Core](https://github.com/qoherent/ria). This example is provided as a Jupyter Notebook. You have the option to either run this example locally or in Google Colab. -To run this example locally, you'll need to download this project, the spectrogram sensing dataset, -and set up a Conda virtual environment. If this seems daunting, we recommend running this example on -Google Colab. +To run this example locally, you'll need to download this project and dataset, and set up a Conda +virtual environment. If this seems daunting, we recommend running this example on Google Colab. ### Running this example locally @@ -62,10 +61,10 @@ conda activate spectrogram-segmentation python download_dataset.py ``` This command will create a new directory named `SpectrumSensingDataset` at the project's root. The -MathWorks Spectrum Sensing 5G dataset will be downloaded and unpacked into this directory. +MathWorks Spectrum Sensing dataset will be downloaded and unpacked into this directory automatically. -6. Install a new IPython kernel within the `spectrogram-segmentation` environment: +6. Register the environment kernel with Jupyter: ```commandline ipython kernel install --user --name=spectrogram-segmentation ``` @@ -83,26 +82,26 @@ Team: [How To Use Jupyter Notebooks](https://www.codecademy.com/article/how-to-u Depending on your system specifications and the availability of a CUDA, running this example locally may take several minutes. If a cell is taking too long to execute, you can interrupt its execution by clicking the "Kernel" -menu and selecting "Interrupt Kernel" or by pressing `Ctrl + C` in the terminal where Jupyter notebook is running. +menu and selecting "Interrupt Kernel" or by pressing `Ctrl + C` in the terminal where Jupyter Notebook is running. -9. After you finish exploring, consider removing the sensing dataset from your system and deleting the Conda -environment to free up space. You can delete the Conda environment using the following command: +9. After you finish exploring, consider removing the dataset from your system and deleting the Conda environment to +free up space. You can delete the Conda environment using the following command: ```commandline conda env remove --name spectrogram-segmentation ``` ### Running this example in Google Colab -Coming soon: Don't want the hassle of downloading the project and dataset and setting up a Conda environment? +**Coming soon:** Don't want the hassle of downloading the project and dataset and setting up a Conda environment? We've shared the notebook on Google Colab: [Spectrogram Segmentation](). ## 🤝 Contribution We welcome contributions from the community! Whether it's an enhancement, bug fix, or improved explanation, -your input is valuable. For significant changes or to include another similar example, kindly [contact us](mailto:info@qoherent.ai) -beforehand. +your input is valuable. For significant changes, or if you'd like to prepare a separate tutorial, kindly +[contact us](mailto:info@qoherent.ai) beforehand. If you encounter any issues or to report a security vulnerability, please submit a bug report to the GitHub Issues page [here](https://github.com/qoherent/spectrogram-segmentation/issues). @@ -121,13 +120,14 @@ for sharing. ## 🙏 Attribution The dataset used in this example was prepared by MathWorks and is publicly available under the MIT license -[here](https://www.mathworks.com/supportfiles/spc/SpectrumSensing/SpectrumSenseTrainingDataNetwork.tar.gz). For more information on how the dataset was generated or to generate further spectrum data, please -refer to the aforementioned MathWork's article on Spectrum Sensing. For more information about Qoherent's use of -MATLAB to accelerate intelligent radio research, check out our [customer story](https://www.mathworks.com/company/user_stories/qoherent-uses-matlab-to-accelerate-research-on-next-generation-ai-for-wireless.html). +[here](https://www.mathworks.com/supportfiles/spc/SpectrumSensing/SpectrumSenseTrainingDataNetwork.tar.gz). For more information on how this dataset was generated or to generate further spectrum data, please +refer to MathWork's article on spectrum sensing. For more information about Qoherent's use of MATLAB to accelerate +intelligent radio research, check out our [customer story](https://www.mathworks.com/company/user_stories/qoherent-uses-matlab-to-accelerate-research-on-next-generation-ai-for-wireless.html). + +The DeepLabv3 models used in this example were initially proposed by Chen _et al._ and are further discussed +in their 2017 paper titled '[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)'. The MobileNetV3 +backbone used in this example was developed by Howard _et al._ and is further discussed in their 2019 paper titled +'[Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)'. Models were accessed through [`torchvision`](https://pytorch.org/vision/stable/models/deeplabv3.html). A special thanks to the PyTorch and Lightning teams for providing the foundational machine learning frameworks used in this example. - -The DeepLabv3 models employed in this example were initially proposed by Chen _et al._ and are further discussed -in their 2017 paper titled '[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)'. DeepLabv3 