modelscope-scepter/scepter/modules/model/network/classifier.py

# -*- coding: utf-8 -*-
# Copyright 2021 Alibaba Group Holding Limited. All Rights Reserved.

from collections import OrderedDict
from functools import partial

import torch.nn as nn
from torch.nn.functional import sigmoid, softmax

from scepter.modules.model.metric.registry import METRICS
from scepter.modules.model.network.train_module import TrainModule
from scepter.modules.model.registry import (BACKBONES, HEADS, LOSSES, MODELS,
                                            NECKS)
from scepter.modules.utils.config import Config, dict_to_yaml

_ACTIVATE_MAPPER = {'softmax': partial(softmax, dim=1), 'sigmoid': sigmoid}


@MODELS.register_class()
class Classifier(TrainModule):
    """ Base classifier implementation.

    Args:
        backbones (dict): Defines backbones.
        neck (dict, optional): Defines neck. Use Identity if none.
        head (dict): Defines head.
        act_name (str): Defines activate function, 'softmax' or 'sigmoid'.
        topk (Sequence[int]): Defines how to calculate accuracy metrics.
        freeze_bn (bool): If True, freeze all BatchNorm layers including LayerNorm.
    """
    para_dict = {
        'ACT_NAME': {
            'value':
            'softmax',
            'description':
            'the activation function for logits, select from [softmax, sigmoid]!'
        },
        'FREEZE_BN': {
            'value': False,
            'description': 'if freeze bn of not'
        }
    }

    def __init__(self, cfg, logger=None):
        super().__init__(cfg, logger=logger)
        # Construct model
        self.backbone = BACKBONES.build(cfg.BACKBONE, logger=logger)
        necks_cfg = cfg.get('NECK',
                            Config(cfg_dict={'NAME': 'Identity'}, load=False))
        self.neck = NECKS.build(necks_cfg, logger=logger)
        self.head = HEADS.build(cfg.HEAD, logger=logger)
        freeze_bn = cfg.get('FREEZE_BN', False)
        # Construct loss
        loss = cfg.get('LOSS',
                       Config(cfg_dict={'NAME': 'CrossEntropy'}, load=False))
        self.loss = LOSSES.build(loss, logger=logger)
        act_name = cfg.get('ACT_NAME', 'softmax')
        # Construct activate function
        self.act_fn = _ACTIVATE_MAPPER[act_name]
        self.metric = METRICS.build(cfg.METRIC, logger=logger)
        self.freeze_bn = freeze_bn

    def train(self, mode=True):
        self.training = mode
        super(Classifier, self).train(mode=mode)
        if self.freeze_bn:
            for module in self.modules():
                if isinstance(module,
                              (nn.BatchNorm2d, nn.BatchNorm3d, nn.LayerNorm)):
                    module.train(False)
        return self

    def forward(self, img, label=None, **kwargs):
        return self.forward_train(
            img, label=label) if self.training else self.forward_test(
                img, label=label)  # noqa

    def forward_train(self, img, label=None):
        probs = self.head(self.neck(self.backbone(img)))
        if label is None:
            return probs

        ret = OrderedDict()
        loss = self.loss(probs, label)
        ret['loss'] = loss
        ret['batch_size'] = img.size(0)
        ret.update(self.metric(probs, label))
        return ret

    def forward_test(self, img, label=None):
        logits = self.act_fn(self.head(self.neck(self.backbone(img))))
        if label is not None:
            ret = OrderedDict()
            ret['logits'] = logits
            ret['batch_size'] = img.size(0)
            ret.update(self.metric(logits, label))
            return ret
        return logits

    @staticmethod
    def get_config_template():
        '''
        { "ENV" :
            { "description" : "",
              "A" : {
                    "value": 1.0,
                    "description": ""
               }
            }
        }
        :return:
        '''
        return dict_to_yaml('MODEL',
                            __class__.__name__,
                            Classifier.para_dict,
                            set_name=True)


@MODELS.register_class()
class VideoClassifier(Classifier):
    """ Classifier for video.
    Default input tensor is video.

    """
    def forward(self, video, label=None, **kwargs):
        return self.forward_train(video, label=label) \
            if self.training else self.forward_test(video, label=label)

    @staticmethod
    def get_config_template():
        '''
        { "ENV" :
            { "description" : "",
              "A" : {
                    "value": 1.0,
                    "description": ""
               }
            }
        }
        :return:
        '''
        return dict_to_yaml('MODEL',
                            __class__.__name__,
                            VideoClassifier.para_dict,
                            set_name=True)


@MODELS.register_class()
class VideoClassifier2x(VideoClassifier):
    """ A 2-way classifier for video.

    """
    def forward_train(self, video, label=None):
        probs0, probs1 = self.head(self.neck(self.backbone(video)))
        if label is not None:
            ret = OrderedDict()
            loss = self.loss(probs0, label[:, 0]) + self.loss(
                probs1, label[:, 1])
            ret['loss'] = loss
            ret['batch_size'] = video.size(0)
            acc_0 = self.metric(probs0, label[:, 0])
            acc_0 = {
                key.relace('@', '_0@'): value
                for key, value in acc_0.items()
            }
            acc_1 = self.metric(probs1, label[:, 1])
            acc_1 = {
                key.relace('@', '_1@'): value
                for key, value in acc_1.items()
            }
            ret.update(acc_0)
            ret.update(acc_1)
            return ret
        return {'logits0': self.act_fn(probs0), 'logits1': self.act_fn(probs1)}

    def forward_test(self, video, label=None):
        probs0, probs1 = self.head(self.neck(self.backbone(video)))
        logits0, logits1 = self.act_fn(probs0), self.act_fn(probs1)
        if label is None:
            return {'logits0': logits0, 'logits1': logits1}
        ret = OrderedDict()
        ret['logits0'] = logits0
        ret['logits1'] = logits1
        acc_0 = self.metric(probs0, label[:, 0])
        acc_0 = {key.relace('@', '_0@'): value for key, value in acc_0.items()}
        acc_1 = self.metric(probs1, label[:, 1])
        acc_1 = {key.relace('@', '_1@'): value for key, value in acc_1.items()}
        ret.update(acc_0)
        ret.update(acc_1)
        return ret

    @staticmethod
    def get_config_template():
        '''
        { "ENV" :
            { "description" : "",
              "A" : {
                    "value": 1.0,
                    "description": ""
               }
            }
        }
        :return:
        '''
        return dict_to_yaml('MODEL',
                            __class__.__name__,
                            VideoClassifier2x.para_dict,
                            set_name=True)