import math
from os.path import basename, dirname, join, isfile
import torch
from torch import nn
from torch.nn import functional as nnf
from torch.nn.modules.activation import ReLU
def get_prompt_list(prompt):
if prompt == 'plain':
return ['{}']
elif prompt == 'fixed':
return ['a photo of a {}.']
elif prompt == 'shuffle':
return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
elif prompt == 'shuffle+':
return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
'a bad photo of a {}.', 'a photo of the {}.']
else:
raise ValueError('Invalid value for prompt')
def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
"""
Simplified version of multihead attention (taken from torch source code but without tons of if clauses).
The mlp and layer norm come from CLIP.
x: input.
b: multihead attention module.
"""
x_ = b.ln_1(x)
q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
tgt_len, bsz, embed_dim = q.size()
head_dim = embed_dim // b.attn.num_heads
scaling = float(head_dim) ** -0.5
q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
q = q * scaling
attn_output_weights = torch.bmm(q, k.transpose(1, 2)) # n_heads * batch_size, tokens^2, tokens^2
if attn_mask is not None:
attn_mask_type, attn_mask = attn_mask
n_heads = attn_output_weights.size(0) // attn_mask.size(0)
attn_mask = attn_mask.repeat(n_heads, 1)
if attn_mask_type == 'cls_token':
# the mask only affects similarities compared to the readout-token.
attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
# attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
if attn_mask_type == 'all':
# print(attn_output_weights.shape, attn_mask[:, None].shape)
attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
attn_output = torch.bmm(attn_output_weights, v)
attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn_output = b.attn.out_proj(attn_output)
x = x + attn_output
x = x + b.mlp(b.ln_2(x))
if with_aff:
return x, attn_output_weights
else:
return x
class CLIPDenseBase(nn.Module):
def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
super().__init__()
import clip
# prec = torch.FloatTensor
self.clip_model, _ = clip.load(version, device='cpu', jit=False)
self.model = self.clip_model.visual
# if not None, scale conv weights such that we obtain n_tokens.
self.n_tokens = n_tokens
for p in self.clip_model.parameters():
p.requires_grad_(False)
# conditional
if reduce_cond is not None:
self.reduce_cond = nn.Linear(512, reduce_cond)
for p in self.reduce_cond.parameters():
p.requires_grad_(False)
else:
self.reduce_cond = None
self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
self.reduce = nn.Linear(768, reduce_dim)
self.prompt_list = get_prompt_list(prompt)
# precomputed prompts
import pickle
if isfile('precomputed_prompt_vectors.pickle'):
precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
else:
self.precomputed_prompts = dict()
def rescaled_pos_emb(self, new_size):
assert len(new_size) == 2
a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
return torch.cat([self.model.positional_embedding[:1], b])
def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
with torch.no_grad():
inp_size = x_inp.shape[2:]
if self.n_tokens is not None:
stride2 = x_inp.shape[2] // self.n_tokens
conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
else:
x = self.model.conv1(x_inp) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
if x.shape[1] != standard_n_tokens:
new_shape = int(math.sqrt(x.shape[1]-1))
x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
else:
x = x + self.model.positional_embedding.to(x.dtype)
x = self.model.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
activations, affinities = [], []
for i, res_block in enumerate(self.model.transformer.resblocks):
if mask is not None:
mask_layer, mask_type, mask_tensor = mask
if mask_layer == i or mask_layer == 'all':
# import ipdb; ipdb.set_trace()
size = int(math.sqrt(x.shape[0] - 1))
attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
else:
attn_mask = None
else:
attn_mask = None
x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
if i in extract_layers:
affinities += [aff_per_head]
#if self.n_tokens is not None:
# activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
#else:
activations += [x]
if len(extract_layers) > 0 and i == max(extract_layers) and skip:
print('early skip')
break
x = x.permute(1, 0, 2) # LND -> NLD
x = self.model.ln_post(x[:, 0, :])
if self.model.proj is not None:
x = x @ self.model.proj
return x, activations, affinities
def sample_prompts(self, words, prompt_list=None):
prompt_list = prompt_list if prompt_list is not None else self.prompt_list
prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
prompts = [prompt_list[i] for i in prompt_indices]
return [promt.format(w) for promt, w in zip(prompts, words)]
def get_cond_vec(self, conditional, batch_size):
# compute conditional from a single string
if conditional is not None and type(conditional) == str:
cond = self.compute_conditional(conditional)
cond = cond.repeat(batch_size, 1)
# compute conditional from string list/tuple
elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
assert len(conditional) == batch_size
cond = self.compute_conditional(conditional)
# use conditional directly
elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
cond = conditional
# compute conditional from image
elif conditional is not None and type(conditional) == torch.Tensor:
with torch.no_grad():
cond, _, _ = self.visual_forward(conditional)
else:
raise ValueError('invalid conditional')
return cond
def compute_conditional(self, conditional):
import clip
dev = next(self.parameters()).device
if type(conditional) in {list, tuple}:
text_tokens = clip.tokenize(conditional).to(dev)
cond = self.clip_model.encode_text(text_tokens)
else:
if conditional in self.precomputed_prompts:
cond = self.precomputed_prompts[conditional].float().to(dev)
else:
text_tokens = clip.tokenize([conditional]).to(dev)
cond = self.clip_model.encode_text(text_tokens)[0]
if self.shift_vector is not None:
return cond + self.shift_vector
else:
return cond
def clip_load_untrained(version):
assert version == 'ViT-B/16'
from clip.model import CLIP
from clip.clip import _MODELS, _download
model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
state_dict = model.state_dict()
vision_width = state_dict["visual.conv1.weight"].shape[0]
vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
image_resolution = vision_patch_size * grid_size
embed_dim = state_dict["text_projection"].shape[1]
context_length = state_dict["positional_embedding"].shape[0]
