[M] Symlink isn't handled, rename directory instead
This commit is contained in:
@@ -0,0 +1,6 @@
|
||||
from . import clap
|
||||
from . import audio
|
||||
from . import htsat
|
||||
from . import config
|
||||
from . import pytorch_utils
|
||||
from . import htsat
|
||||
@@ -0,0 +1,182 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
|
||||
from .htsat import HTSATWrapper
|
||||
|
||||
def get_audio_encoder(name: str):
|
||||
if name == "Cnn14":
|
||||
return Cnn14
|
||||
elif name == "HTSAT":
|
||||
return HTSATWrapper
|
||||
else:
|
||||
raise Exception('The audio encoder name {} is incorrect or not supported'.format(name))
|
||||
|
||||
|
||||
class ConvBlock(nn.Module):
|
||||
def __init__(self, in_channels, out_channels):
|
||||
|
||||
super(ConvBlock, self).__init__()
|
||||
|
||||
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=(3, 3), stride=(1, 1),
|
||||
padding=(1, 1), bias=False)
|
||||
|
||||
self.conv2 = nn.Conv2d(in_channels=out_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=(3, 3), stride=(1, 1),
|
||||
padding=(1, 1), bias=False)
|
||||
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
self.bn2 = nn.BatchNorm2d(out_channels)
|
||||
|
||||
|
||||
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
||||
|
||||
x = input
|
||||
x = F.relu_(self.bn1(self.conv1(x)))
|
||||
x = F.relu_(self.bn2(self.conv2(x)))
|
||||
if pool_type == 'max':
|
||||
x = F.max_pool2d(x, kernel_size=pool_size)
|
||||
elif pool_type == 'avg':
|
||||
x = F.avg_pool2d(x, kernel_size=pool_size)
|
||||
elif pool_type == 'avg+max':
|
||||
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
||||
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
||||
x = x1 + x2
|
||||
else:
|
||||
raise Exception('Incorrect argument!')
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class ConvBlock5x5(nn.Module):
|
||||
def __init__(self, in_channels, out_channels):
|
||||
|
||||
super(ConvBlock5x5, self).__init__()
|
||||
|
||||
self.conv1 = nn.Conv2d(in_channels=in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=(5, 5), stride=(1, 1),
|
||||
padding=(2, 2), bias=False)
|
||||
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
|
||||
|
||||
def forward(self, input, pool_size=(2, 2), pool_type='avg'):
|
||||
|
||||
x = input
|
||||
x = F.relu_(self.bn1(self.conv1(x)))
|
||||
if pool_type == 'max':
|
||||
x = F.max_pool2d(x, kernel_size=pool_size)
|
||||
elif pool_type == 'avg':
|
||||
x = F.avg_pool2d(x, kernel_size=pool_size)
|
||||
elif pool_type == 'avg+max':
|
||||
x1 = F.avg_pool2d(x, kernel_size=pool_size)
|
||||
x2 = F.max_pool2d(x, kernel_size=pool_size)
|
||||
x = x1 + x2
|
||||
else:
|
||||
raise Exception('Incorrect argument!')
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class AttBlock(nn.Module):
|
||||
def __init__(self, n_in, n_out, activation='linear', temperature=1.):
|
||||
super(AttBlock, self).__init__()
|
||||
|
||||
self.activation = activation
|
||||
self.temperature = temperature
|
||||
self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
|
||||
self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True)
|
||||
|
||||
self.bn_att = nn.BatchNorm1d(n_out)
|
||||
|
||||
def forward(self, x):
|
||||
# x: (n_samples, n_in, n_time)
|
||||
norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1)
|
||||
cla = self.nonlinear_transform(self.cla(x))
|
||||
x = torch.sum(norm_att * cla, dim=2)
|
||||
return x, norm_att, cla
|
||||
|
||||
def nonlinear_transform(self, x):
|
||||
if self.activation == 'linear':
|
||||
return x
|
||||
elif self.activation == 'sigmoid':
|
||||
return torch.sigmoid(x)
|
||||
|
||||
|
||||
class Cnn14(nn.Module):
|
||||
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
|
||||
fmax, classes_num, out_emb):
|
||||
|
||||
super(Cnn14, self).__init__()
|
||||
|
||||
window = 'hann'
|
||||
center = True
|
||||
pad_mode = 'reflect'
|
||||
ref = 1.0
|
||||
amin = 1e-10
|
||||
top_db = None
|
||||
|
||||
# Spectrogram extractor
|
||||
self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size,
|
||||
win_length=window_size, window=window, center=center, pad_mode=pad_mode,
|
||||
freeze_parameters=True)
|
||||
|
||||
# Logmel feature extractor
|
||||
self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size,
|
||||
n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db,
|
||||
freeze_parameters=True)
|
||||
|
||||
self.bn0 = nn.BatchNorm2d(64)
|
||||
|
||||
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
|
||||
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
|
||||
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
|
||||
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
|
||||
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
|
||||
