[M] Symlink isn't handled, rename directory instead
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# Ke Chen
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# knutchen@ucsd.edu
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# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
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# Model Core
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# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
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# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
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import math
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import random
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint as checkpoint
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from torchlibrosa.stft import Spectrogram, LogmelFilterBank
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from torchlibrosa.augmentation import SpecAugmentation
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from itertools import repeat
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from .pytorch_utils import do_mixup, interpolate
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from . import config
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import collections.abc
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import warnings
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from torch.nn.init import _calculate_fan_in_and_fan_out
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def _ntuple(n):
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def parse(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return tuple(repeat(x, n))
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return parse
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to_1tuple = _ntuple(1)
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to_2tuple = _ntuple(2)
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to_3tuple = _ntuple(3)
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to_4tuple = _ntuple(4)
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to_ntuple = _ntuple
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def drop_path(x, drop_prob: float = 0., training: bool = False):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
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the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
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changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
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'survival rate' as the argument.
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"""
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if drop_prob == 0. or not training:
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return x
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keep_prob = 1 - drop_prob
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shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
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random_tensor.floor_() # binarize
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output = x.div(keep_prob) * random_tensor
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return output
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class DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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class PatchEmbed(nn.Module):
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""" 2D Image to Patch Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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patch_stride = to_2tuple(patch_stride)
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self.img_size = img_size
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self.patch_size = patch_size
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self.patch_stride = patch_stride
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self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
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self.num_patches = self.grid_size[0] * self.grid_size[1]
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self.flatten = flatten
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self.in_chans = in_chans
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self.embed_dim = embed_dim
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padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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def forward(self, x):
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B, C, H, W = x.shape
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assert H == self.img_size[0] and W == self.img_size[1], \
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
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x = self.proj(x)
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if self.flatten:
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x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
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x = self.norm(x)
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return x
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class Mlp(nn.Module):
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""" MLP as used in Vision Transformer, MLP-Mixer and related networks
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"""
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = act_layer()
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x)
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x = self.drop(x)
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return x
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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# Cut & paste from PyTorch official master until it's in a few official releases - RW
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# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
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"The distribution of values may be incorrect.",
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stacklevel=2)
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with torch.no_grad():
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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l = norm_cdf((a - mean) / std)
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u = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [l, u], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * l - 1, 2 * u - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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tensor.clamp_(min=a, max=b)
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return tensor
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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# type: (Tensor, float, float, float, float) -> Tensor
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r"""Fills the input Tensor with values drawn from a truncated
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normal distribution. The values are effectively drawn from the
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normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \leq \text{mean} \leq b`.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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Examples:
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>>> w = torch.empty(3, 5)
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>>> nn.init.trunc_normal_(w)
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"""
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
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fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
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if mode == 'fan_in':
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denom = fan_in
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elif mode == 'fan_out':
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denom = fan_out
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elif mode == 'fan_avg':
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denom = (fan_in + fan_out) / 2
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variance = scale / denom
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if distribution == "truncated_normal":
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# constant is stddev of standard normal truncated to (-2, 2)
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trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
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elif distribution == "normal":
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tensor.normal_(std=math.sqrt(variance))
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elif distribution == "uniform":
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bound = math.sqrt(3 * variance)
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tensor.uniform_(-bound, bound)
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else:
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raise ValueError(f"invalid distribution {distribution}")
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def lecun_normal_(tensor):
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variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
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# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
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# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
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def window_partition(x, window_size):
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"""
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Args:
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x: (B, H, W, C)
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window_size (int): window size
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Returns:
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windows: (num_windows*B, window_size, window_size, C)
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"""
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B, H, W, C = x.shape
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x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows
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def window_reverse(windows, window_size, H, W):
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"""
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Args:
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windows: (num_windows*B, window_size, window_size, C)
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window_size (int): Window size
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H (int): Height of image
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W (int): Width of image
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Returns:
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x: (B, H, W, C)
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"""
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B = int(windows.shape[0] / (H * W / window_size / window_size))
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x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
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return x
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class WindowAttention(nn.Module):
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r""" Window based multi-head self attention (W-MSA) module with relative position bias.
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It supports both of shifted and non-shifted window.
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Args:
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dim (int): Number of input channels.
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window_size (tuple[int]): The height and width of the window.
