Dubhe/model_measuring/kamal/slim/distillation/cc.py

"""
 Copyright 2020 Tianshu AI Platform. 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.
 =============================================================
"""

from .kd import KDDistiller
from kamal.core.tasks.loss import KDLoss

import torch.nn as nn
import torch._ops

import time

class CCDistiller(KDDistiller):

    def __init__(self, logger=None, tb_writer=None ):
        super(CCDistiller, self).__init__( logger, tb_writer )

    def setup(self, student, teacher, dataloader, optimizer, embed_s, embed_t, T=1.0, alpha=1.0, beta=1.0, gamma=1.0, stu_hooks=[], tea_hooks=[], out_flags=[], device=None ):
        super(CCDistiller, self).setup(
            student, teacher, dataloader, optimizer, T=T, gamma=gamma, alpha=alpha, device=device)
        self.embed_s = embed_s.to(self.device).train()
        self.embed_t = embed_t.to(self.device).train()
        self.stu_hooks = stu_hooks
        self.tea_hooks = tea_hooks
        self.out_flags = out_flags

    def additional_kd_loss(self, engine, batch):
        feat_s = [f.feat_out if flag else f.feat_in for (f, flag) in zip(self.stu_hooks, self.out_flags)]
        feat_t = [f.feat_out.detach() if flag else f.feat_in for (f, flag) in zip(self.tea_hooks, self.out_flags)]
        f_s = self.embed_s(feat_s[-1])
        f_t = self.embed_t(feat_t[-1])
        return torch.mean((torch.abs(f_s-f_t)[:-1] * torch.abs(f_s-f_t)[1:]).sum(1))

class LinearEmbed(nn.Module):
    def __init__(self, dim_in=1024, dim_out=128):
        super(LinearEmbed, self).__init__()
        self.linear = nn.Linear(dim_in, dim_out)

    def forward(self, x):
        x = x.view(x.shape[0], -1)
        x = self.linear(x)
        return x