HW9. Representation Learning
Topics: Self-supervised learning, Autoencoders, Representation learning, CLIP(Contrastive Language-Image Pre-training)
In this problem, we are going to implement two representation learning methods: an autoencoder and a recent constrastive learning method. We’ll then test the features that were learned by these models on a “downstream” recognition task, using the STL-10 dataset.
Google Colab Setup
We need to run a few commands to set up our environment on Google Colab. If you are running this notebook on a local machine you can skip this section.
Run the following cell to mount your Google Drive. Follow the link, sign in to your Google account (the same account you used to store this notebook!) and copy the authorization code into the text box that appears below.
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
!pip install torchsummary
!pip install transformers
import pickle
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import cv2
import time
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import torchvision
from torchvision import datasets, models, transforms
from torchsummary import summary
import albumentations as A
import matplotlib.pyplot as plt
from transformers import DistilBertModel, DistilBertConfig, DistilBertTokenizer
from tqdm import tqdm_notebook
from tqdm.autonotebook import tqdm
print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)
# Detect if we have a GPU available
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
print("Using the GPU!")
else:
print("WARNING: Could not find GPU! Using CPU only. If you want to enable GPU, please to go Edit > Notebook Settings > Hardware Accelerator and select GPU.")
np.random.seed(42)
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: torchsummary in /usr/local/lib/python3.7/dist-packages (1.5.1)
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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PyTorch Version: 1.12.1+cu113
Torchvision Version: 0.13.1+cu113
Using the GPU!
Downloading the dataset.
We use PyTorch built-in class to download the STL-10 (http://ai.stanford.edu/~acoates/stl10/) dataset (a subset of ImageNet). The STL-10 dataset contains three partitions: train, test, and unlabeled. The train partition contains 10 image classes, each class with 500 images. The test partition contains 800 images for each class. The unlabeled contains a total of 100,000 images with many classes not in the train/test partitions.
unlabeled_transform = transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.RandomCrop(64),
transforms.ToTensor()])
labeled_transform = transforms.Compose([transforms.CenterCrop(64),
transforms.ToTensor(),])
# We use the PyTorch built-in class to download the STL-10 dataset.
# The 'unlabeled' partition contains 100,000 images without labels.
# It's used for leanring representations with unsupervised learning.
dataset_un = torchvision.datasets.STL10('./data', 'unlabeled', download=True, transform=unlabeled_transform)
dataset_tr = torchvision.datasets.STL10('./data', 'train', download=False, transform=labeled_transform)
dataset_te = torchvision.datasets.STL10('./data', 'test', download=False, transform=labeled_transform)
Files already downloaded and verified
print('# of samples for ulabeled, train, and test, {}, {}, {}'.format(len(dataset_un), len(dataset_tr), len(dataset_te)))
print('Classes in train: {}'.format(dataset_tr.classes))
# of samples for ulabeled, train, and test, 100000, 5000, 8000
Classes in train: ['airplane', 'bird', 'car', 'cat', 'deer', 'dog', 'horse', 'monkey', 'ship', 'truck']
# Visualize the data within the dataset
class_names = dict(zip(range(10), dataset_tr.classes))
dataloader_un = DataLoader(dataset_un, batch_size=64)
dataloader_tr = DataLoader(dataset_tr, batch_size=64)
def imshow(inp, title=None, ax=None, figsize=(5, 5)):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
if ax is None:
fig, ax = plt.subplots(1, figsize=figsize)
ax.imshow(inp)
ax.set_xticks([])
ax.set_yticks([])
if title is not None:
ax.set_title(title)
# Visualize training partition
# Get a batch of training data
inputs, classes = next(iter(dataloader_tr))
# Make a grid from batch
out = torchvision.utils.make_grid(inputs, nrow=8)
fig, ax = plt.subplots(1, figsize=(10, 10))
title = [class_names[x.item()] if (i+1) % 8 != 0 else class_names[x.item()]+'\n' for i, x in enumerate(classes)]
imshow(out, title=' | '.join(title), ax=ax)
# Visualize unlabeled partition
inputs, classes = next(iter(dataloader_un))
out = torchvision.utils.make_grid(inputs, nrow=8)
fig, ax = plt.subplots(1, figsize=(10, 10))
imshow(out, title='unlabeled', ax=ax)
As can be seen from above visualizations, the unlabeled partition contains classes that are not in the training partition. Though not labeled, the unlabeled partition has much more data than the labeled training partition. The large amount of unlabeled label ought to help us learn useful representations. In the next sections, we will use the unlabeled partition to help learn representations that is helpful for downstream tasks.
Problem 1. Autoencoders
We’ll start by implementing a simple self-supervised learning method: an autoencoder. The autoencoder is composed of an encoder and a decoder. The encoder often compresses the original data with a funnel-like architecture, i.e., it throws away redundant information by reducing the layer sizes gradually. The final output size of the encoder is a bottleneck that is much smaller than the size of the original data. The decoder will use this limited amount of information to reconstruct the original data. If the reconstruction is successful, the encoder has arguably captured a useful, concise representation of the original data.
Such representations could help with downstream tasks such as object recognition, semantic segmentation, etc. Here, to test the usefulness of the representation, we’ll train the encoders on the STL-10 dataset, which is designed to evaluate unsupervised learning algorithms. This dataset contains 100,000 unlabeled images, 5,000 labeled training images, and 8,000 labeled test images. To keep training time short, we’ll use 10,000 unlabeled images to learn representations.
We will then use the feature representation that we learned to train an object recognition model (a simple linear classifier) on the 5,000 labeled training images. If the learned representations are useful, we should obtain a performance improvement over only using the small, labeled training set.
1.(a) Build the encoder
We will build a small convolutional autoencoder and train it on the STL-10 dataset. The conv layers in the autoencoder all have kernel size = 4x4, stride = 2, padding = 1.
Please make sure that your encoder has the same architeture as we print below before your proceed to the decoder part.