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
|
||||
|
||||
# out_emb is 2048 for best Cnn14
|
||||
self.fc1 = nn.Linear(2048, out_emb, bias=True)
|
||||
self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True)
|
||||
|
||||
def forward(self, input, mixup_lambda=None):
|
||||
"""
|
||||
Input: (batch_size, data_length)
|
||||
"""
|
||||
|
||||
x = self.spectrogram_extractor(input) # (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)
|
||||
|
||||
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
|
||||
x = F.dropout(x, p=0.2, training=self.training)
|
||||
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
|
||||
x = F.dropout(x, p=0.2, training=self.training)
|
||||
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
|
||||
x = F.dropout(x, p=0.2, training=self.training)
|
||||
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
|
||||
x = F.dropout(x, p=0.2, training=self.training)
|
||||
x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
|
||||
x = F.dropout(x, p=0.2, training=self.training)
|
||||
x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
|
||||
x = F.dropout(x, p=0.2, training=self.training)
|
||||
x = torch.mean(x, dim=3)
|
||||
|
||||
(x1, _) = torch.max(x, dim=2)
|
||||
x2 = torch.mean(x, dim=2)
|
||||
x = x1 + x2
|
||||
x = F.dropout(x, p=0.5, training=self.training)
|
||||
x = F.relu_(self.fc1(x))
|
||||
embedding = F.dropout(x, p=0.5, training=self.training)
|
||||
clipwise_output = torch.sigmoid(self.fc_audioset(x))
|
||||
|
||||
output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding}
|
||||
|
||||
return output_dict
|
||||
@@ -0,0 +1,109 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
from transformers import AutoModel
|
||||
from .audio import get_audio_encoder
|
||||
|
||||
class Projection(nn.Module):
|
||||
def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
|
||||
super().__init__()
|
||||
self.linear1 = nn.Linear(d_in, d_out, bias=False)
|
||||
self.linear2 = nn.Linear(d_out, d_out, bias=False)
|
||||
self.layer_norm = nn.LayerNorm(d_out)
|
||||
self.drop = nn.Dropout(p)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
embed1 = self.linear1(x)
|
||||
embed2 = self.drop(self.linear2(F.gelu(embed1)))
|
||||
embeds = self.layer_norm(embed1 + embed2)
|
||||
return embeds
|
||||
|
||||
class AudioEncoder(nn.Module):
|
||||
def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int,
|
||||
hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None:
|
||||
super().__init__()
|
||||
|
||||
audio_encoder = get_audio_encoder(audioenc_name)
|
||||
|
||||
self.base = audio_encoder(
|
||||
sample_rate, window_size,
|
||||
hop_size, mel_bins, fmin, fmax,
|
||||
classes_num, d_in)
|
||||
|
||||
self.projection = Projection(d_in, d_out)
|
||||
|
||||
def forward(self, x):
|
||||
out_dict = self.base(x)
|
||||
audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output']
|
||||
projected_vec = self.projection(audio_features)
|
||||
return projected_vec, audio_classification_output
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None:
|
||||
super().__init__()
|
||||
self.text_model = text_model
|
||||
self.base = AutoModel.from_pretrained(text_model)
|
||||
|
||||
if 'clip' in text_model:
|
||||
self.clip_text_projection = self.base.text_projection
|
||||
self.base = self.base.text_model
|
||||
if 'base' in text_model:
|
||||
transformer_embed_dim = 512
|
||||
|
||||
self.projection = Projection(transformer_embed_dim, d_out)
|
||||
|
||||
def forward(self, x):
|
||||
if 'clip' in self.text_model:
|
||||
pooled_output = self.base(**x)[1] # get pooled output
|
||||
out = self.clip_text_projection(pooled_output) # get CLS token output
|
||||
elif 'gpt' in self.text_model:
|
||||
batch_size = x['input_ids'].shape[0]
|
||||
hidden_states = self.base(**x)[0] # (batch_size=4, seq_len, 768)
|
||||
|
||||
sequence_lengths = torch.ne(x['input_ids'], 0).sum(-1) - 1 # tensor([13, 14, 18, 17])
|
||||
out = hidden_states[torch.arange(batch_size, device=hidden_states.device), sequence_lengths] # [batch_size, 768] = [4, 768]
|
||||
else:
|
||||
out = self.base(**x)[0]
|
||||
out = out[:, 0, :] # get CLS token output
|
||||
|
||||
projected_vec = self.projection(out)
|
||||
|
||||
return projected_vec
|
||||
|
||||
class CLAP(nn.Module):
|
||||
def __init__(self,
|
||||
# audio
|
||||
audioenc_name: str,
|
||||
sample_rate: int,
|
||||
window_size: int,
|
||||
hop_size: int,
|
||||
mel_bins: int,
|
||||
fmin: int,
|
||||
fmax: int,
|
||||
classes_num: int,
|
||||
out_emb: int,
|
||||
# text
|
||||
text_model: str,
|
||||
transformer_embed_dim: int,
|
||||
# common
|
||||
d_proj: int,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
|
||||
self.audio_encoder = AudioEncoder(