models -were accessed through [`torchvision`](https://pytorch.org/vision/stable/models/deeplabv3.html). From 85d84a44c8442a059ab26aa78c62d6fcf6d979c9 Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Fri, 3 May 2024 11:44:14 -0400 Subject: [PATCH 7/9] Focussing on MobileNetV3. Rather than both MobileNetV3 and ResNet-50. Finding a common set of hyperparameters that work well for both MobileNetV3 and ResNet-50 can be challenging. However, MobileNetV3 is quick to train and lightweight, making it ideal for this tutorial. It offers results comparable to the MATLAB example. The note about other ResNet models has been moved to the next steps section. --- spectrogram_segmentation.ipynb | 101 ++++++++++++++++++--------------- 1 file changed, 54 insertions(+), 47 deletions(-) diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index 4a1b413..b38279d 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -6,7 +6,7 @@ "source": [ "# Spectrogram Segmentation\n", "\n", - "In this example, we use [PyTorch](https://pytorch.org/) and [Lightning](https://lightning.ai/docs/pytorch/stable/) to train deep learning models to differentiate between 5G NR and 4G LTE signals within wideband spectrograms." + "In this example, we use [PyTorch](https://pytorch.org/) and [Lightning](https://lightning.ai/docs/pytorch/stable/) to train a deep learning model to identify and differentiate between 5G NR and 4G LTE signals within wideband spectrograms." ] }, { @@ -17,17 +17,17 @@ "\n", "**[Background](#Background):** Delve into the problem background and learn more about the machine learning frameworks, tools, and datasets used in this example.\n", "\n", - "**[Set-up](#Set-Up):** Install the libraries and initialize the variables necessary to run the code in this notebook.\n", + "**[Set-up](#Set-Up):** Install the libraries necessary to run the code in this notebook.\n", "\n", "**[Data Preprocessing](#Data-Preprocessing):** Load and analyze the Spectrum Sensing dataset.\n", "\n", - "**[Model Training](#Model-Training):** Select and train a deep learning model.\n", + "**[Model Training](#Model-Training):** Configure and train a Deeplabv3 model with a MobileNetV3 backbone.\n", "\n", "**[Model Validation](#Model-Validation):** Assess the performance of the model using a suite of common machine learning metrics\n", "\n", - "**[Challenge Data](#Challange-Data):** Challenge the model on combined frames containing both LTE and NR signal.\n", + "**[Challenge Data](#Challenge-Data):** Challenge the model on combined frames containing both LTE and NR signal.\n", "\n", - "**[Conclusions & Next Step](#Conculsions-&-Next-Steps):** Interpret the results, summarize key learnings, and identify next steps for expanding upon this example." + "**[Conclusions & Next Steps](#Conclusions-&-Next-Steps):** Interpret the results, summarize key learnings, and identify steps for expanding upon this example." ] }, { @@ -46,12 +46,11 @@ "the field of computer vision to the problem of spectrogram analysis. Our task is to assign one of the \n", "following labels to each pixel in the spectrogram: 'LTE', 'NR', or 'Noise'. ('Noise' refers to the absence of signal, representing \n", "a vacant or empty spectrum, also known as whitespace.)\n", + ".\n", "\n", - "The machine learning models utilized in this example are DeepLabV3 models. The DeepLabv3 framework was originally introduced by Chen _et al._ in their 2017 paper titled '[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)'. For an accessible introduction to the DeepLabV3 framework, please check out Isaac Berrios' article: [DeepLabv3: Building Blocks for Robust Segmentation Models](https://medium.com/@itberrios6/deeplabv3-c0c8c93d25a4).\n", + "The machine learning model utilized in this example is a DeepLabV3 model with a MobileNetV3 large backbone. The DeepLabv3 framework was originally introduced by Chen _et al._ in their 2017 paper titled '[Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587) and the MobileNetV3 backbone was developed by Howard _et al._ and is further discussed in their 2019 paper titled '[Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)'. For an accessible introduction to the DeepLabV3 framework, please check out Isaac Berrios' article: [DeepLabv3: Building Blocks for Robust Segmentation Models](https://medium.com/@itberrios6/deeplabv3-c0c8c93d25a4).\n", "\n", - "The dataset used in this example is the Spectrum Sensing dataset, provided by MathWorks. This dataset contains 900 LTE frames, 900 NR frames, and 900 combined frames with both LTE and NR signal. In this example, we train exclusively on the individual LTE and NR examples, excluding the combined frames.