嫌墨迹直接看代码

Q5 Self-Supervised Learning for Image Classification

compute_train_transform CIFAR10Pair.getitem()

题面

解析

这里就是让我们对图片进行转换，具体的转换步骤在上面都写了，我们只需要查阅相应的api进行调用就好了

代码

def compute_train_transform(seed=123456):"""This function returns a composition of data augmentations to a single training image.Complete the following lines. Hint: look at available functions in torchvision.transforms"""random.seed(seed)torch.random.manual_seed(seed)# Transformation that applies color jitter with brightness=0.4, contrast=0.4, saturation=0.4, and hue=0.1color_jitter = transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)train_transform = transforms.Compose([############################################################################### TODO: Start of your code.                                                  ##                                                                            ## Hint: Check out transformation functions defined in torchvision.transforms ## The first operation is filled out for you as an example.############################################################################### Step 1: Randomly resize and crop to 32x32.transforms.RandomResizedCrop(32),# Step 2: Horizontally flip the image with probability 0.5transforms.RandomHorizontalFlip(p=0.5),# Step 3: With a probability of 0.8, apply color jitter (you can use "color_jitter" defined above.transforms.RandomApply(torch.nn.ModuleList([color_jitter]), p=0.8),# Step 4: With a probability of 0.2, convert the image to grayscaletransforms.RandomGrayscale(p=0.2),###############################################################################                               END OF YOUR CODE                             ###############################################################################transforms.ToTensor(),transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])])return train_transformdef compute_test_transform():test_transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])])return test_transformclass CIFAR10Pair(CIFAR10):"""CIFAR10 Dataset."""def __getitem__(self, index):img, target = self.data[index], self.targets[index]img = Image.fromarray(img)x_i = Nonex_j = Noneif self.transform is not None:############################################################################### TODO: Start of your code.                                                  ##                                                                            ## Apply self.transform to the image to produce x_i and x_j in the paper ###############################################################################x_i = self.transform(img)x_j = self.transform(img)###############################################################################                               END OF YOUR CODE                             ###############################################################################if self.target_transform is not None:target = self.target_transform(target)return x_i, x_j, target

输出

注意这里我不知道为啥本地跑出来的结果是有误差的，但是同样的代码放到colab上跑就没事了，很奇怪我只能说

simclr_loss_naive

题面

让我们用简单的方法来计算SimCLR的loss

解析

看看上面的题面，一步一步来就好了

代码

def sim(z_i, z_j):"""Normalized dot product between two vectors.Inputs:- z_i: 1xD tensor.- z_j: 1xD tensor.Returns:- A scalar value that is the normalized dot product between z_i and z_j."""norm_dot_product = None############################################################################### TODO: Start of your code.                                                  ##                                                                            ## HINT: torch.linalg.norm might be helpful.                                  ################################################################################ torch.linalg.norm 相对于对一个向量求范数，返回的是一个标量norm_dot_product = torch.dot(z_i, z_j) / (torch.linalg.norm(z_i) * torch.linalg.norm(z_j))###############################################################################                               END OF YOUR CODE                             ###############################################################################return norm_dot_productdef simclr_loss_naive(out_left, out_right, tau):"""Compute the contrastive loss L over a batch (naive loop version).Input:- out_left: NxD tensor; output of the projection head g(), left branch in SimCLR model.- out_right: NxD tensor; output of the projection head g(), right branch in SimCLR model.Each row is a z-vector for an augmented sample in the batch. The same row in out_left and out_right form a positive pair. In other words, (out_left[k], out_right[k]) form a positive pair for all k=0...N-1.- tau: scalar value, temperature parameter that determines how fast the exponential increases.Returns:- A scalar value; the total loss across all positive pairs in the batch. See notebook for definition."""N = out_left.shape[0]  # total number of training examples# Concatenate out_left and out_right into a 2*N x D tensor.out = torch.cat([out_left, out_right], dim=0)  # [2*N, D]total_loss = 0for k in range(N):  # loop through each positive pair (k, k+N)z_k, z_k_N = out[k], out[k + N]############################################################################### TODO: Start of your code.                                                  ##                                                                            ## Hint: Compute l(k, k+N) and l(k+N, k).                                     ################################################################################ *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****# 计算 l(k, k+N)# 计算左边的分子left_numerator = (sim(z_k, z_k_N) / tau).exp()# 计算左边的分母中需要进行sim运算的元素left_need_sim = out[np.arange(2 * N) != k]# 计算左边的分母left_denominator = torch.tensor([sim(z_k, z_i) / tau for z_i in left_need_sim]).exp().sum()# 计算左边的结果left = -(left_numerator / left_denominator).log()# 计算 l(k+N, k)# 计算右边的分子right_numerator = (sim(z_k_N, z_k) / tau).exp()# 计算右边的分母中需要进行sim运算的元素right_need_sim = out[np.arange(2 * N) != k + N]# 计算右边的分母right_denominator = torch.tensor([sim(z_k_N, z_i) / tau for z_i in right_need_sim]).exp().sum()# 计算右边的结果right = -(right_numerator / right_denominator).log()total_loss += left + right# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****###############################################################################                               END OF YOUR CODE                             ################################################################################ In the end, we need to divide the total loss by 2N, the number of samples in the batch.total_loss = total_loss / (2 * N)return total_loss

