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import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from typing import Callable
from timm.models.layers import Mlp
from fairseq_signals_backbone.models.wav2vec2.wav2vec2_cmsc import Wav2Vec2CMSCModel, Wav2Vec2CMSCConfig
from lightning import LightningModule
from transformers import PreTrainedModel
from .configuration_MELP_Encoder import MELPEncoderConfig
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm (with cast back to input dtype)."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
return x.to(orig_type)
class AttentionalPooler(nn.Module):
def __init__(
self,
d_model: int,
context_dim: int,
n_head: int = 8,
n_queries: int = 256,
norm_layer: Callable = LayerNorm,
):
super().__init__()
self.query = nn.Parameter(torch.randn(n_queries, d_model))
self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim, batch_first=True)
self.ln_q = norm_layer(d_model)
self.ln_k = norm_layer(context_dim)
def forward(self, x: torch.Tensor):
N = x.shape[0]
x = self.ln_k(x)
q = self.ln_q(self.query)
out = self.attn(q.unsqueeze(0).expand(N, -1, -1), x, x, need_weights=False)[0]
return out
def off_diagonal(x):
# return a flattened view of the off-diagonal elements of a square matrix
n, m = x.shape
assert n == m
return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()
class ECGFMModel(LightningModule):
def __init__(self,
model_size: str = "small", # small by default
shared_emb_dim: int = 256,
embed_dim_caption: int = 768,
use_attentional_pool_contrast: bool = False,
use_attentional_pool_caption: bool = False,
n_queries_contrast: int = 10,
n_queries_caption: int = 128,
attn_pooler_heads: int = 8,
norm_layer: nn.Module = nn.LayerNorm,
proj: str = "linear",
drop: float = 0.,
proj_bias: bool = False,
num_leads: int = 12,
softmax_temperature: float = 0.1,
lambd: float = 0.0051,
*args,
**kwargs):
"""" Implementation of ECG-FM model.
Using the Wave2Vec2 model as the ECG encoder: CNN + Transformer
"""
super().__init__()
self.save_hyperparameters()
self.shared_emb_dim = shared_emb_dim
self.num_leads = num_leads
self.temperature = softmax_temperature
if model_size == "small":
self.encoder_embed_dim = 768
self.encoder_attention_heads = 12
self.encoder_layers = 8
self.encoder_ffn_embed_dim = 3072
elif model_size == "base":
self.encoder_embed_dim = 768
self.encoder_attention_heads = 12
self.encoder_layers = 12
self.encoder_ffn_embed_dim = 3072
elif model_size == "large":
self.encoder_embed_dim = 1024
self.encoder_attention_heads = 16
self.encoder_layers = 24
self.encoder_ffn_embed_dim = 4096
else:
raise ValueError(f"Unknown model size: {model_size}")
print("Using ECG encoder with the following configuration:")
print(f"encoder_embed_dim: {self.encoder_embed_dim}")
print(f"encoder_attention_heads: {self.encoder_attention_heads}")
print(f"encoder_layers: {self.encoder_layers}")
print(f"encoder_ffn_embed_dim: {self.encoder_ffn_embed_dim}")
self.init_ecg_encoder()
self.embed_dim_caption = embed_dim_caption
self.use_attentional_pool_contrast = use_attentional_pool_contrast
self.use_attentional_pool_caption = use_attentional_pool_caption
head_layers = OrderedDict()
prev_chs = self.ecg_encoder.cfg.encoder_embed_dim
if use_attentional_pool_contrast:
scale = prev_chs ** -0.5
self.attn_pool_contrast = AttentionalPooler(
d_model=shared_emb_dim,
context_dim=prev_chs,
n_head=attn_pooler_heads,
n_queries=n_queries_contrast)
self.ln_contrast = norm_layer(shared_emb_dim)
self.proj_contrast = nn.Parameter(scale * torch.randn(shared_emb_dim, shared_emb_dim))
else:
assert proj, 'projection layer needed if not using attentional pooling.'
# NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used
if proj == 'linear':
head_layers['drop'] = nn.Dropout(drop)
head_layers['proj'] = nn.Linear(prev_chs, shared_emb_dim, bias=proj_bias)
elif proj == 'mlp':
head_layers['mlp'] = Mlp(prev_chs, 2 * shared_emb_dim, shared_emb_dim, drop=(drop, 0), bias=(True, proj_bias))
self.head = nn.Sequential(head_layers)
if use_attentional_pool_caption:
self.attn_pool_caption = AttentionalPooler(
d_model=embed_dim_caption, context_dim=prev_chs, n_head=attn_pooler_heads, n_queries=n_queries_caption)
self.ln_caption = norm_layer(embed_dim_caption)
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.bn = nn.BatchNorm1d(768, affine=False)
self.lambd = lambd
def init_ecg_encoder(self):
# Here we define Wav2Vec2CMSC model as the ECG encoder
cfg = Wav2Vec2CMSCConfig(
apply_mask = True,
mask_prob = 0.65,
quantize_targets = True,
final_dim = 256,
dropout_input = 0.1,
dropout_features = 0.1,
feature_grad_mult = 0.1,
encoder_embed_dim = self.encoder_embed_dim,
encoder_attention_heads = self.encoder_attention_heads,
in_d = 12,
encoder_layers = self.encoder_layers,
encoder_ffn_embed_dim = self.encoder_ffn_embed_dim
)
self.ecg_encoder = Wav2Vec2CMSCModel(cfg)
def _global_pool(self, x):
return torch.mean(x, dim=1)
@torch.no_grad()
# only used for finetune ...
def ext_ecg_emb(self, ecg, normalize=False):
assert ecg.dim() == 3, "Input tensor must be 3D"
ecg_out = self.ecg_encoder(source=ecg, mask=False, features_only=True)
features = ecg_out["x"]
if self.use_attentional_pool_contrast:
pooled = self.attn_pool_contrast(features)
pooled = self.ln_contrast(pooled)
pooled = torch.mean(pooled, dim=1)
else:
pooled = self._global_pool(features)
if normalize:
pooled = F.normalize(pooled, p=2, dim=-1)
return pooled
def _encode_ecg(self, ecg):
assert ecg.dim() == 3, "Input tensor must be 3D"
ecg_out = self.ecg_encoder(source=ecg, mask=False, features_only=True)
# features = self.ecg_encoder.get_features(net_output=ecg_out, aggregate=False)
# results after CNN-Transformer
features = ecg_out["x"]
if self.use_attentional_pool_contrast:
# hierarchical pooling
pooled = self.attn_pool_contrast(features)
pooled = self.ln_contrast(pooled)
pooled = pooled @ self.proj_contrast.unsqueeze(0)
pooled_beat = pooled.clone()
pooled = torch.mean(pooled, dim=1)
else:
pooled = self._global_pool(features)
pooled = self.head(features)
tokens = None
if self.use_attentional_pool_caption:
tokens = self.attn_pool_caption(features)
tokens = self.ln_caption(tokens)
else:
tokens = None
return pooled, pooled_beat, tokens
def encode_ecg(self, ecg):
ecg_latent, _, _ = self._encode_ecg(ecg)
return ecg_latent
class MELPEncoderModel(PreTrainedModel):
config_class = MELPEncoderConfig
def __init__(self, config: MELPEncoderConfig):
super().__init__(config)
self.ecg_encoder = ECGFMModel(
model_size=config.model_size,
shared_emb_dim=config.shared_emb_dim,
embed_dim_caption=config.embed_dim_caption,
use_attentional_pool_contrast=config.use_attentional_pool_contrast,
use_attentional_pool_caption=config.use_attentional_pool_caption,
n_queries_contrast=config.n_queries_contrast,
n_queries_caption=config.n_queries_caption,
attn_pooler_heads=config.attn_pooler_heads,
proj=config.proj,
drop=config.drop,
proj_bias=config.proj_bias,
num_leads=config.num_leads,
)
def forward(self, tensor: torch.Tensor) -> torch.Tensor:
proj_ecg_emb, ecg_beat_emb, ecg_token_emb = self.ecg_encoder._encode_ecg(tensor)
return {
"proj_ecg_emb": proj_ecg_emb,
"ecg_beat_emb": ecg_beat_emb,
"ecg_token_emb": ecg_token_emb
}
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