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import logging
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import ModelOutput
from .configuration_layout_fidnet_v3 import LayoutFIDNetV3Config
logger = logging.getLogger(__name__)
@dataclass
class LayoutFIDNetV3Output(ModelOutput):
logit_disc: torch.Tensor
logit_cls: torch.Tensor
bbox_pred: torch.Tensor
class TransformerWithToken(nn.Module):
def __init__(
self,
d_model: int,
nhead: int,
dim_feedforward: int,
num_layers: int,
) -> None:
super().__init__()
self.token = nn.Parameter(torch.randn(1, 1, d_model))
token_mask = torch.zeros(1, 1, dtype=torch.bool)
self.register_buffer("token_mask", token_mask)
self.core = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=dim_feedforward,
),
num_layers=num_layers,
)
def forward(self, x, src_key_padding_mask):
# x: [N, B, E]
# padding_mask: [B, N]
# `False` for valid values
# `True` for padded values
B = x.size(1)
token = self.token.expand(-1, B, -1)
x = torch.cat([token, x], dim=0)
token_mask = self.token_mask.expand(B, -1)
padding_mask = torch.cat([token_mask, src_key_padding_mask], dim=1)
x = self.core(x, src_key_padding_mask=padding_mask)
return x
class LayoutFIDNetV3(PreTrainedModel):
config_class = LayoutFIDNetV3Config
def __init__(self, config: LayoutFIDNetV3Config) -> None:
super().__init__(config)
# encoder
self.emb_label = nn.Embedding(config.num_labels, config.d_model)
self.fc_bbox = nn.Linear(4, config.d_model)
self.enc_fc_in = nn.Linear(config.d_model * 2, config.d_model)
self.enc_transformer = TransformerWithToken(
d_model=config.d_model,
dim_feedforward=config.d_model // 2,
nhead=config.nhead,
num_layers=config.num_layers,
)
self.fc_out_disc = nn.Linear(config.d_model, 1)
# decoder
self.pos_token = nn.Parameter(torch.rand(config.max_bbox, 1, config.d_model))
self.dec_fc_in = nn.Linear(config.d_model * 2, config.d_model)
te = nn.TransformerEncoderLayer(
d_model=config.d_model,
nhead=config.nhead,
dim_feedforward=config.d_model // 2,
)
self.dec_transformer = nn.TransformerEncoder(te, num_layers=config.num_layers)
self.fc_out_cls = nn.Linear(config.d_model, config.num_labels)
self.fc_out_bbox = nn.Linear(config.d_model, 4)
def extract_features(
self, bbox: torch.Tensor, label: torch.Tensor, padding_mask: torch.Tensor
) -> torch.Tensor:
b = self.fc_bbox(bbox)
l = self.emb_label(label)
x = self.enc_fc_in(torch.cat([b, l], dim=-1))
x = torch.relu(x).permute(1, 0, 2)
x = self.enc_transformer(x, padding_mask)
return x[0]
def forward(
self,
bbox: torch.Tensor,
label: torch.Tensor,
padding_mask: torch.Tensor,
return_dict: Optional[bool] = None,
) -> Union[Tuple, LayoutFIDNetV3Output]:
B, N, _ = bbox.size()
x = self.extract_features(bbox, label, padding_mask)
logit_disc = self.fc_out_disc(x).squeeze(-1)
x = x.unsqueeze(0).expand(N, -1, -1)
t = self.pos_token[:N].expand(-1, B, -1)
x = torch.cat([x, t], dim=-1)
x = torch.relu(self.dec_fc_in(x))
x = self.dec_transformer(x, src_key_padding_mask=padding_mask)
# x = x.permute(1, 0, 2)[~padding_mask]
x = x.permute(1, 0, 2)
# logit_cls: [B, N, L] bbox_pred: [B, N, 4]
logit_cls = self.fc_out_cls(x)
bbox_pred = torch.sigmoid(self.fc_out_bbox(x))
if not return_dict:
return logit_disc, logit_cls, bbox_pred
return LayoutFIDNetV3Output(
logit_disc=logit_disc, logit_cls=logit_cls, bbox_pred=bbox_pred
)
def convert_from_checkpoint(
repo_id: str, filename: str, config: Optional[LayoutFIDNetV3Config] = None
) -> LayoutFIDNetV3:
from huggingface_hub import hf_hub_download
checkpoint_path = hf_hub_download(repo_id=repo_id, filename=filename)
config = config or LayoutFIDNetV3Config()
model = LayoutFIDNetV3(config)
logger.info(f"Loading model from {checkpoint_path}")
state_dict = torch.load(checkpoint_path, map_location="cpu")["state_dict"]
model.load_state_dict(state_dict, strict=True)
model.eval()
return model
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