|
||||
audioenc_name, out_emb, d_proj,
|
||||
sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num)
|
||||
|
||||
self.caption_encoder = TextEncoder(
|
||||
d_proj, text_model, transformer_embed_dim
|
||||
)
|
||||
|
||||
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
||||
|
||||
def forward(self, audio, text):
|
||||
audio_embed, _ = self.audio_encoder(audio)
|
||||
caption_embed = self.caption_encoder(text)
|
||||
|
||||
return caption_embed, audio_embed, self.logit_scale.exp()
|
||||
@@ -0,0 +1,128 @@
|
||||
# Ke Chen
|
||||
# knutchen@ucsd.edu
|
||||
# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
|
||||
# The configuration for training the model
|
||||
|
||||
exp_name = "exp_htsat_pretrain" # the saved ckpt prefix name of the model
|
||||
workspace = "/home/kechen/Research/HTSAT" # the folder of your code
|
||||
dataset_path = "/home/Research/audioset" # the dataset path
|
||||
desed_folder = "/home/Research/DESED" # the desed file
|
||||
|
||||
dataset_type = "audioset" # "audioset" "esc-50" "scv2"
|
||||
index_type = "full_train" # only works for audioset
|
||||
balanced_data = True # only works for audioset
|
||||
|
||||
loss_type = "clip_bce" #
|
||||
# AudioSet & SCV2: "clip_bce" | ESC-50: "clip_ce"
|
||||
|
||||
# trained from a checkpoint, or evaluate a single model
|
||||
resume_checkpoint = None
|
||||
# "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_1.ckpt"
|
||||
|
||||
esc_fold = 0 # just for esc dataset, select the fold you need for evaluation and (+1) validation
|
||||
|
||||
|
||||
debug = False
|
||||
|
||||
random_seed = 970131 # 19970318 970131 12412 127777 1009 34047
|
||||
batch_size = 32 * 4 # batch size per GPU x GPU number , default is 32 x 4 = 128
|
||||
learning_rate = 1e-3 # 1e-4 also workable
|
||||
max_epoch = 100
|
||||
num_workers = 3
|
||||
|
||||
lr_scheduler_epoch = [10,20,30]
|
||||
lr_rate = [0.02, 0.05, 0.1]
|
||||
|
||||
# these data preparation optimizations do not bring many improvements, so deprecated
|
||||
enable_token_label = False # token label
|
||||
class_map_path = "class_hier_map.npy"
|
||||
class_filter = None
|
||||
retrieval_index = [15382, 9202, 130, 17618, 17157, 17516, 16356, 6165, 13992, 9238, 5550, 5733, 1914, 1600, 3450, 13735, 11108, 3762,
|
||||
9840, 11318, 8131, 4429, 16748, 4992, 16783, 12691, 4945, 8779, 2805, 9418, 2797, 14357, 5603, 212, 3852, 12666, 1338, 10269, 2388, 8260, 4293, 14454, 7677, 11253, 5060, 14938, 8840, 4542, 2627, 16336, 8992, 15496, 11140, 446, 6126, 10691, 8624, 10127, 9068, 16710, 10155, 14358, 7567, 5695, 2354, 8057, 17635, 133, 16183, 14535, 7248, 4560, 14429, 2463, 10773, 113, 2462, 9223, 4929, 14274, 4716, 17307, 4617, 2132, 11083, 1039, 1403, 9621, 13936, 2229, 2875, 17840, 9359, 13311, 9790, 13288, 4750, 17052, 8260, 14900]
|
||||
token_label_range = [0.2,0.6]
|
||||
enable_time_shift = False # shift time
|
||||
enable_label_enhance = False # enhance hierarchical label
|
||||
enable_repeat_mode = False # repeat the spectrogram / reshape the spectrogram
|
||||
|
||||
|
||||
|
||||
# for model's design
|
||||
enable_tscam = True # enbale the token-semantic layer
|
||||
|
||||
# for signal processing
|
||||
sample_rate = 32000 # 16000 for scv2, 32000 for audioset and esc-50
|
||||
clip_samples = sample_rate * 10 # audio_set 10-sec clip
|
||||
window_size = 1024
|
||||
hop_size = 320 # 160 for scv2, 320 for audioset and esc-50
|
||||
mel_bins = 64
|
||||
fmin = 50
|
||||
fmax = 14000
|
||||
shift_max = int(clip_samples * 0.5)
|
||||
|
||||
# for data collection
|
||||
classes_num = 527 # esc: 50 | audioset: 527 | scv2: 35
|
||||
patch_size = (25, 4) # deprecated
|
||||
crop_size = None # int(clip_samples * 0.5) deprecated
|
||||
|
||||
# for htsat hyperparamater
|
||||
htsat_window_size = 8
|
||||
htsat_spec_size = 256
|
||||
htsat_patch_size = 4
|
||||
htsat_stride = (4, 4)
|
||||
htsat_num_head = [4,8,16,32]
|
||||
htsat_dim = 96
|
||||
htsat_depth = [2,2,6,2]
|
||||
|
||||
swin_pretrain_path = None
|
||||
# "/home/Research/model_backup/pretrain/swin_tiny_c24_patch4_window8_256.pth"
|
||||
|
||||
# Some Deprecated Optimization in the model design, check the model code for details
|
||||
htsat_attn_heatmap = False
|
||||
htsat_hier_output = False
|
||||
htsat_use_max = False
|
||||
|
||||
|
||||
# for ensemble test
|
||||
|
||||
ensemble_checkpoints = []
|
||||
ensemble_strides = []
|
||||
|
||||
|
||||
# weight average folder
|
||||
wa_folder = "/home/version_0/checkpoints/"
|
||||
# weight average output filename
|
||||
wa_model_path = "HTSAT_AudioSet_Saved_x.ckpt"
|
||||
|
||||
esm_model_pathes = [
|
||||
"/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_1.ckpt",
|
||||
"/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_2.ckpt",
