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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import nn
import logging
logger = logging.getLogger("dinov2")
try:
from xformers.ops import cross_entropy
def lossfunc(t, s, temp):
s = s.float()
t = t.float()
if s.ndim == 2:
return -cross_entropy(s.unsqueeze(0), t.unsqueeze(0), temp, bw_inplace=True).squeeze(0)
elif s.ndim == 3:
return -cross_entropy(s, t, temp, bw_inplace=True)
except ImportError:
def lossfunc(t, s, temp):
return torch.sum(t * F.log_softmax(s / temp, dim=-1), dim=-1)
class iBOTPatchLoss(nn.Module):
def __init__(self, patch_out_dim, student_temp=0.1, center_momentum=0.9):
super().__init__()
self.student_temp = student_temp
self.center_momentum = center_momentum
self.register_buffer("center", torch.zeros(1, 1, patch_out_dim))
self.updated = True
self.reduce_handle = None
self.len_teacher_patch_tokens = None
self.async_batch_center = None
@torch.no_grad()
def softmax_center_teacher(self, teacher_patch_tokens, teacher_temp):
self.apply_center_update()
# teacher centering and sharpening
#
# WARNING:
# as self.center is a float32, everything gets casted to float32 afterwards
#
# teacher_patch_tokens = teacher_patch_tokens.float()
# return F.softmax((teacher_patch_tokens.sub_(self.center.to(teacher_patch_tokens.dtype))).mul_(1 / teacher_temp), dim=-1)
return F.softmax((teacher_patch_tokens - self.center) / teacher_temp, dim=-1)
# this is experimental, keep everything in float16 and let's see what happens:
# return F.softmax((teacher_patch_tokens.sub_(self.center)) / teacher_temp, dim=-1)
@torch.no_grad()
def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_masked_patches_tensor, n_iterations=3):
teacher_output = teacher_output.float()
# world_size = dist.get_world_size() if dist.is_initialized() else 1
Q = torch.exp(teacher_output / teacher_temp).t() # Q is K-by-B for consistency with notations from our paper
# B = Q.shape[1] * world_size # number of samples to assign
B = n_masked_patches_tensor
dist.all_reduce(B)
K = Q.shape[0] # how many prototypes
# make the matrix sums to 1
sum_Q = torch.sum(Q)
if dist.is_initialized():
dist.all_reduce(sum_Q)
Q /= sum_Q
for it in range(n_iterations):
# normalize each row: total weight per prototype must be 1/K
sum_of_rows = torch.sum(Q, dim=1, keepdim=True)
if dist.is_initialized():
dist.all_reduce(sum_of_rows)
Q /= sum_of_rows
Q /= K
# normalize each column: total weight per sample must be 1/B
Q /= torch.sum(Q, dim=0, keepdim=True)
Q /= B
Q *= B # the columns must sum to 1 so that Q is an assignment
return Q.t()
def forward(self, student_patch_tokens, teacher_patch_tokens, student_masks_flat):
"""
Cross-entropy between softmax outputs of the teacher and student networks.
student_patch_tokens: (B, N, D) tensor
teacher_patch_tokens: (B, N, D) tensor
student_masks_flat: (B, N) tensor
"""
t = teacher_patch_tokens
s = student_patch_tokens
loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1)
loss = torch.sum(loss * student_masks_flat.float(), dim=-1) / student_masks_flat.sum(dim=-1).clamp(min=1.0)
return -loss.mean()
def forward_masked(
self,
student_patch_tokens_masked,
teacher_patch_tokens_masked,
student_masks_flat,
n_masked_patches=None,
masks_weight=None,
):
t = teacher_patch_tokens_masked
s = student_patch_tokens_masked
# loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1)
loss = lossfunc(t, s, self.student_temp)
if masks_weight is None:
masks_weight = (
(1 / student_masks_flat.sum(-1).clamp(min=1.0))
.unsqueeze(-1)
.expand_as(student_masks_flat)[student_masks_flat]
)
if n_masked_patches is not None:
loss = loss[:n_masked_patches]
loss = loss * masks_weight
return -loss.sum() / student_masks_flat.shape[0]
@torch.no_grad()
def update_center(self, teacher_patch_tokens):
self.reduce_center_update(teacher_patch_tokens)
@torch.no_grad()
def reduce_center_update(self, teacher_patch_tokens):
self.updated = False
self.len_teacher_patch_tokens = len(teacher_patch_tokens)
self.async_batch_center = torch.sum(teacher_patch_tokens.mean(1), dim=0, keepdim=True)
if dist.is_initialized():
self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True)
@torch.no_grad()
def apply_center_update(self):
if self.updated is False:
world_size = dist.get_world_size() if dist.is_initialized() else 1
if self.reduce_handle is not None:
self.reduce_handle.wait()
_t = self.async_batch_center / (self.len_teacher_patch_tokens * world_size)
self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum)
self.updated = True
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