105 lines
3.5 KiB
Python
105 lines
3.5 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 torch_geometric.nn.conv import GCNConv
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from .utils import left_bias, right_bias, build_ocr_graph
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from .base import CNN, MindNetTF
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from memory_efficient_attention_pytorch import Attention
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class TFToMnet(nn.Module):
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"""
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Implicit ToM net. Supports any subset of modalities
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Possible aggregations: sum, mult, attn, concat
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"""
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def __init__(self, hidden_dim, device, resnet=False, dropout=0.1, mods=['rgb', 'pose', 'gaze', 'ocr', 'bbox']):
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super(TFToMnet, self).__init__()
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# ---- Images ----#
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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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# ---- OCR and bbox -----#
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self.ocr_x, self.ocr_edge_index, self.ocr_edge_attr = build_ocr_graph(device)
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self.ocr_gnn = GCNConv(-1, hidden_dim)
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self.bbox_ff = nn.Linear(108, 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_left = MindNetTF(hidden_dim, dropout, mods)
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self.mind_net_right = MindNetTF(hidden_dim, dropout, mods)
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self.left = nn.Linear(hidden_dim, 27)
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self.right = nn.Linear(hidden_dim, 27)
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self.left.bias.data = torch.tensor(left_bias).log()
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self.right.bias.data = torch.tensor(right_bias).log()
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def forward(self, images, poses, gazes, bboxes, ocr_tensor=None):
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assert images is not None
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assert poses is not None
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assert gazes is not None
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assert bboxes is not None
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batch_size, sequence_len, channels, height, width = images.shape
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bbox_feat = self.act(self.bbox_ff(bboxes))
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rgb_feat = []
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for i in range(sequence_len):
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images_i = images[:,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 = self.dropout(self.act(self.rgb_ff(rgb_feat)))
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ocr_feat = self.dropout(self.act(self.ocr_gnn(self.ocr_x,
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self.ocr_edge_index,
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self.ocr_edge_attr)))
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ocr_feat = ocr_feat.mean(0).unsqueeze(0).repeat(batch_size, sequence_len, 1)
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out_left, feats_left = self.mind_net_left(rgb_feat, ocr_feat, poses[:, :, 0, :], gazes[:, :, 0, :], bbox_feat)
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out_right, feats_right = self.mind_net_right(rgb_feat, ocr_feat, poses[:, :, 1, :], gazes[:, :, 1, :], bbox_feat)
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l = self.dropout(self.act(out_left))
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r = self.dropout(self.act(out_right))
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left_beliefs = self.left(l)
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right_beliefs = self.right(r)
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return left_beliefs, right_beliefs, feats_left + feats_right
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if __name__ == "__main__":
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images = torch.ones(3, 22, 3, 128, 128)
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poses = torch.ones(3, 22, 2, 75)
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gazes = torch.ones(3, 22, 2, 3)
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bboxes = torch.ones(3, 22, 108)
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model = TFToMnet(64, 'cpu', False, 0.5)
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out = model(images, poses, gazes, bboxes, None)
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print(out[0].shape)
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