Very simple multi-layer perceptron (also called FFN)

config files. only support .py file as config now. ref: mmcv.utils.config Example: >>> cfg = Config(dict(a=1, b=dict(b1=[0,

Track a series of values and provide access to smoothed values over a window or the global series average.

ResNet backbone with frozen BatchNorm.

A basic Swin Transformer layer for one stage. Args: dim (int): Number of feature channels depth (int): Depths of this stage.

This is the Cross-Attention Detector module that performs object detection

Multilayer perceptron.

Image to Patch Embedding Args: patch_size (int): Patch token size. Default: 4. in_chans (int): Number of input image channels. De

Absolute pos embedding, learned.

This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized t

This module converts the model's output into the format expected by the coco api

Swin Transformer backbone. A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` - https:/

Swin Transformer Block. Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. window_siz

Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. Args:

argparse action to split an argument into KEY=VALUE form on the first = and append to a dictionary. List options should be passed as comm

BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt

Inherit from this class and define ``__nice__`` to "nicely" print your objects. Defines ``__str__`` and ``__repr__`` in terms of ``__nice__``

Patch Merging Layer Args: dim (int): Number of input channels. norm_layer (nn.Module, optional): Normalization layer. Default: n

This is a more standard version of the position embedding, very similar to the one used by the Attention is all you need paper, generalized t

Randomly selects between transforms1 and transforms2, with probability p for transforms1 and (1 - p) for transforms2

Randomly selects between transforms1 and transforms2, with probability p for transforms1 and (1 - p) for transforms2