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num_heads (int): Number of attention heads.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
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attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
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proj_drop (float, optional): Dropout ratio of output. Default: 0.0
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"""
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def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.dim = dim
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self.window_size = window_size # Wh, Ww
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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# define a parameter table of relative position bias
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
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# get pair-wise relative position index for each token inside the window
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coords_h = torch.arange(self.window_size[0])
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coords_w = torch.arange(self.window_size[1])
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coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
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coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
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relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
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relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
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relative_coords[:, :, 1] += self.window_size[1] - 1
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relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
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relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
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self.register_buffer("relative_position_index", relative_position_index)
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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trunc_normal_(self.relative_position_bias_table, std=.02)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, x, mask=None):
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"""
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Args:
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x: input features with shape of (num_windows*B, N, C)
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mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
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"""
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B_, N, C = x.shape
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qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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q = q * self.scale
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attn = (q @ k.transpose(-2, -1))
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relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
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attn = attn + relative_position_bias.unsqueeze(0)
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if mask is not None:
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nW = mask.shape[0]
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attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
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attn = attn.view(-1, self.num_heads, N, N)
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attn = self.softmax(attn)
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else:
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attn = self.softmax(attn)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x, attn
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def extra_repr(self):
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return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
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# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
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class SwinTransformerBlock(nn.Module):
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r""" Swin Transformer Block.
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Args:
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dim (int): Number of input channels.
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input_resolution (tuple[int]): Input resulotion.
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num_heads (int): Number of attention heads.
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window_size (int): Window size.
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shift_size (int): Shift size for SW-MSA.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
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qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
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drop (float, optional): Dropout rate. Default: 0.0
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attn_drop (float, optional): Attention dropout rate. Default: 0.0
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drop_path (float, optional): Stochastic depth rate. Default: 0.0
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act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
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norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
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"""
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def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
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mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
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act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'):
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super().__init__()
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self.dim = dim
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self.input_resolution = input_resolution
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self.num_heads = num_heads
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self.window_size = window_size
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self.shift_size = shift_size
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self.mlp_ratio = mlp_ratio
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self.norm_before_mlp = norm_before_mlp
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if min(self.input_resolution) <= self.window_size:
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# if window size is larger than input resolution, we don't partition windows
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self.shift_size = 0
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self.window_size = min(self.input_resolution)
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assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
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self.norm1 = norm_layer(dim)
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self.attn = WindowAttention(
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dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
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qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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if self.norm_before_mlp == 'ln':
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self.norm2 = nn.LayerNorm(dim)
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elif self.norm_before_mlp == 'bn':
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self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2)
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else:
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raise NotImplementedError
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
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if self.shift_size > 0:
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# calculate attention mask for SW-MSA
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H, W = self.input_resolution
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img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
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h_slices = (slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None))
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w_slices = (slice(0, -self.window_size),
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slice(-self.window_size, -self.shift_size),
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slice(-self.shift_size, None))
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cnt = 0
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for h in h_slices:
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for w in w_slices:
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img_mask[:, h, w, :] = cnt
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cnt += 1
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mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
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mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
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attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
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attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
else:
|
||||
attn_mask = None
|
||||
|
||||
self.register_buffer("attn_mask", attn_mask)
|
||||
|
||||
def forward(self, x):
|
||||
# pdb.set_trace()
|
||||
H, W = self.input_resolution
|
||||
# print("H: ", H)
|
||||
# print("W: ", W)
|
||||
# pdb.set_trace()
|
||||
B, L, C = x.shape
|
||||
# assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
shifted_x = x
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows, attn = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x, attn
|
||||
|
||||
def extra_repr(self):
|
||||
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
|
||||
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
|
||||
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
r""" Patch Merging Layer.
|
||||
Args:
|
||||
input_resolution (tuple[int]): Resolution of input feature.
