feature: automatic mask generation
mask generation working but not integratedpull/9/head
Bryce
2 years ago
parent
8238e59067
commit
7087c4a680
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from functools import lru_cache
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import torch
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from torchvision import transforms
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from imaginairy import PKG_ROOT
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from imaginairy.vendored.clipseg import CLIPDensePredT
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weights_url = "https://github.com/timojl/clipseg/raw/master/weights/rd64-uni.pth"
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@lru_cache()
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def clip_mask_model():
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model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64)
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model.eval()
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model.load_state_dict(
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torch.load(
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f'{PKG_ROOT}/vendored/clipseg/rd64-uni.pth',
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map_location=torch.device("cpu")),
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strict=False
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)
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return model
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def get_img_mask(img, mask_descriptions):
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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transforms.Resize((352, 352)),
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])
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img = transform(img).unsqueeze(0)
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with torch.no_grad():
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preds = clip_mask_model()(img.repeat(len(mask_descriptions), 1, 1, 1), mask_descriptions)[0]
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return preds
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import math
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from os.path import basename, dirname, join, isfile
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import torch
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from torch import nn
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from torch.nn import functional as nnf
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from torch.nn.modules.activation import ReLU
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def precompute_clip_vectors():
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from trails.initialization import init_dataset
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lvis = init_dataset('LVIS_OneShot3', split='train', mask='text_label', image_size=224, aug=1, normalize=True,
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reduce_factor=None, add_bar=False, negative_prob=0.5)
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all_names = list(lvis.category_names.values())
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from imaginairy.vendored import clip
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from models.clip_prompts import imagenet_templates
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clip_model = clip.load("ViT-B/32", device='cuda', jit=False)[0]
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prompt_vectors = {}
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for name in all_names[:100]:
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with torch.no_grad():
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conditionals = [t.format(name).replace('_', ' ') for t in imagenet_templates]
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text_tokens = clip.tokenize(conditionals).cuda()
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cond = clip_model.encode_text(text_tokens).cpu()
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for cond, vec in zip(conditionals, cond):
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prompt_vectors[cond] = vec.cpu()
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import pickle
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pickle.dump(prompt_vectors, open('precomputed_prompt_vectors.pickle', 'wb'))
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def get_prompt_list(prompt):
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if prompt == 'plain':
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return ['{}']
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elif prompt == 'fixed':
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return ['a photo of a {}.']
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elif prompt == 'shuffle':
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return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
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elif prompt == 'shuffle+':
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return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
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'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
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'a bad photo of a {}.', 'a photo of the {}.']
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elif prompt == 'shuffle_clip':
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from models.clip_prompts import imagenet_templates
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return imagenet_templates
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else:
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raise ValueError('Invalid value for prompt')
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def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
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"""
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Simplified version of multihead attention (taken from torch source code but without tons of if clauses).
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The mlp and layer norm come from CLIP.
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x: input.
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b: multihead attention module.
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"""
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x_ = b.ln_1(x)
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q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
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tgt_len, bsz, embed_dim = q.size()
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head_dim = embed_dim // b.attn.num_heads
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scaling = float(head_dim) ** -0.5
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q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
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k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
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v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
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q = q * scaling
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attn_output_weights = torch.bmm(q, k.transpose(1, 2)) # n_heads * batch_size, tokens^2, tokens^2
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if attn_mask is not None:
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attn_mask_type, attn_mask = attn_mask
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n_heads = attn_output_weights.size(0) // attn_mask.size(0)
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attn_mask = attn_mask.repeat(n_heads, 1)
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if attn_mask_type == 'cls_token':
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# the mask only affects similarities compared to the readout-token.
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attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
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# attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
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if attn_mask_type == 'all':
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# print(attn_output_weights.shape, attn_mask[:, None].shape)
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attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
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attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
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attn_output = torch.bmm(attn_output_weights, v)
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attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
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attn_output = b.attn.out_proj(attn_output)
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x = x + attn_output
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x = x + b.mlp(b.ln_2(x))
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if with_aff:
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return x, attn_output_weights
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else:
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return x
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class CLIPDenseBase(nn.Module):
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def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
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super().__init__()
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from imaginairy.vendored import clip
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# prec = torch.FloatTensor
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self.clip_model, _ = clip.load(version, device='cpu', jit=False)
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self.model = self.clip_model.visual
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# if not None, scale conv weights such that we obtain n_tokens.