|
||||
"/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_3.ckpt",
|
||||
"/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_4.ckpt",
|
||||
"/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_5.ckpt",
|
||||
"/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_6.ckpt"
|
||||
]
|
||||
|
||||
# for framewise localization
|
||||
heatmap_dir = "/home/Research/heatmap_output"
|
||||
test_file = "htsat-test-ensemble"
|
||||
fl_local = False # indicate if we need to use this dataset for the framewise detection
|
||||
fl_dataset = "/home/Research/desed/desed_eval.npy"
|
||||
fl_class_num = [
|
||||
"Speech", "Frying", "Dishes", "Running_water",
|
||||
"Blender", "Electric_shaver_toothbrush", "Alarm_bell_ringing",
|
||||
"Cat", "Dog", "Vacuum_cleaner"
|
||||
]
|
||||
|
||||
# map 527 classes into 10 classes
|
||||
fl_audioset_mapping = [
|
||||
[0,1,2,3,4,5,6,7],
|
||||
[366, 367, 368],
|
||||
[364],
|
||||
[288, 289, 290, 291, 292, 293, 294, 295, 296, 297],
|
||||
[369],
|
||||
[382],
|
||||
[310, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402],
|
||||
[81, 82, 83, 84, 85],
|
||||
[74, 75, 76, 77, 78, 79],
|
||||
[377]
|
||||
]
|
||||
@@ -0,0 +1,942 @@
|
||||
# Ke Chen
|
||||
# knutchen@ucsd.edu
|
||||
# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
|
||||
# Model Core
|
||||
# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
|
||||
# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
|
||||
|
||||
|
||||
import math
|
||||
import random
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
|
||||
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
|
||||
from torchlibrosa.augmentation import SpecAugmentation
|
||||
|
||||
from itertools import repeat
|
||||
|
||||
from .pytorch_utils import do_mixup, interpolate
|
||||
from . import config
|
||||
|
||||
import collections.abc
|
||||
import warnings
|
||||
|
||||
from torch.nn.init import _calculate_fan_in_and_fan_out
|
||||
|
||||
def _ntuple(n):
|
||||
def parse(x):
|
||||
if isinstance(x, collections.abc.Iterable):
|
||||
return x
|
||||
return tuple(repeat(x, n))
|
||||
return parse
|
||||
|
||||
to_1tuple = _ntuple(1)
|
||||
to_2tuple = _ntuple(2)
|
||||
to_3tuple = _ntuple(3)
|
||||
to_4tuple = _ntuple(4)
|
||||
to_ntuple = _ntuple
|
||||
|
||||
|
||||
def drop_path(x, drop_prob: float = 0., training: bool = False):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
|
||||
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
|
||||
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
|
||||
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
|
||||
'survival rate' as the argument.
|
||||
"""
|
||||
if drop_prob == 0. or not training:
|
||||
return x
|
||||
keep_prob = 1 - drop_prob
|
||||
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
||||
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
|
||||
random_tensor.floor_() # binarize
|
||||
output = x.div(keep_prob) * random_tensor
|
||||
return output
|
||||
|
||||
|
||||
class DropPath(nn.Module):
|
||||
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
||||
"""
|
||||
def __init__(self, drop_prob=None):
|
||||
super(DropPath, self).__init__()
|
||||
self.drop_prob = drop_prob
|
||||
|
||||
def forward(self, x):
|
||||
return drop_path(x, self.drop_prob, self.training)
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" 2D Image to Patch Embedding
|
||||
"""
|
||||
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patch_stride = to_2tuple(patch_stride)
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.patch_stride = patch_stride
|
||||
self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
|
||||
self.num_patches = self.grid_size[0] * self.grid_size[1]
|
||||
self.flatten = flatten
|
||||
self.in_chans = in_chans
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
|
||||
|
||||
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
|
||||
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
B, C, H, W = x.shape
|
||||
assert H == self.img_size[0] and W == self.img_size[1], \
|
||||
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
|
||||
x = self.proj(x)
|
||||
if self.flatten:
|
||||
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
|
||||
x = self.norm(x)
|
||||
return x
|
||||
|
||||
class Mlp(nn.Module):
|
||||
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
|
||||
"""
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
|
||||
# Cut & paste from PyTorch official master until it's in a few official releases - RW
|
||||
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
|
||||
def norm_cdf(x):
|
||||
# Computes standard normal cumulative distribution function
|
||||
return (1. + math.erf(x / math.sqrt(2.))) / 2.