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.input_resolution = input_resolution
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
x: B, H*W, C
|
||||
"""
|
||||
H, W = self.input_resolution
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
def extra_repr(self):
|
||||
return f"input_resolution={self.input_resolution}, dim={self.dim}"
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int]): Input resolution.
|
||||
depth (int): Number of blocks.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Local window size.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
|
||||
norm_before_mlp='ln'):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
|
||||
num_heads=num_heads, window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop, attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer, norm_before_mlp=norm_before_mlp)
|
||||
for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x):
|
||||
attns = []
|
||||
for blk in self.blocks:
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x)
|
||||
else:
|
||||
x, attn = blk(x)
|
||||
if not self.training:
|
||||
attns.append(attn.unsqueeze(0))
|
||||
if self.downsample is not None:
|
||||
x = self.downsample(x)
|
||||
if not self.training:
|
||||
attn = torch.cat(attns, dim = 0)
|
||||
attn = torch.mean(attn, dim = 0)
|
||||
return x, attn
|
||||
|
||||
def extra_repr(self):
|
||||
return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
|
||||
|
||||
|
||||
# The Core of HTSAT
|
||||
class HTSAT_Swin_Transformer(nn.Module):
|
||||
r"""HTSAT based on the Swin Transformer
|
||||
Args:
|
||||
spec_size (int | tuple(int)): Input Spectrogram size. Default 256
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4
|
||||
path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4
|
||||
in_chans (int): Number of input image channels. Default: 1 (mono)
|
||||
num_classes (int): Number of classes for classification head. Default: 527
|
||||
embed_dim (int): Patch embedding dimension. Default: 96
|
||||
depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer.
|
||||
num_heads (tuple(int)): Number of attention heads in different layers.
|
||||
window_size (int): Window size. Default: 8
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
|
||||
drop_rate (float): Dropout rate. Default: 0
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.1
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
|
||||
config (module): The configuration Module from config.py
|
||||
"""
|
||||
|
||||
def __init__(self, spec_size=256, patch_size=4, patch_stride=(4,4),
|
||||
in_chans=1, num_classes=527,
|
||||
embed_dim=96, depths=[2, 2, 6, 2], num_heads=[4, 8, 16, 32],
|
||||
window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
|
||||
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False, patch_norm=True,
|
||||
use_checkpoint=False, norm_before_mlp='ln', config = None, **kwargs):
|
||||
super(HTSAT_Swin_Transformer, self).__init__()
|
||||
|
||||
self.config = config
|
||||
self.spec_size = spec_size
|
||||
self.patch_stride = patch_stride
|
||||
self.patch_size = patch_size
|
||||
self.window_size = window_size
|
||||
self.embed_dim = embed_dim
|
||||
self.depths = depths
|
||||
self.ape = ape
|
||||
self.in_chans = in_chans
|
||||
self.num_classes = num_classes
|
||||
self.num_heads = num_heads
|
||||
self.num_layers = len(self.depths)
|
||||
self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1))
|
||||
|
||||
self.drop_rate = drop_rate
|
||||
self.attn_drop_rate = attn_drop_rate
|
||||
self.drop_path_rate = drop_path_rate
|
||||
|
||||
self.qkv_bias = qkv_bias
|
||||
self.qk_scale = None
|
||||
|
||||
self.patch_norm = patch_norm
|
||||
self.norm_layer = norm_layer if self.patch_norm else None
|
||||
self.norm_before_mlp = norm_before_mlp
|
||||
self.mlp_ratio = mlp_ratio
|
||||
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# process mel-spec ; used only once
|
||||
self.freq_ratio = self.spec_size // self.config.mel_bins
|
||||
window = 'hann'
|
||||
center = True
|
||||
