matanyone2/model/big_modules.py

"""
big_modules.py - This file stores higher-level network blocks.

x - usually denotes features that are shared between objects.
g - usually denotes features that are not shared between objects 
    with an extra "num_objects" dimension (batch_size * num_objects * num_channels * H * W).

The trailing number of a variable usually denotes the stride
"""

from typing import Iterable
from omegaconf import DictConfig
import torch
import torch.nn as nn
import torch.nn.functional as F

from matanyone2.model.group_modules import MainToGroupDistributor, GroupFeatureFusionBlock, GConv2d
from matanyone2.model.utils import resnet
from matanyone2.model.modules import SensoryDeepUpdater, SensoryUpdater_fullscale, DecoderFeatureProcessor, MaskUpsampleBlock
from matanyone2.utils.device import safe_autocast

class UncertPred(nn.Module):
    def __init__(self, model_cfg: DictConfig):
        super().__init__()
        self.conv1x1_v2 = nn.Conv2d(model_cfg.pixel_dim*2 + 1 + model_cfg.value_dim, 64, kernel_size=1, stride=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.conv3x3 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1, groups=1, bias=False, dilation=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.conv3x3_out = nn.Conv2d(32, 1, kernel_size=3, stride=1, padding=1, groups=1, bias=False, dilation=1)
    
    def forward(self, last_frame_feat: torch.Tensor, cur_frame_feat: torch.Tensor, last_mask: torch.Tensor, mem_val_diff:torch.Tensor):
        last_mask = F.interpolate(last_mask, size=last_frame_feat.shape[-2:], mode='area')
        x = torch.cat([last_frame_feat, cur_frame_feat, last_mask, mem_val_diff], dim=1)
        x = self.conv1x1_v2(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv3x3(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3x3_out(x)
        return x
    
    # override the default train() to freeze BN statistics
    def train(self, mode=True):
        self.training = False
        for module in self.children():
            module.train(False)
        return self

class PixelEncoder(nn.Module):
    def __init__(self, model_cfg: DictConfig):
        super().__init__()

        self.is_resnet = 'resnet' in model_cfg.pixel_encoder.type
        # if model_cfg.pretrained_resnet is set in the model_cfg we get the value
        # else default to True
        is_pretrained_resnet = getattr(model_cfg,"pretrained_resnet",True)
        if self.is_resnet:
            if model_cfg.pixel_encoder.type == 'resnet18':
                network = resnet.resnet18(pretrained=is_pretrained_resnet)
            elif model_cfg.pixel_encoder.type == 'resnet50':
                network = resnet.resnet50(pretrained=is_pretrained_resnet)
            else:
                raise NotImplementedError
            self.conv1 = network.conv1
            self.bn1 = network.bn1
            self.relu = network.relu
            self.maxpool = network.maxpool

            self.res2 = network.layer1
            self.layer2 = network.layer2
            self.layer3 = network.layer3
        else:
            raise NotImplementedError

    def forward(self, x: torch.Tensor, seq_length=None) -> (torch.Tensor, torch.Tensor, torch.Tensor):
        f1 = x
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        f2 = x
        x = self.maxpool(x)
        f4 = self.res2(x)
        f8 = self.layer2(f4)
        f16 = self.layer3(f8)

        return f16, f8, f4, f2, f1

    # override the default train() to freeze BN statistics
    def train(self, mode=True):
        self.training = False
        for module in self.children():
            module.train(False)
        return self


class KeyProjection(nn.Module):
    def __init__(self, model_cfg: DictConfig):
        super().__init__()
        in_dim = model_cfg.pixel_encoder.ms_dims[0]
        mid_dim = model_cfg.pixel_dim
        key_dim = model_cfg.key_dim

        self.pix_feat_proj = nn.Conv2d(in_dim, mid_dim, kernel_size=1)
        self.key_proj = nn.Conv2d(mid_dim, key_dim, kernel_size=3, padding=1)
        # shrinkage
        self.d_proj = nn.Conv2d(mid_dim, 1, kernel_size=3, padding=1)
        # selection
        self.e_proj = nn.Conv2d(mid_dim, key_dim, kernel_size=3, padding=1)

        nn.init.orthogonal_(self.key_proj.weight.data)
        nn.init.zeros_(self.key_proj.bias.data)

    def forward(self, x: torch.Tensor, *, need_s: bool,
                need_e: bool) -> (torch.Tensor, torch.Tensor, torch.Tensor):
        x = self.pix_feat_proj(x)
        shrinkage = self.d_proj(x)**2 + 1 if (need_s) else None
        selection = torch.sigmoid(self.e_proj(x)) if (need_e) else None

        return self.key_proj(x), shrinkage, selection


class MaskEncoder(nn.Module):
    def __init__(self, model_cfg: DictConfig, single_object=False):
        super().__init__()
        pixel_dim = model_cfg.pixel_dim
        value_dim = model_cfg.value_dim
        sensory_dim = model_cfg.sensory_dim
        final_dim = model_cfg.mask_encoder.final_dim