\n", - "\n", - "To ensure comparability with results obtained using MathWorks' AI-based network, we use the hyperparameter configuration from MathWorks' spectrum sensing example: [Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals](https://www.mathworks.com/help/comm/ug/spectrum-sensing-with-deep-learning-to-identify-5g-and-lte-signals.html)." + "The dataset used in this example is the Spectrum Sensing dataset, provided by MathWorks. This dataset contains 900 LTE frames, 900 NR frames, and 900 combined frames with both LTE and NR signal. In this example, we train exclusively on the individual LTE and NR examples, excluding the combined frames." ] }, { @@ -81,7 +80,7 @@ "import glob\n", "import os\n", "import statistics\n", - "from typing import Optional\n", + "from typing import Any, Optional\n", "\n", "import lightning as L\n", "import matplotlib.pyplot as plt\n", @@ -107,6 +106,7 @@ "from torchvision.models.segmentation import (\n", " deeplabv3_mobilenet_v3_large,\n", " deeplabv3_resnet50,\n", + " deeplabv3_resnet101,\n", ")\n", "from torchvision.transforms.v2 import (\n", " Compose,\n", @@ -134,7 +134,7 @@ "\n", "In semantic segmentation, the input data typically consists of images (in this case, spectrograms), while the output data consists of pixel-wise labels (masks) where each pixel is assigned a category label (in this case, either 'LTE', 'NR', or 'Noise'). \n", "\n", - "In this example, we use [supervised learning](https://www.ibm.com/topics/supervised-learning) techniques to train our model. These techniques require both input spectrograms and the corresponding target masks for training. For each frame in the dataset, we have two separate files:\n", + "We will use [supervised learning](https://www.ibm.com/topics/supervised-learning) techniques to train our model. These techniques require both input spectrograms and the corresponding target masks for training. For each frame in the dataset, we have two separate files:\n", "\n", "- A `.png` file containing the spectrogram image to use as input to the model.\n", "\n", @@ -201,10 +201,10 @@ "\n", "Both the spectrograms and masks are 256 x 256 pixel images. However, the spectrograms are three channeled, while the masks are single-channeled. This is because the spectrograms are full RGB images, whereas the masks are ternary-valued images, where each pixel takes one of three discrete values:\n", "- `0`: Represents noise.\n", - "- `127`: Representing 5G NR signal.\n", - "- `255`: Representing 4G LTE signal.\n", + "- `127`: Representing NR signal.\n", + "- `255`: Representing LTE signal.\n", "\n", - "To prepare our spectrograms for training, we will convert them from PIL Images to Tensor objects. As required by our models, the images have to be loaded in to a range of `[0, 1]` and then normalized using a mean of `[0.485, 0.456, 0.406]` and a standard deviation of `[0.229, 0.224, 0.225]`. To prepare our masks for training, we will convert them to Tensor objects, remove the extraneous channel dimension, and update the pixel values such that `0` represents noise, `1` represents NR signal, and `2` represents LTE signal." + "To prepare our spectrograms for training, we will convert them from PIL Images to Tensor objects. As required by our model, the images have to be loaded into the range `[0, 1]` and then normalized using a mean of `[0.485, 0.456, 0.406]` and a standard deviation of `[0.229, 0.224, 0.225]`. To prepare our masks for training, we will convert them to Tensor objects, remove the extraneous channel dimension, and update the pixel values such that `0` represents noise, `1` represents NR signal, and `2` represents LTE signal." ] }, { @@ -230,9 +230,7 @@ "transform = Compose(\n", " [\n", " PILToTensor(),\n", - " ToDtype(\n", - " torch.float, scale=True\n", - " ), \n", + " ToDtype(torch.float, scale=True), \n", " Normalize(mean=mean, std=std),\n", " ]\n", ")\n", @@ -381,9 +379,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It looks like our data is mostly noise! A classification dataset like this—with skewed class proportions—is called imbalanced.\n", + "It looks like our dataset is mostly noise! A classification dataset like this—with skewed class proportions—is called imbalanced.\n", "\n", - "An imbalanced dataset can result in biased and poorly performing models. Models trained on imbalanced data tends to focus more on the majority classes and may not learn enough about the minority classes. In our case, the majority class is noise, while the minority classes are the NR and LTE signals we want to identify and classify. To ensure the development of a fair, accurate, and robust model, we will need to address this class imbalance. \n", + "An imbalanced dataset can result in biased and poorly performing models. Models trained on imbalanced data tends to focus more on the majority classes and may not learn enough about the minority classes. To ensure the development of a fair, accurate, and robust model, we will need to address this class imbalance. \n", "\n", "But first, let's split the dataset into separate training and validation sets. The training dataset is the portion of the dataset that will be used to train the model, while the validation dataset will be held in reserve and used to evaluate the performance of the trained model. Let's start with a simple 80/20 split, where 80% of the dataset is used for training and 20% for validation." ] @@ -421,10 +419,10 @@ "metadata": {}, "outputs": [], "source": [ - "mini_batch_size = 4\n", + "batch_size = 4\n", "\n", - "train_loader = DataLoader(train_set, batch_size=mini_batch_size, shuffle=True)\n", - "val_loader = DataLoader(val_set, batch_size=mini_batch_size, shuffle=False)\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n", + "val_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\n", "\n", "spects, masks = next(iter(train_loader))\n", "\n", @@ -436,7 +434,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's examine a batch of spectrograms along with their corresponding masks. Note that the following plotting code is optimized for small batch sizes and may not render as nicely with larger batch sizes." + "Let's examine a batch of spectrograms along with their corresponding masks. Please note that the following plotting code is optimized for small batch sizes and may not render as nicely with larger batch sizes." ] }, { @@ -446,7 +444,7 @@ "outputs": [], "source": [ "def plot_spects(spects: list[Image.Image]) -> None:\n", - " fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + " fig, axes = plt.subplots(figsize=[batch_size * 2, 3], nrows=1, ncols=batch_size, sharey=True)\n", " fig.text(0.5, 0.75, \"Spectrograms\", fontsize=title_font_size, ha=\"center\")\n", " axes[0].set_ylabel(\"Time [arb. units]\", fontsize=label_font_size)\n", " fig.text(0.5, 0.12, \"Freq. [arb. units]\", fontsize=label_font_size, ha=\"center\")\n", @@ -460,7 +458,7 @@ "\n", "\n", "def plot_masks(masks: list[Image.Image], prediction: bool = False) -> None:\n", - " fig, axes = plt.subplots(figsize=[mini_batch_size * 2, 3], nrows=1, ncols=mini_batch_size, sharey=True)\n", + " fig, axes = plt.subplots(figsize=[batch_size * 2, 3], nrows=1, ncols=batch_size, sharey=True)\n", " if prediction:\n", " fig.text(0.5, 0.75, \"Model Predictions\", fontsize=title_font_size, ha=\"center\")\n", " else:\n", @@ -487,15 +485,20 @@ "plot_masks(masks=[inv_target_transform(i) for i in masks])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** You can view different batches from the dataset by rerunning the previous few code cells." + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Training\n", "\n", - "Let's start by choosing a model. For this example, we suggest choosing between DeepLabV3 models with ResNet-50 or MobileNetV3 backbones. ResNet-50 is the deeper and more complex, and generally offers better model performance, whereas MobileNetV3 is designed to be lightweight and efficient. Because both models provide the same interface, and either will work with this example.\n", - "\n", - "Note: DeepLabV3 also provides a deeper ResNet-101 model. Feel free to experiment with it if you're interested, but we suggest 101 layers is overkill for the task at hand and likely requires a larger dataset to train effectively." + "In this example, we'll use a DeepLabV3 model with a MobileNetV3 large backbones. This model is designed to be lightweight and efficient, making it ideal for edge computing devices and quick proof-of-concept demonstrations" ] }, { @@ -505,8 +508,6 @@ "outputs": [], "source": [ "n_classes = 3 # We are dealing with three classes: Noise, NR, and LTE.\n", - "\n", - "# model = deeplabv3_resnet50(num_classes=n_classes)\n", "model = deeplabv3_mobilenet_v3_large(num_classes=n_classes)" ] }, @@ -551,7 +552,6 @@ "outputs": [], "source": [ "class SegmentationSGD(L.LightningModule):\n", - " \"\"\"LightningModule for training and evaluating a segmentation model using the SGD optimizer. \"\"\"\n", "\n", " def __init__(\n", " self,\n", @@ -660,7 +660,7 @@ "source": [ "Now that we have our model, weighted loss function, and Lightning Module, we are prepared to train our model. If available, we will leverage GPU acceleration. Otherwise, the training process will default to using the CPU. Please be patient; model training time may vary depending on the current hardware configuration and could take a few minutes.\n", "\n", - "The number of epochs determines how many times the entire dataset will be used to train the model. We will begin training with 10 epochs." + "The number of epochs determines how many times the entire dataset will be used to train the model. For this specific model and dataset, 10 epochs should be more than sufficient." ] }, { @@ -696,7 +696,7 @@ "source": [ "# Model Validation\n", "\n", - "Having trained our model, the next step is to evaluate its performance. To accomplish this, we'll use a suite of standard machine learning metrics. But first, let's take quick look at a random batch of predictions and true labels.\n", + "Having trained our model, the next step is to evaluate its performance. To accomplish this, we'll use a suite of standard machine learning metrics. But first, let's take a look at a random batch of predictions and true labels.\n", "\n", "Because the model returns the unnormalized probabilities corresponding to the predictions of each class. We need to use `argmax()` to get the maximum prediction of each class. The result is a ternary-valued image for each example in the batch." ] @@ -734,7 +734,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Looks pretty good! To get a more ojective sense, let's turn to the metrics. Let's start with model accuracy, calculated as the ratio of correctly predicted pixels to the total number of pixels." + "Looks pretty good! But to get a more ojective sense, let's turn to the metrics. Let's start with model accuracy, calculated as the ratio of correctly predicted pixels to the total number of pixels.