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#!/usr/bin/env python3
# coding=utf-8
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.module.edge_classifier import EdgeClassifier
from model.module.anchor_classifier import AnchorClassifier
from utility.cross_entropy import cross_entropy, binary_cross_entropy
from utility.hungarian_matching import get_matching, reorder, match_anchor, match_label
from utility.utils import create_padding_mask
class AbstractHead(nn.Module):
def __init__(self, dataset, args, config, initialize: bool):
super(AbstractHead, self).__init__()
self.edge_classifier = self.init_edge_classifier(dataset, args, config, initialize)
self.label_classifier = self.init_label_classifier(dataset, args, config, initialize)
self.anchor_classifier = self.init_anchor_classifier(dataset, args, config, initialize, mode="anchor")
self.source_anchor_classifier = self.init_anchor_classifier(dataset, args, config, initialize, mode="source_anchor")
self.target_anchor_classifier = self.init_anchor_classifier(dataset, args, config, initialize, mode="target_anchor")
self.query_length = args.query_length
self.focal = args.focal
self.dataset = dataset
def forward(self, encoder_output, decoder_output, encoder_mask, decoder_mask, batch):
output = {}
decoder_lens = self.query_length * batch["every_input"][1]
output["label"] = self.forward_label(decoder_output)
output["anchor"] = self.forward_anchor(decoder_output, encoder_output, encoder_mask, mode="anchor") # shape: (B, T_l, T_w)
output["source_anchor"] = self.forward_anchor(decoder_output, encoder_output, encoder_mask, mode="source_anchor") # shape: (B, T_l, T_w)
output["target_anchor"] = self.forward_anchor(decoder_output, encoder_output, encoder_mask, mode="target_anchor") # shape: (B, T_l, T_w)
cost_matrices = self.create_cost_matrices(output, batch, decoder_lens)
matching = get_matching(cost_matrices)
decoder_output = reorder(decoder_output, matching, batch["labels"][0].size(1))
output["edge presence"], output["edge label"] = self.forward_edge(decoder_output)
return self.loss(output, batch, matching, decoder_mask)
def predict(self, encoder_output, decoder_output, encoder_mask, decoder_mask, batch, **kwargs):
every_input, word_lens = batch["every_input"]
decoder_lens = self.query_length * word_lens
batch_size = every_input.size(0)
label_pred = self.forward_label(decoder_output)
anchor_pred = self.forward_anchor(decoder_output, encoder_output, encoder_mask, mode="anchor") # shape: (B, T_l, T_w)
source_anchor_pred = self.forward_anchor(decoder_output, encoder_output, encoder_mask, mode="source_anchor") # shape: (B, T_l, T_w)
target_anchor_pred = self.forward_anchor(decoder_output, encoder_output, encoder_mask, mode="target_anchor") # shape: (B, T_l, T_w)
labels = [[] for _ in range(batch_size)]
anchors, source_anchors, target_anchors = [[] for _ in range(batch_size)], [[] for _ in range(batch_size)], [[] for _ in range(batch_size)]
for b in range(batch_size):
label_indices = self.inference_label(label_pred[b, :decoder_lens[b], :]).cpu()
for t in range(label_indices.size(0)):
label_index = label_indices[t].item()
if label_index == 0:
continue
decoder_output[b, len(labels[b]), :] = decoder_output[b, t, :]
labels[b].append(label_index)
if anchor_pred is None:
anchors[b].append(list(range(t // self.query_length, word_lens[b])))
else:
anchors[b].append(self.inference_anchor(anchor_pred[b, t, :word_lens[b]]).cpu())
if source_anchor_pred is None:
source_anchors[b].append(list(range(t // self.query_length, word_lens[b])))
else:
source_anchors[b].append(self.inference_anchor(source_anchor_pred[b, t, :word_lens[b]]).cpu())
if target_anchor_pred is None:
target_anchors[b].append(list(range(t // self.query_length, word_lens[b])))
else:
target_anchors[b].append(self.inference_anchor(target_anchor_pred[b, t, :word_lens[b]]).cpu())
decoder_output = decoder_output[:, : max(len(l) for l in labels), :]
edge_presence, edge_labels = self.forward_edge(decoder_output)
outputs = [
self.parser.parse(
{
"labels": labels[b],
"anchors": anchors[b],
"source anchors": source_anchors[b],
"target anchors": target_anchors[b],
"edge presence": self.inference_edge_presence(edge_presence, b),
"edge labels": self.inference_edge_label(edge_labels, b),
"id": batch["id"][b].cpu(),
"tokens": batch["every_input"][0][b, : word_lens[b]].cpu(),
"token intervals": batch["token_intervals"][b, :, :].cpu(),
},
**kwargs
)
for b in range(batch_size)
]
return outputs
def loss(self, output, batch, matching, decoder_mask):
batch_size = batch["every_input"][0].size(0)
device = batch["every_input"][0].device
T_label = batch["labels"][0].size(1)
T_input = batch["every_input"][0].size(1)
T_edge = batch["edge_presence"].size(1)
input_mask = create_padding_mask(batch_size, T_input, batch["every_input"][1], device) # shape: (B, T_input)
label_mask = create_padding_mask(batch_size, T_label, batch["labels"][1], device) # shape: (B, T_label)
edge_mask = torch.eye(T_label, T_label, device=device, dtype=torch.bool).unsqueeze(0) # shape: (1, T_label, T_label)
edge_mask = edge_mask | label_mask.unsqueeze(1) | label_mask.unsqueeze(2) # shape: (B, T_label, T_label)
if T_edge != T_label:
edge_mask = F.pad(edge_mask, (T_edge - T_label, 0, T_edge - T_label, 0), value=0)
edge_label_mask = (batch["edge_presence"] == 0) | edge_mask
if output["edge label"] is not None:
batch["edge_labels"] = (
batch["edge_labels"][0][:, :, :, :output["edge label"].size(-1)],
batch["edge_labels"][1],
)
losses = {}
losses.update(self.loss_label(output, batch, decoder_mask, matching))
losses.update(self.loss_anchor(output, batch, input_mask, matching, mode="anchor"))
losses.update(self.loss_anchor(output, batch, input_mask, matching, mode="source_anchor"))
losses.update(self.loss_anchor(output, batch, input_mask, matching, mode="target_anchor"))
losses.update(self.loss_edge_presence(output, batch, edge_mask))
losses.update(self.loss_edge_label(output, batch, edge_label_mask.unsqueeze(-1)))
stats = {f"{key}": value.detach().cpu().item() for key, value in losses.items()}
total_loss = sum(losses.values()) / len(losses)
return total_loss, stats
@torch.no_grad()
def create_cost_matrices(self, output, batch, decoder_lens):
batch_size = len(batch["labels"][1])
decoder_lens = decoder_lens.cpu()
matrices = []
for b in range(batch_size):
label_cost_matrix = self.label_cost_matrix(output, batch, decoder_lens, b)
anchor_cost_matrix = self.anchor_cost_matrix(output, batch, decoder_lens, b)
cost_matrix = label_cost_matrix * anchor_cost_matrix
matrices.append(cost_matrix.cpu())
return matrices
def init_edge_classifier(self, dataset, args, config, initialize: bool):
if not config["edge presence"] and not config["edge label"]:
return None
return EdgeClassifier(dataset, args, initialize, presence=config["edge presence"], label=config["edge label"])
def init_label_classifier(self, dataset, args, config, initialize: bool):
if not config["label"]:
return None
classifier = nn.Sequential(
nn.Dropout(args.dropout_label),
nn.Linear(args.hidden_size, len(dataset.label_field.vocab) + 1, bias=True)
)
if initialize:
classifier[1].bias.data = dataset.label_freqs.log()
return classifier
def init_anchor_classifier(self, dataset, args, config, initialize: bool, mode="anchor"):
if not config[mode]:
return None
return AnchorClassifier(dataset, args, initialize, mode=mode)
def forward_edge(self, decoder_output):
if self.edge_classifier is None:
return None, None
return self.edge_classifier(decoder_output)
def forward_label(self, decoder_output):
