mask_former/modeling/heads/big_pixel_decoder.py

# Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import Callable, Dict, Optional, Union

import fvcore.nn.weight_init as weight_init
from detectron2.config import configurable
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
from torch import nn
from torch.nn import functional as F

from ..transformer.position_encoding import PositionEmbeddingSine
from ..transformer.transformer import TransformerEncoder, TransformerEncoderLayer

@SEM_SEG_HEADS_REGISTRY.register()
class BigPixelDecoder(nn.Module):
    @configurable
    def __init__(
            self,
            input_shape: Dict[str, ShapeSpec],
            *,
            conv_dim: int,
            mask_dim: int,
            norm: Optional[Union[str, Callable]] = None,
    ):
        """
        NOTE: this interface is experimental.
        Args:
            input_shape: shapes (channels and stride) of the input features
            conv_dims: number of output channels for the intermediate conv layers.
            mask_dim: number of output channels for the final conv layer.
            norm (str or callable): normalization for all conv layers
        """
        super().__init__()

        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
        feature_channels = [v.channels for k, v in input_shape]

        lateral_convs = []
        output_convs = []

        use_bias = norm == ""
        for idx, in_channels in enumerate(feature_channels):
            if idx == len(self.in_features) - 1:
                output_norm = get_norm(norm, conv_dim)
                output_conv = Conv2d(
                    in_channels,
                    conv_dim,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    bias=use_bias,
                    norm=output_norm,
                    activation=F.relu,
                )
                weight_init.c2_xavier_fill(output_conv)
                self.add_module("layer_{}".format(idx + 1), output_conv)

                lateral_convs.append(None)
                output_convs.append(output_conv)
            else:
                lateral_norm = get_norm(norm, conv_dim)
                output_norm = get_norm(norm, conv_dim)

                lateral_conv = Conv2d(
                    in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
                )
                output_conv = Conv2d(
                    conv_dim,
                    conv_dim,
                    kernel_size=3,
                    stride=1,
                    padding=1,
                    bias=use_bias,
                    norm=output_norm,
                    activation=F.relu,
                )
                weight_init.c2_xavier_fill(lateral_conv)
                weight_init.c2_xavier_fill(output_conv)
                self.add_module("adapter_{}".format(idx + 1), lateral_conv)
                self.add_module("layer_{}".format(idx + 1), output_conv)

                lateral_convs.append(lateral_conv)
                output_convs.append(output_conv)
        # Place convs into top-down order (from low to high resolution)
        # to make the top-down computation in forward clearer.
        self.lateral_convs = lateral_convs[::-1]
        self.output_convs = output_convs[::-1]

        self.mask_dim = mask_dim
        # self.mask_features = Conv2d(
        #     conv_dim,
        #     mask_dim,
        #     kernel_size=3,
        #     stride=1,
        #     padding=1,
        # )

        # weight_init.c2_xavier_fill(self.mask_features)

        self.mask_features = nn.Sequential(
            Conv2d(
                conv_dim,
                conv_dim,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=use_bias,
                norm=output_norm,
                activation=F.relu,
            ),
            nn.UpsamplingNearest2d(scale_factor=2),
            Conv2d(
                conv_dim,
                conv_dim,
                kernel_size=1,
                stride=1,
                padding=1,
                bias=use_bias,
                norm=output_norm,
                activation=F.relu,
            ),
            Conv2d(
                conv_dim,
                conv_dim,
                kernel_size=3,
                stride=1,
                padding=1,
                bias=use_bias,
                norm=output_norm,
                activation=F.relu,
            ),
            nn.UpsamplingNearest2d(scale_factor=2),
            Conv2d(
                conv_dim,
                conv_dim,
                kernel_size=1,
                stride=1,
                padding=1,
                bias=use_bias,
                norm=output_norm,
                activation=F.relu,
            ),
            Conv2d(
                conv_dim,
                mask_dim,
                kernel_size=3,
                stride=1,
                padding=1,
            )
        )

        for name, module in self.mask_features.named_modules():
            if 'Conv2d' in name:
                weight_init.c2_xavier_fill(module)

    @classmethod
    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
        ret = {}
        ret["input_shape"] = {
            k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
        }
        ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
        ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
        ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
        return ret

    def forward_features(self, features):
        # Reverse feature maps into top-down order (from low to high resolution)
        for idx, f in enumerate(self.in_features[::-1]):
            x = features[f]
            lateral_conv = self.lateral_convs[idx]
            output_conv = self.output_convs[idx]
            if lateral_conv is None:
                y = output_conv(x)
            else:
                cur_fpn = lateral_conv(x)
                # Following FPN implementation, we use nearest upsampling here
                y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
                y = output_conv(y)
        return self.mask_features(y), None

    def forward(self, features, targets=None):
        logger = logging.getLogger(__name__)
        logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.")
        return self.forward_features(features)


class TransformerEncoderOnly(nn.Module):
    def __init__(
            self,
            d_model=512,
            nhead=8,
            num_encoder_layers=6,
            dim_feedforward=2048,
            dropout=0.1,
            activation="relu",
            normalize_before=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)

        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, 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)
        if mask is not None:
            mask = mask.flatten(1)

        memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
        return memory.permute(1, 2, 0).view(bs, c, h, w)