pad_mode = 'reflect'
|
||||
ref = 1.0
|
||||
amin = 1e-10
|
||||
top_db = None
|
||||
self.interpolate_ratio = 32 # Downsampled ratio
|
||||
# Spectrogram extractor
|
||||
self.spectrogram_extractor = Spectrogram(n_fft=config.window_size, hop_length=config.hop_size,
|
||||
win_length=config.window_size, window=window, center=center, pad_mode=pad_mode,
|
||||
freeze_parameters=True)
|
||||
# Logmel feature extractor
|
||||
self.logmel_extractor = LogmelFilterBank(sr=config.sample_rate, n_fft=config.window_size,
|
||||
n_mels=config.mel_bins, fmin=config.fmin, fmax=config.fmax, ref=ref, amin=amin, top_db=top_db,
|
||||
freeze_parameters=True)
|
||||
# Spec augmenter
|
||||
self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
|
||||
freq_drop_width=8, freq_stripes_num=2) # 2 2
|
||||
self.bn0 = nn.BatchNorm2d(self.config.mel_bins)
|
||||
|
||||
|
||||
# split spctrogram into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
img_size=self.spec_size, patch_size=self.patch_size, in_chans=self.in_chans,
|
||||
embed_dim=self.embed_dim, norm_layer=self.norm_layer, patch_stride = patch_stride)
|
||||
|
||||
num_patches = self.patch_embed.num_patches
|
||||
patches_resolution = self.patch_embed.grid_size
|
||||
self.patches_resolution = patches_resolution
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=self.drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(dim=int(self.embed_dim * 2 ** i_layer),
|
||||
input_resolution=(patches_resolution[0] // (2 ** i_layer),
|
||||
patches_resolution[1] // (2 ** i_layer)),
|
||||
depth=self.depths[i_layer],
|
||||
num_heads=self.num_heads[i_layer],
|
||||
window_size=self.window_size,
|
||||
mlp_ratio=self.mlp_ratio,
|
||||
qkv_bias=self.qkv_bias, qk_scale=self.qk_scale,
|
||||
drop=self.drop_rate, attn_drop=self.attn_drop_rate,
|
||||
drop_path=dpr[sum(self.depths[:i_layer]):sum(self.depths[:i_layer + 1])],
|
||||
norm_layer=self.norm_layer,
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint,
|
||||
norm_before_mlp=self.norm_before_mlp)
|
||||
self.layers.append(layer)
|
||||
|
||||
self.norm = self.norm_layer(self.num_features)
|
||||
self.avgpool = nn.AdaptiveAvgPool1d(1)
|
||||
self.maxpool = nn.AdaptiveMaxPool1d(1)
|
||||
|
||||
if self.config.enable_tscam:
|
||||
SF = self.spec_size // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] // self.freq_ratio
|
||||
self.tscam_conv = nn.Conv2d(
|
||||
in_channels = self.num_features,
|
||||
out_channels = self.num_classes,
|
||||
kernel_size = (SF,3),
|
||||
padding = (0,1)
|
||||
)
|
||||
self.head = nn.Linear(num_classes, num_classes)
|
||||
else:
|
||||
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'absolute_pos_embed'}
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay_keywords(self):
|
||||
return {'relative_position_bias_table'}
|
||||
|
||||
def forward_features(self, x):
|
||||
frames_num = x.shape[2]
|
||||
x = self.patch_embed(x)
|
||||
if self.ape:
|
||||
x = x + self.absolute_pos_embed
|
||||
x = self.pos_drop(x)
|
||||
for i, layer in enumerate(self.layers):
|
||||
x, attn = layer(x)
|
||||
|
||||
if self.config.enable_tscam:
|
||||
# for x
|
||||
x = self.norm(x)
|
||||
B, N, C = x.shape
|
||||
SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
|
||||
ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
|
||||
x = x.permute(0,2,1).contiguous().reshape(B, C, SF, ST)
|
||||
B, C, F, T = x.shape
|
||||
# group 2D CNN
|
||||
c_freq_bin = F // self.freq_ratio
|
||||
x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
|
||||
x = x.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
|
||||
|
||||
# get latent_output
|
||||
latent_output = self.avgpool(torch.flatten(x,2))
|
||||
latent_output = torch.flatten(latent_output, 1)
|
||||
|
||||
# display the attention map, if needed
|
||||
if self.config.htsat_attn_heatmap:
|
||||
# for attn
|
||||
attn = torch.mean(attn, dim = 1)
|
||||
attn = torch.mean(attn, dim = 1)
|
||||
attn = attn.reshape(B, SF, ST)
|
||||
c_freq_bin = SF // self.freq_ratio
|
||||
