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"""
modules.py - This file stores the rather boring network blocks.
x - usually means features that only depends on the image
g - usually means features that also depends on the mask.
They might have an extra "group" or "num_objects" dimension, hence
batch_size * num_objects * num_channels * H * W
The trailing number of a variable usually denote the stride
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.group_modules import *
from model import resnet
from model.cbam import CBAM
class FeatureFusionBlock(nn.Module):
def __init__(self, x_in_dim, g_in_dim, g_mid_dim, g_out_dim):
super().__init__()
self.distributor = MainToGroupDistributor()
self.block1 = GroupResBlock(x_in_dim+g_in_dim, g_mid_dim)
self.attention = CBAM(g_mid_dim)
self.block2 = GroupResBlock(g_mid_dim, g_out_dim)
def forward(self, x, g):
batch_size, num_objects = g.shape[:2]
g = self.distributor(x, g)
g = self.block1(g)
r = self.attention(g.flatten(start_dim=0, end_dim=1))
r = r.view(batch_size, num_objects, *r.shape[1:])
g = self.block2(g+r)
return g
class HiddenUpdater(nn.Module):
# Used in the decoder, multi-scale feature + GRU
def __init__(self, g_dims, mid_dim, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.g16_conv = GConv2D(g_dims[0], mid_dim, kernel_size=1)
self.g8_conv = GConv2D(g_dims[1], mid_dim, kernel_size=1)
self.g4_conv = GConv2D(g_dims[2], mid_dim, kernel_size=1)
self.transform = GConv2D(mid_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)
nn.init.xavier_normal_(self.transform.weight)
def forward(self, g, h):
g = self.g16_conv(g[0]) + self.g8_conv(downsample_groups(g[1], ratio=1/2)) + \
self.g4_conv(downsample_groups(g[2], ratio=1/4))
g = torch.cat([g, h], 2)
# defined slightly differently than standard GRU,
# namely the new value is generated before the forget gate.
# might provide better gradient but frankly it was initially just an
# implementation error that I never bothered fixing
values = self.transform(g)
forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])
update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])
new_value = torch.tanh(values[:,:,self.hidden_dim*2:])
new_h = forget_gate*h*(1-update_gate) + update_gate*new_value
return new_h
class HiddenReinforcer(nn.Module):
# Used in the value encoder, a single GRU
def __init__(self, g_dim, hidden_dim):
super().__init__()
self.hidden_dim = hidden_dim
self.transform = GConv2D(g_dim+hidden_dim, hidden_dim*3, kernel_size=3, padding=1)
nn.init.xavier_normal_(self.transform.weight)
def forward(self, g, h):
g = torch.cat([g, h], 2)
# defined slightly differently than standard GRU,
# namely the new value is generated before the forget gate.
# might provide better gradient but frankly it was initially just an
# implementation error that I never bothered fixing
values = self.transform(g)
forget_gate = torch.sigmoid(values[:,:,:self.hidden_dim])
update_gate = torch.sigmoid(values[:,:,self.hidden_dim:self.hidden_dim*2])
new_value = torch.tanh(values[:,:,self.hidden_dim*2:])
new_h = forget_gate*h*(1-update_gate) + update_gate*new_value
return new_h
class ValueEncoder(nn.Module):
def __init__(self, value_dim, hidden_dim, single_object=False):
super().__init__()
self.single_object = single_object
network = resnet.resnet18(pretrained=True, extra_dim=1 if single_object else 2)
self.conv1 = network.conv1
self.bn1 = network.bn1
self.relu = network.relu # 1/2, 64
self.maxpool = network.maxpool
self.layer1 = network.layer1 # 1/4, 64
self.layer2 = network.layer2 # 1/8, 128
self.layer3 = network.layer3 # 1/16, 256
self.distributor = MainToGroupDistributor()