vocab_size = state_dict["token_embedding.weight"].shape[0]
transformer_width = state_dict["ln_final.weight"].shape[0]
transformer_heads = transformer_width // 64
transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size,
context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
class CLIPDensePredT(CLIPDenseBase):
def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
extra_blocks=0, reduce_cond=None, fix_shift=False,
learn_trans_conv_only=False, limit_to_clip_only=False, upsample=False,
add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None, complex_trans_conv=False):
super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
# device = 'cpu'
self.extract_layers = extract_layers
self.cond_layer = cond_layer
self.limit_to_clip_only = limit_to_clip_only
self.process_cond = None
self.rev_activations = rev_activations
depth = len(extract_layers)
if add_calibration:
self.calibration_conds = 1
self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
self.add_activation1 = True
self.version = version
self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
if fix_shift:
# self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
# self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
else:
self.shift_vector = None
if trans_conv is None:
trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
else:
# explicitly define transposed conv kernel size
trans_conv_ks = (trans_conv, trans_conv)
if not complex_trans_conv:
self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
else:
assert trans_conv_ks[0] == trans_conv_ks[1]
tp_kernels = (trans_conv_ks[0] // 4, trans_conv_ks[0] // 4)
self.trans_conv = nn.Sequential(
nn.Conv2d(reduce_dim, reduce_dim, kernel_size=3, padding=1),
nn.ReLU(),
nn.ConvTranspose2d(reduce_dim, reduce_dim // 2, kernel_size=tp_kernels[0], stride=tp_kernels[0]),
nn.ReLU(),
nn.ConvTranspose2d(reduce_dim // 2, 1, kernel_size=tp_kernels[1], stride=tp_kernels[1]),
)
# self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
assert len(self.extract_layers) == depth
self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
# refinement and trans conv
if learn_trans_conv_only:
for p in self.parameters():
p.requires_grad_(False)
for p in self.trans_conv.parameters():
p.requires_grad_(True)
self.prompt_list = get_prompt_list(prompt)
def forward(self, inp_image, conditional=None, return_features=False, mask=None):
assert type(return_features) == bool
inp_image = inp_image.to(self.model.positional_embedding.device)
if mask is not None:
raise ValueError('mask not supported')
# x_inp = normalize(inp_image)
x_inp = inp_image
bs, dev = inp_image.shape[0], x_inp.device
cond = self.get_cond_vec(conditional, bs)
visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
activation1 = activations[0]
activations = activations[1:]
_activations = activations[::-1] if not self.rev_activations else activations
a = None
for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
if a is not None:
a = reduce(activation) + a
else:
a = reduce(activation)
if i == self.cond_layer:
if self.reduce_cond is not None:
cond = self.reduce_cond(cond)
a = self.film_mul(cond) * a + self.film_add(cond)
a = block(a)
for block in self.extra_blocks:
a = a + block(a)
a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
size = int(math.sqrt(a.shape[2]))
a = a.view(bs, a.shape[1], size, size)
a = self.trans_conv(a)
if self.n_tokens is not None:
a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True)
if self.upsample_proj is not None:
a = self.upsample_proj(a)
a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
if return_features:
return a, visual_q, cond, [activation1] + activations
else:
return a,
class CLIPDensePredTMasked(CLIPDensePredT):
def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4,
prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False,
refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):
super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim,
n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond,
fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
n_tokens=n_tokens)
def visual_forward_masked(self, img_s, seg_s):
return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))
def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):
if seg_s is None:
cond = cond_or_img_s
else:
img_s = cond_or_img_s
with torch.no_grad():
cond, _, _ = self.visual_forward_masked(img_s, seg_s)
return super().forward(img_q, cond, return_features=return_features)
class CLIPDenseBaseline(CLIPDenseBase):
def __init__(self, version='ViT-B/32', cond_layer=0,
extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed',
reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
device = 'cpu'
# self.cond_layer = cond_layer
self.extract_layer = extract_layer
self.limit_to_clip_only = limit_to_clip_only
self.shift_vector = None
self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
assert reduce2_dim is not None
self.reduce2 = nn.Sequential(
nn.Linear(reduce_dim, reduce2_dim),
nn.ReLU(),
nn.Linear(reduce2_dim, reduce_dim)
)
trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
def forward(self, inp_image, conditional=None, return_features=False):
inp_image = inp_image.to(self.model.positional_embedding.device)
# x_inp = normalize(inp_image)
x_inp = inp_image
bs, dev = inp_image.shape[0], x_inp.device
cond = self.get_cond_vec(conditional, bs)
visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])
a = activations[0]
a = self.reduce(a)
a = self.film_mul(cond) * a + self.film_add(cond)
if self.reduce2 is not None:
a = self.reduce2(a)
# the original model would execute a transformer block here
a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
size = int(math.sqrt(a.shape[2]))
a = a.view(bs, a.shape[1], size, size)
a = self.trans_conv(a)
if return_features:
return a, visual_q, cond, activations
else:
return a,
class CLIPSegMultiLabel(nn.Module):
def __init__(self, model) -> None:
super().__init__()
from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
self.pascal_classes = VOC
from clip.clipseg import CLIPDensePredT
from general_utils import load_model
# self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
self.clipseg = load_model(model, strict=False)
self.clipseg.eval()
def forward(self, x):
bs = x.shape[0]
out = torch.ones(21, bs, 352, 352).to(x.device) * -10
for class_id, class_name in enumerate(self.pascal_classes):
fac = 3 if class_name == 'background' else 1
with torch.no_grad():
pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac
out[class_id] += pred
out = out.permute(1, 0, 2, 3)
return out
# construct output tensor