Encoder archiecture
Layer (type) Output Shape Param #
Conv2d-1 [-1, 12, 32, 32] 588
ReLU-2 [-1, 12, 32, 32] 0
Conv2d-3 [-1, 24, 16, 16] 4,632
ReLU-4 [-1, 24, 16, 16] 0
Conv2d-5 [-1, 48, 8, 8] 18,480
ReLU-6 [-1, 48, 8, 8] 0
Conv2d-7 [-1, 24, 4, 4] 18,456
ReLU-8 [-1, 24, 4, 4] 0 Total params: 42,156 Trainable params: 42,156 Non-trainable params: 0 Input size (MB): 0.05 Forward/backward pass size (MB): 0.33 Params size (MB): 0.16 Estimated Total Size (MB): 0.54
class Encoder(nn.Module):
def __init__(self, in_channels=3):
super(Encoder, self).__init__()
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Build an encoder with the architecture as specified above. #
##############################################################################
in_out_channels = [3, 12, 24, 48, 24]
layers = []
for i in range(len(in_out_channels) - 1):
conv = torch.nn.Conv2d(in_channels = in_out_channels[i], out_channels = in_out_channels[i + 1], kernel_size = 4, stride = 2, padding = 1)
layers.append(conv)
layers.append(torch.nn.ReLU())
self.encoder = torch.nn.Sequential(*layers)
##############################################################################
# END OF YOUR CODE #
##############################################################################
def forward(self, x):
'''
Given an image x, return the encoded latent representation h.
Args:
x: torch.tensor
Return:
h: torch.tensor
'''
h = self.encoder(x)
return h
# Print out the neural network architectures and activation dimensions.
# Verify that your network has the same architecture as the one we printed above.
encoder = Encoder().to(device)
summary(encoder, [(3, 64, 64)])
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 12, 32, 32] 588
ReLU-2 [-1, 12, 32, 32] 0
Conv2d-3 [-1, 24, 16, 16] 4,632
ReLU-4 [-1, 24, 16, 16] 0
Conv2d-5 [-1, 48, 8, 8] 18,480
ReLU-6 [-1, 48, 8, 8] 0
Conv2d-7 [-1, 24, 4, 4] 18,456
ReLU-8 [-1, 24, 4, 4] 0
================================================================
Total params: 42,156
Trainable params: 42,156
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.05
Forward/backward pass size (MB): 0.33
Params size (MB): 0.16
Estimated Total Size (MB): 0.54
----------------------------------------------------------------
1.(b) Build the decoder
Next, we build the decoder to reconstruct the image from the latent representation extracted by the encoder. Please implement the decoder following the architectrue printed here.
Layer (type) Output Shape Param #
ConvTranspose2d-1 [-1, 48, 8, 8] 18,480
ReLU-2 [-1, 48, 8, 8] 0
ConvTranspose2d-3 [-1, 24, 16, 16] 18,456
ReLU-4 [-1, 24, 16, 16] 0
ConvTranspose2d-5 [-1, 12, 32, 32] 4,620
ReLU-6 [-1, 12, 32, 32] 0
ConvTranspose2d-7 [-1, 3, 64, 64] 579
Sigmoid-8 [-1, 3, 64, 64] 0
Total params: 42,135
Trainable params: 42,135
Non-trainable params: 0
Input size (MB): 0.00
Forward/backward pass size (MB): 0.52
Params size (MB): 0.16
Estimated Total Size (MB): 0.68
class Decoder(nn.Module):
def __init__(self, out_channels=3, feat_dim=64):
super(Decoder, self).__init__()
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Build the decoder as specified above. #
##############################################################################
in_out_channels = [24, 48, 24, 12, out_channels]
layers = []
for i in range(len(in_out_channels) - 1):
conv = torch.nn.ConvTranspose2d(in_channels = in_out_channels[i], out_channels = in_out_channels[i + 1], kernel_size = 4, stride = 2, padding = 1)
layers.append(conv)
if i == (len(in_out_channels) - 2):
layers.append(torch.nn.Sigmoid())
else:
layers.append(torch.nn.ReLU())
self.decoder = torch.nn.Sequential(*layers)
##############################################################################
# END OF YOUR CODE #
##############################################################################
def forward(self, h):
'''
Given latent representation h, reconstruct an image patch of size 64 x 64.
Args:
h: torch.tensor
Return:
x: torch.tensor
'''
x = self.decoder(h)
return x
# Print out the neural network architectures and activation dimensions.
# Verify that your network has the same architecture as the one we printed above.
decoder = Decoder().to(device)
summary(decoder, [(24, 4, 4)])
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
ConvTranspose2d-1 [-1, 48, 8, 8] 18,480
ReLU-2 [-1, 48, 8, 8] 0
ConvTranspose2d-3 [-1, 24, 16, 16] 18,456
ReLU-4 [-1, 24, 16, 16] 0
ConvTranspose2d-5 [-1, 12, 32, 32] 4,620
ReLU-6 [-1, 12, 32, 32] 0
ConvTranspose2d-7 [-1, 3, 64, 64] 579
Sigmoid-8 [-1, 3, 64, 64] 0
================================================================
Total params: 42,135
Trainable params: 42,135
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.52
Params size (MB): 0.16
Estimated Total Size (MB): 0.68
----------------------------------------------------------------
1.(c) Put together the autoencoder
Now we have the encoder and the decoder classes. We only need to implement another Autoencoder class to wrap the encoder and the decoder together.
class Autoencoder(nn.Module):
def __init__(self, in_channels=3, feat_dim=64):
super(Autoencoder, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder()
def forward(self, x):
'''
Compress and reconstruct the input image with encoder and decoder.
Args:
x: torch.tensor
Return:
x_: torch.tensor
'''
h = self.encoder(x)
x_ = self.decoder(h)
return x_
# verify that your aueconder's output size is 3 x 64 x 64
ae = Autoencoder().to(device)
summary(ae, (3, 64, 64))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 12, 32, 32] 588
ReLU-2 [-1, 12, 32, 32] 0
Conv2d-3 [-1, 24, 16, 16] 4,632
ReLU-4 [-1, 24, 16, 16] 0
Conv2d-5 [-1, 48, 8, 8] 18,480
ReLU-6 [-1, 48, 8, 8] 0
Conv2d-7 [-1, 24, 4, 4] 18,456
ReLU-8 [-1, 24, 4, 4] 0
Encoder-9 [-1, 24, 4, 4] 0
ConvTranspose2d-10 [-1, 48, 8, 8] 18,480
ReLU-11 [-1, 48, 8, 8] 0
ConvTranspose2d-12 [-1, 24, 16, 16] 18,456
ReLU-13 [-1, 24, 16, 16] 0
ConvTranspose2d-14 [-1, 12, 32, 32] 4,620
ReLU-15 [-1, 12, 32, 32] 0
ConvTranspose2d-16 [-1, 3, 64, 64] 579
Sigmoid-17 [-1, 3, 64, 64] 0
Decoder-18 [-1, 3, 64, 64] 0
================================================================
Total params: 84,291
Trainable params: 84,291
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.05
Forward/backward pass size (MB): 0.95
Params size (MB): 0.32
Estimated Total Size (MB): 1.31
----------------------------------------------------------------
1.(d) Training the autoencoder
Now, we’ll train the autoencoder to reconstruct images from the unlabeled set of STL-10. Note that the reconstructed images will contain significant artifacts, due to the limited size of the bottleneck between the encoder and decoder, and the small network size.
