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import torch
import torch.nn as nn
def dense_connector_sti(image_features, image_forward_outs, is_siglip=True):
avg_pooling_k8 = nn.AvgPool1d(kernel_size=8, stride=8)
if not is_siglip:
image_features_1 = image_forward_outs.hidden_states[7][:, 1:].to(image_features.dtype)
image_features_2 = image_forward_outs.hidden_states[15][:, 1:].to(image_features.dtype)
else:
image_features_1 = image_forward_outs.hidden_states[7][:, :].to(image_features.dtype)
image_features_2 = image_forward_outs.hidden_states[15][:, :].to(image_features.dtype)
image_features_1 = avg_pooling_k8(image_features_1.permute(0, 2, 1)).permute(0, 2, 1)
image_features_2 = avg_pooling_k8(image_features_2.permute(0, 2, 1)).permute(0, 2, 1)
return torch.cat([image_features_1, image_features_2], dim=-2)
def dense_connector_sci(image_features,image_forward_outs, is_siglip=True):
if not is_siglip:
image_features_1 = image_forward_outs.hidden_states[7][:, 1:].to(image_features.dtype)
image_features_2 = image_forward_outs.hidden_states[15][:, 1:].to(image_features.dtype)
else:
image_features_1 = image_forward_outs.hidden_states[7][:, :].to(image_features.dtype)
image_features_2 = image_forward_outs.hidden_states[15][:, :].to(image_features.dtype)
return torch.cat([image_features_1, image_features_2], dim=-1)
def dense_connector_dci(image_features,image_forward_outs, is_siglip=True):
image_features_1 = []
image_features_2 = []
if not is_siglip:
for i in range(0, 12):
image_features_1.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype))
image_features_1 = torch.stack(image_features_1, dim=0)
image_features_1 = torch.sum(image_features_1, dim=0) / 12
for i in range(12, 24):
image_features_2.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype))
image_features_2 = torch.stack(image_features_2, dim=0)
image_features_2 = torch.sum(image_features_2, dim=0) / 12
else:
for i in range(0, 13):
image_features_1.append(image_forward_outs.hidden_states[i][:, :].to(image_features.dtype))
image_features_1 = torch.stack(image_features_1, dim=0)
image_features_1 = torch.sum(image_features_1, dim=0) / 13
for i in range(13, 26):
image_features_2.append(image_forward_outs.hidden_states[i][:, :].to(image_features.dtype))
image_features_2 = torch.stack(image_features_2, dim=0)
image_features_2 = torch.sum(image_features_2, dim=0) / 13
return torch.cat([image_features_1, image_features_2], dim=-1)
def dense_connector(image_features, image_forward_outs, is_siglip=True, mm_dense_connector_type='dci'):
if mm_dense_connector_type == 'sti':
image_features_dc = dense_connector_sti(image_features, image_forward_outs, is_siglip)
image_features = torch.cat((image_features, image_features_dc), dim=-2)
elif mm_dense_connector_type == 'sci':
image_features_dc = dense_connector_sci(image_features, image_forward_outs, is_siglip)
image_features = torch.cat((image_features, image_features_dc), dim=-1)
elif mm_dense_connector_type == 'dci':
image_features_dc = dense_connector_dci(image_features, image_forward_outs, is_siglip)
image_features = torch.cat((image_features, image_features_dc), dim=-1)
else:
raise NotImplementedError()
return image_features
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