Spaces:
Shad0ws
/
Runtime error

File size: 20,667 Bytes
9eb3654
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
import json
import logging
import os
import pathlib
import re
from copy import deepcopy
from pathlib import Path
from typing import Optional, Tuple, Union, Dict, Any
import torch

from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .model import CLIP, CustomCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
    get_cast_dtype
from .openai import load_openai_model
from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model
from .transform import image_transform
from .tokenizer import HFTokenizer, tokenize
from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed


_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs


def _natural_key(string_):
    return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]


def _rescan_model_configs():
    global _MODEL_CONFIGS

    config_ext = ('.json',)
    config_files = []
    for config_path in _MODEL_CONFIG_PATHS:
        if config_path.is_file() and config_path.suffix in config_ext:
            config_files.append(config_path)
        elif config_path.is_dir():
            for ext in config_ext:
                config_files.extend(config_path.glob(f'*{ext}'))

    for cf in config_files:
        with open(cf, "r", encoding="utf8") as f:
            model_cfg = json.load(f)
            if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
                _MODEL_CONFIGS[cf.stem] = model_cfg

    _MODEL_CONFIGS = dict(sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0])))


_rescan_model_configs()  # initial populate of model config registry


def list_models():
    """ enumerate available model architectures based on config files """
    return list(_MODEL_CONFIGS.keys())


def add_model_config(path):
    """ add model config path or file and update registry """
    if not isinstance(path, Path):
        path = Path(path)
    _MODEL_CONFIG_PATHS.append(path)
    _rescan_model_configs()


def get_model_config(model_name):
    if model_name in _MODEL_CONFIGS:
        return deepcopy(_MODEL_CONFIGS[model_name])
    else:
        return None


def get_tokenizer(model_name):
    config = get_model_config(model_name)
    tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize
    return tokenizer


# loading openai CLIP weights when is_openai=True for training
def load_state_dict(checkpoint_path: str, map_location: str='cpu', model_key: str='model|module|state_dict', is_openai: bool=False, skip_list: list=[]):
    if is_openai:
        model = torch.jit.load(checkpoint_path, map_location="cpu").eval()
        state_dict = model.state_dict()
        for key in ["input_resolution", "context_length", "vocab_size"]:
            state_dict.pop(key, None)
    else:
        checkpoint = torch.load(checkpoint_path, map_location=map_location)
        for mk in model_key.split('|'):
            if isinstance(checkpoint, dict) and mk in checkpoint:
                state_dict = checkpoint[mk]
                break
            else:
                state_dict = checkpoint
        if next(iter(state_dict.items()))[0].startswith('module'):
            state_dict = {k[7:]: v for k, v in state_dict.items()}
    
    for k in skip_list:
        if k in list(state_dict.keys()):
            logging.info(f"Removing key {k} from pretrained checkpoint")
            del state_dict[k]

    if os.getenv('RoPE') == '1':
        for k in list(state_dict.keys()):
            if 'freqs_cos' in k or 'freqs_sin' in k:
                del state_dict[k]
    return state_dict



def load_checkpoint(model, checkpoint_path, model_key="model|module|state_dict", strict=True):
    state_dict = load_state_dict(checkpoint_path, model_key=model_key, is_openai=False)
    # detect old format and make compatible with new format
    if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
        state_dict = convert_to_custom_text_state_dict(state_dict)
    if 'text.logit_scale' in state_dict and hasattr(model, 'logit_scale'):
        state_dict['logit_scale'] = state_dict['text.logit_scale']
        del state_dict['text.logit_scale']

    # resize_clip_pos_embed for CLIP and open CLIP
    if 'visual.positional_embedding' in state_dict:
        resize_clip_pos_embed(state_dict, model)
    # specified to eva_vit_model
    elif 'visual.pos_embed' in state_dict:
        resize_evaclip_pos_embed(state_dict, model)