# We train on 10,000 unsupervised samples instead of 100,000 samples to speed up training
n = 10000
dataset_un_subset, _ = torch.utils.data.random_split(dataset_un, [n,100000-n])
dataloader_un = DataLoader(dataset_un_subset, batch_size=128, shuffle=True)
dataloader_tr = DataLoader(dataset_tr, batch_size=128, shuffle=True)
dataloader_te = DataLoader(dataset_te, batch_size=128, shuffle=False)
def visualize_recon(model, dataloader):
'''
Helper function for visualizing reconstruction performance.
Randomly sample 8 images and plot the original/reconstructed images.
'''
model.eval()
img = next(iter(dataloader))[0][:8].to(device)
out = model(img)
fig, ax = plt.subplots(1, 1, figsize=(15,10))
inp = torchvision.utils.make_grid(torch.cat((img, out), dim=2), nrow=8)
imshow(inp.detach().cpu(), ax=ax)
model.train()
plt.show()
def train_ae(model, dataloader, epochs=200):
'''
Train autoencoder model.
Args:
model: torch.nn.module.
dataloader: DataLoader. The unlabeled partition of the STL dataset.
'''
optimizer = optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.MSELoss()
loss_traj = []
for epoch in tqdm_notebook(range(epochs)):
loss_epoch = 0
for x, _ in dataloader:
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Train the autoencoder on one minibatch. #
##############################################################################
x = x.to(device)
outputs = model(x)
loss = criterion(outputs, x)
optimizer.zero_grad()
loss.backward()
optimizer.step()
##############################################################################
# END OF YOUR CODE #
##############################################################################
loss_epoch += loss.detach()
loss_traj.append(loss_epoch)
if epoch % 10 == 0:
print('Epoch {}, loss {:.3f}'.format(epoch, loss_epoch))
visualize_recon(model, dataloader)
return model, loss_traj
# Train the autoencoder for 100 epochs
ae = Autoencoder().to(device)
ae, ae_loss_traj = train_ae(ae, dataloader_un, epochs=100)
torch.save(ae.state_dict(), 'ae.pth')
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
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Epoch 0, loss 3.850
Epoch 10, loss 1.084
Epoch 20, loss 0.866
Epoch 30, loss 0.787
Epoch 40, loss 0.744
Epoch 50, loss 0.722
Epoch 60, loss 0.685
Epoch 70, loss 0.668
Epoch 80, loss 0.648
Epoch 90, loss 0.622
After training the autoencoder on the 100,000 images for 100 epochs, we see that autoencoder has leanred to approximately recontruct the image.
1.(e) Train a linear classifier
Now, we ask how useful the features are for object recongition. We’ll train a linear clasifier that takes the output of the encoder as its features. During training, we freeze the parameters of the encoder. To verify the effectiveness of unsupervised pretraining, we compare the linear classifier accuracy against two baselines:
- Supervised: train the encoder together with the linear classifier on the training set for 100 epochs.
- Random weights: freeze the parameters of a randomly initialized encoder during training.
# latent representation dimension (the output dimension of the encoder)
feat_dim = 24 * 4 * 4
def train_classifier(encoder, cls, dataloader, epochs=100, supervised=False):
'''
Args:
encoder: trained/untrained encoder for unsupervised/supervised training.
cls: linear classifier.
dataloader: train partition.
supervised:
Return:
cls: linear clssifier.
'''
optimizer = optim.Adam(cls.parameters(), lr=0.001, weight_decay=1e-4)
if supervised:
optimizer = optim.Adam(list(cls.parameters())+list(encoder.parameters()), lr=0.001, weight_decay=1e-4)
criterion = nn.CrossEntropyLoss()
loss_traj = []
accuracy_traj = []
for epoch in tqdm_notebook(range(epochs)):
loss_epoch = 0
corrects_epoch = 0
for x, y in dataloader:
batch_size = x.size(0)
x = x.float()
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: update the parameters of the classifer. If in supervised mode, the #
# parameter of the encoder is also updated. #
##############################################################################
x = x.to(device)
y = y.to(device)
x = encoder(x)
x = x.reshape(x.shape[0], -1)
outs = cls(x)
loss = criterion(outs, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
##############################################################################
# END OF YOUR CODE #
##############################################################################
_, preds = torch.max(outs, 1)
corrects_epoch += torch.sum(preds == y.data)
loss_epoch += loss.detach()
loss_traj.append(loss_epoch)
epoch_acc = corrects_epoch.double() / len(dataloader.dataset)
accuracy_traj.append(epoch_acc)
if epoch % 10 == 0:
print('Epoch {}, loss {:.3f}, train accuracy {}'.format(epoch, loss_epoch, epoch_acc))
return cls, loss_traj
def test(encoder, cls, dataloader):
'''
Calculate the accuracy of the trained linear classifier on the test set.
