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69c07f8 a4688bc 85c9d5d 69c07f8 85c9d5d 69c07f8 85c9d5d 69c07f8 a4688bc 69c07f8 85c9d5d bad2121 69c07f8 a4688bc 69c07f8 85c9d5d 69c07f8 85c9d5d 69c07f8 85c9d5d 69c07f8 85c9d5d 69c07f8 85c9d5d 69c07f8 85c9d5d 69c07f8 85c9d5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 | """ CLAP Model
Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
Adapted to the Audio Task.
"""
from dataclasses import dataclass
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
import logging
from .htsat import create_htsat_model
from transformers import RobertaModel, RobertaConfig
# removed: OrderedDict, email.mime, typing, freeze_batch_norm_2d, BertModel, BartModel, BatchEncoding — dead after removing visual/transformer/bert/bart branches
class MLPLayers(nn.Module):
def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1):
super(MLPLayers, self).__init__()
self.nonlin = nonlin
self.dropout = dropout
sequence = []
for u0, u1 in zip(units[:-1], units[1:]):
sequence.append(nn.Linear(u0, u1))
sequence.append(self.nonlin)
sequence.append(nn.Dropout(self.dropout))
sequence = sequence[:-2]
self.sequential = nn.Sequential(*sequence)
def forward(self, X):
X = self.sequential(X)
return X
# removed: Bottleneck, AttentionPool2d, ModifiedResNet, LayerNorm, QuickGELU, ResidualAttentionBlock, Transformer, VisualTransformer
# — all CLIP visual encoder / transformer text encoder classes, not used with HTSAT+roberta inference
# Audio Config Class
@dataclass
class CLAPAudioCfp:
model_type: str = "PANN"
model_name: str = "Cnn14"
sample_rate: int = 48000
# Param
audio_length: int = 1024
window_size: int = 1024
hop_size: int = 1024
fmin: int = 50
fmax: int = 14000
class_num: int = 527
mel_bins: int = 64
clip_samples: int = 480000
@dataclass
class CLAPTextCfg:
context_length: int
vocab_size: int
width: int
heads: int
layers: int
model_type: str
class CLAP(nn.Module):
def __init__(
self,
embed_dim: int,
audio_cfg: CLAPAudioCfp,
text_cfg: CLAPTextCfg,
quick_gelu: bool = False,
enable_fusion: bool = False,
fusion_type: str = "None",
joint_embed_shape: int = 512,
mlp_act: str = "relu",
):
super().__init__()
if isinstance(audio_cfg, dict):
audio_cfg = CLAPAudioCfp(**audio_cfg)
if isinstance(text_cfg, dict):
text_cfg = CLAPTextCfg(**text_cfg)
self.audio_cfg = audio_cfg
self.text_cfg = text_cfg
self.enable_fusion = enable_fusion
self.fusion_type = fusion_type
self.joint_embed_shape = joint_embed_shape
self.mlp_act = mlp_act
self.context_length = text_cfg.context_length
# removed: act_layer/QuickGELU — only used by transformer text branch
if mlp_act == "relu":
mlp_act_layer = nn.ReLU()
elif mlp_act == "gelu":
mlp_act_layer = nn.GELU()
else:
raise NotImplementedError
# audio branch — removed: PANN branch, only HTSAT used
self.audio_branch = create_htsat_model(audio_cfg, enable_fusion, fusion_type)
# text branch — removed: transformer/bert/bart branches, only roberta used
if text_cfg.model_type == "roberta":
self.text_branch = RobertaModel.from_pretrained("roberta-base", revision="e2da8e2f811d1448a5b465c236feacd80ffbac7b")
self.text_transform = MLPLayers(
units=[
self.joint_embed_shape,
self.joint_embed_shape,
self.joint_embed_shape,
],
dropout=0.1,
)
self.text_projection = nn.Sequential(
nn.Linear(768, self.joint_embed_shape),
mlp_act_layer,
nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
)
# removed: bart branch — only roberta used
else:
logging.error(f"Model config for {text_cfg.model_type} not found")
raise RuntimeError(f"Model config for {text_cfg.model_type} not found.")