\n", + "\n", + "**Note:** You can view different predictions by rerunning the previous few code cells." ] }, { @@ -750,7 +752,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The accuracy can give us a quick sense of the model's overall performance. However, it's important to note that accuracy alone doesn't tell the whole story. In fact, because of the imbalance in our dataset, a reasonably high accuracy could be achieved by always predicting noise. The simple accuracy provided above is an unweighted mean of the accuracies over each class.\n", + "The accuracy can give us a quick sense of the model's overall performance. However, accuracy alone doesn't tell the whole story. In fact, because of the imbalance in our dataset, a reasonably high accuracy could be achieved by always predicting noise. The simple accuracy provided above is an unweighted mean of the accuracies over each class.\n", "\n", "To gain a better understanding of our model's ability to predict specific classes, let's take a look at the confusion matrix, which provides a more comprehensive overview of model capability. The diagonal elements represent the correct predictions and off-diagonal elements indicate prediction errors." ] @@ -761,6 +763,7 @@ "metadata": {}, "outputs": [], "source": [ + "model.to(device)\n", "confusion_matrix = MulticlassConfusionMatrix(num_classes=n_classes, normalize='true').to(device)\n", "\n", "with torch.no_grad():\n", @@ -779,13 +782,13 @@ "source": [ "Finally, let's generate a more comprehensive report, complete with the following metrics:\n", "\n", - "- **Recall:** The recall (sensitivity) measures the ability of the model to identify all relevant pixels. A higher recall indicates that the model is better at identifying positive instances.\n", + "- **Recall:** The recall (sensitivity) measures the ability of the model to identify the relevant pixels. A higher recall indicates that the model is better at identifying signal.\n", "\n", - "- **Precision:** The precision assesses the accuracy of positive predictions. A higher precision indicates that when the model predicts a positive outcome, it is more likely to be correct.\n", + "- **Precision:** The precision assesses the accuracy of positive predictions. A higher precision indicates that when the model predicts signal, it is more likely to be correct.\n", "\n", "- **F1 Score:** The F1 score combines both recall and precision into a single value, providing a more balanced measure of the model's performance. A higher F1 indicates a model with both good precision and recall (fewer false positives and false negatives overall).\n", "\n", - "- **Intersection over Union (IoU):** The IoU, commonly called Jaccard's Index, quantifies the overlap between the predicted bounding box or segmented region and the ground truth bounding box or annotated region from a dataset. A higher IoU value indicates a better alignment between the predicted and actual regions, reflecting a more accurate model." + "- **Intersection over Union (IoU):** The IoU, commonly called Jaccard's Index, quantifies the overlap between the predicted bounding box or segmented region and the ground truth. A higher IoU value indicates a better alignment between the predicted and actual regions, reflecting a more accurate model." ] }, { @@ -841,7 +844,7 @@ "source": [ "# Challenge Data\n", "\n", - "In machine leaning, generalization refers to the ability of a trained model to perform well on new data that it hasn't been trained on. As an easy way to test the generalization of our model, let's test on combined frames with both LTE and NR signal. As a reminder, such frames were excluded from testing." + "In machine leaning, generalization refers to the ability of a trained model to perform well on new data that it hasn't been trained on. As an easy way to test the generalization of our model, let's test on the combined frames with both LTE and NR signal. As a reminder, such frames were excluded from training." ] }, { @@ -850,10 +853,10 @@ "metadata": {}, "outputs": [], "source": [ - "challenge_data_root = os.path.join(data_root, \"LTE_NR\")\n", - "challenge_dataset = SpectrumSensing(root=challenge_data_root, transform=transform, target_transform=target_transform)\n", + "challenge_data = os.path.join(data_root, \"LTE_NR\")\n", + "challenge_dataset = SpectrumSensing(root=challenge_data, transform=transform, target_transform=target_transform)\n", "\n", - "challenge_loader = DataLoader(challenge_dataset, batch_size=mini_batch_size, shuffle=True)" + "challenge_loader = DataLoader(challenge_dataset, batch_size=batch_size, shuffle=True)" ] }, { @@ -886,6 +889,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "**Note:** You can view different batches from the dataset by rerunning the previous few code cells.