if self.label_classifier is None:
return None
return torch.log_softmax(self.label_classifier(decoder_output), dim=-1)
def forward_anchor(self, decoder_output, encoder_output, encoder_mask, mode="anchor"):
classifier = getattr(self, f"{mode}_classifier")
if classifier is None:
return None
return classifier(decoder_output, encoder_output, encoder_mask)
def inference_label(self, prediction):
prediction = prediction.exp()
return torch.where(
prediction[:, 0] > prediction[:, 1:].sum(-1),
torch.zeros(prediction.size(0), dtype=torch.long, device=prediction.device),
prediction[:, 1:].argmax(dim=-1) + 1
)
def inference_anchor(self, prediction):
return prediction.sigmoid()
def inference_edge_presence(self, prediction, example_index: int):
if prediction is None:
return None
N = prediction.size(1)
mask = torch.eye(N, N, device=prediction.device, dtype=torch.bool)
return prediction[example_index, :, :].sigmoid().masked_fill(mask, 0.0).cpu()
def inference_edge_label(self, prediction, example_index: int):
if prediction is None:
return None
return prediction[example_index, :, :, :].cpu()
def loss_edge_presence(self, prediction, target, mask):
if self.edge_classifier is None or prediction["edge presence"] is None:
return {}
return {"edge presence": binary_cross_entropy(prediction["edge presence"], target["edge_presence"].float(), mask)}
def loss_edge_label(self, prediction, target, mask):
if self.edge_classifier is None or prediction["edge label"] is None:
return {}
return {"edge label": binary_cross_entropy(prediction["edge label"], target["edge_labels"][0].float(), mask)}
def loss_label(self, prediction, target, mask, matching):
if self.label_classifier is None or prediction["label"] is None:
return {}
prediction = prediction["label"]
target = match_label(
target["labels"][0], matching, prediction.shape[:-1], prediction.device, self.query_length
)
return {"label": cross_entropy(prediction, target, mask, focal=self.focal)}
def loss_anchor(self, prediction, target, mask, matching, mode="anchor"):
if getattr(self, f"{mode}_classifier") is None or prediction[mode] is None:
return {}
prediction = prediction[mode]
target, anchor_mask = match_anchor(target[mode], matching, prediction.shape, prediction.device)
mask = anchor_mask.unsqueeze(-1) | mask.unsqueeze(-2)
return {mode: binary_cross_entropy(prediction, target.float(), mask)}
def label_cost_matrix(self, output, batch, decoder_lens, b: int):
if output["label"] is None:
return 1.0
target_labels = batch["anchored_labels"][b] # shape: (num_nodes, num_inputs, num_classes)
label_prob = output["label"][b, : decoder_lens[b], :].exp().unsqueeze(0) # shape: (1, num_queries, num_classes)
tgt_label = target_labels.repeat_interleave(self.query_length, dim=1) # shape: (num_nodes, num_queries, num_classes)
cost_matrix = ((tgt_label * label_prob).sum(-1) * label_prob[:, :, 1:].sum(-1)).t().sqrt() # shape: (num_queries, num_nodes)
return cost_matrix
def anchor_cost_matrix(self, output, batch, decoder_lens, b: int):
if output["anchor"] is None:
return 1.0
num_nodes = batch["labels"][1][b]
word_lens = batch["every_input"][1]
target_anchors, _ = batch["anchor"]
pred_anchors = output["anchor"].sigmoid()
tgt_align = target_anchors[b, : num_nodes, : word_lens[b]] # shape: (num_nodes, num_inputs)
align_prob = pred_anchors[b, : decoder_lens[b], : word_lens[b]] # shape: (num_queries, num_inputs)
align_prob = align_prob.unsqueeze(1).expand(-1, num_nodes, -1) # shape: (num_queries, num_nodes, num_inputs)
align_prob = torch.where(tgt_align.unsqueeze(0).bool(), align_prob, 1.0 - align_prob) # shape: (num_queries, num_nodes, num_inputs)
cost_matrix = align_prob.log().mean(-1).exp() # shape: (num_queries, num_nodes)
return cost_matrix
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