'''
cls.eval()
loss_epoch = 0
corrects_epoch = 0
for x, y in dataloader:
x = x.float()
batch_size = x.size(0)
x, y = x.to(device), y.to(device)
h = encoder(x).view(batch_size, -1)
outs = cls(h)
_, preds = torch.max(outs, 1)
corrects_epoch += torch.sum(preds == y.data)
epoch_acc = corrects_epoch.double() / len(dataloader.dataset)
print('Test accuracy {}'.format(epoch_acc))
# Method I: unsupervised pretraining + training linear classifier
# Freeze the parameters of the trained autoencoder
# Train a linear classifier using the features
linear_cls = nn.Sequential(nn.Linear(feat_dim, 10)).to(device)
cls_unsupervised, loss_traj_unsupervised = train_classifier(ae.encoder, linear_cls, dataloader_tr, epochs=100, supervised=False)
test(ae.encoder, cls_unsupervised, dataloader_te)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
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Epoch 0, loss 96.943, train accuracy 0.1834
Epoch 10, loss 76.281, train accuracy 0.3326
Epoch 20, loss 73.675, train accuracy 0.3612
Epoch 30, loss 70.213, train accuracy 0.383
Epoch 40, loss 69.307, train accuracy 0.3916
Epoch 50, loss 68.762, train accuracy 0.40280000000000005
Epoch 60, loss 68.250, train accuracy 0.4
Epoch 70, loss 68.650, train accuracy 0.4066
Epoch 80, loss 68.351, train accuracy 0.4036
Epoch 90, loss 66.161, train accuracy 0.4242
Test accuracy 0.310125
# Method II: supervised training
# Train the encoder together with the linear classifier
linear_cls = nn.Sequential(nn.Linear(feat_dim, 10)).to(device)
encoder = Autoencoder().to(device).encoder
cls_supervised, loss_traj_supervised = train_classifier(encoder, linear_cls, dataloader_tr, epochs=100, supervised=True)
test(encoder, cls_supervised, dataloader_te)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
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Epoch 0, loss 89.633, train accuracy 0.15660000000000002
Epoch 10, loss 57.520, train accuracy 0.47700000000000004
Epoch 20, loss 46.515, train accuracy 0.5770000000000001
Epoch 30, loss 35.532, train accuracy 0.6842
Epoch 40, loss 26.493, train accuracy 0.7654000000000001
Epoch 50, loss 16.725, train accuracy 0.8574
Epoch 60, loss 7.928, train accuracy 0.9430000000000001
Epoch 70, loss 4.986, train accuracy 0.9606
Epoch 80, loss 1.430, train accuracy 0.9942000000000001
Epoch 90, loss 0.207, train accuracy 1.0
Test accuracy 0.418125
# Method III: random encoder + training linear classifier
# We freeze the parameters of the randomly initialized encoder during training
linear_cls = nn.Sequential(nn.Linear(feat_dim, 10)).to(device)
encoder = Autoencoder().to(device).encoder
cls_random, loss_traj_random = train_classifier(encoder, linear_cls, dataloader_tr, epochs=100, supervised=False)
test(encoder, cls_random, dataloader_te)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
0%| | 0/100 [00:00<?, ?it/s]
Epoch 0, loss 92.079, train accuracy 0.1034
Epoch 10, loss 91.191, train accuracy 0.2048
Epoch 20, loss 90.510, train accuracy 0.24500000000000002
Epoch 30, loss 89.974, train accuracy 0.2462
Epoch 40, loss 89.459, train accuracy 0.2576
Epoch 50, loss 88.874, train accuracy 0.2664
Epoch 60, loss 88.679, train accuracy 0.26180000000000003
Epoch 70, loss 88.344, train accuracy 0.26280000000000003
Epoch 80, loss 88.118, train accuracy 0.2748
Epoch 90, loss 87.935, train accuracy 0.25880000000000003
Test accuracy 0.253125
With pretrained encoder, the linear classifier should achieve about 30% accuracy on the test set. With the supervised approach, the linear classifier should achieve an accuracy above 40%. The random encoder approach performs the worse among these three. The observation that the pretrained encoder outperforms the random encoder confirms that unsupervised pretraining has learned a useful represenation. However, the quality of this learned representation is not good because it only performs slightly better than the random encoder. In the next part, we’ll explore contrastive multiview coding, which learns a more useful representation than the autoencoder.
del dataset_un
del dataset_te
del dataset_tr
Problem 2. Contrastive Language-Image Pre-training (CLIP)
In this part, we will implement a simplified version of the Contrastive Language-Image Pre-training (CLIP). This is a variation of the contrastive representation learning method we discussed in class. In a sentence, this model learns the relationship between a natural language sentence and the image this sentence describes. We’ll learn a vector representation for images and a vector representation for natural language texts. Learning directly from raw texts gives us a broader source of supervision compared to the standard crowd-sourced labeling for image classification. It also has an advantage over most unsupervised or self-supervised learning approaches, since it doesn’t just learn a representation, but also connects the representation to the natural language.
The core idea behind CLIP is that, when an image matches with a natural language sentence, their vector representations should have a large dot product. Whereas, the dot product between the representations of an image and a text that are not related to each other should be small.
We will learn a vector embedding for images using contrastive learning: in this representation, the input image and it corresponding caption should have a large dot product, while dot products between an image and other unrelated captions should be small. We first extract the feature embeddings for the image and its respective caption using a ImageEncoder (ResNet50) and a TextEncoder (DistilBert) respectively. Next, we project both these embeddings into a common vector space for matching as illustrated in part Figure 2. Finally, we will use InfoNCE loss to train the contrastive model:
\[L_{constrast}^{I,T} = -\frac{1}{N} \sum_{i = 1}^{N} log \frac{exp(I_i \cdot T_i/\tau)}{\sum_{j = 1}^{N}exp(I_{i} \cdot T_j/\tau)}\]where $I_i$ and $T_i$ are the image and text embedings for the $i_{th}$ sample in a minibatch, and $N$ denotes the batch size. The constant $\tau$ is used to control the range of logits. The denominator sums over all of the other text embeddings in the batch.
We will minimize a symmetric objective function:
\[L = \frac{L_{contrast}^{I, T} + L_{contrast}^{T, I}}{2}\]where the second term is defined:
\[L_{constrast}^{T,I} = -\frac{1}{N} \sum_{i = 1}^{N} log \frac{exp(T_i \cdot I_i/\tau)}{\sum_{j = 1}^{N}exp(T_{i} \cdot I_j/\tau)}\]Conceptually, $L_{contrast}^{T,I}$ matches the text to the image (rather than the image-to-text, as in $L_{contrast}^{I,T}$). Its denominator sums over the image embeddings. By minimizing the above loss function, we learn representations for the image and text such that the positive pairs (respective images and their captions) will produce high dot product while giving low dot product for negative samples (image and other unrelated captions).