输出

sim_positive_pairs

题面

这里跟之前的区别在于之前只需要算两个向量之间的sim，现在算的是两个矩阵之间的sim

解析

看代码注释吧

代码

def sim_positive_pairs(out_left, out_right):"""Normalized dot product between positive pairs.Inputs:- out_left: NxD tensor; output of the projection head g(), left branch in SimCLR model.- out_right: NxD tensor; output of the projection head g(), right branch in SimCLR model.Each row is a z-vector for an augmented sample in the batch.The same row in out_left and out_right form a positive pair.Returns:- A Nx1 tensor; each row k is the normalized dot product between out_left[k] and out_right[k]."""pos_pairs = None############################################################################### TODO: Start of your code.                                                  ##                                                                            ## HINT: torch.linalg.norm might be helpful.                                  ################################################################################ *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****# 看看公式，sim 是一个除法，但是可以看做两个除法相乘norm_left = out_left / torch.linalg.norm(out_left, dim=1, keepdim=True)norm_right = out_right / torch.linalg.norm(out_right, dim=1, keepdim=True)pos_pairs = torch.sum(norm_left * norm_right, dim=1, keepdim=True)# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****###############################################################################                               END OF YOUR CODE                             ###############################################################################return pos_pairs

输出

compute_sim_matrix

题面

这个任务应该就是让我们输出sim_matrix,matrix是一个2N * 2N的矩阵，matrix[i,j]表示sim(i,j)
所以就处理下直接乘以自己的转置就好了

解析

看上面或者看代码，理解一下

代码

def compute_sim_matrix(out):"""Compute a 2N x 2N matrix of normalized dot products between all pairs of augmented examples in a batch.Inputs:- out: 2N x D tensor; each row is the z-vector (output of projection head) of a single augmented example.There are a total of 2N augmented examples in the batch.Returns:- sim_matrix: 2N x 2N tensor; each element i, j in the matrix is the normalized dot product between out[i] and out[j]."""sim_matrix = None############################################################################### TODO: Start of your code.                                                  ################################################################################ *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****out_norm = out / torch.linalg.norm(out, dim=1, keepdim=True)sim_matrix = out_norm @ out_norm.T# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****###############################################################################                               END OF YOUR CODE                             ###############################################################################return sim_matrix

输出

simclr_loss_vectorized

题面

就是让我们实现序列化的计算loss,按照步骤一步一步来就好了

解析

看注释吧

代码

def simclr_loss_vectorized(out_left, out_right, tau, device='cuda'):"""Compute the contrastive loss L over a batch (vectorized version). No loops are allowed.Inputs and output are the same as in simclr_loss_naive."""N = out_left.shape[0]# Concatenate out_left and out_right into a 2*N x D tensor.out = torch.cat([out_left, out_right], dim=0)  # [2*N, D]# Compute similarity matrix between all pairs of augmented examples in the batch.sim_matrix = compute_sim_matrix(out)  # [2*N, 2*N]############################################################################### TODO: Start of your code. Follow the hints.                                ################################################################################ Step 1: Use sim_matrix to compute the denominator value for all augmented samples.# Hint: Compute e^{sim / tau} and store into exponential, which should have shape 2N x 2N.exponential = (sim_matrix / tau).exp().to(device)# This binary mask zeros out terms where k=i.mask = (torch.ones_like(exponential, device=device) - torch.eye(2 * N, device=device)).to(device).bool()# We apply the binary mask.exponential = exponential.masked_select(mask).view(2 * N, -1)  # [2*N, 2*N-1]# Hint: Compute the denominator values for all augmented samples. This should be a 2N x 1 vector.denom = exponential.sum(dim=1)# Step 2: Compute similarity between positive pairs.# You can do this in two ways: # Option 1: Extract the corresponding indices from sim_matrix. # Option 2: Use sim_positive_pairs().# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****# 计算出所有的正样本对的相似度sim_pairs = sim_positive_pairs(out_left, out_right).to(device)# 拼接矩阵，因为正样本对是对称的，所以拼接两次sim_pairs = torch.cat([sim_pairs, sim_pairs], dim=0)# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****# Step 3: Compute the numerator value for all augmented samples.numerator = None# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****numerator = (sim_pairs / tau).exp()# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****# Step 4: Now that you have the numerator and denominator for all augmented samples, compute the total loss.loss = None# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****loss = torch.mean(-torch.log(numerator / denom))# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****###############################################################################                               END OF YOUR CODE                             ###############################################################################return loss

输出

train

题面

解析

这里就是让我们将x_i和x_j变成经过处理的gi 和 gj ，然后计算loss就好了

注意一下，这个训练预计要吃5G的显存，如果你的显卡显存不够，量力而行，我的显卡是1060显存3个g，所以为了训练这个模型他额外吃了系统内存，但是降低了训练速度。

代码

输出

训练了差不多一个小时

结语

这样这个实验就做完了，虽然自己实现了simSLR,但是感觉对这个模型的总体还不是特别清楚，细节部分倒是清楚了，等到时候所有cs231n作业做完再回过头理解一下吧