112 lines
4.2 KiB
Python
112 lines
4.2 KiB
Python
|
import torch
|
|||
|
|
import torch.nn as nn
|
|||
|
|
import torchvision.models as models
|
|||
|
|
from .base import CNN, MindNetLSTM
|
|||
|
|
import numpy as np
|
|||
|
|
|
|||
|
|
|
|||
|
|
class ImplicitToMnet(nn.Module):
|
|||
|
|
"""
|
|||
|
|
Implicit ToM net. Supports any subset of modalities
|
|||
|
|
Possible aggregations: sum, mult, attn, concat
|
|||
|
|
"""
|
|||
|
|
def __init__(self, hidden_dim, device, resnet=False, dropout=0.1, mods=['rgb_3', 'rgb_1', 'pose', 'gaze', 'bbox']):
|
|||
|
|
super(ImplicitToMnet, self).__init__()
|
|||
|
|
|
|||
|
|
self.mods = mods
|
|||
|
|
|
|||
|
|
# ---- 3rd POV Images, object and bbox ----#
|
|||
|
|
if resnet:
|
|||
|
|
resnet = models.resnet34(weights="IMAGENET1K_V1")
|
|||
|
|
self.cnn = nn.Sequential(
|
|||
|
|
*(list(resnet.children())[:-1])
|
|||
|
|
)
|
|||
|
|
for param in self.cnn.parameters():
|
|||
|
|
param.requires_grad = False
|
|||
|
|
self.rgb_ff = nn.Linear(512, hidden_dim)
|
|||
|
|
else:
|
|||
|
|
self.cnn = CNN(hidden_dim)
|
|||
|
|
self.rgb_ff = nn.Linear(hidden_dim, hidden_dim)
|
|||
|
|
self.bbox_ff = nn.Linear(4, hidden_dim)
|
|||
|
|
|
|||
|
|
# ---- Others ----#
|
|||
|
|
self.act = nn.GELU()
|
|||
|
|
self.dropout = nn.Dropout(dropout)
|
|||
|
|
self.device = device
|
|||
|
|
|
|||
|
|
# ---- Mind nets ----#
|
|||
|
|
self.mind_net_1 = MindNetLSTM(hidden_dim, dropout, mods=mods)
|
|||
|
|
self.mind_net_2 = MindNetLSTM(hidden_dim, dropout, mods=[m for m in mods if m != 'gaze'])
|
|||
|
|
|
|||
|
|
self.m1 = nn.Linear(hidden_dim, 4)
|
|||
|
|
self.m2 = nn.Linear(hidden_dim, 4)
|
|||
|
|
self.m12 = nn.Linear(hidden_dim, 4)
|
|||
|
|
self.m21 = nn.Linear(hidden_dim, 4)
|
|||
|
|
self.mc = nn.Linear(hidden_dim, 4)
|
|||
|
|
|
|||
|
|
def forward(self, img_3rd_pov, img_tracker, img_battery, pose1, pose2, bbox, tracker_id, gaze):
|
|||
|
|
|
|||
|
|
batch_size, sequence_len, channels, height, width = img_3rd_pov.shape
|
|||
|
|
|
|||
|
|
if 'bbox' in self.mods:
|
|||
|
|
bbox_feat = self.dropout(self.act(self.bbox_ff(bbox)))
|
|||
|
|
else:
|
|||
|
|
bbox_feat = None
|
|||
|
|
|
|||
|
|
if 'rgb_3' in self.mods:
|
|||
|
|
rgb_feat = []
|
|||
|
|
for i in range(sequence_len):
|
|||
|
|
images_i = img_3rd_pov[:,i]
|
|||
|
|
img_i_feat = self.cnn(images_i)
|
|||
|
|
img_i_feat = img_i_feat.view(batch_size, -1)
|
|||
|
|
rgb_feat.append(img_i_feat)
|
|||
|
|
rgb_feat = torch.stack(rgb_feat, 1)
|
|||
|
|
rgb_feat_3rd_pov = self.dropout(self.act(self.rgb_ff(rgb_feat)))
|
|||
|
|
else:
|
|||
|
|
rgb_feat_3rd_pov = None
|
|||
|
|
|
|||
|
|
if tracker_id == 'skele1':
|
|||
|
|
out_1, cell_1, feats_1 = self.mind_net_1(rgb_feat_3rd_pov, bbox_feat, img_tracker, pose1, gaze)
|
|||
|
|
out_2, cell_2, feats_2 = self.mind_net_2(rgb_feat_3rd_pov, bbox_feat, img_battery, pose2, gaze=None)
|
|||
|
|
else:
|
|||
|
|
out_1, cell_1, feats_1 = self.mind_net_1(rgb_feat_3rd_pov, bbox_feat, img_tracker, pose2, gaze)
|
|||
|
|
out_2, cell_2, feats_2 = self.mind_net_2(rgb_feat_3rd_pov, bbox_feat, img_battery, pose1, gaze=None)
|
|||
|
|
|
|||
|
|
if self.aggr == 'no_tom':
|
|||
|
|
m1 = self.m1(out_1).mean(1)
|
|||
|
|
m2 = self.m2(out_2).mean(1)
|
|||
|
|
m12 = self.m12(out_1).mean(1)
|
|||
|
|
m21 = self.m21(out_2).mean(1)
|
|||
|
|
mc = self.mc(out_1*out_2).mean(1) # NOTE: if no_tom then mc is computed starting from the concat of out_1 and out_2
|
|||
|
|
|
|||
|
|
return m1, m2, m12, m21, mc, [out_1, cell_1, out_2, cell_2] + feats_1 + feats_2
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def count_parameters(model):
|
|||
|
|
#return sum(p.numel() for p in model.parameters() if p.requires_grad)
|
|||
|
|
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
|
|||
|
|
return sum([np.prod(p.size()) for p in model_parameters])
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == "__main__":
|
|||
|
|
|
|||
|
|
img_3rd_pov = torch.ones(3, 5, 3, 128, 128)
|
|||
|
|
img_tracker = torch.ones(3, 5, 3, 128, 128)
|
|||
|
|
img_battery = torch.ones(3, 5, 3, 128, 128)
|
|||
|
|
pose1 = torch.ones(3, 5, 26, 3)
|
|||
|
|
pose2 = torch.ones(3, 5, 26, 3)
|
|||
|
|
bbox = torch.ones(3, 5, 13, 4)
|
|||
|
|
tracker_id = 'skele1'
|
|||
|
|
gaze = torch.ones(3, 5, 2)
|
|||
|
|
|
|||
|
|
model = ImplicitToMnet(hidden_dim=64, device='cpu', resnet=False, dropout=0.5)
|
|||
|
|
print(count_parameters(model))
|
|||
|
|
breakpoint()
|
|||
|
|
|
|||
|
|
for agg in ['no_tom', 'concat', 'sum', 'mult', 'attn']:
|
|||
|
|
model = ImplicitToMnet(hidden_dim=64, device='cpu', resnet=False, dropout=0.5)
|
|||
|
|
out = model(img_3rd_pov, img_tracker, img_battery, pose1, pose2, bbox, tracker_id, gaze)
|
|||
|
|
|
|||
|
|
print(agg, out[0].shape)
|