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knn and decision tree
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pranavbrkr
parent
4492dc6677
commit
6c8663ed46
@ -1311,7 +1311,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.5"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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14032
Phase 3/task_3.ipynb
14032
Phase 3/task_3.ipynb
File diff suppressed because it is too large
Load Diff
227
Phase 3/utils.py
227
Phase 3/utils.py
@ -37,86 +37,12 @@ from pymongo import MongoClient
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import matplotlib.pyplot as plt
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class GridPartition:
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"""Class transform to partition image into (rows, cols) grid"""
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def __init__(self, rows, cols):
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self.rows = rows
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self.cols = cols
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def __call__(self, img):
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# img is in (C,(H,W)) format, so first element is channel
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img_width, img_height = img.size()[1:]
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cell_width = img_width // self.cols
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cell_height = img_height // self.rows
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grids = []
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for i in range(self.rows):
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for j in range(self.cols):
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left = j * cell_width
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right = left + cell_width
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top = i * cell_height
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bottom = top + cell_height
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# Slice out
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grid = img[:, left:right, top:bottom]
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grids.append(grid)
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return grids
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def compute_gradient_histogram(grid_cell):
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"""Compute HOG using [-1,0,1] masks for gradient"""
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histograms = []
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# Convert grid cell to NumPy array
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grid_array = np.array(grid_cell, dtype=np.float32)
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grid_array = grid_array.reshape(
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grid_array.shape[1], grid_array.shape[2]
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) # ignore extra dimension
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# Compute the gradient using first-order central differences
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dx = cv2.Sobel(
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grid_array, cv2.CV_32F, dx=1, dy=0, ksize=1
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) # first order x derivative = [-1, 0, 1]
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dy = cv2.Sobel(
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grid_array, cv2.CV_32F, dx=0, dy=1, ksize=1
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) # first order y derivative = [-1, 0, 1]^T
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# Compute magnitude and direction of gradients
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magnitude = np.sqrt(dx**2 + dy**2)
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direction = np.arctan2(dy, dx) * 180 / np.pi # in degrees
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# Compute HOG - 9 bins, counted across the range of -180 to 180 degrees, weighted by gradient magnitude
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histogram, _ = np.histogram(direction, bins=9, range=(-180, 180), weights=magnitude)
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histograms.append(histogram)
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return histograms
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def compute_histograms_for_grid(grid):
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histograms = [compute_gradient_histogram(grid_cell) for grid_cell in grid]
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return np.array(histograms).flatten()
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def combine_histograms(grid_histograms):
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return torch.Tensor(grid_histograms).view(10, 10, 9)
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HOG_transform = transforms.Compose(
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[
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transforms.Grayscale(num_output_channels=1), # grayscale transform
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transforms.Resize((100, 300)), # resize to H:W=100:300
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GridPartition(
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rows=10, cols=10
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), # partition into grid of 10 rows, 10 columns as a list
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compute_histograms_for_grid,
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combine_histograms,
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]
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)
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valid_classification_methods = {
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"m-nn": 1,
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"decision tree": 2,
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"ppr": 3,
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}
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def getCollection(db, collection):
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"""Load feature descriptor collection from MongoDB"""
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@ -124,30 +50,6 @@ def getCollection(db, collection):
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return client[db][collection]
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def datasetTransform(image):
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"""Transform while loading dataset as scaled tensors of shape (channels, (img_shape))"""
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return transforms.Compose(
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[
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transforms.ToTensor() # ToTensor by default scales to [0,1] range, the input range for ResNet
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]
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)(image)
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def loadDataset(dataset):
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"""Load TorchVision dataset with the defined transform"""
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return dataset(
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root=getenv("DATASET_PATH"),
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download=False, # True if you wish to download for first time
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transform=datasetTransform,
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)
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dataset = loadDataset(Caltech101)
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NUM_LABELS = 101
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NUM_IMAGES = 4339
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def euclidean_distance_measure(img_1_fd, img_2_fd):
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img_1_fd_reshaped = img_1_fd.flatten()
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img_2_fd_reshaped = img_2_fd.flatten()
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@ -156,100 +58,6 @@ def euclidean_distance_measure(img_1_fd, img_2_fd):
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return math.dist(img_1_fd_reshaped, img_2_fd_reshaped)
