File size: 14,641 Bytes
2e5e07d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import os
import math
import torch
import logging
import subprocess
import numpy as np
import torch.distributed as dist

# from torch._six import inf
from torch import inf
from PIL import Image
from typing import Union, Iterable
from collections import OrderedDict
from torch.utils.tensorboard import SummaryWriter   
_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]

#################################################################################
#                             Training Helper Functions                         #
#################################################################################
def fetch_files_by_numbers(start_number, count, file_list):
    file_numbers = range(start_number, start_number + count)
    found_files = []
    for file_number in file_numbers:
        file_number_padded = str(file_number).zfill(2)
        for file_name in file_list:
            if file_name.endswith(file_number_padded + '.csv'):
                found_files.append(file_name)
                break  # Stop searching once a file is found for the current number
    return found_files

#################################################################################
#                             Training Clip Gradients                           #
#################################################################################

def get_grad_norm(
        parameters: _tensor_or_tensors, norm_type: float = 2.0) -> torch.Tensor:
    r"""
    Copy from torch.nn.utils.clip_grad_norm_

    Clips gradient norm of an iterable of parameters.

    The norm is computed over all gradients together, as if they were
    concatenated into a single vector. Gradients are modified in-place.

    Args:
        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
            single Tensor that will have gradients normalized
        max_norm (float or int): max norm of the gradients
        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
            infinity norm.
        error_if_nonfinite (bool): if True, an error is thrown if the total
            norm of the gradients from :attr:`parameters` is ``nan``,
            ``inf``, or ``-inf``. Default: False (will switch to True in the future)

    Returns:
        Total norm of the parameter gradients (viewed as a single vector).
    """
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    grads = [p.grad for p in parameters if p.grad is not None]
    norm_type = float(norm_type)
    if len(grads) == 0:
        return torch.tensor(0.)
    device = grads[0].device
    if norm_type == inf:
        norms = [g.detach().abs().max().to(device) for g in grads]
        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
    else:
        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
    return total_norm

def clip_grad_norm_(
        parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0,
        error_if_nonfinite: bool = False, clip_grad = True) -> torch.Tensor:
    r"""
    Copy from torch.nn.utils.clip_grad_norm_

    Clips gradient norm of an iterable of parameters.

    The norm is computed over all gradients together, as if they were
    concatenated into a single vector. Gradients are modified in-place.

    Args:
        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
            single Tensor that will have gradients normalized
        max_norm (float or int): max norm of the gradients
        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
            infinity norm.
        error_if_nonfinite (bool): if True, an error is thrown if the total
            norm of the gradients from :attr:`parameters` is ``nan``,
            ``inf``, or ``-inf``. Default: False (will switch to True in the future)

    Returns:
        Total norm of the parameter gradients (viewed as a single vector).
    """
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    grads = [p.grad for p in parameters if p.grad is not None]
    max_norm = float(max_norm)
    norm_type = float(norm_type)
    if len(grads) == 0:
        return torch.tensor(0.)
    device = grads[0].device
    if norm_type == inf:
        norms = [g.detach().abs().max().to(device) for g in grads]
        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
    else:
        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
    # print(total_norm)

    if clip_grad:
        if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
            raise RuntimeError(
                f'The total norm of order {norm_type} for gradients from '
                '`parameters` is non-finite, so it cannot be clipped. To disable '
                'this error and scale the gradients by the non-finite norm anyway, '
                'set `error_if_nonfinite=False`')
        clip_coef = max_norm / (total_norm + 1e-6)
        # Note: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
        # avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
        # when the gradients do not reside in CPU memory.
        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
        for g in grads:
            g.detach().mul_(clip_coef_clamped.to(g.device))
        # gradient_cliped = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
        # print(gradient_cliped)
    return total_norm

def separation_content_motion(video_clip):
    """
    separate coontent and motion in a given video
    Args:
        video_clip, a give video clip, [B F C H W]

    Return:
        base frame, [B, 1, C, H, W]
        motions, [B, F-1, C, H, W], 
        the first is base frame, 
        the second is motions based on base frame
    """
    total_frames = video_clip.shape[1]
    base_frame = video_clip[0]
    motions = [video_clip[i] - base_frame for i in range(1, total_frames)]
    motions = torch.cat(motions, dim=1)
    return base_frame, motions

def get_experiment_dir(root_dir, args):
    if args.use_compile:
        root_dir += '-Compile' # speedup by torch compile
    if args.fixed_spatial:
        root_dir += '-FixedSpa'
    if args.enable_xformers_memory_efficient_attention:
        root_dir += '-Xfor'
    if args.gradient_checkpointing:
        root_dir += '-Gc'
    if args.mixed_precision:
        root_dir += '-Amp'
    if args.image_size == 512:
        root_dir += '-512'
    return root_dir

