init

Phase 2/task_8.ipynb

@@ -432,7 +432,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

Phase 3/utils.py

@@ -0,0 +1,285 @@
+# All imports
+# Math
+import math
+import random
+import cv2
+import numpy as np
+from scipy.stats import pearsonr
+
+# from scipy.sparse.linalg import svds
+# from sklearn.decomposition import NMF
+from sklearn.decomposition import LatentDirichletAllocation
+
+# from sklearn.cluster import KMeans
+
+# Torch
+import torch
+import torchvision.transforms as transforms
+from torchvision.datasets import Caltech101
+from torchvision.models import resnet50, ResNet50_Weights
+
+import tensorly as tl
+
+# OS and env
+import json
+import os
+from os import getenv
+from dotenv import load_dotenv
+import warnings
+from joblib import dump, load
+
+load_dotenv()
+
+# MongoDB
+from pymongo import MongoClient
+
+# Visualizing
+import matplotlib.pyplot as plt
+
+
+class GridPartition:
+    """Class transform to partition image into (rows, cols) grid"""
+
+    def __init__(self, rows, cols):
+        self.rows = rows
+        self.cols = cols
+
+    def __call__(self, img):
+        # img is in (C,(H,W)) format, so first element is channel
+        img_width, img_height = img.size()[1:]
+        cell_width = img_width // self.cols
+        cell_height = img_height // self.rows
+
+        grids = []
+        for i in range(self.rows):
+            for j in range(self.cols):
+                left = j * cell_width
+                right = left + cell_width
+
+                top = i * cell_height
+                bottom = top + cell_height
+
+                # Slice out
+                grid = img[:, left:right, top:bottom]
+                grids.append(grid)
+
+        return grids
+
+
+
+def compute_gradient_histogram(grid_cell):
+    """Compute HOG using [-1,0,1] masks for gradient"""
+    histograms = []
+
+    # Convert grid cell to NumPy array
+    grid_array = np.array(grid_cell, dtype=np.float32)
+    grid_array = grid_array.reshape(
+        grid_array.shape[1], grid_array.shape[2]
+    )  # ignore extra dimension
+
+    # Compute the gradient using first-order central differences
+    dx = cv2.Sobel(
+        grid_array, cv2.CV_32F, dx=1, dy=0, ksize=1
+    )  # first order x derivative = [-1, 0, 1]
+    dy = cv2.Sobel(
+        grid_array, cv2.CV_32F, dx=0, dy=1, ksize=1
+    )  # first order y derivative = [-1, 0, 1]^T
+
+    # Compute magnitude and direction of gradients
+    magnitude = np.sqrt(dx**2 + dy**2)
+    direction = np.arctan2(dy, dx) * 180 / np.pi  # in degrees
+
+    # Compute HOG - 9 bins, counted across the range of -180 to 180 degrees, weighted by gradient magnitude
+    histogram, _ = np.histogram(direction, bins=9, range=(-180, 180), weights=magnitude)
+
+    histograms.append(histogram)
+
+    return histograms
+
+
+def compute_histograms_for_grid(grid):
+    histograms = [compute_gradient_histogram(grid_cell) for grid_cell in grid]
+    return np.array(histograms).flatten()
+
+
+def combine_histograms(grid_histograms):
+    return torch.Tensor(grid_histograms).view(10, 10, 9)
+
+HOG_transform = transforms.Compose(
+    [
+        transforms.Grayscale(num_output_channels=1),  # grayscale transform
+        transforms.Resize((100, 300)),  # resize to H:W=100:300
+        GridPartition(
+            rows=10, cols=10
+        ),  # partition into grid of 10 rows, 10 columns as a list
+        compute_histograms_for_grid,
+        combine_histograms,
+    ]
+)
+
+
+def getCollection(db, collection):
+    """Load feature descriptor collection from MongoDB"""
+    client = MongoClient("mongodb://localhost:27017")
+    return client[db][collection]
+
+
+def datasetTransform(image):
+    """Transform while loading dataset as scaled tensors of shape (channels, (img_shape))"""
+    return transforms.Compose(
+        [
+            transforms.ToTensor()  # ToTensor by default scales to [0,1] range, the input range for ResNet
+        ]
+    )(image)
+
+
+def loadDataset(dataset):
+    """Load TorchVision dataset with the defined transform"""
+    return dataset(
+        root=getenv("DATASET_PATH"),
+        download=False,  # True if you wish to download for first time
+        transform=datasetTransform,
+    )
+
+
+
+dataset = loadDataset(Caltech101)
+NUM_LABELS = 101
+NUM_IMAGES = 4339