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def loadResnet():
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"""Load ResNet50 pre-trained model with default weights"""
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# Load model
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model = resnet50(weights=ResNet50_Weights.DEFAULT)
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# try to use Nvidia GPU
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if torch.cuda.is_available():
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dev = torch.device("cuda")
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torch.cuda.empty_cache()
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else:
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dev = torch.device("cpu")
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model = model.to(dev)
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model.eval() # switch to inference mode - important! since we're using pre-trained model
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return model, dev
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model, dev = loadResnet()
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class FeatureExtractor(torch.nn.Module):
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"""Feature extractor module for all layers at once"""
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def __init__(self, model, layers):
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super().__init__()
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self.model = model
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self.layers = layers
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self._features = {layer: None for layer in layers} # store layer outputs here
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# Create hooks for all specified layers at once
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for layer_id in layers:
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layer = dict(self.model.named_modules())[
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layer_id
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] # get actual layer in the model
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layer.register_forward_hook(
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self.save_outputs_hook(layer_id)
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) # register feature extractor hook on layer
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# Hook to save output of layer
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def save_outputs_hook(self, layer_id):
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def fn(_module, _input, output):
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self._features[layer_id] = output
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return fn
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# Forward pass returns extracted features
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def forward(self, input):
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_ = self.model(input)
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return self._features
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def resnet_extractor(image):
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"""Extract image features from avgpool, layer3 and fc layers of ResNet50"""
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resized_image = (
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torch.Tensor(np.array(transforms.Resize((224, 224))(image)).flatten())
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.view(1, 3, 224, 224)
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.to(dev)
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)
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# Attach all hooks on model and extract features
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resnet_features = FeatureExtractor(model=model, layers=["avgpool", "layer3", "fc"])
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features = resnet_features(resized_image)
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avgpool_2048 = features["avgpool"]
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# Reshape the vector into row pairs of elements and average across rows
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avgpool_1024_fd = torch.mean(avgpool_2048.view(-1, 2), axis=1)
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layer3_1024_14_14 = features["layer3"]
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# Reshape the vector into 1024 rows of 196 elements and average across rows
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layer3_1024_fd = torch.mean(layer3_1024_14_14.view(1024, -1), axis=1)
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fc_1000_fd = features["fc"].view(1000)
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return (
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avgpool_1024_fd.detach().cpu().tolist(),
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layer3_1024_fd.detach().cpu().tolist(),
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fc_1000_fd.detach().cpu().tolist(),
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)
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def resnet_output(image):
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"""Get image features from ResNet50 (full execution) and apply a softmax layer"""
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resized_image = (
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torch.Tensor(np.array(transforms.Resize((224, 224))(image)).flatten())
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.view(1, 3, 224, 224)
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.to(dev)
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)
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with torch.no_grad():
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features = model(resized_image)
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features = torch.nn.Softmax()(features)
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return features.detach().cpu().tolist()
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valid_feature_models = {
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"cm": "cm_fd",
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"hog": "hog_fd",
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@ -258,28 +66,3 @@ valid_feature_models = {
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"fc": "fc_fd",
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"resnet": "resnet_fd",
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}
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def predict_m_nn_classifier(fd_collection, m, feature_model, selected_image_fd):
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"""
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Create the m-NN classifier from the selected feature space
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"""
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assert (
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feature_model in valid_feature_models.values()
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), "feature_moel should be one of " + str(list(valid_feature_models.keys()))
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all_images = list(fd_collection.find())
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feature_ids = [img["image_id"] for img in all_images]
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feature_vectors = np.array(
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[np.array(img[feature_model]).flatten() for img in all_images]
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)
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distances = []
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for fd, id in zip(feature_vectors, feature_ids):
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distances.append({"image_id": id, "distance": euclidean_distance_measure(selected_image_fd, fd)})
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distances = sorted(distances, key=lambda x: x["distance"])
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return distances[:10]
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