attn = attn.reshape(B, SF // c_freq_bin, c_freq_bin, ST)
|
||||
attn = attn.permute(0,2,1,3).contiguous().reshape(B, c_freq_bin, -1)
|
||||
attn = attn.mean(dim = 1)
|
||||
attn_max = torch.max(attn, dim = 1, keepdim = True)[0]
|
||||
attn_min = torch.min(attn, dim = 1, keepdim = True)[0]
|
||||
attn = ((attn * 0.15) + (attn_max * 0.85 - attn_min)) / (attn_max - attn_min)
|
||||
attn = attn.unsqueeze(dim = 2)
|
||||
|
||||
x = self.tscam_conv(x)
|
||||
x = torch.flatten(x, 2) # B, C, T
|
||||
|
||||
if self.config.htsat_attn_heatmap:
|
||||
fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous() * attn, 8 * self.patch_stride[1])
|
||||
else:
|
||||
fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1])
|
||||
|
||||
x = self.avgpool(x)
|
||||
x = torch.flatten(x, 1)
|
||||
|
||||
if self.config.loss_type == "clip_ce":
|
||||
output_dict = {
|
||||
'framewise_output': fpx, # already sigmoided
|
||||
'clipwise_output': x,
|
||||
'latent_output': latent_output
|
||||
}
|
||||
else:
|
||||
output_dict = {
|
||||
'framewise_output': fpx, # already sigmoided
|
||||
'clipwise_output': torch.sigmoid(x),
|
||||
'latent_output': latent_output
|
||||
}
|
||||
|
||||
else:
|
||||
x = self.norm(x) # B N C
|
||||
B, N, C = x.shape
|
||||
|
||||
fpx = x.permute(0,2,1).contiguous().reshape(B, C, frames_num // (2 ** (len(self.depths) + 1)), frames_num // (2 ** (len(self.depths) + 1)) )
|
||||
B, C, F, T = fpx.shape
|
||||
c_freq_bin = F // self.freq_ratio
|
||||
fpx = fpx.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
|
||||
fpx = fpx.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
|
||||
fpx = torch.sum(fpx, dim = 2)
|
||||
fpx = interpolate(fpx.permute(0,2,1).contiguous(), 8 * self.patch_stride[1])
|
||||
x = self.avgpool(x.transpose(1, 2)) # B C 1
|
||||
x = torch.flatten(x, 1)
|
||||
if self.num_classes > 0:
|
||||
x = self.head(x)
|
||||
fpx = self.head(fpx)
|
||||
output_dict = {'framewise_output': torch.sigmoid(fpx),
|
||||
'clipwise_output': torch.sigmoid(x)}
|
||||
return output_dict
|
||||
|
||||
def crop_wav(self, x, crop_size, spe_pos = None):
|
||||
time_steps = x.shape[2]
|
||||
tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device)
|
||||
for i in range(len(x)):
|
||||
if spe_pos is None:
|
||||
crop_pos = random.randint(0, time_steps - crop_size - 1)
|
||||
else:
|
||||
crop_pos = spe_pos
|
||||
tx[i][0] = x[i, 0, crop_pos:crop_pos + crop_size,:]
|
||||
return tx
|
||||
|
||||
# Reshape the wavform to a img size, if you want to use the pretrained swin transformer model
|
||||
def reshape_wav2img(self, x):
|
||||
B, C, T, F = x.shape
|
||||
target_T = int(self.spec_size * self.freq_ratio)
|
||||
target_F = self.spec_size // self.freq_ratio
|
||||
assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
|
||||
# to avoid bicubic zero error
|
||||
if T < target_T:
|
||||
x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
|
||||
if F < target_F:
|
||||
x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)
|
||||
x = x.permute(0,1,3,2).contiguous()
|
||||
x = x.reshape(x.shape[0], x.shape[1], x.shape[2], self.freq_ratio, x.shape[3] // self.freq_ratio)
|
||||
# print(x.shape)
|
||||
x = x.permute(0,1,3,2,4).contiguous()
|
||||
x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4])
|
||||
return x
|
||||
|
||||
# Repeat the wavform to a img size, if you want to use the pretrained swin transformer model
|
||||
def repeat_wat2img(self, x, cur_pos):
|
||||
B, C, T, F = x.shape
|
||||
target_T = int(self.spec_size * self.freq_ratio)
|
||||
target_F = self.spec_size // self.freq_ratio
|
||||
assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
|
||||
# to avoid bicubic zero error
|
||||
if T < target_T:
|
||||
x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
|
||||
if F < target_F:
|
||||
x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)
|
||||
x = x.permute(0,1,3,2).contiguous() # B C F T
|
||||
x = x[:,:,:,cur_pos:cur_pos + self.spec_size]
|
||||
x = x.repeat(repeats = (1,1,4,1))
|
||||
return x
|
||||
|
||||
