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| """ | |
| This file provides fine stage LETR definition | |
| """ | |
| import io | |
| from collections import defaultdict | |
| from typing import List, Optional | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| from PIL import Image | |
| from .misc import NestedTensor, nested_tensor_from_tensor_list | |
| import copy | |
| class LETRstack(nn.Module): | |
| def __init__(self, letr, args): | |
| super().__init__() | |
| self.letr = letr | |
| self.backbone = self.letr.backbone | |
| if args.layer1_frozen: | |
| # freeze backbone, encoder, decoder | |
| for n, p in self.named_parameters(): | |
| p.requires_grad_(False) | |
| hidden_dim, nheads = letr.transformer.d_model, letr.transformer.nhead | |
| # add new input proj layer | |
| channel = [256, 512, 1024, 2048] | |
| self.input_proj = nn.Conv2d(channel[args.layer2_num], hidden_dim, kernel_size=1) | |
| # add new transformer encoder decoder | |
| self.transformer = Transformer( d_model=args.second_hidden_dim, dropout=args.second_dropout, nhead=args.second_nheads, | |
| dim_feedforward=args.second_dim_feedforward, num_encoder_layers=args.second_enc_layers, | |
| num_decoder_layers=args.second_dec_layers, normalize_before=args.second_pre_norm, return_intermediate_dec=True,) | |
| # output layer | |
| self.class_embed = nn.Linear(hidden_dim, 1 + 1) | |
| self.lines_embed = MLP(hidden_dim, hidden_dim, 4, 3) | |
| self.aux_loss=args.aux_loss | |
| self.args = args | |
| def forward(self, samples, postprocessors=None, targets=None, criterion=None): | |
| if isinstance(samples, (list, torch.Tensor)): | |
| samples = nested_tensor_from_tensor_list(samples) | |
| # backbone | |
| features, pos = self.letr.backbone(samples) | |
| # layer 1 | |
| l1_num = self.args.layer1_num | |
| src1, mask1 = features[l1_num].decompose() | |
| assert mask1 is not None | |
| # layer 1 transformer | |
| hs1, _ = self.letr.transformer(self.letr.input_proj(src1), mask1, self.letr.query_embed.weight, pos[l1_num]) | |
| # layer 2 | |
| l2_num = self.args.layer2_num | |
| src2, mask2 = features[l2_num].decompose() | |
| src2 = self.input_proj(src2) | |
| # layer 2 transformer | |
| hs2, memory, _ = self.transformer(src2, mask2, hs1[-1], pos[l2_num]) | |
| outputs_class = self.class_embed(hs2) | |
| outputs_coord = self.lines_embed(hs2).sigmoid() | |
| out = {} | |
| out["pred_logits"] = outputs_class[-1] | |
| out["pred_lines"] = outputs_coord[-1] | |
| if self.aux_loss: | |
| out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord) | |
| return out, None | |
| def _set_aux_loss(self, outputs_class, outputs_coord): | |
| # this is a workaround to make torchscript happy, as torchscript | |
| # doesn't support dictionary with non-homogeneous values, such | |
| # as a dict having both a Tensor and a list. | |
| return [{'pred_logits': a, 'pred_lines': b} | |
| for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] | |
| def _set_aux_loss_POST(self, outputs_class, outputs_coord): | |
| # this is a workaround to make torchscript happy, as torchscript | |
| # doesn't support dictionary with non-homogeneous values, such | |
| # as a dict having both a Tensor and a list. | |
| return [{'POST_pred_lines': b} for b in outputs_coord[:-1]] | |
| def _expand(tensor, length: int): | |
| return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1) | |
| class MLP(nn.Module): | |
| """ Very simple multi-layer perceptron (also called FFN)""" | |
| def __init__(self, input_dim, hidden_dim, output_dim, num_layers): | |
| super().__init__() | |
| self.num_layers = num_layers | |
| h = [hidden_dim] * (num_layers - 1) | |
| self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) | |
| def forward(self, x): | |
| for i, layer in enumerate(self.layers): | |
| x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) | |
| return x | |
| class Transformer(nn.Module): | |
| def __init__(self, d_model=512, nhead=8, num_encoder_layers=6, | |
| num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False, | |
