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A10G
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"""
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 omegaconf import DictConfig
import torch
import torch.nn as nn
import torch.nn.functional as F
from tracker.model.group_modules import *
from tracker.model.utils import resnet
from tracker.model.modules import *
class PixelEncoder(nn.Module):
def __init__(self, model_cfg: DictConfig):
super().__init__()
self.is_resnet = 'resnet' in model_cfg.pixel_encoder.type
if self.is_resnet:
if model_cfg.pixel_encoder.type == 'resnet18':
network = resnet.resnet18(pretrained=True)
elif model_cfg.pixel_encoder.type == 'resnet50':
network = resnet.resnet50(pretrained=True)
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) -> (torch.Tensor, torch.Tensor, torch.Tensor):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
f4 = self.res2(x)
f8 = self.layer2(f4)
f16 = self.layer3(f8)
return f16, f8, f4
# 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.mask_encoder.type == 'resnet18':
network = resnet.resnet18(pretrained=True, extra_dim=extra_dim)
elif model_cfg.mask_encoder.type == 'resnet50':
network = resnet.resnet50(pretrained=True, 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([up_dims[0], up_dims[1], up_dims[2] + 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])
self.pred = 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) -> (torch.Tensor, torch.Tensor):
batch_size, num_objects = memory_readout.shape[:2]
f8, f4 = 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)
with torch.cuda.amp.autocast(enabled=False):
logits = self.pred(F.relu(p4.flatten(start_dim=0, end_dim=1).float()))
if update_sensory:
p4 = torch.cat(
[p4, logits.view(batch_size, actual_chunk_size, 1, *logits.shape[-2:])], 2)
if fast_path:
new_sensory = self.sensory_update([p16, p8, p4], sensory)
else:
new_sensory[:,
i:i + chunk_size] = self.sensory_update([p16, p8, p4],
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
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