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# -*- coding: utf-8 -*-
import random
import torch
import torch.nn as nn
from .base_model import CaptionModel
from .utils import repeat_tensor
import audio_to_text.captioning.models.decoder
class TransformerModel(CaptionModel):
def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs):
if not hasattr(self, "compatible_decoders"):
self.compatible_decoders = (
audio_to_text.captioning.models.decoder.TransformerDecoder,
)
super().__init__(encoder, decoder, **kwargs)
def seq_forward(self, input_dict):
cap = input_dict["cap"]
cap_padding_mask = (cap == self.pad_idx).to(cap.device)
cap_padding_mask = cap_padding_mask[:, :-1]
output = self.decoder(
{
"word": cap[:, :-1],
"attn_emb": input_dict["attn_emb"],
"attn_emb_len": input_dict["attn_emb_len"],
"cap_padding_mask": cap_padding_mask
}
)
return output
def prepare_decoder_input(self, input_dict, output):
decoder_input = {
"attn_emb": input_dict["attn_emb"],
"attn_emb_len": input_dict["attn_emb_len"]
}
t = input_dict["t"]
###############
# determine input word
################
if input_dict["mode"] == "train" and random.random() < input_dict["ss_ratio"]: # training, scheduled sampling
word = input_dict["cap"][:, :t+1]
else:
start_word = torch.tensor([self.start_idx,] * input_dict["attn_emb"].size(0)).unsqueeze(1).long()
if t == 0:
word = start_word
else:
word = torch.cat((start_word, output["seq"][:, :t]), dim=-1)
# word: [N, T]
decoder_input["word"] = word
cap_padding_mask = (word == self.pad_idx).to(input_dict["attn_emb"].device)
decoder_input["cap_padding_mask"] = cap_padding_mask
return decoder_input
def prepare_beamsearch_decoder_input(self, input_dict, output_i):
decoder_input = {}
t = input_dict["t"]
i = input_dict["sample_idx"]
beam_size = input_dict["beam_size"]
###############
# prepare attn embeds
################
if t == 0:
attn_emb = repeat_tensor(input_dict["attn_emb"][i], beam_size)
attn_emb_len = repeat_tensor(input_dict["attn_emb_len"][i], beam_size)
output_i["attn_emb"] = attn_emb
output_i["attn_emb_len"] = attn_emb_len
decoder_input["attn_emb"] = output_i["attn_emb"]
decoder_input["attn_emb_len"] = output_i["attn_emb_len"]
###############
# determine input word
################
start_word = torch.tensor([self.start_idx,] * beam_size).unsqueeze(1).long()
if t == 0:
word = start_word
else:
word = torch.cat((start_word, output_i["seq"]), dim=-1)
decoder_input["word"] = word
cap_padding_mask = (word == self.pad_idx).to(input_dict["attn_emb"].device)
decoder_input["cap_padding_mask"] = cap_padding_mask
return decoder_input
class M2TransformerModel(CaptionModel):
def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs):
if not hasattr(self, "compatible_decoders"):
self.compatible_decoders = (
captioning.models.decoder.M2TransformerDecoder,
)
super().__init__(encoder, decoder, **kwargs)
self.check_encoder_compatibility()
def check_encoder_compatibility(self):
assert isinstance(self.encoder, captioning.models.encoder.M2TransformerEncoder), \
f"only M2TransformerModel is compatible with {self.__class__.__name__}"
def seq_forward(self, input_dict):
cap = input_dict["cap"]
output = self.decoder(
{
"word": cap[:, :-1],
"attn_emb": input_dict["attn_emb"],
"attn_emb_mask": input_dict["attn_emb_mask"],
}
)
return output
def prepare_decoder_input(self, input_dict, output):
decoder_input = {
"attn_emb": input_dict["attn_emb"],
"attn_emb_mask": input_dict["attn_emb_mask"]
}
t = input_dict["t"]
###############
# determine input word
################
if input_dict["mode"] == "train" and random.random() < input_dict["ss_ratio"]: # training, scheduled sampling
word = input_dict["cap"][:, :t+1]
else:
start_word = torch.tensor([self.start_idx,] * input_dict["attn_emb"].size(0)).unsqueeze(1).long()
if t == 0:
word = start_word
else:
word = torch.cat((start_word, output["seq"][:, :t]), dim=-1)
# word: [N, T]
decoder_input["word"] = word
return decoder_input
def prepare_beamsearch_decoder_input(self, input_dict, output_i):