    # resize_clip_pos_embed(state_dict, model)
    incompatible_keys = model.load_state_dict(state_dict, strict=strict)
    logging.info(f"incompatible_keys.missing_keys: {incompatible_keys.missing_keys}")
    return incompatible_keys

def load_clip_visual_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]):
    state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list)

    for k in list(state_dict.keys()):
        if not k.startswith('visual.'):
            del state_dict[k]
    for k in list(state_dict.keys()):
        if k.startswith('visual.'):
            new_k = k[7:]
            state_dict[new_k] = state_dict[k]
            del state_dict[k]
    return state_dict

def load_clip_text_state_dict(checkpoint_path: str, map_location: str='cpu', is_openai: bool=False, skip_list:list=[]):
    state_dict = load_state_dict(checkpoint_path, map_location=map_location, is_openai=is_openai, skip_list=skip_list)

    for k in list(state_dict.keys()):
        if k.startswith('visual.'):
            del state_dict[k]
    return state_dict

def get_pretrained_tag(pretrained_model):
    pretrained_model = pretrained_model.lower()
    if "laion" in pretrained_model or "open_clip" in pretrained_model:
        return "open_clip"
    elif "openai" in pretrained_model:
        return "clip"
    elif "eva" in pretrained_model and "clip" in pretrained_model:
        return "eva_clip"
    else:
        return "other"

def load_pretrained_checkpoint(
        model,
        visual_checkpoint_path,
        text_checkpoint_path,
        strict=True,
        visual_model=None,
        text_model=None,
        model_key="model|module|state_dict",
        skip_list=[]):
    visual_tag = get_pretrained_tag(visual_model)
    text_tag = get_pretrained_tag(text_model)

    logging.info(f"num of model state_dict keys: {len(model.state_dict().keys())}")
    visual_incompatible_keys, text_incompatible_keys = None, None
    if visual_checkpoint_path:
        if visual_tag == "eva_clip" or visual_tag == "open_clip":
            visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=False, skip_list=skip_list)
        elif visual_tag == "clip":
            visual_state_dict = load_clip_visual_state_dict(visual_checkpoint_path, is_openai=True, skip_list=skip_list)
        else:
            visual_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list)
    
        # resize_clip_pos_embed for CLIP and open CLIP
        if 'positional_embedding' in visual_state_dict:
            resize_visual_pos_embed(visual_state_dict, model)
        # specified to EVA model
        elif 'pos_embed' in visual_state_dict:
            resize_eva_pos_embed(visual_state_dict, model)

        visual_incompatible_keys = model.visual.load_state_dict(visual_state_dict, strict=strict)
        logging.info(f"num of loaded visual_state_dict keys: {len(visual_state_dict.keys())}")
        logging.info(f"visual_incompatible_keys.missing_keys: {visual_incompatible_keys.missing_keys}")

    if text_checkpoint_path:
        if text_tag == "eva_clip" or text_tag == "open_clip":
            text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=False, skip_list=skip_list)
        elif text_tag == "clip":
            text_state_dict = load_clip_text_state_dict(text_checkpoint_path, is_openai=True, skip_list=skip_list)
        else:
            text_state_dict = load_state_dict(visual_checkpoint_path, model_key=model_key, is_openai=False, skip_list=skip_list)

        text_incompatible_keys = model.text.load_state_dict(text_state_dict, strict=strict)
        
        logging.info(f"num of loaded text_state_dict keys: {len(text_state_dict.keys())}")
        logging.info(f"text_incompatible_keys.missing_keys: {text_incompatible_keys.missing_keys}")

    return visual_incompatible_keys, text_incompatible_keys

def create_model(
        model_name: str,
        pretrained: Optional[str] = None,
        precision: str = 'fp32',
        device: Union[str, torch.device] = 'cpu',
        jit: bool = False,
        force_quick_gelu: bool = False,
        force_custom_clip: bool = False,
        force_patch_dropout: Optional[float] = None,
        pretrained_image: str = '',
        pretrained_text: str = '',
        pretrained_hf: bool = True,
        pretrained_visual_model: str = None,
        pretrained_text_model: str = None,
        cache_dir: Optional[str] = None,
        skip_list: list  = [],
):
    model_name = model_name.replace('/', '-')  # for callers using old naming with / in ViT names
    if isinstance(device, str):
        device = torch.device(device)

    if pretrained and pretrained.lower() == 'openai':
        logging.info(f'Loading pretrained {model_name} from OpenAI.')
        model = load_openai_model(
            model_name,
            precision=precision,
            device=device,
            jit=jit,
            cache_dir=cache_dir,
        )
    else:
        model_cfg = get_model_config(model_name)
        if model_cfg is not None:
            logging.info(f'Loaded {model_name} model config.')
        else:
            logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
            raise RuntimeError(f'Model config for {model_name} not found.')

