You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
179 lines
6.7 KiB
Python
179 lines
6.7 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
|
|
import numpy as np
|
|
from abc import abstractmethod
|
|
|
|
import paddle
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
|
|
from ..builder import build_loss
|
|
from paddlevideo.utils import get_logger, get_dist_info
|
|
|
|
logger = get_logger("paddlevideo")
|
|
|
|
|
|
class BaseHead(nn.Layer):
|
|
"""Base class for head part.
|
|
|
|
All head should subclass it.
|
|
All subclass should overwrite:
|
|
|
|
- Methods: ```init_weights```, initializing weights.
|
|
- Methods: ```forward```, forward function.
|
|
|
|
Args:
|
|
num_classes (int): The number of classes to be classified.
|
|
in_channels (int): The number of channels in input feature.
|
|
loss_cfg (dict): Config for building loss. Default: dict(type='CrossEntropyLoss').
|
|
ls_eps (float): label smoothing epsilon. Default: 0. .
|
|
|
|
"""
|
|
def __init__(
|
|
self,
|
|
num_classes=None,
|
|
in_channels=None,
|
|
loss_cfg=dict(
|
|
name="CrossEntropyLoss"
|
|
), #TODO(shipping): only pass a name or standard build cfg format.
|
|
#multi_class=False, NOTE(shipping): not supported now.
|
|
ls_eps=0.):
|
|
|
|
super().__init__()
|
|
self.num_classes = num_classes
|
|
self.in_channels = in_channels
|
|
self.loss_func = build_loss(loss_cfg)
|
|
#self.multi_class = multi_class NOTE(shipping): not supported now
|
|
self.ls_eps = ls_eps
|
|
|
|
@abstractmethod
|
|
def forward(self, x):
|
|
"""Define how the head is going to run.
|
|
"""
|
|
raise NotImplemented
|
|
|
|
def loss(self, scores, labels, valid_mode=False, if_top5=True, **kwargs):
|
|
"""Calculate the loss accroding to the model output ```scores```,
|
|
and the target ```labels```.
|
|
|
|
Args:
|
|
scores (paddle.Tensor): The output of the model.
|
|
labels (paddle.Tensor): The target output of the model.
|
|
|
|
Returns:
|
|
losses (dict): A dict containing field 'loss'(mandatory) and 'top1_acc', 'top5_acc'(optional).
|
|
|
|
"""
|
|
if len(labels) == 1: #commonly case
|
|
labels = labels[0]
|
|
losses = dict()
|
|
if self.ls_eps != 0. and not valid_mode: # label_smooth
|
|
loss = self.label_smooth_loss(scores, labels, **kwargs)
|
|
else:
|
|
loss = self.loss_func(scores, labels, **kwargs)
|
|
if if_top5:
|
|
top1, top5 = self.get_acc(scores, labels, valid_mode)
|
|
losses['top1'] = top1
|
|
losses['top5'] = top5
|
|
losses['loss'] = loss
|
|
else:
|
|
top1 = self.get_acc(scores, labels, valid_mode, if_top5)
|
|
losses['top1'] = top1
|
|
losses['loss'] = loss
|
|
return losses
|
|
# MRI目前二分类无top5
|
|
elif len(labels) == 3: # mix_up
|
|
labels_a, labels_b, lam = labels
|
|
lam = lam[0] # get lam value
|
|
losses = dict()
|
|
if self.ls_eps != 0:
|
|
loss_a = self.label_smooth_loss(scores, labels_a, **kwargs)
|
|
loss_b = self.label_smooth_loss(scores, labels_b, **kwargs)
|
|
else:
|
|
loss_a = self.loss_func(scores, labels_a, **kwargs)
|
|
loss_b = self.loss_func(scores, labels_b, **kwargs)
|
|
loss = lam * loss_a + (1 - lam) * loss_b
|
|
|
|
if if_top5:
|
|
top1a, top5a = self.get_acc(scores, labels_a, valid_mode)
|
|
top1b, top5b = self.get_acc(scores, labels_b, valid_mode)
|
|
top1 = lam * top1a + (1 - lam) * top1b
|
|
top5 = lam * top5a + (1 - lam) * top5b
|
|
losses['top1'] = top1
|
|
losses['top5'] = top5
|
|
losses['loss'] = loss
|
|
|
|
else:
|
|
top1a = self.get_acc(scores, labels_a, valid_mode, if_top5)
|
|
top1b = self.get_acc(scores, labels_b, valid_mode, if_top5)
|
|
top1 = lam * top1a + (1 - lam) * top1b
|
|
losses['top1'] = top1
|
|
losses['loss'] = loss
|
|
|
|
return losses
|
|
else:
|
|
raise NotImplemented
|
|
|
|
def label_smooth_loss(self, scores, labels, **kwargs):
|
|
"""
|
|
Args:
|
|
scores (paddle.Tensor): [N, num_classes]
|
|
labels (paddle.Tensor): [N, ]
|
|
Returns:
|
|
paddle.Tensor: [1,]
|
|
"""
|
|
if paddle.is_compiled_with_custom_device('npu'):
|
|
"""
|
|
Designed for the lack of temporary operators of NPU,
|
|
main idea is to split smooth loss into uniform distribution loss
|
|
and hard label calculation
|
|
"""
|
|
hard_loss = (1.0 - self.ls_eps) * F.cross_entropy(scores, labels)
|
|
uniform_loss = (self.ls_eps / self.num_classes) * (
|
|
-F.log_softmax(scores, -1).sum(-1).mean(0))
|
|
loss = hard_loss + uniform_loss
|
|
else:
|
|
labels = F.one_hot(labels, self.num_classes)
|
|
labels = F.label_smooth(labels, epsilon=self.ls_eps)
|
|
labels = paddle.squeeze(labels, axis=1)
|
|
loss = self.loss_func(scores, labels, soft_label=True, **kwargs)
|
|
return loss
|
|
|
|
def get_acc(self, scores, labels, valid_mode, if_top5=True):
|
|
if if_top5:
|
|
top1 = paddle.metric.accuracy(input=scores, label=labels, k=1)
|
|
top5 = paddle.metric.accuracy(input=scores, label=labels, k=5)
|
|
_, world_size = get_dist_info()
|
|
#NOTE(shipping): deal with multi cards validate
|
|
if world_size > 1 and valid_mode: #reduce sum when valid
|
|
paddle.distributed.all_reduce(
|
|
top1, op=paddle.distributed.ReduceOp.SUM)
|
|
top1 = top1 / world_size
|
|
paddle.distributed.all_reduce(
|
|
top5, op=paddle.distributed.ReduceOp.SUM)
|
|
top5 = top5 / world_size
|
|
|
|
return top1, top5
|
|
else:
|
|
top1 = paddle.metric.accuracy(input=scores, label=labels, k=1)
|
|
_, world_size = get_dist_info()
|
|
#NOTE(shipping): deal with multi cards validate
|
|
if world_size > 1 and valid_mode: #reduce sum when valid
|
|
paddle.distributed.all_reduce(
|
|
top1, op=paddle.distributed.ReduceOp.SUM)
|
|
top1 = top1 / world_size
|
|
|
|
return top1
|