def forward(self, x: torch.Tensor, mixup_lambda = None, infer_mode = False):# out_feat_keys: List[str] = None):
|
||||
x = self.spectrogram_extractor(x) # (batch_size, 1, time_steps, freq_bins)
|
||||
x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
|
||||
|
||||
|
||||
x = x.transpose(1, 3)
|
||||
x = self.bn0(x)
|
||||
x = x.transpose(1, 3)
|
||||
if self.training:
|
||||
x = self.spec_augmenter(x)
|
||||
if self.training and mixup_lambda is not None:
|
||||
x = do_mixup(x, mixup_lambda)
|
||||
|
||||
if infer_mode:
|
||||
# in infer mode. we need to handle different length audio input
|
||||
frame_num = x.shape[2]
|
||||
target_T = int(self.spec_size * self.freq_ratio)
|
||||
repeat_ratio = math.floor(target_T / frame_num)
|
||||
x = x.repeat(repeats=(1,1,repeat_ratio,1))
|
||||
x = self.reshape_wav2img(x)
|
||||
output_dict = self.forward_features(x)
|
||||
elif self.config.enable_repeat_mode:
|
||||
if self.training:
|
||||
cur_pos = random.randint(0, (self.freq_ratio - 1) * self.spec_size - 1)
|
||||
x = self.repeat_wat2img(x, cur_pos)
|
||||
output_dict = self.forward_features(x)
|
||||
else:
|
||||
output_dicts = []
|
||||
for cur_pos in range(0, (self.freq_ratio - 1) * self.spec_size + 1, self.spec_size):
|
||||
tx = x.clone()
|
||||
tx = self.repeat_wat2img(tx, cur_pos)
|
||||
output_dicts.append(self.forward_features(tx))
|
||||
clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
|
||||
framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
|
||||
for d in output_dicts:
|
||||
clipwise_output += d["clipwise_output"]
|
||||
framewise_output += d["framewise_output"]
|
||||
clipwise_output = clipwise_output / len(output_dicts)
|
||||
framewise_output = framewise_output / len(output_dicts)
|
||||
|
||||
output_dict = {
|
||||
'framewise_output': framewise_output,
|
||||
'clipwise_output': clipwise_output
|
||||
}
|
||||
else:
|
||||
if x.shape[2] > self.freq_ratio * self.spec_size:
|
||||
if self.training:
|
||||
x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size)
|
||||
x = self.reshape_wav2img(x)
|
||||
output_dict = self.forward_features(x)
|
||||
else:
|
||||
# Change: Hard code here
|
||||
overlap_size = 344 #(x.shape[2] - 1) // 4
|
||||
output_dicts = []
|
||||
crop_size = 689 #(x.shape[2] - 1) // 2
|
||||
for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size):
|
||||
tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos)
|
||||
tx = self.reshape_wav2img(tx)
|
||||
output_dicts.append(self.forward_features(tx))
|
||||
clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
|
||||
framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
|
||||
latent_output = torch.zeros_like(output_dicts[0]["latent_output"]).float().to(x.device)
|
||||
for d in output_dicts:
|
||||
clipwise_output += d["clipwise_output"]
|
||||
framewise_output += d["framewise_output"]
|
||||
latent_output += d["latent_output"]
|
||||
clipwise_output = clipwise_output / len(output_dicts)
|
||||
framewise_output = framewise_output / len(output_dicts)
|
||||
latent_output = latent_output / len(output_dicts)
|
||||
output_dict = {
|
||||
'framewise_output': framewise_output,
|
||||
'clipwise_output': clipwise_output,
|
||||
'latent_output': latent_output,
|
||||
}
|
||||
else: # this part is typically used, and most easy one
|
||||
x = self.reshape_wav2img(x)
|
||||
output_dict = self.forward_features(x)
|
||||
# x = self.head(x)
|
||||
return output_dict
|
||||
|
||||
class HTSATWrapper(nn.Module):
|
||||
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
||||
fmax, classes_num, out_emb):
|
||||
super().__init__()
|
||||
|
||||
# print("parameters are being overidden when using HTSAT")
|
||||
# print("HTSAT only support loading a pretrained model on AudioSet")
|
||||
# @TODO later look at what parameters are same and can be merged
|
||||
|
||||
self.htsat = HTSAT_Swin_Transformer(config=config)
|
||||
|
||||
def forward(self, x):
|
||||
out_dict = self.htsat(x)
|
||||
out_dict['embedding'] = out_dict['latent_output']
|
||||
return out_dict
|
||||
Reference in New Issue
Block a user