self.text_branch_type = text_cfg.model_type
# text branch parameters
# audio branch parameters
self.audio_transform = MLPLayers(
units=[
self.joint_embed_shape,
self.joint_embed_shape,
self.joint_embed_shape,
],
dropout=0.1,
)
# below here is text branch parameters
# ============================================================================================================
self.audio_projection = nn.Sequential(
nn.Linear(embed_dim, self.joint_embed_shape),
mlp_act_layer,
nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
)
self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
# removed: attn_mask buffer — only used by transformer text branch
self.init_text_branch_parameters()
def init_text_branch_parameters(self):
# removed: transformer/bert/bart init branches
nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07))
nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07))
# removed: build_attention_mask — only used by transformer text branch
# removed: encode_audio — audio modality not used in inference
def encode_text(self, text, device):
# removed: transformer/bert/bart branches — only roberta used
if self.text_branch_type == "roberta":
x = self.text_branch(
input_ids=text["input_ids"].to(device=device, non_blocking=True),
attention_mask=text["attention_mask"].to(
device=device, non_blocking=True
),
)["pooler_output"]
x = self.text_projection(x)
else:
raise RuntimeError(f"Model type {self.text_branch_type} not found.")
return x
# removed: forward, get_logit_scale — training/contrastive loss helpers, not used in inference
def get_text_embedding(self, data):
"""Get the text embedding from the model
Parameters
----------
data: torch.Tensor
a tensor of text embedding
Returns
----------
text_embed: torch.Tensor
a tensor of text_embeds (N, D)
"""
device = next(self.parameters()).device
for k in data:
data[k] = data[k].to(device)
text_embeds = self.encode_text(data, device=device)
text_embeds = F.normalize(text_embeds, dim=-1)
return text_embeds
# removed: get_audio_embedding, audio_infer — audio modality not used in inference
def convert_weights_to_fp16(model: nn.Module):
"""Convert applicable model parameters to fp16"""
def _convert_weights_to_fp16(l):
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
l.weight.data = l.weight.data.half()
if l.bias is not None:
l.bias.data = l.bias.data.half()
if isinstance(l, nn.MultiheadAttention):
for attr in [
*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
"in_proj_bias",
"bias_k",
"bias_v",
]:
tensor = getattr(l, attr)
if tensor is not None:
tensor.data = tensor.data.half()
for name in ["text_projection", "proj"]:
if hasattr(l, name):
attr = getattr(l, name)
if attr is not None:
attr.data = attr.data.half()
model.apply(_convert_weights_to_fp16)
# Ignore the state dict of the vision part
def build_model_from_openai_state_dict(
state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = "None"
):
embed_dim = model_cfg["embed_dim"]
audio_cfg = model_cfg["audio_cfg"]
text_cfg = model_cfg["text_cfg"]
context_length = state_dict["positional_embedding"].shape[0]
vocab_size = state_dict["token_embedding.weight"].shape[0]
transformer_width = state_dict["ln_final.weight"].shape[0]
transformer_heads = transformer_width // 64
transformer_layers = len(
set(
k.split(".")[2]
for k in state_dict
if k.startswith(f"transformer.resblocks")
)
)
audio_cfg = CLAPAudioCfp(**audio_cfg)
text_cfg = CLAPTextCfg(**text_cfg)
model = CLAP(
embed_dim,
audio_cfg=audio_cfg,
text_cfg=text_cfg,
quick_gelu=True, # OpenAI models were trained with QuickGELU
enable_fusion=enable_fusion,
fusion_type=fusion_type,
)
state_dict["logit_scale_a"] = state_dict["logit_scale"]
state_dict["logit_scale_t"] = state_dict["logit_scale"]
pop_keys = list(state_dict.keys())[::]
# pop the visual branch saved weights
for key in pop_keys:
if key.startswith("visual."):
state_dict.pop(key, None)
for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]:
state_dict.pop(key, None)
# not use fp16
# convert_weights_to_fp16(model)
model.load_state_dict(state_dict, strict=False)
return model.eval()
# removed: trace_model — JIT tracing utility, not used in inference
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