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self.n_tokens = n_tokens
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for p in self.clip_model.parameters():
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p.requires_grad_(False)
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# conditional
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if reduce_cond is not None:
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self.reduce_cond = nn.Linear(512, reduce_cond)
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for p in self.reduce_cond.parameters():
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p.requires_grad_(False)
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else:
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self.reduce_cond = None
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self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
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self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
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self.reduce = nn.Linear(768, reduce_dim)
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self.prompt_list = get_prompt_list(prompt)
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# precomputed prompts
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import pickle
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if isfile('precomputed_prompt_vectors.pickle'):
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precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
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self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
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else:
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self.precomputed_prompts = dict()
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def rescaled_pos_emb(self, new_size):
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assert len(new_size) == 2
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a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
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b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
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return torch.cat([self.model.positional_embedding[:1], b])
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def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
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with torch.no_grad():
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inp_size = x_inp.shape[2:]
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if self.n_tokens is not None:
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stride2 = x_inp.shape[2] // self.n_tokens
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conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
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x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
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else:
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x = self.model.conv1(x_inp) # shape = [*, width, grid, grid]
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x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
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x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
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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]
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standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
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if x.shape[1] != standard_n_tokens:
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new_shape = int(math.sqrt(x.shape[1]-1))
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x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
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else:
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x = x + self.model.positional_embedding.to(x.dtype)
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x = self.model.ln_pre(x)
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x = x.permute(1, 0, 2) # NLD -> LND
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activations, affinities = [], []
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for i, res_block in enumerate(self.model.transformer.resblocks):
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if mask is not None:
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mask_layer, mask_type, mask_tensor = mask
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if mask_layer == i or mask_layer == 'all':
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# import ipdb; ipdb.set_trace()
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size = int(math.sqrt(x.shape[0] - 1))
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attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
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else:
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attn_mask = None
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else:
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attn_mask = None
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x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
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if i in extract_layers:
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affinities += [aff_per_head]
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#if self.n_tokens is not None:
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# activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
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#else:
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activations += [x]
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if len(extract_layers) > 0 and i == max(extract_layers) and skip:
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print('early skip')
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break
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.model.ln_post(x[:, 0, :])
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if self.model.proj is not None:
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x = x @ self.model.proj
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return x, activations, affinities
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def sample_prompts(self, words, prompt_list=None):
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prompt_list = prompt_list if prompt_list is not None else self.prompt_list
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prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
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prompts = [prompt_list[i] for i in prompt_indices]
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return [promt.format(w) for promt, w in zip(prompts, words)]
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def get_cond_vec(self, conditional, batch_size):
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# compute conditional from a single string
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if conditional is not None and type(conditional) == str:
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cond = self.compute_conditional(conditional)
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cond = cond.repeat(batch_size, 1)
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# compute conditional from string list/tuple
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elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
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assert len(conditional) == batch_size
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cond = self.compute_conditional(conditional)
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# use conditional directly
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elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
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cond = conditional
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# compute conditional from image
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elif conditional is not None and type(conditional) == torch.Tensor:
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with torch.no_grad():
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cond, _, _ = self.visual_forward(conditional)
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else:
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raise ValueError('invalid conditional')
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return cond
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def compute_conditional(self, conditional):
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from imaginairy.vendored import clip
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dev = next(self.parameters()).device
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if type(conditional) in {list, tuple}:
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text_tokens = clip.tokenize(conditional).to(dev)
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cond = self.clip_model.encode_text(text_tokens)
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else:
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if conditional in self.precomputed_prompts:
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cond = self.precomputed_prompts[conditional].float().to(dev)
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else:
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text_tokens = clip.tokenize([conditional]).to(dev)
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cond = self.clip_model.encode_text(text_tokens)[0]
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if self.shift_vector is not None:
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return cond + self.shift_vector
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else:
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return cond
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def clip_load_untrained(version):
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assert version == 'ViT-B/16'
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from clip.model import CLIP
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from clip.clip import _MODELS, _download
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model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
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state_dict = model.state_dict()
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vision_width = state_dict["visual.conv1.weight"].shape[0]
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vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
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vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
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grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
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image_resolution = vision_patch_size * grid_size
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embed_dim = state_dict["text_projection"].shape[1]
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context_length = state_dict["positional_embedding"].shape[0]
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vocab_size = state_dict["token_embedding.weight"].shape[0]
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transformer_width = state_dict["ln_final.weight"].shape[0]
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transformer_heads = transformer_width // 64
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transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
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return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size,
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context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
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class CLIPDensePredT(CLIPDenseBase):
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def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
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extra_blocks=0, reduce_cond=None, fix_shift=False,
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learn_trans_conv_only=False, limit_to_clip_only=False, upsample=False,
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add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None):
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super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
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# device = 'cpu'
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self.extract_layers = extract_layers
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self.cond_layer = cond_layer
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self.limit_to_clip_only = limit_to_clip_only
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self.process_cond = None
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self.rev_activations = rev_activations
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depth = len(extract_layers)
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if add_calibration:
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self.calibration_conds = 1
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self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
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self.add_activation1 = True
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self.version = version
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self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
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if fix_shift:
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# self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
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self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
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# self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
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else:
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self.shift_vector = None
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if trans_conv is None:
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trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
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else:
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# explicitly define transposed conv kernel size
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trans_conv_ks = (trans_conv, trans_conv)
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self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
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assert len(self.extract_layers) == depth
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self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
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self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
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self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
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# refinement and trans conv
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if learn_trans_conv_only:
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for p in self.parameters():
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p.requires_grad_(False)
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for p in self.trans_conv.parameters():
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p.requires_grad_(True)
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self.prompt_list = get_prompt_list(prompt)
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def forward(self, inp_image, conditional=None, return_features=False, mask=None):
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assert type(return_features) == bool
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inp_image = inp_image.to(self.model.positional_embedding.device)
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if mask is not None:
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raise ValueError('mask not supported')
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# x_inp = normalize(inp_image)
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x_inp = inp_image
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bs, dev = inp_image.shape[0], x_inp.device
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cond = self.get_cond_vec(conditional, bs)
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visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
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activation1 = activations[0]
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activations = activations[1:]
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_activations = activations[::-1] if not self.rev_activations else activations
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a = None
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||||
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 models.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
|
||||
|
||||
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Reference in New Issue