ResNet

class paddle.vision.models. ResNet ( block, depth=50, width=64, num_classes=1000, with_pool=True, groups=1 ) [source]

ResNet model from “Deep Residual Learning for Image Recognition”

Parameters
  • Block (BasicBlock|BottleneckBlock) – block module of model.

  • depth (int, optional) – layers of resnet, Default: 50.

  • width (int, optional) – base width per convolution group for each convolution block, Default: 64.

  • num_classes (int, optional) – output dim of last fc layer. If num_classes <=0, last fc layer will not be defined. Default: 1000.

  • with_pool (bool, optional) – use pool before the last fc layer or not. Default: True.

  • groups (int, optional) – number of groups for each convolution block, Default: 1.

Examples

import paddle
from paddle.vision.models import ResNet
from paddle.vision.models.resnet import BottleneckBlock, BasicBlock

# build ResNet with 18 layers
resnet18 = ResNet(BasicBlock, 18)

# build ResNet with 50 layers
resnet50 = ResNet(BottleneckBlock, 50)

# build Wide ResNet model
wide_resnet50_2 = ResNet(BottleneckBlock, 50, width=64*2)

# build ResNeXt model
resnext50_32x4d = ResNet(BottleneckBlock, 50, width=4, groups=32)

x = paddle.rand([1, 3, 224, 224])
out = resnet18(x)

print(out.shape)
# [1, 1000]
forward ( x )

forward

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters
  • *inputs (tuple) – unpacked tuple arguments

  • **kwargs (dict) – unpacked dict arguments