157 lines
6 KiB
Python
157 lines
6 KiB
Python
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import torch
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import torch.nn as nn
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import torchvision.models as models
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from .base import CNN, MindNetLSTM
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from memory_efficient_attention_pytorch import Attention
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class CommonMindToMnet(nn.Module):
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"""
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img: bs, 3, 128, 128
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pose: bs, 26, 3
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gaze: bs, 2 NOTE: only tracker has gaze
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bbox: bs, 4
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"""
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def __init__(self, hidden_dim, device, resnet=False, dropout=0.1, aggr='sum', mods=['rgb_1', 'rgb_3', 'pose', 'gaze', 'bbox']):
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super(CommonMindToMnet, self).__init__()
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self.aggr = aggr
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self.mods = mods
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# ---- 3rd POV Images, object and bbox ----#
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if resnet:
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resnet = models.resnet34(weights="IMAGENET1K_V1")
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self.cnn = nn.Sequential(
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*(list(resnet.children())[:-1])
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)
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#for param in self.cnn.parameters():
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# param.requires_grad = False
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self.rgb_ff = nn.Linear(512, hidden_dim)
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else:
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self.cnn = CNN(hidden_dim)
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self.rgb_ff = nn.Linear(hidden_dim, hidden_dim)
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self.bbox_ff = nn.Linear(4, hidden_dim)
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# ---- Others ----#
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self.act = nn.GELU()
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self.dropout = nn.Dropout(dropout)
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self.device = device
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# ---- Mind nets ----#
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self.mind_net_1 = MindNetLSTM(hidden_dim, dropout, mods=mods)
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self.mind_net_2 = MindNetLSTM(hidden_dim, dropout, mods=[m for m in mods if m != 'gaze'])
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if aggr != 'no_tom': self.cm_proj = nn.Linear(hidden_dim*2, hidden_dim)
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self.ln_1 = nn.LayerNorm(hidden_dim)
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self.ln_2 = nn.LayerNorm(hidden_dim)
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if aggr == 'attn':
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self.attn_left = Attention(
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dim = hidden_dim,
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dim_head = hidden_dim // 4,
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heads = 4,
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memory_efficient = True,
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q_bucket_size = hidden_dim,
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k_bucket_size = hidden_dim)
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self.attn_right = Attention(
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dim = hidden_dim,
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dim_head = hidden_dim // 4,
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heads = 4,
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memory_efficient = True,
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q_bucket_size = hidden_dim,
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k_bucket_size = hidden_dim)
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self.m1 = nn.Linear(hidden_dim, 4)
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self.m2 = nn.Linear(hidden_dim, 4)
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self.m12 = nn.Linear(hidden_dim, 4)
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self.m21 = nn.Linear(hidden_dim, 4)
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self.mc = nn.Linear(hidden_dim, 4)
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def forward(self, img_3rd_pov, img_tracker, img_battery, pose1, pose2, bbox, tracker_id, gaze):
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batch_size, sequence_len, channels, height, width = img_3rd_pov.shape
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if 'bbox' in self.mods:
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bbox_feat = self.dropout(self.act(self.bbox_ff(bbox)))
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else:
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bbox_feat = None
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if 'rgb_3' in self.mods:
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rgb_feat = []
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for i in range(sequence_len):
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images_i = img_3rd_pov[:,i]
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img_i_feat = self.cnn(images_i)
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img_i_feat = img_i_feat.view(batch_size, -1)
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rgb_feat.append(img_i_feat)
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rgb_feat = torch.stack(rgb_feat, 1)
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rgb_feat_3rd_pov = self.dropout(self.act(self.rgb_ff(rgb_feat)))
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else:
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rgb_feat_3rd_pov = None
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if tracker_id == 'skele1':
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out_1, cell_1, feats_1 = self.mind_net_1(rgb_feat_3rd_pov, bbox_feat, img_tracker, pose1, gaze)
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out_2, cell_2, feats_2 = self.mind_net_2(rgb_feat_3rd_pov, bbox_feat, img_battery, pose2, gaze=None)
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else:
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out_1, cell_1, feats_1 = self.mind_net_1(rgb_feat_3rd_pov, bbox_feat, img_tracker, pose2, gaze)
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out_2, cell_2, feats_2 = self.mind_net_2(rgb_feat_3rd_pov, bbox_feat, img_battery, pose1, gaze=None)
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if self.aggr == 'no_tom':
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m1 = self.m1(out_1).mean(1)
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m2 = self.m2(out_2).mean(1)
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m12 = self.m12(out_1).mean(1)
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m21 = self.m21(out_2).mean(1)
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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
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return m1, m2, m12, m21, mc, [out_1, out_2] + feats_1 + feats_2
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common_mind = self.cm_proj(torch.cat([cell_1, cell_2], -1)) # (bs, 1, h)
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if self.aggr == 'attn':
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p1 = self.attn_left(x=out_1, context=common_mind)
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p2 = self.attn_right(x=out_2, context=common_mind)
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elif self.aggr == 'mult':
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p1 = out_1 * common_mind
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p2 = out_2 * common_mind
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elif self.aggr == 'sum':
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p1 = out_1 + common_mind
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p2 = out_2 + common_mind
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elif self.aggr == 'concat':
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p1 = torch.cat([out_1, common_mind], 1)
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p2 = torch.cat([out_2, common_mind], 1)
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else: raise ValueError
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p1 = self.act(p1)
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p1 = self.ln_1(p1)
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p2 = self.act(p2)
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p2 = self.ln_2(p2)
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if self.aggr == 'mult' or self.aggr == 'sum' or self.aggr == 'attn':
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m1 = self.m1(p1).mean(1)
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m2 = self.m2(p2).mean(1)
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m12 = self.m12(p1).mean(1)
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m21 = self.m21(p2).mean(1)
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mc = self.mc(p1*p2).mean(1)
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if self.aggr == 'concat':
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m1 = self.m1(p1).mean(1)
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m2 = self.m2(p2).mean(1)
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m12 = self.m12(p1).mean(1)
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m21 = self.m21(p2).mean(1)
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mc = self.mc(p1*p2).mean(1) # NOTE: here I multiply p1 and p2
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return m1, m2, m12, m21, mc, [out_1, out_2, common_mind] + feats_1 + feats_2
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if __name__ == "__main__":
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img_3rd_pov = torch.ones(3, 5, 3, 128, 128)
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img_tracker = torch.ones(3, 5, 3, 128, 128)
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img_battery = torch.ones(3, 5, 3, 128, 128)
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pose1 = torch.ones(3, 5, 26, 3)
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pose2 = torch.ones(3, 5, 26, 3)
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bbox = torch.ones(3, 5, 13, 4)
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tracker_id = 'skele1'
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gaze = torch.ones(3, 5, 2)
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mods = ['pose', 'bbox', 'rgb_3']
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for agg in ['no_tom']:
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model = CommonMindToMnet(hidden_dim=64, device='cpu', resnet=False, dropout=0.5, aggr=agg, mods=mods)
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out = model(img_3rd_pov, img_tracker, img_battery, pose1, pose2, bbox, tracker_id, gaze)
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print(out[0].shape)
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