        if 'rope' in model_cfg.get('vision_cfg', {}):
            if model_cfg['vision_cfg']['rope']:
                os.environ['RoPE'] = "1"
        else:
            os.environ['RoPE'] = "0"

        if force_quick_gelu:
            # override for use of QuickGELU on non-OpenAI transformer models
            model_cfg["quick_gelu"] = True
        
        if force_patch_dropout is not None:
            # override the default patch dropout value
            model_cfg['vision_cfg']["patch_dropout"] = force_patch_dropout

        cast_dtype = get_cast_dtype(precision)
        custom_clip = model_cfg.pop('custom_text', False) or force_custom_clip or ('hf_model_name' in model_cfg['text_cfg'])


        if custom_clip:
            if 'hf_model_name' in model_cfg.get('text_cfg', {}):
                model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
            model = CustomCLIP(**model_cfg, cast_dtype=cast_dtype)
        else:
            model = CLIP(**model_cfg, cast_dtype=cast_dtype)

        pretrained_cfg = {}
        if pretrained:
            checkpoint_path = ''
            pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
            if pretrained_cfg:
                checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
            elif os.path.exists(pretrained):
                checkpoint_path = pretrained

            if checkpoint_path:
                logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
                load_checkpoint(model,
                               checkpoint_path,
                               model_key="model|module|state_dict",
                               strict=False
                               ) 
            else:
                error_str = (
                    f'Pretrained weights ({pretrained}) not found for model {model_name}.'
                    f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
                logging.warning(error_str)
                raise RuntimeError(error_str)
        else:
            visual_checkpoint_path = ''
            text_checkpoint_path = ''
            
            if pretrained_image:
                pretrained_visual_model = pretrained_visual_model.replace('/', '-')  # for callers using old naming with / in ViT names
                pretrained_image_cfg = get_pretrained_cfg(pretrained_visual_model, pretrained_image)
                if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
                    # pretrained weight loading for timm models set via vision_cfg
                    model_cfg['vision_cfg']['timm_model_pretrained'] = True
                elif pretrained_image_cfg:
                    visual_checkpoint_path = download_pretrained(pretrained_image_cfg, cache_dir=cache_dir)
                elif os.path.exists(pretrained_image):
                    visual_checkpoint_path = pretrained_image
                else:
                    logging.warning(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.')
                    raise RuntimeError(f'Pretrained weights ({visual_checkpoint_path}) not found for model {model_name}.visual.')

            if pretrained_text:
                pretrained_text_model = pretrained_text_model.replace('/', '-')  # for callers using old naming with / in ViT names
                pretrained_text_cfg = get_pretrained_cfg(pretrained_text_model, pretrained_text)
                if pretrained_image_cfg:
                    text_checkpoint_path = download_pretrained(pretrained_text_cfg, cache_dir=cache_dir)
                elif os.path.exists(pretrained_text):
                    text_checkpoint_path = pretrained_text
                else:
                    logging.warning(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.')
                    raise RuntimeError(f'Pretrained weights ({text_checkpoint_path}) not found for model {model_name}.text.')
            
            if visual_checkpoint_path:
                logging.info(f'Loading pretrained {model_name}.visual weights ({visual_checkpoint_path}).')
            if text_checkpoint_path:
                logging.info(f'Loading pretrained {model_name}.text weights ({text_checkpoint_path}).')

            if visual_checkpoint_path or text_checkpoint_path:
                load_pretrained_checkpoint(
                    model,
                    visual_checkpoint_path,
                    text_checkpoint_path,
                    strict=False,
                    visual_model=pretrained_visual_model,
                    text_model=pretrained_text_model,
                    model_key="model|module|state_dict",
                    skip_list=skip_list
                )
        
        if "fp16" in precision or "bf16" in precision:
            logging.info(f'convert precision to {precision}')
            model = model.to(torch.bfloat16) if 'bf16' in precision else model.to(torch.float16)

        model.to(device=device)

