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https://github.com/open-mmlab/mmpretrain.git
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6474ea2fc0
* add efficient backbone * Update Readme and metafile * Add unit tests * fix confict * fix lint * update efficientformer head unit tests * update README * fix unit test * fix Readme * fix example * fix typo * recover api modification * Update EfficiemtFormer Backbone * fix unit tests * add efficientformer to readme and model zoo
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch.nn as nn
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import torch.nn.functional as F
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from ..builder import HEADS
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from .cls_head import ClsHead
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@HEADS.register_module()
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class EfficientFormerClsHead(ClsHead):
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"""EfficientFormer classifier head.
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Args:
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num_classes (int): Number of categories excluding the background
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category.
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in_channels (int): Number of channels in the input feature map.
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distillation (bool): Whether use a additional distilled head.
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Defaults to True.
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init_cfg (dict): The extra initialization configs. Defaults to
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``dict(type='Normal', layer='Linear', std=0.01)``.
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"""
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def __init__(self,
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num_classes,
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in_channels,
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distillation=True,
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init_cfg=dict(type='Normal', layer='Linear', std=0.01),
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*args,
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**kwargs):
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super(EfficientFormerClsHead, self).__init__(
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init_cfg=init_cfg, *args, **kwargs)
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self.in_channels = in_channels
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self.num_classes = num_classes
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self.dist = distillation
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if self.num_classes <= 0:
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raise ValueError(
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f'num_classes={num_classes} must be a positive integer')
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self.head = nn.Linear(self.in_channels, self.num_classes)
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if self.dist:
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self.dist_head = nn.Linear(self.in_channels, self.num_classes)
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def pre_logits(self, x):
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if isinstance(x, tuple):
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x = x[-1]
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return x
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def simple_test(self, x, softmax=True, post_process=True):
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"""Inference without augmentation.
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Args:
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x (tuple[tuple[tensor, tensor]]): The input features.
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Multi-stage inputs are acceptable but only the last stage will
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be used to classify. Every item should be a tuple which
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includes patch token and cls token. The cls token will be used
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to classify and the shape of it should be
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``(num_samples, in_channels)``.
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softmax (bool): Whether to softmax the classification score.
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post_process (bool): Whether to do post processing the
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inference results. It will convert the output to a list.
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Returns:
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Tensor | list: The inference results.
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- If no post processing, the output is a tensor with shape
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``(num_samples, num_classes)``.
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- If post processing, the output is a multi-dimentional list of
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float and the dimensions are ``(num_samples, num_classes)``.
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"""
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x = self.pre_logits(x)
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cls_score = self.head(x)
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if self.dist:
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cls_score = (cls_score + self.dist_head(x)) / 2
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if softmax:
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pred = (
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F.softmax(cls_score, dim=1) if cls_score is not None else None)
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else:
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pred = cls_score
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if post_process:
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return self.post_process(pred)
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else:
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return pred
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def forward_train(self, x, gt_label, **kwargs):
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if self.dist:
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raise NotImplementedError(
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"MMClassification doesn't support to train"
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' the distilled version EfficientFormer.')
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else:
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x = self.pre_logits(x)
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cls_score = self.head(x)
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losses = self.loss(cls_score, gt_label, **kwargs)
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return losses
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