\n", + "\n", "Now, let's evaluate the same metrics as we did above in the [Model Validation](#Model-Validation) section, but now for the challenge dataset. Given that these combined frames represent a more challenging problem, we anticipate the model's capabilities to be somewhat diminished, yet we still anticipate reasonable results." ] }, @@ -904,7 +909,7 @@ "source": [ "# Conclusions & Next Steps\n", "\n", - "In this example, we used PyTorch and PyTorch Lightning to train DeepLabV3 models to identify and differentiate between 5G NR and 4G LTE signals within wideband spectrograms, showcasing one of the ways we can leverage machine learning to identify things in the wireless spectrum. This involved data analysis and preprocessing, model selection, choosing a loss function and optimizer, model training, model performance validation, and finally testing the model's generalization on combined frames containing both NR and LTE signals, \n", + "In this example, we used PyTorch and PyTorch Lightning to train DeepLabV3 models to identify and differentiate between 5G NR and 4G LTE signals within wideband spectrograms, showcasing one of the ways we can leverage machine learning to identify things in the wireless spectrum. This involved data analysis and preprocessing, choosing a loss function and optimizer, model training, model performance validation, and finally testing the model's generalization on combined frames containing both NR and LTE signals, \n", "\n", "The capability to differentiate and recognize various signals finds direct applications in spectrum sensing, which is fundamental to autonomous spectrum management, and brings us one step closer to more holistic cognitive radio solutions! 📡🚀" ] @@ -915,15 +920,17 @@ "source": [ "We hope this example was informative. Here are some next steps you can take to further explore and expand upon what you've learned:\n", "\n", - "- **Experiment with the Hyperparameters:** Adjust the values of hyperparameters such as the number of training epochs, mini-batch size, and learning rate, and observe how these configurations influence model training and performance. After gaining insights through manual hyperparameter tuning, explore automated approaches using tools like [Ray Tune](https://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html) or [Optuna](https://optuna.org/).\n", + "- **Experiment with the Hyperparameters:** Adjust the values of hyperparameters such as the number of training epochs, batch size, and learning rate, and observe how these configurations influence model training and performance. After gaining insights through manual hyperparameter tuning, explore automated approaches using tools like [Ray Tune](https://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html) or [Optuna](https://optuna.org/).\n", + "\n", + "- **Experiment with DeepLabV3's ResNet Models:** DeepLabV3 also provides models with ResNet-50 and ResNet-101 backbones. These ResNet models are deeper and more complex, and generally offers better model performance than MobileNetV3, which is designed to be lightweight and efficient. Because all DeepLabV3 models implement the same interface, no code changes are required. However, some hyperparameter tuning and/or a larger dataset may be required to train these models effectively. These models have already been imported for your convenience.\n", "\n", "- **Explore Alternative Solutions to Class Imbalance:** In this example, we addressed class imbalance in our dataset using a weighted cross-entropy loss function. Research and implement alternative strategies or loss functions designed to address imbalance in image datasets.\n", "\n", - "- **Integrate Combined Frames:** In this example, we trained exclusively on the individual NR and LTE frames. Try integrating the combined frames that contain both the NR and LTE signals into the training process, and evaluate the effect on model performance and generalization.\n", + "- **Integrate Combined Frames:** In this example, we trained exclusively on the individual NR and LTE frames. Try integrating the combined frames that contain both the NR and LTE signals into the training process, and evaluate the effect on model performance.\n", "\n", - "- **Test your Model on Captured Radio Data:** If you have radio hardware available, consider testing your model on real recordings of live radio data. Check out this article from MathWorks for more information on how to capture 5G NR and LTE signals: [Capture and Label NR and LTE Signals for AI Training](https://www.mathworks.com/help/wireless-testbench/ug/capture-and-label-nr-and-lte-signals-for-ai-training.html).\n", + "- **Test your Model on Captured Radio Data:** If you have radio hardware available, consider testing your model on real recordings of live radio data. Check out this article from MathWorks for more information on how to capture NR and LTE signals: [Capture and Label NR and LTE Signals for AI Training](https://www.mathworks.com/help/wireless-testbench/ug/capture-and-label-nr-and-lte-signals-for-ai-training.html).\n", "\n", - "- **Explore RIA Core on GitHub:** At Qoherent, we're building [Radio Intelligence Applications](https://qoherent.ai/radiointelligenceapps-project/) (RIA) to drive the creation of intelligent radios. Check out [RIA Core](https://github.com/qoherent/ria)—the free and open-source foundation of RIA—and consider contributing to the project." + "- **Explore RIA Core on GitHub:** At Qoherent, we're building [Radio Intelligence Applications](https://qoherent.ai/radiointelligenceapps-project/) (RIA) to drive the creation of intelligent radios. Check out [RIA Core](https://github.com/qoherent/ria)—the free and open-source foundation of RIA—and consider contributing to the project. ⭐" ] } ], From c0c0d2842c648147345bed2cf9356659d4f6416c Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Fri, 3 May 2024 12:06:50 -0400 Subject: [PATCH 8/9] Minor typo fixes. --- spectrogram_segmentation.