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import torch
import torch.nn.functional as F
import numpy as np
def contrastive_correlation_loss(
vfm_feats,
clip_feats,
samples = 20,
shift = 0.12):
"""
input:
vfm_self_attn_feats-> [bs,c,h,w]
clip_self_attn_feats->[bs,c,h,w]
return
pos_intra_loss
"""
coord_shape = [vfm_feats.shape[0], samples, samples, 2]
coords = torch.rand(coord_shape, device=vfm_feats.device) * 2 - 1
sampled_vfm_feats = sample(vfm_feats, coords)
sampled_clip_feats = sample(clip_feats, coords)
with torch.no_grad():
# Comes straight from backbone which is currently frozen. this saves mem.
normd_sampled_vfm_feats=norm(sampled_vfm_feats)
vfm_self_attn = tensor_correlation(normd_sampled_vfm_feats,normd_sampled_vfm_feats)
old_mean = vfm_self_attn.mean()
vfm_self_attn -= vfm_self_attn.mean([3, 4], keepdim=True)
vfm_self_attn = vfm_self_attn - vfm_self_attn.mean() + old_mean
normd_sampled_clip_feats=norm(sampled_clip_feats)
clip_self_attn = tensor_correlation(normd_sampled_clip_feats, normd_sampled_clip_feats)
min_val = 0.0
loss = - clip_self_attn.clamp(min_val) * (vfm_self_attn - shift)
return loss.mean()
def sample(t: torch.Tensor, coords: torch.Tensor):
return F.grid_sample(t, coords.permute(0, 2, 1, 3), padding_mode='border', align_corners=True)
def tensor_correlation(a, b):
return torch.einsum("nchw,ncij->nhwij", a, b)
def norm(t):
return F.normalize(t, dim=1, eps=1e-10)
def mask2box(mask):
ys, xs = np.where(mask)
y0, y1 = ys.min(), ys.max()
x0, x1 = xs.min(), xs.max()
return x0, y0, x1, y1
def get_freeze_keys(args):
if args.model=="EVA02-CLIP-B-16":
if args.mode=="qk_vfm_distill":
freeze_keys=ViTB_16_QK_Distill_keys
elif args.mode=="ss_vfm_distill":
freeze_keys=ViTB_16_SS_Distill_keys
else:
freeze_keys=ViTB_16_Only_V_Distill_keys
else:
if args.mode=="qk_vfm_distill":
freeze_keys=ViTL_14_QK_Distill_keys
elif args.mode=="ss_vfm_distill":
freeze_keys=ViTL_14_SS_Distill_keys
else:
freeze_keys=ViTL_14_Only_V_Distill_keys
return freeze_keys
BASE_ViTB_16_freeze_keys=[
'logit_scale',
'visual.blocks.11.norm2.weight',
'visual.blocks.11.norm2.bias',
'visual.blocks.11.mlp.w1.weight',
'visual.blocks.11.mlp.w1.bias',
'visual.blocks.11.mlp.w2.weight',
'visual.blocks.11.mlp.w2.bias',
'visual.blocks.11.mlp.w3.weight',
'visual.blocks.11.mlp.w3.bias',
'visual.blocks.11.mlp.ffn_ln.weight',
'visual.blocks.11.mlp.ffn_ln.bias']
BASE_ViTL_14_freeze_keys=[
'logit_scale',
'visual.blocks.23.norm2.weight',
'visual.blocks.23.norm2.bias',
'visual.blocks.23.mlp.w1.weight',
'visual.blocks.23.mlp.w1.bias',
'visual.blocks.23.mlp.w2.weight',
'visual.blocks.23.mlp.w2.bias',
'visual.blocks.23.mlp.w3.weight',
'visual.blocks.23.mlp.w3.bias',
'visual.blocks.23.mlp.ffn_ln.weight',
'visual.blocks.23.mlp.ffn_ln.bias']
ViTB_16_SS_Distill_keys=['visual.blocks.11.attn.k_proj.weight'] + BASE_ViTB_16_freeze_keys
ViTL_14_SS_Distill_keys=['visual.blocks.23.attn.k_proj.weight'] + BASE_ViTL_14_freeze_keys
ViTB_16_Only_V_Distill_keys=['visual.blocks.11.attn.q_bias',
'visual.blocks.11.attn.q_proj.weight',
'visual.blocks.11.attn.k_proj.weight',] + BASE_ViTB_16_freeze_keys
ViTL_14_Only_V_Distill_keys=['visual.blocks.23.attn.q_bias',
'visual.blocks.23.attn.q_proj.weight',
'visual.blocks.23.attn.k_proj.weight',] + BASE_ViTL_14_freeze_keys
ViTB_16_QK_Distill_keys = BASE_ViTB_16_freeze_keys
ViTL_14_QK_Distill_keys = BASE_ViTL_14_freeze_keys |