modeling/vision/encoder/transformer_encoder_fpn.py

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
from torch.cuda.amp import autocast

import fvcore.nn.weight_init as weight_init
from detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm

from .build import register_encoder
from .transformer_blocks import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn
from ...modules import PositionEmbeddingSine
from ...utils import configurable


# This is a modified FPN decoder.
class BasePixelDecoder(nn.Module):
    def __init__(
        self,
        input_shape: Dict[str, ShapeSpec],
        *,
        conv_dim: int,
        mask_dim: int,
        mask_on: bool,
        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_on = mask_on
        if self.mask_on:
            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.maskformer_num_feature_levels = 3  # always use 3 scales

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

    def forward_features(self, features):
        multi_scale_features = []
        num_cur_levels = 0
        # 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)
            if num_cur_levels < self.maskformer_num_feature_levels:
                multi_scale_features.append(y)
                num_cur_levels += 1
        
        mask_features = self.mask_features(y) if self.mask_on else None
        return mask_features, None, multi_scale_features

    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)


# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.
class TransformerEncoderPixelDecoder(BasePixelDecoder):
    @configurable
    def __init__(
        self,
        input_shape: Dict[str, ShapeSpec],
        *,
        transformer_dropout: float,
        transformer_nheads: int,
        transformer_dim_feedforward: int,
        transformer_enc_layers: int,
        transformer_pre_norm: bool,
        conv_dim: int,
        mask_dim: int,
        mask_on: int,
        norm: Optional[Union[str, Callable]] = None,
    ):
        """
        NOTE: this interface is experimental.
        Args:
            input_shape: shapes (channels and stride) of the input features
            transformer_dropout: dropout probability in transformer
            transformer_nheads: number of heads in transformer
            transformer_dim_feedforward: dimension of feedforward network
            transformer_enc_layers: number of transformer encoder layers
            transformer_pre_norm: whether to use pre-layernorm or not
            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, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm, mask_on=mask_on)

        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_strides = [v.stride for k, v in input_shape]
        feature_channels = [v.channels for k, v in input_shape]

        in_channels = feature_channels[len(self.in_features) - 1]
        self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)
        weight_init.c2_xavier_fill(self.input_proj)
        self.transformer = TransformerEncoderOnly(
            d_model=conv_dim,
            dropout=transformer_dropout,
            nhead=transformer_nheads,
            dim_feedforward=transformer_dim_feedforward,
            num_encoder_layers=transformer_enc_layers,
            normalize_before=transformer_pre_norm,
        )
        N_steps = conv_dim // 2
        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)

        # update layer
        use_bias = norm == ""
        output_norm = get_norm(norm, conv_dim)
        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(output_conv)
        delattr(self, "layer_{}".format(len(self.in_features)))
        self.add_module("layer_{}".format(len(self.in_features)), output_conv)
        self.output_convs[0] = output_conv

    @classmethod
    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
        enc_cfg = cfg['MODEL']['ENCODER']
        dec_cfg = cfg['MODEL']['DECODER']

        ret = super().from_config(cfg, input_shape)
        ret["transformer_dropout"] = dec_cfg['DROPOUT']
        ret["transformer_nheads"] = dec_cfg['NHEADS']
        ret["transformer_dim_feedforward"] = dec_cfg['DIM_FEEDFORWARD']
        ret["transformer_enc_layers"] = enc_cfg['TRANSFORMER_ENC_LAYERS']  # a separate config
        ret["transformer_pre_norm"] = dec_cfg['PRE_NORM']

        ret['mask_on'] = cfg['MODEL']['DECODER']['MASK']
        return ret

    def forward_features(self, features):
        multi_scale_features = []
        num_cur_levels = 0
        
        # 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:
                transformer = self.input_proj(x)
                pos = self.pe_layer(x)
                transformer = self.transformer(transformer, None, pos)
                y = output_conv(transformer)
                # save intermediate feature as input to Transformer decoder
                transformer_encoder_features = transformer
            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)
            if num_cur_levels < self.maskformer_num_feature_levels:
                multi_scale_features.append(y)
                num_cur_levels += 1

        mask_features = self.mask_features(y) if self.mask_on else None
        return mask_features, transformer_encoder_features, multi_scale_features

    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)



@register_encoder
def get_transformer_encoder_fpn(cfg, input_shape):
    """
    Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`.
    """
    model = TransformerEncoderPixelDecoder(cfg, input_shape)    
    forward_features = getattr(model, "forward_features", None)
    if not callable(forward_features):
        raise ValueError(
            "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
            f"Please implement forward_features for {name} to only return mask features."
        )
    return model