self.fuser = FeatureFusionBlock(1024, 256, value_dim, value_dim)
if hidden_dim > 0:
self.hidden_reinforce = HiddenReinforcer(value_dim, hidden_dim)
else:
self.hidden_reinforce = None
def forward(self, image, image_feat_f16, h, masks, others, is_deep_update=True):
# image_feat_f16 is the feature from the key encoder
if not self.single_object:
g = torch.stack([masks, others], 2)
else:
g = masks.unsqueeze(2)
g = self.distributor(image, g)
batch_size, num_objects = g.shape[:2]
g = g.flatten(start_dim=0, end_dim=1)
g = self.conv1(g)
g = self.bn1(g) # 1/2, 64
g = self.maxpool(g) # 1/4, 64
g = self.relu(g)
g = self.layer1(g) # 1/4
g = self.layer2(g) # 1/8
g = self.layer3(g) # 1/16
g = g.view(batch_size, num_objects, *g.shape[1:])
g = self.fuser(image_feat_f16, g)
if is_deep_update and self.hidden_reinforce is not None:
h = self.hidden_reinforce(g, h)
return g, h
class KeyEncoder(nn.Module):
def __init__(self):
super().__init__()
network = resnet.resnet50(pretrained=True)
self.conv1 = network.conv1
self.bn1 = network.bn1
self.relu = network.relu # 1/2, 64
self.maxpool = network.maxpool
self.res2 = network.layer1 # 1/4, 256
self.layer2 = network.layer2 # 1/8, 512
self.layer3 = network.layer3 # 1/16, 1024
def forward(self, f):
x = self.conv1(f)
x = self.bn1(x)
x = self.relu(x) # 1/2, 64
x = self.maxpool(x) # 1/4, 64
f4 = self.res2(x) # 1/4, 256
f8 = self.layer2(f4) # 1/8, 512
f16 = self.layer3(f8) # 1/16, 1024
return f16, f8, f4
class UpsampleBlock(nn.Module):
def __init__(self, skip_dim, g_up_dim, g_out_dim, scale_factor=2):
super().__init__()
self.skip_conv = nn.Conv2d(skip_dim, g_up_dim, kernel_size=3, padding=1)
self.distributor = MainToGroupDistributor(method='add')
self.out_conv = GroupResBlock(g_up_dim, g_out_dim)
self.scale_factor = scale_factor
def forward(self, skip_f, up_g):
skip_f = self.skip_conv(skip_f)
g = upsample_groups(up_g, ratio=self.scale_factor)
g = self.distributor(skip_f, g)
g = self.out_conv(g)
return g
class KeyProjection(nn.Module):
def __init__(self, in_dim, keydim):
super().__init__()
self.key_proj = nn.Conv2d(in_dim, keydim, kernel_size=3, padding=1)
# shrinkage
self.d_proj = nn.Conv2d(in_dim, 1, kernel_size=3, padding=1)
# selection
self.e_proj = nn.Conv2d(in_dim, keydim, 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, need_s, need_e):
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 Decoder(nn.Module):
def __init__(self, val_dim, hidden_dim):
super().__init__()
self.fuser = FeatureFusionBlock(1024, val_dim+hidden_dim, 512, 512)
if hidden_dim > 0:
self.hidden_update = HiddenUpdater([512, 256, 256+1], 256, hidden_dim)
else:
self.hidden_update = None
self.up_16_8 = UpsampleBlock(512, 512, 256) # 1/16 -> 1/8
self.up_8_4 = UpsampleBlock(256, 256, 256) # 1/8 -> 1/4
self.pred = nn.Conv2d(256, 1, kernel_size=3, padding=1, stride=1)
def forward(self, f16, f8, f4, hidden_state, memory_readout, h_out=True):
batch_size, num_objects = memory_readout.shape[:2]
if self.hidden_update is not None:
g16 = self.fuser(f16, torch.cat([memory_readout, hidden_state], 2))
else:
g16 = self.fuser(f16, memory_readout)
g8 = self.up_16_8(f8, g16)
g4 = self.up_8_4(f4, g8)
logits = self.pred(F.relu(g4.flatten(start_dim=0, end_dim=1)))
if h_out and self.hidden_update is not None:
g4 = torch.cat([g4, logits.view(batch_size, num_objects, 1, *logits.shape[-2:])], 2)
hidden_state = self.hidden_update([g16, g8, g4], hidden_state)
else:
hidden_state = None
logits = F.interpolate(logits, scale_factor=4, mode='bilinear', align_corners=False)
logits = logits.view(batch_size, num_objects, *logits.shape[-2:])
return hidden_state, logits
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