+
+
+def euclidean_distance_measure(img_1_fd, img_2_fd):
+    img_1_fd_reshaped = img_1_fd.flatten()
+    img_2_fd_reshaped = img_2_fd.flatten()
+
+    # Calculate Euclidean distance
+    return math.dist(img_1_fd_reshaped, img_2_fd_reshaped)
+
+
+def loadResnet():
+    """Load ResNet50 pre-trained model with default weights"""
+    # Load model
+    model = resnet50(weights=ResNet50_Weights.DEFAULT)
+
+    # try to use Nvidia GPU
+    if torch.cuda.is_available():
+        dev = torch.device("cuda")
+        torch.cuda.empty_cache()
+    else:
+        dev = torch.device("cpu")
+
+    model = model.to(dev)
+    model.eval()  # switch to inference mode - important! since we're using pre-trained model
+    return model, dev
+
+
+model, dev = loadResnet()
+
+class FeatureExtractor(torch.nn.Module):
+    """Feature extractor module for all layers at once"""
+
+    def __init__(self, model, layers):
+        super().__init__()
+        self.model = model
+        self.layers = layers
+        self._features = {layer: None for layer in layers}  # store layer outputs here
+
+        # Create hooks for all specified layers at once
+        for layer_id in layers:
+            layer = dict(self.model.named_modules())[
+                layer_id
+            ]  # get actual layer in the model
+            layer.register_forward_hook(
+                self.save_outputs_hook(layer_id)
+            )  # register feature extractor hook on layer
+
+    # Hook to save output of layer
+    def save_outputs_hook(self, layer_id):
+        def fn(_module, _input, output):
+            self._features[layer_id] = output
+
+        return fn
+
+    # Forward pass returns extracted features
+    def forward(self, input):
+        _ = self.model(input)
+        return self._features
+
+
+
+def resnet_extractor(image):
+    """Extract image features from avgpool, layer3 and fc layers of ResNet50"""
+    resized_image = (
+        torch.Tensor(np.array(transforms.Resize((224, 224))(image)).flatten())
+        .view(1, 3, 224, 224)
+        .to(dev)
+    )
+
+    # Attach all hooks on model and extract features
+    resnet_features = FeatureExtractor(model=model, layers=["avgpool", "layer3", "fc"])
+    features = resnet_features(resized_image)
+
+    avgpool_2048 = features["avgpool"]
+    # Reshape the vector into row pairs of elements and average across rows
+    avgpool_1024_fd = torch.mean(avgpool_2048.view(-1, 2), axis=1)
+
+    layer3_1024_14_14 = features["layer3"]
+    # Reshape the vector into 1024 rows of 196 elements and average across rows
+    layer3_1024_fd = torch.mean(layer3_1024_14_14.view(1024, -1), axis=1)
+
+    fc_1000_fd = features["fc"].view(1000)
+
+    return (
+        avgpool_1024_fd.detach().cpu().tolist(),
+        layer3_1024_fd.detach().cpu().tolist(),
+        fc_1000_fd.detach().cpu().tolist(),
+    )
+
+
+def resnet_output(image):
+    """Get image features from ResNet50 (full execution) and apply a softmax layer"""
+    resized_image = (
+        torch.Tensor(np.array(transforms.Resize((224, 224))(image)).flatten())
+        .view(1, 3, 224, 224)
+        .to(dev)
+    )
+
+    with torch.no_grad():
+        features = model(resized_image)
+        features = torch.nn.Softmax()(features)
+
+    return features.detach().cpu().tolist()
+
+valid_feature_models = {
+    "cm": "cm_fd",
+    "hog": "hog_fd",
+    "avgpool": "avgpool_fd",
+    "layer3": "layer3_fd",
+    "fc": "fc_fd",
+    "resnet": "resnet_fd",
+}
+
+def predict_m_nn_classifier(fd_collection, m, feature_model, selected_image_fd):
+    """
+    Create the m-NN classifier from the selected feature space
+    """
+
+    assert (
+        feature_model in valid_feature_models.values()
+    ), "feature_moel should be one of " + str(list(valid_feature_models.keys()))
+
+    all_images = list(fd_collection.find())
+    feature_ids = [img["image_id"] for img in all_images]
+
+    feature_vectors = np.array(
+        [np.array(img[feature_model]).flatten() for img in all_images]
+    )
+
+    distances = []
+
+    for fd, id in zip(feature_vectors, feature_ids):
+        distances.append({"image_id": id, "distance": euclidean_distance_measure(selected_image_fd, fd)})
+
+    distances = sorted(distances, key=lambda x: x["distance"])
+
+    return distances[:10]