We will complete the implementation as follows:
Contrastive learning. We will implement a simplified version of the network and loss function. In this simplified model, the language network backbone will be frozen2, and the visual backbone will be pretrained using ImageNet.
Linear probing evaluation We will again train a linear classifier for object recognition, using the learned network. There should be a significant performance improvement compared to the autoencoder representations, e.g., 75% (or higher) vs. 44%.
Text-to-image retrieval. Next, we will use our model for cross-modal retrieval. Given a text query, e.g., “dogs in the park” we will find the k best-matching images. To do this, we will find the images that have the highest dot products in our learned embedding space.
Setup
As the authors suggest, in CLIP the dataset plays a major role for the performance and we need both image and texts for training the CLIP model. We will use flickr8k to train our model. This is a small dataset with 8000 images, which would run well given the limited comuptation units on Colab. Each image in the dataset is paired with 5 captions, we will just use one of the 5 captions for simplicity. Below we displayed some sample captions corresponding to an image.
!wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1csewExbHtYIcOr8BWMrGd8DqX_awmP1t' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1csewExbHtYIcOr8BWMrGd8DqX_awmP1t" -O flickr8k.zip && rm -rf /tmp/cookies.txt
!unzip flickr8k.zip
--2022-11-29 05:33:47-- https://docs.google.com/uc?export=download&confirm=&id=1csewExbHtYIcOr8BWMrGd8DqX_awmP1t
Resolving docs.google.com (docs.google.com)... 108.177.120.139, 108.177.120.102, 108.177.120.138, ...
Connecting to docs.google.com (docs.google.com)|108.177.120.139|:443... connected.
HTTP request sent, awaiting response... 303 See Other
Location: https://doc-04-7g-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rq4n63cslae2dme9vv135r2bfdcrr1em/1669700025000/09755012129748709098/*/1csewExbHtYIcOr8BWMrGd8DqX_awmP1t?e=download&uuid=cc117454-5aa9-45df-baff-574c0d2ea9ad [following]
Warning: wildcards not supported in HTTP.
--2022-11-29 05:33:47-- https://doc-04-7g-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/rq4n63cslae2dme9vv135r2bfdcrr1em/1669700025000/09755012129748709098/*/1csewExbHtYIcOr8BWMrGd8DqX_awmP1t?e=download&uuid=cc117454-5aa9-45df-baff-574c0d2ea9ad
Resolving doc-04-7g-docs.googleusercontent.com (doc-04-7g-docs.googleusercontent.com)... 74.125.124.132, 2607:f8b0:4001:c14::84
Connecting to doc-04-7g-docs.googleusercontent.com (doc-04-7g-docs.googleusercontent.com)|74.125.124.132|:443... connected.
HTTP request sent, awaiting response... 429 Too Many Requests
2022-11-29 05:33:47 ERROR 429: Too Many Requests.
Archive: flickr8k.zip
End-of-central-directory signature not found. Either this file is not
a zipfile, or it constitutes one disk of a multi-part archive. In the
latter case the central directory and zipfile comment will be found on
the last disk(s) of this archive.
unzip: cannot find zipfile directory in one of flickr8k.zip or
flickr8k.zip.zip, and cannot find flickr8k.zip.ZIP, period.
!unzip "/content/drive/My Drive/eecs504/hw9/flickr8k.zip"
[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.[0m
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# dataset pre-processing
df = pd.read_csv("captions.txt")
img_color = cv2.imread('/content/Images/'+ str(df['image'][0]),1)
plt.imshow(cv2.cvtColor(img_color, cv2.COLOR_BGR2RGB))
df['id'] = [id_ for id_ in range(df.shape[0] // 5) for _ in range(5)]
df.to_csv("captions.csv", index=False)
df = pd.read_csv("captions.csv")
df.head()
| image | caption | id | |
|---|---|---|---|
| 0 | 1000268201_693b08cb0e.jpg | A child in a pink dress is climbing up a set o... | 0 |
| 1 | 1000268201_693b08cb0e.jpg | A girl going into a wooden building . | 0 |
| 2 | 1000268201_693b08cb0e.jpg | A little girl climbing into a wooden playhouse . | 0 |
| 3 | 1000268201_693b08cb0e.jpg | A little girl climbing the stairs to her playh... | 0 |
| 4 | 1000268201_693b08cb0e.jpg | A little girl in a pink dress going into a woo... | 0 |
Here are some configuration variables, hyperparameter and utility functions that we will be using through this section:
image_path = "/content/Images"
captions_path = "/content"
batch_size = 32
num_workers = 2
head_lr = 1e-3
image_encoder_lr = 1e-4
text_encoder_lr = 1e-5
weight_decay = 1e-3
patience = 1
factor = 0.8
epochs = 3
image_encoder_model = 'resnet50'
image_embedding = 2048
text_encoder_model = "distilbert-base-uncased"
text_embedding = 768
text_tokenizer = "distilbert-base-uncased"
max_length = 200
pretrained = True # for both image encoder and text encoder
trainable = True # for both image encoder and text encoder
temperature = 0.07
image_size = 64
# for projection head; used for both image and text encoders
projection_dim = 256
class AvgMeter:
def __init__(self, name="Metric"):
self.name = name
self.reset()
def reset(self):
self.avg, self.sum, self.count = [0] * 3
def update(self, val, count=1):
self.count += count
self.sum += val * count
self.avg = self.sum / self.count
def __repr__(self):
text = f"{self.name}: {self.avg:.4f}"
return text
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group["lr"]
Dataset
Below we are writing the custom dataloader that can handle both the images and texts necessary to train the CLIP model. It can be noted from the CLIP figure that, we need to encode both images and their describing texts. Since, we are not going to feed raw text to our text encoder! We will use the DistilBERT (which is smaller than BERT but performs nearly as well as BERT) model as our text encoder. Before that, we need to tokenize the sentences (captions) with DistilBERT tokenizer and then feed the token ids (input_ids) and the attention masks to DistilBERT. We have implemented the tokenization scheme and you do not have to implement anything for this custom dataloader.