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index b38279d..2b9a190 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -410,7 +410,7 @@ "source": [ "In machine learning, data loaders facilitate easy access to samples, efficiently load and batch data, and offer numerous other features to streamline data preprocessing, management, and integration within the training loop. Let's create data loaders for both the training and validation datasets.\n", "\n", - "In PyTorch, the `DataLoader` class allows us to pass a `batch_size` argument, which controls the number of samples used in each pass through the network. Using a small number of training examples each pass is called mini-batching, and can improve efficiency, stabilize training dynamics, and enable scalable training on large datasets. Choosing an appropriate mini-batch size depends on several factors, including the available memory on your hardware, training efficiency constraints, and generalization requirements. However, as with everything in machine learning, we ultimately rely on empirical testing to determine the optimal configuration that maximizes model performance for each specific task and dataset. In this example, we'll start with mini-batches containing 4 samples each, which will easily fit on any CPU/GPU without issue." + "In PyTorch, the `DataLoader` class allows us to pass a `batch_size` argument, which controls the number of samples used in each pass through the network. Using a small number of training examples each pass is called mini-batching, and can improve efficiency, stabilize training dynamics, and enable scalable training on large datasets. Choosing an appropriate mini-batch size depends on several factors, including the available memory on your hardware, training efficiency constraints, and generalization requirements. However, as with everything in machine learning, we ultimately rely on empirical testing to determine the optimal configuration that maximizes model performance for each specific task and dataset. In this example, we'll use mini-batches containing 4 samples each, which will easily fit on any CPU/GPU without issue and provide reasonable generalization performance." ] }, { @@ -889,7 +889,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Note:** You can view different batches from the dataset by rerunning the previous few code cells.\n", + "**Note:** You can view different examples by rerunning the previous few code cells.\n", "\n", "Now, let's evaluate the same metrics as we did above in the [Model Validation](#Model-Validation) section, but now for the challenge dataset. Given that these combined frames represent a more challenging problem, we anticipate the model's capabilities to be somewhat diminished, yet we still anticipate reasonable results." ] @@ -920,7 +920,7 @@ "source": [ "We hope this example was informative. Here are some next steps you can take to further explore and expand upon what you've learned:\n", "\n", - "- **Experiment with the Hyperparameters:** Adjust the values of hyperparameters such as the number of training epochs, batch size, and learning rate, and observe how these configurations influence model training and performance. After gaining insights through manual hyperparameter tuning, explore automated approaches using tools like [Ray Tune](https://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html) or [Optuna](https://optuna.org/).\n", + "- **Experiment with the Hyperparameters:** Adjust the values of hyperparameters such as the number of training epochs, batch size, and learning rate, and observe how these configurations influence model training, performance, and generalization capabilities. After gaining insights through manual hyperparameter tuning, explore automated approaches using tools like [Ray Tune](https://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html) or [Optuna](https://optuna.org/).\n", "\n", "- **Experiment with DeepLabV3's ResNet Models:** DeepLabV3 also provides models with ResNet-50 and ResNet-101 backbones. These ResNet models are deeper and more complex, and generally offers better model performance than MobileNetV3, which is designed to be lightweight and efficient. Because all DeepLabV3 models implement the same interface, no code changes are required. However, some hyperparameter tuning and/or a larger dataset may be required to train these models effectively. These models have already been imported for your convenience.