""".. _lightly-simsiam-tutorial-4: Tutorial 4: Train SimSiam on Satellite Images ============================================== In this tutorial we will train a SimSiam model in old-school PyTorch style on a set of satellite images of Italy. We will showcase how the generated embeddings can be used for exploration and better understanding of the raw data. You can read up on the model in the paper `Exploring Simple Siamese Representation Learning `_. We will be using a dataset of satellite images from ESAs Sentinel-2 satellite over Italy. If you're interested, you can get your own data from the `Copernicus Open Access Hub `_. The original images have been cropped into smaller tiles due to their immense size and the dataset has been balanced based on a simple clustering of the mean RGB color values to prevent a surplus of images of the sea. In this tutorial you will learn: - How to work with the SimSiam model - How to do self-supervised learning using PyTorch - How to check whether your embeddings have collapsed """ # %% # Imports # ------- # # Import the Python frameworks we need for this tutorial. import math import numpy as np import torch import torch.nn as nn import torchvision from lightly.data import LightlyDataset from lightly.loss import NegativeCosineSimilarity from lightly.models.modules.heads import SimSiamPredictionHead, SimSiamProjectionHead from lightly.transforms import SimCLRTransform, utils # %% # Configuration # ------------- # # We set some configuration parameters for our experiment. # # The default configuration with a batch size and input resolution of 256 # requires 16GB of GPU memory. num_workers = 8 batch_size = 128 seed = 1 epochs = 50 input_size = 256 # dimension of the embeddings num_ftrs = 512 # dimension of the output of the prediction and projection heads out_dim = proj_hidden_dim = 512 # the prediction head uses a bottleneck architecture pred_hidden_dim = 128 # %% # Let's set the seed for our experiments and the path to our data # seed torch and numpy torch.manual_seed(0) np.random.seed(0) # set the path to the dataset path_to_data = "/datasets/sentinel-2-italy-v1/" # %% # Setup data augmentations and loaders # ------------------------------------ # Since we're working on satellite images, it makes sense to use horizontal and # vertical flips as well as random rotation transformations. We apply weak color # jitter to learn an invariance of the model with respect to slight changes in # the color of the water. # # define the augmentations for self-supervised learning transform = SimCLRTransform( input_size=input_size, # require invariance to flips and rotations hf_prob=0.5, vf_prob=0.5, rr_prob=0.5, # satellite images are all taken from the same height # so we use only slight random cropping min_scale=0.5, # use a weak color jitter for invariance w.r.t small color changes cj_prob=0.2, cj_bright=0.1, cj_contrast=0.1, cj_hue=0.1, cj_sat=0.1, ) # create a lightly dataset for training with augmentations dataset_train_simsiam = LightlyDataset(input_dir=path_to_data, transform=transform) # create a dataloader for training dataloader_train_simsiam = torch.utils.data.DataLoader( dataset_train_simsiam, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=num_workers, ) # create a torchvision transformation for embedding the dataset after training # here, we resize the images to match the input size during training and apply # a normalization of the color channel based on statistics from imagenet test_transforms = torchvision.transforms.Compose( [ torchvision.transforms.Resize((input_size, input_size)), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( mean=utils.IMAGENET_NORMALIZE["mean"], std=utils.IMAGENET_NORMALIZE["std"], ), ] ) # create a lightly dataset for embedding dataset_test = LightlyDataset(input_dir=path_to_data, transform=test_transforms) # create a dataloader for embedding dataloader_test = torch.utils.data.DataLoader( dataset_test, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers, ) # %% # Create the SimSiam model # ------------------------ # # Create a ResNet backbone and remove the classification head class SimSiam(nn.Module): def __init__(self, backbone, num_ftrs, proj_hidden_dim, pred_hidden_dim, out_dim): super().