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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from warnings import warn
import torch
from torch.nn import functional as F
from torch.nn.modules.loss import _Loss
class ContrastiveLoss(_Loss):
"""
Compute the Contrastive loss defined in:
Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International
conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)
Adapted from:
https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5
"""
def __init__(self, temperature: float = 0.5, batch_size: int = -1) -> None:
"""
Args:
temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
Raises:
ValueError: When an input of dimension length > 2 is passed
ValueError: When input and target are of different shapes
"""
super().__init__()
self.temperature = temperature
if batch_size != -1:
warn("batch_size is no longer required to be set. It will be estimated dynamically in the forward call")
def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Args:
input: the shape should be B[F].
target: the shape should be B[F].
"""
if len(target.shape) > 2 or len(input.shape) > 2:
raise ValueError(
f"Either target or input has dimensions greater than 2 where target "
f"shape is ({target.shape}) and input shape is ({input.shape})"
)
if target.shape != input.shape:
raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
temperature_tensor = torch.as_tensor(self.temperature).to(input.device)
batch_size = input.shape[0]
negatives_mask = ~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool)
negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)
repr = torch.cat([input, target], dim=0)
sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)
sim_ij = torch.diag(sim_matrix, batch_size)
sim_ji = torch.diag(sim_matrix, -batch_size)
positives = torch.cat([sim_ij, sim_ji], dim=0)
nominator = torch.exp(positives / temperature_tensor)
denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)
loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
return torch.sum(loss_partial) / (2 * batch_size)
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