| return_intermediate_dec=False): | |
| super().__init__() | |
| encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before) | |
| encoder_norm = nn.LayerNorm(d_model) if normalize_before else None | |
| self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm) | |
| decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout, activation, normalize_before) | |
| decoder_norm = nn.LayerNorm(d_model) | |
| self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm, | |
| return_intermediate=return_intermediate_dec) | |
| self._reset_parameters() | |
| self.d_model = d_model | |
| self.nhead = nhead | |
| def _reset_parameters(self): | |
| for p in self.parameters(): | |
| if p.dim() > 1: | |
| nn.init.xavier_uniform_(p) | |
| def forward(self, src, mask, query_embed, pos_embed): | |
| # flatten NxCxHxW to HWxNxC | |
| bs, c, h, w = src.shape | |
| src = src.flatten(2).permute(2, 0, 1) | |
| pos_embed = pos_embed.flatten(2).permute(2, 0, 1) | |
| mask = mask.flatten(1) | |
| query_embed = query_embed.permute(1, 0, 2) | |
| tgt = torch.zeros_like(query_embed) | |
| memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed) | |
| hs, attn_output_weights = self.decoder(tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed) | |
| return hs.transpose(1, 2), memory, attn_output_weights | |
| class TransformerEncoder(nn.Module): | |
| def __init__(self, encoder_layer, num_layers, norm=None): | |
| super().__init__() | |
| self.layers = _get_clones(encoder_layer, num_layers) | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| def forward(self, src, | |
| mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| output = src | |
| for layer in self.layers: | |
| output = layer(output, src_mask=mask, | |
| src_key_padding_mask=src_key_padding_mask, pos=pos) | |
| if self.norm is not None: | |
| output = self.norm(output) | |
| return output | |
| class TransformerDecoder(nn.Module): | |
| def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): | |
| super().__init__() | |
| self.layers = _get_clones(decoder_layer, num_layers) | |
| self.num_layers = num_layers | |
| self.norm = norm | |
| self.return_intermediate = return_intermediate | |
| def forward(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| output = tgt | |
| intermediate = [] | |
| attn_output_weights_list = [] | |
| for layer in self.layers: | |
| output, attn_output_weights = layer(output, memory, tgt_mask=tgt_mask, | |
| memory_mask=memory_mask, | |
| tgt_key_padding_mask=tgt_key_padding_mask, | |
| memory_key_padding_mask=memory_key_padding_mask, | |
| pos=pos, query_pos=query_pos) | |
| if self.return_intermediate: | |
| intermediate.append(self.norm(output)) | |
| attn_output_weights_list.append(attn_output_weights) | |
| if self.norm is not None: | |
| output = self.norm(output) | |
| if self.return_intermediate: | |
| intermediate.pop() | |
| intermediate.append(output) | |
| if self.return_intermediate: | |
| return torch.stack(intermediate), attn_output_weights_list | |
| return output.unsqueeze(0), attn_output_weights | |
| class TransformerEncoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False): | |
| super().__init__() | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| # Implementation of Feedforward model | |
| self.linear1 = nn.Linear(d_model, dim_feedforward) | |
| self.dropout = nn.Dropout(dropout) | |
| self.linear2 = nn.Linear(dim_feedforward, d_model) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.activation = _get_activation_fn(activation) | |
| self.normalize_before = normalize_before | |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): | |
| return tensor if pos is None else tensor + pos | |
| def forward_post(self, | |
| src, | |
| src_mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| q = k = self.with_pos_embed(src, pos) | |
| src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, | |
| key_padding_mask=src_key_padding_mask)[0] | |
| src = src + self.dropout1(src2) | |
| src = self.norm1(src) | |
| src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) | |