decoder_input = {}
t = input_dict["t"]
i = input_dict["sample_idx"]
beam_size = input_dict["beam_size"]
###############
# prepare attn embeds
################
if t == 0:
attn_emb = repeat_tensor(input_dict["attn_emb"][i], beam_size)
attn_emb_mask = repeat_tensor(input_dict["attn_emb_mask"][i], beam_size)
output_i["attn_emb"] = attn_emb
output_i["attn_emb_mask"] = attn_emb_mask
decoder_input["attn_emb"] = output_i["attn_emb"]
decoder_input["attn_emb_mask"] = output_i["attn_emb_mask"]
###############
# determine input word
################
start_word = torch.tensor([self.start_idx,] * beam_size).unsqueeze(1).long()
if t == 0:
word = start_word
else:
word = torch.cat((start_word, output_i["seq"]), dim=-1)
decoder_input["word"] = word
return decoder_input
class EventEncoder(nn.Module):
"""
Encode the Label information in AudioCaps and AudioSet
"""
def __init__(self, emb_dim, vocab_size=527):
super(EventEncoder, self).__init__()
self.label_embedding = nn.Parameter(
torch.randn((vocab_size, emb_dim)), requires_grad=True)
def forward(self, word_idxs):
indices = word_idxs / word_idxs.sum(dim=1, keepdim=True)
embeddings = indices @ self.label_embedding
return embeddings
class EventCondTransformerModel(TransformerModel):
def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs):
if not hasattr(self, "compatible_decoders"):
self.compatible_decoders = (
captioning.models.decoder.EventTransformerDecoder,
)
super().__init__(encoder, decoder, **kwargs)
self.label_encoder = EventEncoder(decoder.emb_dim, 527)
self.train_forward_keys += ["events"]
self.inference_forward_keys += ["events"]
# def seq_forward(self, input_dict):
# cap = input_dict["cap"]
# cap_padding_mask = (cap == self.pad_idx).to(cap.device)
# cap_padding_mask = cap_padding_mask[:, :-1]
# output = self.decoder(
# {
# "word": cap[:, :-1],
# "attn_emb": input_dict["attn_emb"],
# "attn_emb_len": input_dict["attn_emb_len"],
# "cap_padding_mask": cap_padding_mask
# }
# )
# return output
def prepare_decoder_input(self, input_dict, output):
decoder_input = super().prepare_decoder_input(input_dict, output)
decoder_input["events"] = self.label_encoder(input_dict["events"])
return decoder_input
def prepare_beamsearch_decoder_input(self, input_dict, output_i):
decoder_input = super().prepare_beamsearch_decoder_input(input_dict, output_i)
t = input_dict["t"]
i = input_dict["sample_idx"]
beam_size = input_dict["beam_size"]
if t == 0:
output_i["events"] = repeat_tensor(self.label_encoder(input_dict["events"])[i], beam_size)
decoder_input["events"] = output_i["events"]
return decoder_input
class KeywordCondTransformerModel(TransformerModel):
def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs):
if not hasattr(self, "compatible_decoders"):
self.compatible_decoders = (
captioning.models.decoder.KeywordProbTransformerDecoder,
)
super().__init__(encoder, decoder, **kwargs)
self.train_forward_keys += ["keyword"]
self.inference_forward_keys += ["keyword"]
def seq_forward(self, input_dict):
cap = input_dict["cap"]
cap_padding_mask = (cap == self.pad_idx).to(cap.device)
cap_padding_mask = cap_padding_mask[:, :-1]
keyword = input_dict["keyword"]
output = self.decoder(
{
"word": cap[:, :-1],
"attn_emb": input_dict["attn_emb"],
"attn_emb_len": input_dict["attn_emb_len"],
"keyword": keyword,
"cap_padding_mask": cap_padding_mask
}
)
return output
def prepare_decoder_input(self, input_dict, output):
decoder_input = super().prepare_decoder_input(input_dict, output)
decoder_input["keyword"] = input_dict["keyword"]
return decoder_input
def prepare_beamsearch_decoder_input(self, input_dict, output_i):
decoder_input = super().prepare_beamsearch_decoder_input(input_dict, output_i)
t = input_dict["t"]
i = input_dict["sample_idx"]
beam_size = input_dict["beam_size"]
if t == 0:
output_i["keyword"] = repeat_tensor(input_dict["keyword"][i],
beam_size)
decoder_input["keyword"] = output_i["keyword"]
return decoder_input
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