InceptionV3¶
- class mmpretrain.models.backbones.InceptionV3(num_classes=1000, aux_logits=False, dropout=0.5, init_cfg=[{'type': 'TruncNormal', 'layer': ['Conv2d', 'Linear'], 'std': 0.1}, {'type': 'Constant', 'layer': 'BatchNorm2d', 'val': 1}])[源代码]¶
Inception V3 backbone.
A PyTorch implementation of Rethinking the Inception Architecture for Computer Vision
This implementation is modified from https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py. Licensed under the BSD 3-Clause License.
- 参数:
num_classes (int) – The number of categroies. Defaults to 1000.
aux_logits (bool) – Whether to enable the auxiliary branch. If False, the auxiliary logits output will be None. Defaults to False.
dropout (float) – Dropout rate. Defaults to 0.5.
init_cfg (dict, optional) – The config of initialization. Defaults to use trunc normal with
std=0.1
for all Conv2d and Linear layers and constant withval=1
for all BatchNorm2d layers.
示例
>>> import torch >>> from mmpretrain.models import build_backbone >>> >>> inputs = torch.rand(2, 3, 299, 299) >>> cfg = dict(type='InceptionV3', num_classes=100) >>> backbone = build_backbone(cfg) >>> aux_out, out = backbone(inputs) >>> # The auxiliary branch is disabled by default. >>> assert aux_out is None >>> print(out.shape) torch.Size([2, 100]) >>> cfg = dict(type='InceptionV3', num_classes=100, aux_logits=True) >>> backbone = build_backbone(cfg) >>> aux_out, out = backbone(inputs) >>> print(aux_out.shape, out.shape) torch.Size([2, 100]) torch.Size([2, 100])