|
||||
|
||||
if (mean < a - 2 * std) or (mean > b + 2 * std):
|
||||
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
|
||||
"The distribution of values may be incorrect.",
|
||||
stacklevel=2)
|
||||
|
||||
with torch.no_grad():
|
||||
# Values are generated by using a truncated uniform distribution and
|
||||
# then using the inverse CDF for the normal distribution.
|
||||
# Get upper and lower cdf values
|
||||
l = norm_cdf((a - mean) / std)
|
||||
u = norm_cdf((b - mean) / std)
|
||||
|
||||
# Uniformly fill tensor with values from [l, u], then translate to
|
||||
# [2l-1, 2u-1].
|
||||
tensor.uniform_(2 * l - 1, 2 * u - 1)
|
||||
|
||||
# Use inverse cdf transform for normal distribution to get truncated
|
||||
# standard normal
|
||||
tensor.erfinv_()
|
||||
|
||||
# Transform to proper mean, std
|
||||
tensor.mul_(std * math.sqrt(2.))
|
||||
tensor.add_(mean)
|
||||
|
||||
# Clamp to ensure it's in the proper range
|
||||
tensor.clamp_(min=a, max=b)
|
||||
return tensor
|
||||
|
||||
|
||||
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
|
||||
# type: (Tensor, float, float, float, float) -> Tensor
|
||||
r"""Fills the input Tensor with values drawn from a truncated
|
||||
normal distribution. The values are effectively drawn from the
|
||||
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
|
||||
with values outside :math:`[a, b]` redrawn until they are within
|
||||
the bounds. The method used for generating the random values works
|
||||
best when :math:`a \leq \text{mean} \leq b`.
|
||||
Args:
|
||||
tensor: an n-dimensional `torch.Tensor`
|
||||
mean: the mean of the normal distribution
|
||||
std: the standard deviation of the normal distribution
|
||||
a: the minimum cutoff value
|
||||
b: the maximum cutoff value
|
||||
Examples:
|
||||
>>> w = torch.empty(3, 5)
|
||||
>>> nn.init.trunc_normal_(w)
|
||||
"""
|
||||
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
|
||||
|
||||
|
||||
def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
|
||||
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
|
||||
if mode == 'fan_in':
|
||||
denom = fan_in
|
||||
elif mode == 'fan_out':
|
||||
denom = fan_out
|
||||
elif mode == 'fan_avg':
|
||||
denom = (fan_in + fan_out) / 2
|
||||
|
||||
variance = scale / denom
|
||||
|
||||
if distribution == "truncated_normal":
|
||||
# constant is stddev of standard normal truncated to (-2, 2)
|
||||
trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
|
||||
elif distribution == "normal":
|
||||
tensor.normal_(std=math.sqrt(variance))
|
||||
elif distribution == "uniform":
|
||||
bound = math.sqrt(3 * variance)
|
||||
tensor.uniform_(-bound, bound)
|
||||
else:
|
||||
raise ValueError(f"invalid distribution {distribution}")
|
||||
|
||||
|
||||
def lecun_normal_(tensor):
|
||||
variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
|
||||
|
||||
|
||||
# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
|
||||
# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
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
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
"""
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x, attn
|
||||
|
||||
def extra_repr(self):
|
||||
return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
|
||||
|
||||
|
||||
# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
r""" Swin Transformer Block.
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
input_resolution (tuple[int]): Input resulotion.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
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, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.input_resolution = input_resolution
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
self.norm_before_mlp = norm_before_mlp
|
||||
if min(self.input_resolution) <= self.window_size:
|
||||
# if window size is larger than input resolution, we don't partition windows
|
||||
self.shift_size = 0
|
||||
self.window_size = min(self.input_resolution)
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
if self.norm_before_mlp == 'ln':
|
||||
self.norm2 = nn.LayerNorm(dim)
|
||||
elif self.norm_before_mlp == 'bn':
|
||||
self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
if self.shift_size > 0:
|
||||
# calculate attention mask for SW-MSA
|
||||
H, W = self.input_resolution
|
||||
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
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
|
||||
@@ -0,0 +1,199 @@
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import functional as nnf
|
||||
from enum import Enum
|
||||
from transformers import GPT2LMHeadModel
|
||||
from typing import Tuple, Optional
|
||||
|
||||
def get_clapcap(name: str):
|
||||
if name == "ClapCaption":
|
||||