| src = src + self.dropout2(src2) | |
| src = self.norm2(src) | |
| return src | |
| def forward_pre(self, src, | |
| src_mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| src2 = self.norm1(src) | |
| q = k = self.with_pos_embed(src2, pos) | |
| src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask, | |
| key_padding_mask=src_key_padding_mask)[0] | |
| src = src + self.dropout1(src2) | |
| src2 = self.norm2(src) | |
| src2 = self.linear2(self.dropout(self.activation(self.linear1(src2)))) | |
| src = src + self.dropout2(src2) | |
| return src | |
| def forward(self, src, | |
| src_mask: Optional[Tensor] = None, | |
| src_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None): | |
| if self.normalize_before: | |
| return self.forward_pre(src, src_mask, src_key_padding_mask, pos) | |
| return self.forward_post(src, src_mask, src_key_padding_mask, pos) | |
| class TransformerDecoderLayer(nn.Module): | |
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, | |
| activation="relu", normalize_before=False): | |
| super().__init__() | |
| self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) | |
| # Implementation of Feedforward model | |
| self.linear1 = nn.Linear(d_model, dim_feedforward) | |
| self.dropout = nn.Dropout(dropout) | |
| self.linear2 = nn.Linear(dim_feedforward, d_model) | |
| self.norm1 = nn.LayerNorm(d_model) | |
| self.norm2 = nn.LayerNorm(d_model) | |
| self.norm3 = nn.LayerNorm(d_model) | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.dropout3 = nn.Dropout(dropout) | |
| self.activation = _get_activation_fn(activation) | |
| self.normalize_before = normalize_before | |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): | |
| return tensor if pos is None else tensor + pos | |
| def forward_post(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| q = k = self.with_pos_embed(tgt, query_pos) | |
| tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt = self.norm1(tgt) | |
| tgt2, attn_output_weights = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), | |
| key=self.with_pos_embed(memory, pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask) | |
| tgt = tgt + self.dropout2(tgt2) | |
| tgt = self.norm2(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| tgt = self.norm3(tgt) | |
| return tgt, attn_output_weights | |
| def forward_pre(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| tgt2 = self.norm1(tgt) | |
| q = k = self.with_pos_embed(tgt2, query_pos) | |
| tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, | |
| key_padding_mask=tgt_key_padding_mask)[0] | |
| tgt = tgt + self.dropout1(tgt2) | |
| tgt2 = self.norm2(tgt) | |
| tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), | |
| key=self.with_pos_embed(memory, pos), | |
| value=memory, attn_mask=memory_mask, | |
| key_padding_mask=memory_key_padding_mask)[0] | |
| tgt = tgt + self.dropout2(tgt2) | |
| tgt2 = self.norm3(tgt) | |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) | |
| tgt = tgt + self.dropout3(tgt2) | |
| return tgt | |
| def forward(self, tgt, memory, | |
| tgt_mask: Optional[Tensor] = None, | |
| memory_mask: Optional[Tensor] = None, | |
| tgt_key_padding_mask: Optional[Tensor] = None, | |
| memory_key_padding_mask: Optional[Tensor] = None, | |
| pos: Optional[Tensor] = None, | |
| query_pos: Optional[Tensor] = None): | |
| if self.normalize_before: | |
| return self.forward_pre(tgt, memory, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) | |
| return self.forward_post(tgt, memory, tgt_mask, memory_mask, | |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) | |
| def _get_clones(module, N): | |
| return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) | |
| def _get_activation_fn(activation): | |
| """Return an activation function given a string""" | |
| if activation == "relu": | |
| return F.relu | |
| if activation == "gelu": | |
| return F.gelu | |
| if activation == "glu": | |
| return F.glu | |
| raise RuntimeError(F"activation should be relu/gelu, not {activation}.") | |