\n", "\n", From 6251e3740c6d89094c133e814d25339847aad4b4 Mon Sep 17 00:00:00 2001 From: Michael Luciuk Date: Fri, 3 May 2024 12:23:30 -0400 Subject: [PATCH 9/9] Linting and formatting fixes. --- spectrogram_segmentation.ipynb | 68 ++++++++++++++++------------------ 1 file changed, 32 insertions(+), 36 deletions(-) diff --git a/spectrogram_segmentation.ipynb b/spectrogram_segmentation.ipynb index 2b9a190..227a676 100644 --- a/spectrogram_segmentation.ipynb +++ b/spectrogram_segmentation.ipynb @@ -103,7 +103,7 @@ " MulticlassRecall,\n", ")\n", "from torchvision.datasets import VisionDataset\n", - "from torchvision.models.segmentation import (\n", + "from torchvision.models.segmentation import ( # noqa: F401\n", " deeplabv3_mobilenet_v3_large,\n", " deeplabv3_resnet50,\n", " deeplabv3_resnet101,\n", @@ -218,6 +218,7 @@ "\n", "mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", "\n", + "\n", "class Squeeze(torch.nn.Module):\n", " def forward(self, target: Tensor):\n", " return torch.squeeze(target)\n", @@ -226,22 +227,18 @@ "class DivideBy127(torch.nn.Module):\n", " def forward(self, target: Tensor):\n", " return torch.div(target, 127, rounding_mode=\"floor\")\n", - " \n", + "\n", + "\n", "transform = Compose(\n", " [\n", " PILToTensor(),\n", - " ToDtype(torch.float, scale=True), \n", + " ToDtype(torch.float, scale=True),\n", " Normalize(mean=mean, std=std),\n", " ]\n", ")\n", "\n", "target_transform = Compose(\n", - " [\n", - " PILToTensor(), \n", - " Squeeze(), \n", - " DivideBy127(), # Mapping 0 -> 0, 127 -> 1, and 255 -> 2.\n", - " ToDtype(torch.long)\n", - " ]\n", + " [PILToTensor(), Squeeze(), DivideBy127(), ToDtype(torch.long)] # Mapping 0 -> 0, 127 -> 1, and 255 -> 2.\n", ")" ] }, @@ -300,12 +297,7 @@ " ]\n", ")\n", "\n", - "inv_target_transform = Compose(\n", - " [\n", - " ToDtype(dtype=torch.uint8),\n", - " ToPILImage()\n", - " ]\n", - ")\n", + "inv_target_transform = Compose([ToDtype(dtype=torch.uint8), ToPILImage()])\n", "\n", "training_example = inv_transform(training_example)\n", "corresponding_mask = inv_target_transform(corresponding_mask)\n", @@ -587,7 +579,7 @@ " def training_step(self, batch: Tensor, batch_idx: int) -> Tensor:\n", " \"\"\"Defines a single training step.\"\"\"\n", " image, target = batch\n", - " preds = self(image)['out']\n", + " preds = self(image)[\"out\"]\n", " loss = self.loss_function(preds, target)\n", " self.train_accuracy(preds, target)\n", " self.log(name=\"train_accuracy\", value=self.train_accuracy, prog_bar=True)\n", @@ -597,7 +589,7 @@ " def validation_step(self, batch: Tensor, batch_idx: int) -> Tensor:\n", " \"\"\"Defines a single validation step.\"\"\"\n", " image, target = batch\n", - " preds = self(image)['out']\n", + " preds = self(image)[\"out\"]\n", " loss = self.loss_function(preds, target)\n", " self.val_accuracy(preds, target)\n", " self.log(name=\"val_accuracy\", value=self.val_accuracy, prog_bar=True)\n", @@ -611,10 +603,7 @@ " )\n", " lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=self.step_size, gamma=self.gamma)\n", "\n", - " return {\n", - " \"optimizer\": optimizer, \n", - " \"lr_scheduler\": lr_scheduler\n", - " }" + " return {\"optimizer\": optimizer, \"lr_scheduler\": lr_scheduler}" ] }, { @@ -764,7 +753,7 @@ "outputs": [], "source": [ "model.to(device)\n", - "confusion_matrix = MulticlassConfusionMatrix(num_classes=n_classes, normalize='true').to(device)\n", + "confusion_matrix = MulticlassConfusionMatrix(num_classes=n_classes, normalize=\"true\").to(device)\n", "\n", "with torch.no_grad():\n", " for spect, mask in val_loader:\n", @@ -798,35 +787,42 @@ "outputs": [], "source": [ "def print_metrics_report(dataloader: DataLoader) -> None:\n", - " \"\"\"Compute accuracy, recall, precision, F1 score, and IoU (Intersection over Union), and print a report containing these metrics. \"\"\"\n", + " \"\"\"Compute accuracy, recall, precision, F1 score, and IoU (Intersection over Union),\n", + " and print a report containing these metrics.\"\"\"\n", " metrics = [\n", " MulticlassAccuracy(num_classes=n_classes, average=None),\n", " MulticlassRecall(num_classes=n_classes, average=None),\n", " MulticlassPrecision(num_classes=n_classes, average=None),\n", " MulticlassF1Score(num_classes=n_classes, average=None),\n", - " MulticlassJaccardIndex(num_classes=n_classes, average=None)\n", + " MulticlassJaccardIndex(num_classes=n_classes, average=None),\n", " ]\n", " metrics = [m.to(device) for m in metrics]\n", - " \n", + "\n", " with torch.no_grad():\n", " for spect, mask in dataloader:\n", " spect, mask = spect.to(device), mask.to(device)\n", " pred = (model(spect)[\"out\"]).argmax(dim=1)\n", " for m in metrics:\n", " m.update(pred, mask)\n", - " \n", + "\n", " metrics = [m.compute().cpu().numpy() for m in metrics]\n", " metrics = [np.append(m, statistics.mean(m)) for m in metrics]\n", - " \n", - " df = pd.DataFrame({\n", - " \"Class\": np.append(np.asarray(labels), \"Mean\"),\n", - " \"Accuracy\": metrics[0],\n", - " \"Recall\": metrics[1],\n", - " \"Precision\": metrics[2],\n", - " \"F1 Score\": metrics[3],\n", - " \"Jaccard Index\": metrics[4]\n", - " })\n", - " print(tabulate(df, headers='keys', tablefmt='grid', showindex=False, numalign=\"center\", stralign=\"center\", floatfmt=\".2f\"))" + "\n", + " df = pd.DataFrame(\n", + " {\n", + " \"Class\": np.append(np.asarray(labels), \"Mean\"),\n", + " \"Accuracy\": metrics[0],\n", + " \"Recall\": metrics[1],\n", + " \"Precision\": metrics[2],\n", + " \"F1 Score\": metrics[3],\n", + " \"Jaccard Index\": metrics[4],\n", + " }\n", + " )\n", + " print(\n", + " tabulate(\n", + " df, headers=\"keys\", tablefmt=\"grid\", showindex=False, numalign=\"center\", stralign=\"center\", floatfmt=\".2f\"\n", + " )\n", + " )" ] }, {