return ClapCaptionModel
|
||||
else:
|
||||
raise Exception('The ClapCap model {} is incorrect or not supported'.format(name))
|
||||
|
||||
class MappingType(Enum):
|
||||
MLP = 'mlp'
|
||||
Transformer = 'transformer'
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
|
||||
super(MLP, self).__init__()
|
||||
layers = []
|
||||
for i in range(len(sizes) - 1):
|
||||
layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))
|
||||
if i < len(sizes) - 2:
|
||||
layers.append(act())
|
||||
self.model = nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.model(x)
|
||||
|
||||
|
||||
class MlpTransformer(nn.Module):
|
||||
def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.):
|
||||
super().__init__()
|
||||
out_d = out_d if out_d is not None else in_dim
|
||||
self.fc1 = nn.Linear(in_dim, h_dim)
|
||||
self.act = act
|
||||
self.fc2 = nn.Linear(h_dim, out_d)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.dropout(x)
|
||||
x = self.fc2(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
|
||||
def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.):
|
||||
super().__init__()
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim_self // num_heads
|
||||
self.scale = head_dim ** -0.5
|
||||
self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)
|
||||
self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)
|
||||
self.project = nn.Linear(dim_self, dim_self)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
|
||||
def forward(self, x, y=None, mask=None):
|
||||
y = y if y is not None else x
|
||||
b, n, c = x.shape
|
||||
_, m, d = y.shape
|
||||
# b n h dh
|
||||
queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads)
|
||||
# b m 2 h dh
|
||||
keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads)
|
||||
keys, values = keys_values[:, :, 0], keys_values[:, :, 1]
|
||||
attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale
|
||||
if mask is not None:
|
||||
if mask.dim() == 2:
|
||||
mask = mask.unsqueeze(1)
|
||||
attention = attention.masked_fill(mask.unsqueeze(3), float("-inf"))
|
||||
attention = attention.softmax(dim=2)
|
||||
out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c)
|
||||
out = self.project(out)
|
||||
return out, attention
|
||||
|
||||
|
||||
class TransformerLayer(nn.Module):
|
||||
|
||||
def forward_with_attention(self, x, y=None, mask=None):
|
||||
x_, attention = self.attn(self.norm1(x), y, mask)
|
||||
x = x + x_
|
||||
x = x + self.mlp(self.norm2(x))
|
||||
return x, attention
|
||||
|
||||
def forward(self, x, y=None, mask=None):
|
||||
x = x + self.attn(self.norm1(x), y, mask)[0]
|
||||
x = x + self.mlp(self.norm2(x))
|
||||
return x
|
||||
|
||||
def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu,
|
||||
norm_layer: nn.Module = nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim_self)
|
||||
self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout)
|
||||
self.norm2 = norm_layer(dim_self)
|
||||
self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout)
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None,
|
||||
mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False):
|
||||
super(Transformer, self).__init__()
|
||||
dim_ref = dim_ref if dim_ref is not None else dim_self
|
||||
self.enc_dec = enc_dec
|
||||
if enc_dec:
|
||||
num_layers = num_layers * 2
|
||||
layers = []
|
||||
for i in range(num_layers):
|
||||
if i % 2 == 0 and enc_dec: # cross
|
||||
layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
|
||||
elif enc_dec: # self
|
||||
layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
|
||||
else: # self or cross
|
||||
layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
|
||||
self.layers = nn.ModuleList(layers)
|
||||
|
||||
def forward_with_attention(self, x, y=None, mask=None):
|
||||
attentions = []
|
||||
for layer in self.layers:
|
||||
x, att = layer.forward_with_attention(x, y, mask)
|
||||
attentions.append(att)
|
||||
return x, attentions
|
||||
|
||||
def forward(self, x, y=None, mask=None):
|
||||
for i, layer in enumerate(self.layers):
|
||||
if i % 2 == 0 and self.enc_dec: # cross
|
||||
x = layer(x, y)
|
||||
elif self.enc_dec: # self
|
||||
x = layer(x, x, mask)
|
||||
else: # self or cross
|
||||
x = layer(x, y, mask)
|
||||
return x
|
||||
|
||||
|
||||
class TransformerMapper(nn.Module):
|
||||
def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8):
|
||||
super(TransformerMapper, self).__init__()
|
||||
self.clip_length = clip_length
|
||||
self.transformer = Transformer(dim_embedding, 8, num_layers)
|
||||
self.linear = nn.Linear(dim_clip, clip_length * dim_embedding)
|
||||
self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.linear(x).view(x.shape[0], self.clip_length, -1)
|
||||
prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape)
|
||||
prefix = torch.cat((x, prefix), dim=1)
|
||||
out = self.transformer(prefix)[:, self.clip_length:]
|
||||
return out
|
||||
|
||||
class ClapCaptionModel(nn.Module):
|
||||
def __init__(self, clap, text_decoder: str, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512,
|
||||
num_layers: int = 8, normalize_prefix: bool = True, mapping_type: str = None,\
|
||||
freeze_audio_encoder_weights: bool = True, freeze_gpt_weights: bool = True):
|
||||
super(ClapCaptionModel, self).__init__()
|
||||
self.clap = clap.audio_encoder
|
||||
self.prefix_length = prefix_length
|
||||
self.normalize_prefix = normalize_prefix
|
||||
self.gpt = GPT2LMHeadModel.from_pretrained(text_decoder)
|
||||
self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
|
||||
if mapping_type == 'mlp':
|
||||
self.clap_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2,
|
||||
self.gpt_embedding_size * prefix_length))
|
||||
else:
|
||||
self.clap_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length,
|
||||
clip_length, num_layers)
|
||||
|
||||
# Freeze all CLAP parameters
|
||||
if freeze_audio_encoder_weights:
|
||||
for p in self.clap.parameters():
|
||||
p.requires_grad = False
|
||||
|
||||
if freeze_gpt_weights:
|
||||
for p in self.gpt.parameters():
|
||||
p.requires_grad = False
|
||||
|
||||
def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:
|
||||
return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)
|
||||
|
||||
def forward(self, audios: torch.Tensor, tokens: torch.Tensor, mask: Optional[torch.Tensor] = None,
|
||||
labels: Optional[torch.Tensor] = None):
|
||||
# get audio embeddings
|
||||
prefix, _ = self.clap(audios)
|
||||
# normalize prefix (audio embedding)
|
||||
if self.normalize_prefix:
|
||||
prefix = prefix / prefix.norm(2, -1).reshape(-1,1)
|
||||
|
||||
embedding_text = self.gpt.transformer.wte(tokens['input_ids'])
|
||||
prefix_projections = self.clap_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)
|
||||
embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)
|
||||
if labels is not None:
|
||||
dummy_token = self.get_dummy_token(tokens['input_ids'].shape[0], tokens['input_ids'].device)
|
||||
labels = torch.cat((dummy_token, tokens), dim=1)
|
||||
out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)
|
||||
return out
|
||||
@@ -0,0 +1,182 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
def move_data_to_device(x, device):
|
||||
if 'float' in str(x.dtype):
|
||||
x = torch.Tensor(x)
|
||||
elif 'int' in str(x.dtype):
|
||||
x = torch.LongTensor(x)
|
||||
else:
|
||||
return x
|
||||
|
||||
return x.to(device)
|
||||
|
||||
|
||||
def do_mixup(x, mixup_lambda):
|
||||
"""Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes
|
||||
(1, 3, 5, ...).
|
||||
Args:
|
||||
x: (batch_size * 2, ...)
|
||||
mixup_lambda: (batch_size * 2,)
|
||||
Returns:
|
||||
out: (batch_size, ...)
|
||||
"""
|
||||
out = (x[0 :: 2].transpose(0, -1) * mixup_lambda[0 :: 2] + \
|
||||
x[1 :: 2].transpose(0, -1) * mixup_lambda[1 :: 2]).transpose(0, -1)
|
||||
return out
|
||||
|
||||
|
||||
def append_to_dict(dict, key, value):
|
||||
if key in dict.keys():
|
||||
dict[key].append(value)
|
||||
else:
|
||||
dict[key] = [value]
|
||||
|
||||
|
||||
def interpolate(x, ratio):
|
||||
"""Interpolate data in time domain. This is used to compensate the
|
||||
resolution reduction in downsampling of a CNN.
|
||||
|
||||
Args:
|
||||
x: (batch_size, time_steps, classes_num)
|
||||
ratio: int, ratio to interpolate
|
||||
Returns:
|
||||
upsampled: (batch_size, time_steps * ratio, classes_num)
|
||||
"""
|
||||
(batch_size, time_steps, classes_num) = x.shape
|
||||
upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)
|
||||
upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)
|
||||
return upsampled
|
||||
|
||||
|
||||
def pad_framewise_output(framewise_output, frames_num):
|
||||
"""Pad framewise_output to the same length as input frames. The pad value
|
||||
is the same as the value of the last frame.
|
||||
Args:
|
||||
framewise_output: (batch_size, frames_num, classes_num)
|
||||
frames_num: int, number of frames to pad
|
||||
Outputs:
|
||||
output: (batch_size, frames_num, classes_num)
|
||||
"""
|
||||
pad = framewise_output[:, -1 :, :].repeat(1, frames_num - framewise_output.shape[1], 1)
|
||||
"""tensor for padding"""
|
||||
|
||||
output = torch.cat((framewise_output, pad), dim=1)
|
||||
"""(batch_size, frames_num, classes_num)"""
|
||||
|
||||
return output
|
||||
|
||||
|
||||
def count_parameters(model):
|
||||
return sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||||
|
||||
|
||||
def count_flops(model, audio_length):
|
||||
"""Count flops. Code modified from others' implementation.
