File size: 10,692 Bytes
8dd41a8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
import math
from posixpath import basename, dirname, join
# import clip
from clip.model import convert_weights
import torch
import json
from torch import nn
from torch.nn import functional as nnf
from torch.nn.modules import activation
from torch.nn.modules.activation import ReLU
from torchvision import transforms

normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))

from torchvision.models import ResNet


def process_prompts(conditional, prompt_list, conditional_map):
    # DEPRECATED
            
    # randomly sample a synonym
    words = [conditional_map[int(i)] for i in conditional]
    words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
    words = [w.replace('_', ' ') for w in words]

    if prompt_list is not None:
        prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
        prompts = [prompt_list[i] for i in prompt_indices]
    else:
        prompts = ['a photo of {}'] * (len(words))

    return [promt.format(w) for promt, w in zip(prompts, words)]


class VITDenseBase(nn.Module):
    
    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():

            x_inp = nnf.interpolate(x_inp, (384, 384))

            x = self.model.patch_embed(x_inp)
            cls_token = self.model.cls_token.expand(x.shape[0], -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
            if self.model.dist_token is None:
                x = torch.cat((cls_token, x), dim=1)
            else:
                x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
            x = self.model.pos_drop(x + self.model.pos_embed)

            activations = []
            for i, block in enumerate(self.model.blocks):
                x = block(x)

                if i in extract_layers:
                    # permute to be compatible with CLIP
                    activations += [x.permute(1,0,2)]                

            x = self.model.norm(x)
            x = self.model.head(self.model.pre_logits(x[:, 0]))

            # again for CLIP compatibility
            # x = x.permute(1, 0, 2)

        return x, activations, None

    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]
        
        return cond


class VITDensePredT(VITDenseBase):

    def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed', 
                 depth=3, 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, process_cond=None, not_pretrained=False):
        super().__init__()
        # 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
        
        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

        import timm 
        self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
        self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)

        for p in self.model.parameters():
            p.requires_grad_(False)

        import clip
        self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
        # del self.clip_model.visual
        
        
        self.token_shape = (14, 14)

        # 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 = AVAILABLE_BLOCKS['film'](512, 128)
        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)
        
        # DEPRECATED
        # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
        
        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)])

        trans_conv_ks = (16, 16)
        self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)

        # 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)

        if prompt == 'fixed':
            self.prompt_list = ['a photo of a {}.']
        elif prompt == 'shuffle':
            self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
        elif prompt == 'shuffle+':
            self.prompt_list = ['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 {}.']
        elif prompt == 'shuffle_clip':
            from models.clip_prompts import imagenet_templates
            self.prompt_list = imagenet_templates

        if process_cond is not None:
            if process_cond == 'clamp' or process_cond[0] == 'clamp':

                val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2

                def clamp_vec(x):
                    return torch.clamp(x, -val, val)

                self.process_cond = clamp_vec

            elif process_cond.endswith('.pth'):
                
                shift = torch.load(process_cond)
                def add_shift(x):
                    return x + shift.to(x.device)

                self.process_cond = add_shift

        import pickle
        precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
        self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}


    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

        inp_image_size = inp_image.shape[2:]

        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:]

        a = None
        for i, (activation, block, reduce) in enumerate(zip(activations[::-1], 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)

        if self.trans_conv is not None:
            a = self.trans_conv(a)

        if self.upsample_proj is not None:
            a = self.upsample_proj(a)
            a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')

        a = nnf.interpolate(a, inp_image_size)

        if return_features:
            return a, visual_q, cond, [activation1] + activations
        else:
            return a,