models/letr_stack.py

"""
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

    @torch.jit.unused
    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])]

    @torch.jit.unused
    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}.")