        self.single_object = single_object
        extra_dim = 1 if single_object else 2

        # if model_cfg.pretrained_resnet is set in the model_cfg we get the value
        # else default to True
        is_pretrained_resnet = getattr(model_cfg,"pretrained_resnet",True)
        if model_cfg.mask_encoder.type == 'resnet18':
            network = resnet.resnet18(pretrained=is_pretrained_resnet, extra_dim=extra_dim)
        elif model_cfg.mask_encoder.type == 'resnet50':
            network = resnet.resnet50(pretrained=is_pretrained_resnet, extra_dim=extra_dim)
        else:
            raise NotImplementedError
        self.conv1 = network.conv1
        self.bn1 = network.bn1
        self.relu = network.relu
        self.maxpool = network.maxpool

        self.layer1 = network.layer1
        self.layer2 = network.layer2
        self.layer3 = network.layer3

        self.distributor = MainToGroupDistributor()
        self.fuser = GroupFeatureFusionBlock(pixel_dim, final_dim, value_dim)

        self.sensory_update = SensoryDeepUpdater(value_dim, sensory_dim)

    def forward(self,
                image: torch.Tensor,
                pix_feat: torch.Tensor,
                sensory: torch.Tensor,
                masks: torch.Tensor,
                others: torch.Tensor,
                *,
                deep_update: bool = True,
                chunk_size: int = -1) -> (torch.Tensor, torch.Tensor):
        # ms_features are from the key encoder
        # we only use the first one (lowest resolution), following XMem
        if self.single_object:
            g = masks.unsqueeze(2)
        else:
            g = torch.stack([masks, others], dim=2)

        g = self.distributor(image, g)

        batch_size, num_objects = g.shape[:2]
        if chunk_size < 1 or chunk_size >= num_objects:
            chunk_size = num_objects
            fast_path = True
            new_sensory = sensory
        else:
            if deep_update:
                new_sensory = torch.empty_like(sensory)
            else:
                new_sensory = sensory
            fast_path = False

        # chunk-by-chunk inference
        all_g = []
        for i in range(0, num_objects, chunk_size):
            if fast_path:
                g_chunk = g
            else:
                g_chunk = g[:, i:i + chunk_size]
            actual_chunk_size = g_chunk.shape[1]
            g_chunk = g_chunk.flatten(start_dim=0, end_dim=1)

            g_chunk = self.conv1(g_chunk)
            g_chunk = self.bn1(g_chunk)      # 1/2, 64
            g_chunk = self.maxpool(g_chunk)  # 1/4, 64
            g_chunk = self.relu(g_chunk)

            g_chunk = self.layer1(g_chunk)   # 1/4
            g_chunk = self.layer2(g_chunk)   # 1/8
            g_chunk = self.layer3(g_chunk)   # 1/16

            g_chunk = g_chunk.view(batch_size, actual_chunk_size, *g_chunk.shape[1:])
            g_chunk = self.fuser(pix_feat, g_chunk)
            all_g.append(g_chunk)
            if deep_update:
                if fast_path:
                    new_sensory = self.sensory_update(g_chunk, sensory)
                else:
                    new_sensory[:, i:i + chunk_size] = self.sensory_update(
                        g_chunk, sensory[:, i:i + chunk_size])
        g = torch.cat(all_g, dim=1)

        return g, new_sensory

    # override the default train() to freeze BN statistics
    def train(self, mode=True):
        self.training = False
        for module in self.children():
            module.train(False)
        return self


class PixelFeatureFuser(nn.Module):
    def __init__(self, model_cfg: DictConfig, single_object=False):
        super().__init__()
        value_dim = model_cfg.value_dim
        sensory_dim = model_cfg.sensory_dim
        pixel_dim = model_cfg.pixel_dim
        embed_dim = model_cfg.embed_dim
        self.single_object = single_object

        self.fuser = GroupFeatureFusionBlock(pixel_dim, value_dim, embed_dim)
        if self.single_object:
            self.sensory_compress = GConv2d(sensory_dim + 1, value_dim, kernel_size=1)
        else:
            self.sensory_compress = GConv2d(sensory_dim + 2, value_dim, kernel_size=1)

    def forward(self,
                pix_feat: torch.Tensor,
                pixel_memory: torch.Tensor,
                sensory_memory: torch.Tensor,
                last_mask: torch.Tensor,
                last_others: torch.Tensor,
                *,
                chunk_size: int = -1) -> torch.Tensor:
        batch_size, num_objects = pixel_memory.shape[:2]

        if self.single_object:
            last_mask = last_mask.unsqueeze(2)
        else:
            last_mask = torch.stack([last_mask, last_others], dim=2)

