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Running on Zero
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aff3c6f 02e04fb aff3c6f 02e04fb aff3c6f 02e04fb aff3c6f 02e04fb aff3c6f 02e04fb aff3c6f 02e04fb aff3c6f 02e04fb aff3c6f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | from .backbone import Backbone
from .transformer import TransformerEncoder
from .ope import OPEModule
from .positional_encoding import PositionalEncodingsFixed
from .regression_head import DensityMapRegressor
import torch
from torch import nn
from torch.nn import functional as F
class LOCA(nn.Module):
def __init__(
self,
image_size: int,
num_encoder_layers: int,
num_ope_iterative_steps: int,
num_objects: int,
emb_dim: int,
num_heads: int,
kernel_dim: int,
backbone_name: str,
swav_backbone: bool,
train_backbone: bool,
reduction: int,
dropout: float,
layer_norm_eps: float,
mlp_factor: int,
norm_first: bool,
activation: nn.Module,
norm: bool,
zero_shot: bool,
):
super(LOCA, self).__init__()
self.emb_dim = emb_dim
self.num_objects = num_objects
self.reduction = reduction
self.kernel_dim = kernel_dim
self.image_size = image_size
self.zero_shot = zero_shot
self.num_heads = num_heads
self.num_encoder_layers = num_encoder_layers
self.backbone = Backbone(
backbone_name, pretrained=True, dilation=False, reduction=reduction,
swav=swav_backbone, requires_grad=train_backbone
)
self.input_proj = nn.Conv2d(
self.backbone.num_channels, emb_dim, kernel_size=1
)
if num_encoder_layers > 0:
self.encoder = TransformerEncoder(
num_encoder_layers, emb_dim, num_heads, dropout, layer_norm_eps,
mlp_factor, norm_first, activation, norm
)
self.ope = OPEModule(
num_ope_iterative_steps, emb_dim, kernel_dim, num_objects, num_heads,
reduction, layer_norm_eps, mlp_factor, norm_first, activation, norm, zero_shot
)
self.regression_head = DensityMapRegressor(emb_dim, reduction)
self.aux_heads = nn.ModuleList([
DensityMapRegressor(emb_dim, reduction)
for _ in range(num_ope_iterative_steps - 1)
])
self.pos_emb = PositionalEncodingsFixed(emb_dim)
self.attn_norm = nn.LayerNorm(normalized_shape=(64, 64))
self.fuse = nn.Sequential(
nn.Conv2d(324, 256, kernel_size=1, stride=1),
nn.LeakyReLU(),
nn.LayerNorm((64, 64))
)
def forward_before_reg(self, x, bboxes):
num_objects = bboxes.size(1) if not self.zero_shot else self.num_objects
# backbone
backbone_features = self.backbone(x)
# prepare the encoder input
src = self.input_proj(backbone_features)
bs, c, h, w = src.size()
pos_emb = self.pos_emb(bs, h, w, src.device).flatten(2).permute(2, 0, 1)
src = src.flatten(2).permute(2, 0, 1)
# push through the encoder
if self.num_encoder_layers > 0:
image_features = self.encoder(src, pos_emb, src_key_padding_mask=None, src_mask=None)
else:
image_features = src
# prepare OPE input
f_e = image_features.permute(1, 2, 0).reshape(-1, self.emb_dim, h, w)
all_prototypes = self.ope(f_e, pos_emb, bboxes) # [3, 27, 1, 256]
response_maps_list = []
for i in range(all_prototypes.size(0)):
prototypes = all_prototypes[i, ...].permute(1, 0, 2).reshape(
bs, num_objects, self.kernel_dim, self.kernel_dim, -1
).permute(0, 1, 4, 2, 3).flatten(0, 2)[:, None, ...] # [768, 1, 3, 3]
response_maps = F.conv2d(
torch.cat([f_e for _ in range(num_objects)], dim=1).flatten(0, 1).unsqueeze(0),
prototypes,
bias=None,
padding=self.kernel_dim // 2,
groups=prototypes.size(0)
).view(
bs, num_objects, self.emb_dim, h, w
).max(dim=1)[0]
response_maps_list.append(response_maps)
out = {
"feature_bf_regression": response_maps_list[-1],
"aux_feature_bf_regression": response_maps_list[:-1]
}
return out
def forward_reg(self, response_maps, attn_stack, unet_feature):
attn_stack = self.attn_norm(attn_stack)
attn_stack_mean = torch.mean(attn_stack, dim=1, keepdim=True)
unet_feature = torch.cat([unet_feature, attn_stack], dim=1) # [1, 324, 64, 64]
unet_feature = unet_feature * attn_stack_mean
if unet_feature.shape[1] == 322:
unet_feature = self.fuse1(unet_feature)
else:
unet_feature = self.fuse(unet_feature)
response_maps = response_maps["aux_feature_bf_regression"] + [response_maps["feature_bf_regression"]]
outputs = []
for i in range(len(response_maps)):
response_map = response_maps[i] + unet_feature
if i == len(response_maps) - 1:
predicted_dmaps = self.regression_head(response_map)
else:
predicted_dmaps = self.aux_heads[i](response_map)
outputs.append(predicted_dmaps)
return {"pred": outputs[-1], "aux_pred": outputs[:-1]}
# def forward_reg1(self, response_maps, self_attn):
# response_maps = response_maps["aux_feature_bf_regression"] + [response_maps["feature_bf_regression"]]
# outputs = []
# for i in range(len(response_maps)):
# response_map = response_maps[i] + self_attn
# if i == len(response_maps) - 1:
# predicted_dmaps = self.regression_head(response_map)
# else:
# predicted_dmaps = self.aux_heads[i](response_map)
# outputs.append(predicted_dmaps)
# return {"pred": outputs[-1], "aux_pred": outputs[:-1]}
# def forward_reg_without_unet(self, response_maps, attn_stack):
# # attn_stack = self.attn_norm(attn_stack)
# attn_stack_mean = torch.mean(attn_stack, dim=1, keepdim=True)
# response_maps = response_maps["aux_feature_bf_regression"] + [response_maps["feature_bf_regression"]]
# outputs = []
# for i in range(len(response_maps)):
# response_map = response_maps[i] * attn_stack_mean * 0.5 + response_maps[i]
# if i == len(response_maps) - 1:
# predicted_dmaps = self.regression_head(response_map)
# else:
# predicted_dmaps = self.aux_heads[i](response_map)
# outputs.append(predicted_dmaps)
# return {"pred": outputs[-1], "aux_pred": outputs[:-1]}
def build_model():
"""
Build LOCA with a fixed configuration based on defaults in `loca_args.py`.
The `args` parameter is accepted for backward compatibility but ignored.
"""
return LOCA(
image_size=512,
num_encoder_layers=3,
num_ope_iterative_steps=3,
num_objects=3,
zero_shot=False,
emb_dim=256,
num_heads=8,
kernel_dim=3,
backbone_name="resnet50",
swav_backbone=True,
train_backbone=False, # backbone_lr default is 0 in loca_args.py
reduction=8,
dropout=0.1,
layer_norm_eps=1e-5,
mlp_factor=8,
norm_first=True,
activation=nn.GELU,
norm=True,
)
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