__init__() self.backbone = backbone self.projection_head = SimSiamProjectionHead(num_ftrs, proj_hidden_dim, out_dim) self.prediction_head = SimSiamPredictionHead(out_dim, pred_hidden_dim, out_dim) def forward(self, x): # get representations f = self.backbone(x).flatten(start_dim=1) # get projections z = self.projection_head(f) # get predictions p = self.prediction_head(z) # stop gradient z = z.detach() return z, p # we use a pretrained resnet for this tutorial to speed # up training time but you can also train one from scratch resnet = torchvision.models.resnet18() backbone = nn.Sequential(*list(resnet.children())[:-1]) model = SimSiam(backbone, num_ftrs, proj_hidden_dim, pred_hidden_dim, out_dim) # %% # SimSiam uses a symmetric negative cosine similarity loss and does therefore # not require any negative samples. We build a criterion and an optimizer. # SimSiam uses a symmetric negative cosine similarity loss criterion = NegativeCosineSimilarity() # scale the learning rate lr = 0.05 * batch_size / 256 # use SGD with momentum and weight decay optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4) # %% # Train SimSiam # -------------------- # # To train the SimSiam model, you can use a classic PyTorch training loop: # For every epoch, iterate over all batches in the training data, extract # the two transforms of every image, pass them through the model, and calculate # the loss. Then, simply update the weights with the optimizer. Don't forget to # reset the gradients! # # Since SimSiam doesn't require negative samples, it is a good idea to check # whether the outputs of the model have collapsed into a single direction. For # this we can simply check the standard deviation of the L2 normalized output # vectors. If it is close to one divided by the square root of the output # dimension, everything is fine (you can read # up on this idea `here `_). device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) avg_loss = 0.0 avg_output_std = 0.0 for e in range(epochs): for (x0, x1), _, _ in dataloader_train_simsiam: # move images to the gpu x0 = x0.to(device) x1 = x1.to(device) # run the model on both transforms of the images # we get projections (z0 and z1) and # predictions (p0 and p1) as output z0, p0 = model(x0) z1, p1 = model(x1) # apply the symmetric negative cosine similarity # and run backpropagation loss = 0.5 * (criterion(z0, p1) + criterion(z1, p0)) loss.backward() optimizer.step() optimizer.zero_grad() # calculate the per-dimension standard deviation of the outputs # we can use this later to check whether the embeddings are collapsing output = p0.detach() output = torch.nn.functional.normalize(output, dim=1) output_std = torch.std(output, 0) output_std = output_std.mean() # use moving averages to track the loss and standard deviation w = 0.9 avg_loss = w * avg_loss + (1 - w) * loss.item() avg_output_std = w * avg_output_std + (1 - w) * output_std.item() # the level of collapse is large if the standard deviation of the l2 # normalized output is much smaller than 1 / sqrt(dim) collapse_level = max(0.0, 1 - math.sqrt(out_dim) * avg_output_std) # print intermediate results print( f"[Epoch {e:3d}] " f"Loss = {avg_loss:.2f} | " f"Collapse Level: {collapse_level:.2f} / 1.00" ) # %% # To embed the images in the dataset we simply iterate over the test dataloader # and feed the images to the model backbone. Make sure to disable gradients for # this part. embeddings = [] filenames = [] # disable gradients for faster calculations model.eval() with torch.no_grad(): for i, (x, _, fnames) in enumerate(dataloader_test): # move the images to the gpu x = x.to(device) # embed the images with the pre-trained backbone y = model.backbone(x).flatten(start_dim=1) # store the embeddings and filenames in lists embeddings.append(y) filenames = filenames + list(fnames) # concatenate the embeddings and convert to numpy embeddings = torch.cat(embeddings, dim=0) embeddings = embeddings.cpu().numpy() # %% # Scatter Plot and Nearest Neighbors # ---------------------------------- # Now that we have the embeddings, we can visualize the data with a scatter plot. # Further down, we also check out the nearest neighbors of a few example images. # # As a first step, we make a few additional imports. # for plotting import os import matplotlib.offsetbox as osb import matplotlib.pyplot as plt # for resizing images to thumbnails import torchvision.transforms.functional as functional from matplotlib import rcParams as rcp from PIL import Image # for clustering and 2d representations from sklearn import random_projection # %% # Then, we transform the embeddings using UMAP and rescale them to fit in the # [0, 1] square. # # for the scatter plot we want to transform the images