class CLIPDataset(torch.utils.data.Dataset):
def __init__(self, image_filenames, captions, tokenizer, transforms):
"""
image_filenames and cpations must have the same length; so, if there are
multiple captions for each image, the image_filenames must have repetitive
file names
"""
self.image_filenames = image_filenames
self.captions = list(captions)
self.encoded_captions = tokenizer(
list(captions), padding=True, truncation=True, max_length=max_length
)
self.transforms = transforms
def __getitem__(self, idx):
item = {
key: torch.tensor(values[idx])
for key, values in self.encoded_captions.items()
}
image = cv2.imread(f"{image_path}/{self.image_filenames[idx]}")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = self.transforms(image=image)['image']
item['image'] = torch.tensor(image).permute(2, 0, 1).float()
item['caption'] = self.captions[idx]
return item
def __len__(self):
return len(self.captions)
# data augmentation for images
def get_transforms(mode="train"):
return A.Compose(
[
A.Resize(image_size, image_size, always_apply=True),
A.Normalize(max_pixel_value=255.0, always_apply=True),
]
)
Image Encoder
We use ResNet50 as our image encoder. It encodes each image to a fixed size vector of the model’s output channels (in the case of ResNet50, it’s 2048). We will load the pre-trained ImageNet weights to the ResNet50 model to get faster convergence and the limitation on the dataset size and computation resources.
from torchvision.models.resnet import ResNet50_Weights
class ImageEncoder(nn.Module):
"""
Encode images to a fixed size vector
"""
def __init__(
self, model_name=image_encoder_model, pretrained=pretrained, trainable=trainable
):
super().__init__()
self.model = torchvision.models.resnet50(ResNet50_Weights.IMAGENET1K_V2)
self.model.fc = nn.Identity()
for p in self.model.parameters():
p.requires_grad = trainable
def forward(self, x):
return self.model(x)
Text Encoder
As mentioned above, we use DistilBERT as our text encoder. The output representation is a vector of size 768.
class TextEncoder(nn.Module):
def __init__(self, model_name=text_encoder_model, pretrained=pretrained, trainable=trainable):
super().__init__()
if pretrained:
self.model = DistilBertModel.from_pretrained(model_name)
else:
self.model = DistilBertModel(config=DistilBertConfig())
for p in self.model.parameters():
p.requires_grad = trainable
# we are using the CLS token hidden representation as the sentence's embedding
self.target_token_idx = 0
def forward(self, input_ids, attention_mask):
output = self.model(input_ids=input_ids, attention_mask=attention_mask)
last_hidden_state = output.last_hidden_state
return last_hidden_state[:, self.target_token_idx, :]
2.(a) Projection Head
Now that we have encoded both our images and texts into fixed size vectors (2048 for image and 768 for text), we need to project both embeddings into a new vector space (!) with similar dimensions (256). This allows for both images and texts to be able to compare them and push apart the non-relevant image and texts and pull together those that match.
class ProjectionHead(nn.Module):
def __init__(
self,
embedding_dim,
projection_dim=projection_dim,
):
super().__init__()
'''
Args:
embedding_dim (int): Extracted Image or text feature embedding dimenasion.
'''
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Initialize a single layer linear transformation for the projection #
# head. #
##############################################################################
self.projection = nn.Linear(in_features = embedding_dim, out_features = projection_dim)
##############################################################################
# END OF YOUR CODE #
##############################################################################
def forward(self, x):
'''
Args:
x: Image or text feature embeddings extracted from the ResNet50 and DistilBERT model respectively.
Return:
projected: The projected image and text embeddings.
'''
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Write the forward function and normalize the out of the projection #
# head. hint: use F.normalize() for the normalization #
##############################################################################
projected = F.normalize(self.projection(x))
##############################################################################
# END OF YOUR CODE #
##############################################################################
return projected
2.(b) CLIP
In this section, you will need to use the modules that you just implemented to build the CLIP model. The loss function we minimize is defined as:
\[\mathcal{L}_{\text {contrast }}^{I, T}=-\frac{1}{N}\sum_{i=1}^N \log \frac{\exp(I_{i}\cdot T_{i}/\tau)}{\sum_{j=1}^{N} \exp(I_{i}\cdot T_{j}/\tau)},\]where $I_i$ and $T_i$ are the image feature and its corresponding text representations for the $i_{th}$ sample in a minibatch, and $N$ denotes the batch size. The constant $\tau$ is used to control the range of logits.
We will minimize a symmetric objective function that averages $\mathcal{L}{\text {contrast }}^{I, T}$ and $\mathcal{L}{\text {contrast }}^{T, I}$, i.e.,
\[\mathcal{L}=\frac{\mathcal{L}_{\text {contrast }}^{I, T}+\mathcal{L}_{\text {contrast }}^{T, I}}{2},\]where, \(% \mathcal{L}_{\text {contrast }}^{I, T}=-\frac{1}{N}\sum_{i=1}^N \log \frac{\exp(I_{i}\cdot T_{i})}{\sum_{j=1}^{N} \exp(I_{i}\cdot T_{j})}\cdot\exp(\tau), \mathcal{L}_{\text {contrast }}^{T, I}=-\frac{1}{N}\sum_{i=1}^N \log \frac{\exp(T_{i}\cdot I_{i}/\tau)}{\sum_{j=1}^{N} \exp(T_{i}\cdot I_{j}/\tau)},\)
By minimizing the above loss function, we learn representations for the image and text such that the positive pairs (respective images and their captions) will produce high dot product while giving low dot product for negative samples (image and other unrelated captions).
class CLIPModel(nn.Module):
def __init__(
self,
temperature=temperature,
image_embedding=image_embedding,
text_embedding=text_embedding,
):
super().__init__()
'''
Args:
temperature (float): temperature parameter which controls the range of the logits.
image_embedding (int): Shape of the extracted image embedding
text_embedding (int): Shape of the extracted text embedding
'''
self.image_encoder = None
self.text_encoder = None
self.image_projection = None
self.text_projection = None
self.temperature = temperature
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Initialize the encoders and the projection heads for image and text i.e,
# instantiate the above None variables with their corresponding models#
##############################################################################
self.image_encoder = ImageEncoder()
self.text_encoder = TextEncoder()
self.image_projection = ProjectionHead(embedding_dim = image_embedding, projection_dim = projection_dim)
self.text_projection = ProjectionHead(embedding_dim = text_embedding, projection_dim = projection_dim)
##############################################################################
# END OF YOUR CODE #
##############################################################################
def forward(self, batch):
'''
Args:
batch: batch of images for training.