        if chunk_size < 1:
            chunk_size = num_objects

        # chunk-by-chunk inference
        all_p16 = []
        for i in range(0, num_objects, chunk_size):
            sensory_readout = self.sensory_compress(
                torch.cat([sensory_memory[:, i:i + chunk_size], last_mask[:, i:i + chunk_size]], 2))
            p16 = pixel_memory[:, i:i + chunk_size] + sensory_readout
            p16 = self.fuser(pix_feat, p16)
            all_p16.append(p16)
        p16 = torch.cat(all_p16, dim=1)

        return p16


class MaskDecoder(nn.Module):
    def __init__(self, model_cfg: DictConfig):
        super().__init__()
        embed_dim = model_cfg.embed_dim
        sensory_dim = model_cfg.sensory_dim
        ms_image_dims = model_cfg.pixel_encoder.ms_dims
        up_dims = model_cfg.mask_decoder.up_dims

        assert embed_dim == up_dims[0]

        self.sensory_update = SensoryUpdater_fullscale([up_dims[0], up_dims[1], up_dims[2], up_dims[3], up_dims[4] + 1], sensory_dim,
                                             sensory_dim)

        self.decoder_feat_proc = DecoderFeatureProcessor(ms_image_dims[1:], up_dims[:-1])
        self.up_16_8 = MaskUpsampleBlock(up_dims[0], up_dims[1])
        self.up_8_4 = MaskUpsampleBlock(up_dims[1], up_dims[2])
        # newly add for alpha matte
        self.up_4_2 = MaskUpsampleBlock(up_dims[2], up_dims[3])
        self.up_2_1 = MaskUpsampleBlock(up_dims[3], up_dims[4])

        self.pred_seg = nn.Conv2d(up_dims[-1], 1, kernel_size=3, padding=1)
        self.pred_mat = nn.Conv2d(up_dims[-1], 1, kernel_size=3, padding=1)

    def forward(self,
                ms_image_feat: Iterable[torch.Tensor],
                memory_readout: torch.Tensor,
                sensory: torch.Tensor,
                *,
                chunk_size: int = -1,
                update_sensory: bool = True,
                seg_pass: bool = False,
                last_mask=None,
                sigmoid_residual=False) -> (torch.Tensor, torch.Tensor):

        batch_size, num_objects = memory_readout.shape[:2]
        f8, f4, f2, f1 = self.decoder_feat_proc(ms_image_feat[1:])
        if chunk_size < 1 or chunk_size >= num_objects:
            chunk_size = num_objects
            fast_path = True
            new_sensory = sensory
        else:
            if update_sensory:
                new_sensory = torch.empty_like(sensory)
            else:
                new_sensory = sensory
            fast_path = False

        # chunk-by-chunk inference
        all_logits = []
        for i in range(0, num_objects, chunk_size):
            if fast_path:
                p16 = memory_readout
            else:
                p16 = memory_readout[:, i:i + chunk_size]
            actual_chunk_size = p16.shape[1]

            p8 = self.up_16_8(p16, f8)
            p4 = self.up_8_4(p8, f4)
            p2 = self.up_4_2(p4, f2)
            p1 = self.up_2_1(p2, f1)
            with safe_autocast(enabled=False):
                if seg_pass:
                    if last_mask is not None:
                        res = self.pred_seg(F.relu(p1.flatten(start_dim=0, end_dim=1).float()))
                        if sigmoid_residual:
                            res = (torch.sigmoid(res) - 0.5) * 2  # regularization: (-1, 1) change on last mask
                        logits = last_mask + res
                    else:
                        logits = self.pred_seg(F.relu(p1.flatten(start_dim=0, end_dim=1).float()))
                else:
                    if last_mask is not None:
                        res = self.pred_mat(F.relu(p1.flatten(start_dim=0, end_dim=1).float()))
                        if sigmoid_residual:
                            res = (torch.sigmoid(res) - 0.5) * 2  # regularization: (-1, 1) change on last mask
                        logits = last_mask + res
                    else:
                        logits = self.pred_mat(F.relu(p1.flatten(start_dim=0, end_dim=1).float()))
            ## SensoryUpdater_fullscale
            if update_sensory:
                p1 = torch.cat(
                    [p1, logits.view(batch_size, actual_chunk_size, 1, *logits.shape[-2:])], 2)
                if fast_path:
                    new_sensory = self.sensory_update([p16, p8, p4, p2, p1], sensory)
                else:
                    new_sensory[:,
                                i:i + chunk_size] = self.sensory_update([p16, p8, p4, p2, p1],
                                                                        sensory[:,
                                                                                i:i + chunk_size])
            all_logits.append(logits)
        logits = torch.cat(all_logits, dim=0)
        logits = logits.view(batch_size, num_objects, *logits.shape[-2:])

        return new_sensory, logits