|
||||
"""
|
||||
multiply_adds = True
|
||||
list_conv2d=[]
|
||||
def conv2d_hook(self, input, output):
|
||||
batch_size, input_channels, input_height, input_width = input[0].size()
|
||||
output_channels, output_height, output_width = output[0].size()
|
||||
|
||||
kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
|
||||
bias_ops = 1 if self.bias is not None else 0
|
||||
|
||||
params = output_channels * (kernel_ops + bias_ops)
|
||||
flops = batch_size * params * output_height * output_width
|
||||
|
||||
list_conv2d.append(flops)
|
||||
|
||||
list_conv1d=[]
|
||||
def conv1d_hook(self, input, output):
|
||||
batch_size, input_channels, input_length = input[0].size()
|
||||
output_channels, output_length = output[0].size()
|
||||
|
||||
kernel_ops = self.kernel_size[0] * (self.in_channels / self.groups) * (2 if multiply_adds else 1)
|
||||
bias_ops = 1 if self.bias is not None else 0
|
||||
|
||||
params = output_channels * (kernel_ops + bias_ops)
|
||||
flops = batch_size * params * output_length
|
||||
|
||||
list_conv1d.append(flops)
|
||||
|
||||
list_linear=[]
|
||||
def linear_hook(self, input, output):
|
||||
batch_size = input[0].size(0) if input[0].dim() == 2 else 1
|
||||
|
||||
weight_ops = self.weight.nelement() * (2 if multiply_adds else 1)
|
||||
bias_ops = self.bias.nelement()
|
||||
|
||||
flops = batch_size * (weight_ops + bias_ops)
|
||||
list_linear.append(flops)
|
||||
|
||||
list_bn=[]
|
||||
def bn_hook(self, input, output):
|
||||
list_bn.append(input[0].nelement() * 2)
|
||||
|
||||
list_relu=[]
|
||||
def relu_hook(self, input, output):
|
||||
list_relu.append(input[0].nelement() * 2)
|
||||
|
||||
list_pooling2d=[]
|
||||
def pooling2d_hook(self, input, output):
|
||||
batch_size, input_channels, input_height, input_width = input[0].size()
|
||||
output_channels, output_height, output_width = output[0].size()
|
||||
|
||||
kernel_ops = self.kernel_size * self.kernel_size
|
||||
bias_ops = 0
|
||||
params = output_channels * (kernel_ops + bias_ops)
|
||||
flops = batch_size * params * output_height * output_width
|
||||
|
||||
list_pooling2d.append(flops)
|
||||
|
||||
list_pooling1d=[]
|
||||
def pooling1d_hook(self, input, output):
|
||||
batch_size, input_channels, input_length = input[0].size()
|
||||
output_channels, output_length = output[0].size()
|
||||
|
||||
kernel_ops = self.kernel_size[0]
|
||||
bias_ops = 0
|
||||
|
||||
params = output_channels * (kernel_ops + bias_ops)
|
||||
flops = batch_size * params * output_length
|
||||
|
||||
list_pooling2d.append(flops)
|
||||
|
||||
def foo(net):
|
||||
childrens = list(net.children())
|
||||
if not childrens:
|
||||
if isinstance(net, nn.Conv2d):
|
||||
net.register_forward_hook(conv2d_hook)
|
||||
elif isinstance(net, nn.Conv1d):
|
||||
net.register_forward_hook(conv1d_hook)
|
||||
elif isinstance(net, nn.Linear):
|
||||
net.register_forward_hook(linear_hook)
|
||||
elif isinstance(net, nn.BatchNorm2d) or isinstance(net, nn.BatchNorm1d):
|
||||
net.register_forward_hook(bn_hook)
|
||||
elif isinstance(net, nn.ReLU):
|
||||
net.register_forward_hook(relu_hook)
|
||||
elif isinstance(net, nn.AvgPool2d) or isinstance(net, nn.MaxPool2d):
|
||||
net.register_forward_hook(pooling2d_hook)
|
||||
elif isinstance(net, nn.AvgPool1d) or isinstance(net, nn.MaxPool1d):
|
||||
net.register_forward_hook(pooling1d_hook)
|
||||
else:
|
||||
print('Warning: flop of module {} is not counted!'.format(net))
|
||||
return
|
||||
for c in childrens:
|
||||
foo(c)
|
||||
|
||||
# Register hook
|
||||
foo(model)
|
||||
|
||||
device = device = next(model.parameters()).device
|
||||
input = torch.rand(1, audio_length).to(device)
|
||||
|
||||
out = model(input)
|
||||
|
||||
total_flops = sum(list_conv2d) + sum(list_conv1d) + sum(list_linear) + \
|
||||
sum(list_bn) + sum(list_relu) + sum(list_pooling2d) + sum(list_pooling1d)
|
||||
|
||||
return total_flops
|
||||
@@ -0,0 +1,26 @@
|
||||
import argparse
|
||||
import yaml
|
||||
import sys
|
||||
|
||||
def read_config_as_args(config_path,args=None,is_config_str=False):
|
||||
return_dict = {}
|
||||
|
||||
if config_path is not None:
|
||||
if is_config_str:
|
||||
yml_config = yaml.load(config_path, Loader=yaml.FullLoader)
|
||||
else:
|
||||
with open(config_path, "r") as f:
|
||||
yml_config = yaml.load(f, Loader=yaml.FullLoader)
|
||||
|
||||
if args != None:
|
||||
for k, v in yml_config.items():
|
||||
if k in args.__dict__:
|
||||
args.__dict__[k] = v
|
||||
else:
|
||||
sys.stderr.write("Ignored unknown parameter {} in yaml.\n".format(k))
|
||||
else:
|
||||
for k, v in yml_config.items():
|
||||
return_dict[k] = v
|
||||
|
||||
args = args if args != None else return_dict
|
||||
return argparse.Namespace(**args)
|
||||
Reference in New Issue
Block a user