        # set image / mean metadata from pretrained_cfg if available, or use default
        model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
        model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD

        if jit:
            model = torch.jit.script(model)

    return model


def create_model_and_transforms(
        model_name: str,
        pretrained: Optional[str] = None,
        precision: str = 'fp32',
        device: Union[str, torch.device] = 'cpu',
        jit: bool = False,
        force_quick_gelu: bool = False,
        force_custom_clip: bool = False,
        force_patch_dropout: Optional[float] = None,
        pretrained_image: str = '',
        pretrained_text: str = '',
        pretrained_hf: bool = True,
        pretrained_visual_model: str = None,
        pretrained_text_model: str = None,
        image_mean: Optional[Tuple[float, ...]] = None,
        image_std: Optional[Tuple[float, ...]] = None,
        cache_dir: Optional[str] = None,
        skip_list: list = [],
):
    model = create_model(
        model_name,
        pretrained,
        precision=precision,
        device=device,
        jit=jit,
        force_quick_gelu=force_quick_gelu,
        force_custom_clip=force_custom_clip,
        force_patch_dropout=force_patch_dropout,
        pretrained_image=pretrained_image,
        pretrained_text=pretrained_text,
        pretrained_hf=pretrained_hf,
        pretrained_visual_model=pretrained_visual_model,
        pretrained_text_model=pretrained_text_model,
        cache_dir=cache_dir,
        skip_list=skip_list,
    )

    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
    image_std = image_std or getattr(model.visual, 'image_std', None)
    preprocess_train = image_transform(
        model.visual.image_size,
        is_train=True,
        mean=image_mean,
        std=image_std
    )
    preprocess_val = image_transform(
        model.visual.image_size,
        is_train=False,
        mean=image_mean,
        std=image_std
    )

    return model, preprocess_train, preprocess_val


def create_transforms(
        model_name: str,
        pretrained: Optional[str] = None,
        precision: str = 'fp32',
        device: Union[str, torch.device] = 'cpu',
        jit: bool = False,
        force_quick_gelu: bool = False,
        force_custom_clip: bool = False,
        force_patch_dropout: Optional[float] = None,
        pretrained_image: str = '',
        pretrained_text: str = '',
        pretrained_hf: bool = True,
        pretrained_visual_model: str = None,
        pretrained_text_model: str = None,
        image_mean: Optional[Tuple[float, ...]] = None,
        image_std: Optional[Tuple[float, ...]] = None,
        cache_dir: Optional[str] = None,
        skip_list: list = [],
):
    model = create_model(
        model_name,
        pretrained,
        precision=precision,
        device=device,
        jit=jit,
        force_quick_gelu=force_quick_gelu,
        force_custom_clip=force_custom_clip,
        force_patch_dropout=force_patch_dropout,
        pretrained_image=pretrained_image,
        pretrained_text=pretrained_text,
        pretrained_hf=pretrained_hf,
        pretrained_visual_model=pretrained_visual_model,
        pretrained_text_model=pretrained_text_model,
        cache_dir=cache_dir,
        skip_list=skip_list,
    )


    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
    image_std = image_std or getattr(model.visual, 'image_std', None)
    preprocess_train = image_transform(
        model.visual.image_size,
        is_train=True,
        mean=image_mean,
        std=image_std
    )
    preprocess_val = image_transform(
        model.visual.image_size,
        is_train=False,
        mean=image_mean,
        std=image_std
    )
    del model

    return preprocess_train, preprocess_val

def create_model_from_pretrained(
        model_name: str,
        pretrained: str,
        precision: str = 'fp32',
        device: Union[str, torch.device] = 'cpu',
        jit: bool = False,
        force_quick_gelu: bool = False,
        force_custom_clip: bool = False,
        force_patch_dropout: Optional[float] = None,
        return_transform: bool = True,
        image_mean: Optional[Tuple[float, ...]] = None,
        image_std: Optional[Tuple[float, ...]] = None,
        cache_dir: Optional[str] = None,
        is_frozen: bool = False,
):
    if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained):
        raise RuntimeError(
            f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.'
            f' Use open_clip.list_pretrained() to find one.')

    model = create_model(
        model_name,
        pretrained,
        precision=precision,
        device=device,
        jit=jit,
        force_quick_gelu=force_quick_gelu,
        force_custom_clip=force_custom_clip,
        force_patch_dropout=force_patch_dropout,
        cache_dir=cache_dir,
    )

    if is_frozen:
        for param in model.parameters():
            param.requires_grad = False

    if not return_transform:
        return model

    image_mean = image_mean or getattr(model.visual, 'image_mean', None)
    image_std = image_std or getattr(model.visual, 'image_std', None)
    preprocess = image_transform(
        model.visual.image_size,
        is_train=False,
        mean=image_mean,
        std=image_std
    )

    return model, preprocess