Return:
loss: computed loss.
'''
# get image and text features
image_features = self.image_encoder(batch["image"])
text_features = self.text_encoder(
input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]
)
loss = None
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Project images and text into a new vector space and write the loss function by following the above equations
# you are not allowed to use any for loops while computing the loss.
##############################################################################
image_projected = self.image_projection(image_features)
text_projected = self.text_projection(text_features)
# print("image batch shape: ", batch["image"].shape)
# print("image feature shape: ", image_features.shape)
# print("image projection shape: ", image_projected.shape)
# print("text feature shape: ", text_features.shape)
# print("text projection shape: ", text_projected.shape)
criteria = nn.CrossEntropyLoss()
image_text_matmul = torch.matmul(image_projected, text_projected.t()) / self.temperature
# print("image projection * text projection shape: ", image_text_matmul.shape)
loss_it = criteria(image_text_matmul, torch.arange(image_text_matmul.shape[0], device = device))
text_image_matmul = torch.matmul(text_projected, image_projected.t()) / self.temperature
# print("text projection * image projection shape: ", text_image_matmul.shape)
loss_ti = criteria(text_image_matmul, torch.arange(text_image_matmul.shape[0], device = device))
#loss = torch.tensor([loss_it, loss_ti], requires_grad=True)
loss = (loss_it + loss_ti) / 2
# loss = torch.nn.CrossEntropyLoss(reduction = "mean")
# loss_it = loss(image_fea)
# numerator = torch.exp((image_projected * text_projected) / self.temperature)
# denum_it = torch.sum(torch.exp(torch.matmul(image_projected.t(), text_projected) / self.temperature), axis = 1)
# loss_it = -torch.sum(torch.log(numerator / denum_it)) / numerator.shape[0]
# denum_ti = torch.sum(torch.exp(torch.matmul(text_projected.t(), image_projected) / self.temperature), axis = 1)
# loss_ti = -torch.sum(torch.log(numerator / denum_ti)) / numerator.shape[0]
# loss = torch.tensor([loss_it, loss_ti])
# print("numerator: ", numerator.shape)
# print("denum it: ", denum_it.shape)
# print("denum ti: ", denum_ti.shape)
# print("loss: ", loss)
# print("loss mean: ", loss.mean())
##############################################################################
# END OF YOUR CODE #
##############################################################################
return loss
2.(c) Training
Next we will need to train the CLIP model and here are some utility functions that are necessary to train.
def make_train_valid_dfs():
dataframe = pd.read_csv(f"{captions_path}/captions.csv")
dataframe = dataframe[dataframe.reset_index().index % 5 == 0]
max_id = dataframe["id"].max() + 1
image_ids = np.arange(0, max_id)
np.random.seed(42)
valid_ids = np.random.choice(
image_ids, size=int(0.2 * len(image_ids)), replace=False
)
train_ids = [id_ for id_ in image_ids if id_ not in valid_ids]
train_dataframe = dataframe[dataframe["id"].isin(train_ids)].reset_index(drop=True)
valid_dataframe = dataframe[dataframe["id"].isin(valid_ids)].reset_index(drop=True)
return train_dataframe, valid_dataframe
def build_loaders(dataframe, tokenizer, mode):
transforms = get_transforms(mode=mode)
dataset = CLIPDataset(
dataframe["image"].values,
dataframe["caption"].values,
tokenizer=tokenizer,
transforms=transforms,
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True if mode == "train" else False,
drop_last=True
)
return dataloader
def train_epoch(model, train_loader, optimizer, lr_scheduler, step):
loss_meter = AvgMeter()
tqdm_object = tqdm(train_loader, total=len(train_loader))
for i, batch in enumerate(tqdm_object):
batch = {k: v.to(device) for k, v in batch.items() if k != "caption"}
loss = model(batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step == "batch":
lr_scheduler.step()
count = batch["image"].size(0)
loss_meter.update(loss.item(), count)
tqdm_object.set_postfix(train_loss=loss_meter.avg, lr=get_lr(optimizer))
if i % 100 == 0:
print('loss:', loss.item() / count)
return loss_meter
def valid_epoch(model, valid_loader):
loss_meter = AvgMeter()
tqdm_object = tqdm(valid_loader, total=len(valid_loader))
for batch in tqdm_object:
batch = {k: v.to(device) for k, v in batch.items() if k != "caption"}
loss = model(batch)
count = batch["image"].size(0)
loss_meter.update(loss.item(), count)
tqdm_object.set_postfix(valid_loss=loss_meter.avg)
return loss_meter
Run this cell to train the model.
train_df, valid_df = make_train_valid_dfs()
tokenizer = DistilBertTokenizer.from_pretrained(text_tokenizer)
train_loader = build_loaders(train_df, tokenizer, mode="train")
valid_loader = build_loaders(valid_df, tokenizer, mode="valid")
model = CLIPModel().to(device)
params = [
{"params": model.image_encoder.parameters(), "lr": image_encoder_lr},
{"params": model.text_encoder.parameters(), "lr": text_encoder_lr},
{"params": itertools.chain(
model.image_projection.parameters(), model.text_projection.parameters()
), "lr": head_lr, "weight_decay": weight_decay}
]
optimizer = torch.optim.AdamW(params, weight_decay=0.)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode="min", patience=patience, factor=factor
)
step = "epoch"
best_loss = float('inf')
for epoch in range(epochs):
print(f"Epoch: {epoch + 1}")
model.train()
train_loss = train_epoch(model, train_loader, optimizer, lr_scheduler, step)
model.eval()
with torch.no_grad():
valid_loss = valid_epoch(model, valid_loader)
if valid_loss.avg < best_loss:
best_loss = valid_loss.avg
torch.save(model.state_dict(), "best.pt")
print("Saved Best Model!")
lr_scheduler.step(valid_loss.avg)
/usr/local/lib/python3.7/dist-packages/torchvision/models/_utils.py:136: UserWarning: Using 'weights' as positional parameter(s) is deprecated since 0.13 and will be removed in 0.15. Please use keyword parameter(s) instead.
f"Using {sequence_to_str(tuple(keyword_only_kwargs.keys()), separate_last='and ')} as positional "
Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_projector.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_transform.weight']
- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Epoch: 1
0%| | 0/202 [00:00<?, ?it/s]
loss: 0.11498111486434937
loss: 0.07898600399494171
loss: 0.07101263105869293
0%| | 0/50 [00:00<?, ?it/s]
Saved Best Model!