to a two-dimensional # vector space using a random Gaussian projection projection = random_projection.GaussianRandomProjection(n_components=2) embeddings_2d = projection.fit_transform(embeddings) # normalize the embeddings to fit in the [0, 1] square M = np.max(embeddings_2d, axis=0) m = np.min(embeddings_2d, axis=0) embeddings_2d = (embeddings_2d - m) / (M - m) # %% # Let's start with a nice scatter plot of our dataset! The helper function # below will create one. def get_scatter_plot_with_thumbnails(): """Creates a scatter plot with image overlays.""" # initialize empty figure and add subplot fig = plt.figure() fig.suptitle("Scatter Plot of the Sentinel-2 Dataset") ax = fig.add_subplot(1, 1, 1) # shuffle images and find out which images to show shown_images_idx = [] shown_images = np.array([[1.0, 1.0]]) iterator = [i for i in range(embeddings_2d.shape[0])] np.random.shuffle(iterator) for i in iterator: # only show image if it is sufficiently far away from the others dist = np.sum((embeddings_2d[i] - shown_images) ** 2, 1) if np.min(dist) < 2e-3: continue shown_images = np.r_[shown_images, [embeddings_2d[i]]] shown_images_idx.append(i) # plot image overlays for idx in shown_images_idx: thumbnail_size = int(rcp["figure.figsize"][0] * 2.0) path = os.path.join(path_to_data, filenames[idx]) img = Image.open(path) img = functional.resize(img, thumbnail_size) img = np.array(img) img_box = osb.AnnotationBbox( osb.OffsetImage(img, cmap=plt.cm.gray_r), embeddings_2d[idx], pad=0.2, ) ax.add_artist(img_box) # set aspect ratio ratio = 1.0 / ax.get_data_ratio() ax.set_aspect(ratio, adjustable="box") # get a scatter plot with thumbnail overlays get_scatter_plot_with_thumbnails() # %% # Next, we plot example images and their nearest neighbors (calculated from the # embeddings generated above). This is a very simple approach to find more images # of a certain type where a few examples are already available. For example, # when a subset of the data is already labelled and one class of images is clearly # underrepresented, one can easily query more images of this class from the # unlabelled dataset. # # Let's get to work! The plots are shown below. example_images = [ "S2B_MSIL1C_20200526T101559_N0209_R065_T31TGE/tile_00154.png", # water 1 "S2B_MSIL1C_20200526T101559_N0209_R065_T32SLJ/tile_00527.png", # water 2 "S2B_MSIL1C_20200526T101559_N0209_R065_T32TNL/tile_00556.png", # land "S2B_MSIL1C_20200526T101559_N0209_R065_T31SGD/tile_01731.png", # clouds 1 "S2B_MSIL1C_20200526T101559_N0209_R065_T32SMG/tile_00238.png", # clouds 2 ] def get_image_as_np_array(filename: str): """Loads the image with filename and returns it as a numpy array.""" img = Image.open(filename) return np.asarray(img) def get_image_as_np_array_with_frame(filename: str, w: int = 5): """Returns an image as a numpy array with a black frame of width w.""" img = get_image_as_np_array(filename) ny, nx, _ = img.shape # create an empty image with padding for the frame framed_img = np.zeros((w + ny + w, w + nx + w, 3)) framed_img = framed_img.astype(np.uint8) # put the original image in the middle of the new one framed_img[w:-w, w:-w] = img return framed_img def plot_nearest_neighbors_3x3(example_image: str, i: int): """Plots the example image and its eight nearest neighbors.""" n_subplots = 9 # initialize empty figure fig = plt.figure() fig.suptitle(f"Nearest Neighbor Plot {i + 1}") # example_idx = filenames.index(example_image) # get distances to the cluster center distances = embeddings - embeddings[example_idx] distances = np.power(distances, 2).sum(-1).squeeze() # sort indices by distance to the center nearest_neighbors = np.argsort(distances)[:n_subplots] # show images for plot_offset, plot_idx in enumerate(nearest_neighbors): ax = fig.add_subplot(3, 3, plot_offset + 1) # get the corresponding filename fname = os.path.join(path_to_data, filenames[plot_idx]) if plot_offset == 0: ax.set_title(f"Example Image") plt.imshow(get_image_as_np_array_with_frame(fname)) else: plt.imshow(get_image_as_np_array(fname)) # let's disable the axis plt.axis("off") # show example images for each cluster for i, example_image in enumerate(example_images): plot_nearest_neighbors_3x3(example_image, i) # %% # Next Steps # ------------ # # Interested in exploring other self-supervised models? Check out our other # tutorials: # # - :ref:`lightly-moco-tutorial-2` # - :ref:`lightly-simclr-tutorial-3` # - :ref:`lightly-custom-augmentation-5` # - :ref:`lightly-detectron-tutorial-6` #