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import torch
import torch.nn as nn
from transformers import PreTrainedModel
from typing import List
from .config import LidirlCNNConfig
def torch_max_no_pads(model_out, lengths):
indices = torch.arange(model_out.size(1)).to(model_out.device)
mask = (indices < lengths.view(-1, 1)).unsqueeze(-1).expand(model_out.size())
model_out = torch.where(mask, model_out, torch.tensor(-1e9))
max_pool = torch.max(model_out, 1)[0]
return max_pool
class TransposeModule(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x.transpose(1, 2)
class ProjectionLayer(nn.Module):
"""
Noise-aware labels layer or traditional linear projection
"""
def __init__(self, hidden_dim, label_size, montecarlo_layer=False):
super().__init__()
self.montecarlo_layer = montecarlo_layer
if montecarlo_layer:
self.proj = MCSoftmaxDenseFA(hidden_dim, label_size, 1, logits_only=True)
else:
self.proj = nn.Linear(hidden_dim, label_size)
def forward(self, x):
return self.proj(x)
class ConvolutionalBlock(
nn.Module,
):
"""
Convolutional block
https://jonathanbgn.com/2021/09/30/illustrated-wav2vec-2.html
"""
def __init__(self,
embed_dim : int,
channels : List[int],
kernels : List[int],
strides : List[int]):
super(ConvolutionalBlock, self).__init__()
layers = []
self.embed_dim = embed_dim
input_dimension = embed_dim
for channel, kernel, stride in zip(channels, kernels, strides):
next_layer = nn.Conv1d(
in_channels = input_dimension,
out_channels = channel,
kernel_size = kernel,
stride = stride,
padding = 'valid', # we handle the padding ourselves in the forward function
)
input_dimension = channel
layers.append(TransposeModule())
layers.append(next_layer)
layers.append(TransposeModule())
layers.append(nn.LayerNorm(channel, elementwise_affine=True))
layers.append(nn.GELU())
layers.append(nn.Dropout(0.1))
self.model = nn.Sequential(*layers)
self.output_dim = channels[-1]
self.min_length = 1
for kernel, stride in zip(kernels[::-1], strides[::-1]):
self.min_length = ((self.min_length - 1) * stride) + kernel
def forward(self, inputs, lengths):
# this is our padding trick instead of consistent padding
if inputs.size(1) < self.min_length:
pads = torch.zeros((inputs.size(0), self.min_length - inputs.size(1), self.embed_dim), device=inputs.device)
inputs = torch.cat((inputs, pads), dim=1)
outputs = self.model(inputs)
for layer_i in range(1, len(self.model), 6):
lengths = torch.floor(((lengths - self.model[layer_i].kernel_size[0]) / self.model[layer_i].stride[0]) + 1).to(lengths.device, dtype=torch.long)
lengths[lengths < 1] = 1
return outputs, lengths
class LidirlCNN(PreTrainedModel):
"""
Defines the Lidirl CNN MODEL
"""
config_class = LidirlCNNConfig
def __init__(self, config):
super().__init__(config)
self.encoder = ConvolutionalBlock(config.embed_dim, config.channels, config.kernels, config.strides)
self.embed_layer = nn.Embedding(config.vocab_size, config.embed_dim)
self.proj = ProjectionLayer(self.encoder.output_dim, config.label_size, config.montecarlo_layer)
self.label_size = config.label_size
self.max_length = config.max_length
self.multilabel = config.multilabel
self.monte_carlo = config.montecarlo_layer
self.labels = ["" for _ in config.labels]
for key, value in config.labels.items():
self.labels[value] = key
def forward(self, inputs, lengths):
inputs = inputs[:, :self.max_length]
lengths = lengths.clamp(max=self.max_length)
embeddings = self.embed_layer(inputs)
encoding, lengths = self.encoder(embeddings, lengths=lengths)
max_pool = torch_max_no_pads(encoding, lengths)
projection = self.proj(max_pool)
return projection
def __call__(self, inputs, lengths):
# this is inference only model
with torch.no_grad():
logits = self.forward(inputs, lengths)
if self.multilabel:
probs = torch.sigmoid(logits)
else:
probs = torch.softmax(logits, dim=-1)
return probs
def predict(self, inputs, lengths, threshold=0.5, top_k=None):
probs = self.__call__(inputs, lengths)
if top_k is not None and top_k > 0:
top_k_preds = torch.topk(probs, top_k, dim=1)
pred_labels = []
for pred, prob in zip(top_k_preds.indices, top_k_preds.values):
pred_labels.append([(self.labels[p.item()], pr.item()) for (p, pr) in zip(pred, prob)])
return pred_labels
if self.multilabel:
batch_idx, label_idx = torch.where(probs > threshold)
output = [[] for _ in range(len(inputs))]
for batch, label in zip(batch_idx, label_idx):
label_string = self.labels
output[batch.item()].append(
(self.labels[label.item()], probs[batch, label])
)
return output |