Epoch: 2
0%| | 0/202 [00:00<?, ?it/s]
loss: 0.04536661505699158
loss: 0.050881966948509216
loss: 0.03583340346813202
0%| | 0/50 [00:00<?, ?it/s]
Saved Best Model!
Epoch: 3
0%| | 0/202 [00:00<?, ?it/s]
loss: 0.0393420085310936
loss: 0.028501039370894432
loss: 0.025959571823477745
0%| | 0/50 [00:00<?, ?it/s]
2.(d) Train a Linear Classifier
We’ll train a linear clasifier that takes the output of the encoder (Image Encoder: ResNet50) as its features. During training, we freeze the parameters of the encoder and only train the linear layer.
labeled_transform = transforms.Compose([
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
])
dataset_tr = torchvision.datasets.STL10('./data', 'train', download=False, transform=labeled_transform)
dataset_te = torchvision.datasets.STL10('./data', 'test', download=False, transform=labeled_transform)
dataloader_tr = DataLoader(dataset_tr, batch_size=128, shuffle=True, num_workers=2)
dataloader_te = DataLoader(dataset_te, batch_size=128, shuffle=False, num_workers=2)
model = CLIPModel().to(device)
model.load_state_dict(torch.load("best.pt", map_location=device))
model.eval()
encoder = model.image_encoder
linear_cls = nn.Sequential(
nn.Linear(image_embedding, 10)
).to(device)
cls_clip, loss_traj_clip = train_classifier(encoder, linear_cls, dataloader_tr, epochs=100, supervised=False)
test(encoder, cls_clip, dataloader_te)
Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_projector.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_transform.weight']
- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
0%| | 0/100 [00:00<?, ?it/s]
Epoch 0, loss 53.551, train accuracy 0.6498
Epoch 10, loss 8.225, train accuracy 0.9636
Epoch 20, loss 4.023, train accuracy 0.9900000000000001
Epoch 30, loss 2.499, train accuracy 0.9978
Epoch 40, loss 1.660, train accuracy 0.9992000000000001
Epoch 50, loss 1.218, train accuracy 0.9998
Epoch 60, loss 0.935, train accuracy 1.0
Epoch 70, loss 0.754, train accuracy 1.0
Epoch 80, loss 0.630, train accuracy 1.0
Epoch 90, loss 0.542, train accuracy 1.0
Test accuracy 0.801625
With pretrained encoder, the linear classifier should achieve an accuracy > 75% on the test set. Notice that the supervised flag is set to ‘False’ which means we are updating the weights of the final linear layer.
Report results
So far, we have trained linear classifiers on top of autoencoder representations and CLIP representations.
With CLIP, we learn a more useful representation than autoencoder
Please report the test accuracy of all four linear classifiers below.
Autoencoder-based
method 1 accuracy: 0.31
method 2 accuracy: 0.41
method 3 accuracy: 0.25
CLIP accuracy: 0.80
2.(e) CLIP Inference
Now we are doing a interesting downstream task to see what CLIP has learned. We will give the model a piece of text, and let the model retrieve the most relevant images from the dataset.
First, we need to project all the images into a common search space.
def get_image_embeddings(df, model_path):
tokenizer = DistilBertTokenizer.from_pretrained(text_tokenizer)
dataloader = build_loaders(df, tokenizer, mode="valid")
model = CLIPModel().to(device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
image_embeddings = []
with torch.no_grad():
for batch in tqdm(dataloader):
image_features = model.image_encoder(batch["image"].to(device))
image_embedding = model.image_projection(image_features)
image_embeddings.append(image_embedding)
return model, torch.cat(image_embeddings)
df, _ = make_train_valid_dfs()
model, image_embeddings = get_image_embeddings(df, "best.pt")
Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_projector.bias', 'vocab_transform.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_transform.weight']
- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
0%| | 0/202 [00:00<?, ?it/s]
In this function retrieve_images, we will get the model, image embeddings and a text query. You need to calculate the similarities between the text and all the images and return the best k of them.
def retrieve_images(model, image_embeddings, query, image_filenames, k=9):
tokenizer = DistilBertTokenizer.from_pretrained(text_tokenizer)
encoded_query = tokenizer([query])
batch = {
key: torch.tensor(values).to(device)
for key, values in encoded_query.items()
}
with torch.no_grad():
text_features = model.text_encoder(
input_ids=batch["input_ids"], attention_mask=batch["attention_mask"]
)
text_embeddings = model.text_projection(text_features)
##############################################################################
# YOUR CODE HERE #
##############################################################################
# TODO: Please get the top k similar images to the given text query and pass them to the 'matches' variable.
# Note: You cannot use any for loops in this function.
##############################################################################
matches = None # Please keep this as the variable name for the matches
text_image_matmul = torch.matmul(text_embeddings, image_embeddings.t())
indexes = torch.topk(text_image_matmul, k)[1].tolist()
matches = image_filenames[indexes]
##############################################################################
# END OF YOUR CODE #
##############################################################################
_, axes = plt.subplots(3, 3, figsize=(10, 10))
for match, ax in zip(matches, axes.flatten()):
image = cv2.imread(f"{image_path}/{match}")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ax.imshow(image)
ax.axis("off")
plt.show()
Now we can query the model and get some results!
retrieve_images(model,
image_embeddings,
query="dogs in park",
image_filenames=train_df['image'].values)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
retrieve_images(model,
image_embeddings,
query="dogs on grass",
image_filenames=train_df['image'].values)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
retrieve_images(model,
image_embeddings,
query="dogs running",
image_filenames=train_df['image'].values)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
retrieve_images(model,
image_embeddings,
query="humans dancing and singing",
image_filenames=train_df['image'].values)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
retrieve_images(model,
image_embeddings,
query="humans doing outdoor activities",
image_filenames=train_df['image'].values)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
retrieve_images(model,
image_embeddings,
query="humans riding bikes",
image_filenames=train_df['image'].values)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