#################################################################################
#                             Training Logger                                   #
#################################################################################

def create_logger(logging_dir):
    """
    Create a logger that writes to a log file and stdout.
    """
    if dist.get_rank() == 0:  # real logger
        logging.basicConfig(
            level=logging.INFO,
            # format='[\033[34m%(asctime)s\033[0m] %(message)s',
            format='[%(asctime)s] %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
        )
        logger = logging.getLogger(__name__)
        
    else:  # dummy logger (does nothing)
        logger = logging.getLogger(__name__)
        logger.addHandler(logging.NullHandler())
    return logger

def create_accelerate_logger(logging_dir, is_main_process=False):
    """
    Create a logger that writes to a log file and stdout.
    """
    if is_main_process:  # real logger
        logging.basicConfig(
            level=logging.INFO,
            # format='[\033[34m%(asctime)s\033[0m] %(message)s',
            format='[%(asctime)s] %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S',
            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
        )
        logger = logging.getLogger(__name__)
    else:  # dummy logger (does nothing)
        logger = logging.getLogger(__name__)
        logger.addHandler(logging.NullHandler())
    return logger


def create_tensorboard(tensorboard_dir):
    """
    Create a tensorboard that saves losses.
    """
    if dist.get_rank() == 0:  # real tensorboard 
        # tensorboard 
        writer = SummaryWriter(tensorboard_dir)

    return writer

def write_tensorboard(writer, *args):
    '''
    write the loss information to a tensorboard file.
    Only for pytorch DDP mode.
    '''
    if dist.get_rank() == 0:  # real tensorboard
        writer.add_scalar(args[0], args[1], args[2])

#################################################################################
#                      EMA Update/ DDP Training Utils                           #
#################################################################################

@torch.no_grad()
def update_ema(ema_model, model, decay=0.9999):
    """
    Step the EMA model towards the current model.
    """
    ema_params = OrderedDict(ema_model.named_parameters())
    model_params = OrderedDict(model.named_parameters())

    for name, param in model_params.items():
        # TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
        if param.requires_grad:
            ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)

def requires_grad(model, flag=True):
    """
    Set requires_grad flag for all parameters in a model.
    """
    for p in model.parameters():
        p.requires_grad = flag

def cleanup():
    """
    End DDP training.
    """
    dist.destroy_process_group()
    

def setup_distributed(backend="nccl", port=None):
    """Initialize distributed training environment.
    support both slurm and torch.distributed.launch
    see torch.distributed.init_process_group() for more details
    """
    num_gpus = torch.cuda.device_count()

    if "SLURM_JOB_ID" in os.environ:
        rank = int(os.environ["SLURM_PROCID"])
        world_size = int(os.environ["SLURM_NTASKS"])
        node_list = os.environ["SLURM_NODELIST"]
        addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1")
        # specify master port
        if port is not None:
            os.environ["MASTER_PORT"] = str(port)
        elif "MASTER_PORT" not in os.environ:
            # os.environ["MASTER_PORT"] = "29566"
            os.environ["MASTER_PORT"] = str(29566 + num_gpus)
        if "MASTER_ADDR" not in os.environ:
            os.environ["MASTER_ADDR"] = addr
        os.environ["WORLD_SIZE"] = str(world_size)
        os.environ["LOCAL_RANK"] = str(rank % num_gpus)
        os.environ["RANK"] = str(rank)
    else:
        rank = int(os.environ["RANK"])
        world_size = int(os.environ["WORLD_SIZE"])

    # torch.cuda.set_device(rank % num_gpus)

    dist.init_process_group(
        backend=backend,
        world_size=world_size,
        rank=rank,
    )

#################################################################################
#                             Testing  Utils                                    #
#################################################################################

def save_video_grid(video, nrow=None):
    b, t, h, w, c = video.shape
    
    if nrow is None:
        nrow = math.ceil(math.sqrt(b))
    ncol = math.ceil(b / nrow)
    padding = 1
    video_grid = torch.zeros((t, (padding + h) * nrow + padding,
                           (padding + w) * ncol + padding, c), dtype=torch.uint8)
    
    print(video_grid.shape)
    for i in range(b):
        r = i // ncol
        c = i % ncol
        start_r = (padding + h) * r
        start_c = (padding + w) * c
        video_grid[:, start_r:start_r + h, start_c:start_c + w] = video[i]
    
    return video_grid

def save_videos_grid_tav(videos: torch.Tensor, path: str, rescale=False, n_rows=4, fps=8):
    from einops import rearrange
    import imageio
    import torchvision

    videos = rearrange(videos, "b c t h w -> t b c h w")
    outputs = []
    for x in videos:
        x = torchvision.utils.make_grid(x, nrow=n_rows)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = (x * 255).numpy().astype(np.uint8)
        outputs.append(x)

    # os.makedirs(os.path.dirname(path), exist_ok=True)
    imageio.mimsave(path, outputs, fps=fps)


#################################################################################
#                             MMCV  Utils                                    #
#################################################################################


def collect_env():
    # Copyright (c) OpenMMLab. All rights reserved.
    from mmcv.utils import collect_env as collect_base_env
    from mmcv.utils import get_git_hash
    """Collect the information of the running environments."""
    
    env_info = collect_base_env()
    env_info['MMClassification'] = get_git_hash()[:7]

    for name, val in env_info.items():
        print(f'{name}: {val}')
    
    print(torch.cuda.get_arch_list())
    print(torch.version.cuda)


#################################################################################
#                      Long video generation  Utils                             #
#################################################################################
    
def mask_generation_before(mask_type, shape, dtype, device, dropout_prob=0.0, use_image_num=0):
    b, f, c, h, w = shape
    if mask_type.startswith('first'):
        num = int(mask_type.split('first')[-1])
        mask_f = torch.cat([torch.zeros(1, num, 1, 1, 1, dtype=dtype, device=device),
                           torch.ones(1, f-num, 1, 1, 1, dtype=dtype, device=device)], dim=1)
        mask = mask_f.expand(b, -1, c, h, w)
    elif mask_type.startswith('all'):
        mask = torch.ones(b,f,c,h,w,dtype=dtype,device=device)
    elif mask_type.startswith('onelast'):
        num = int(mask_type.split('onelast')[-1])
        mask_one = torch.zeros(1,1,1,1,1, dtype=dtype, device=device)
        mask_mid = torch.ones(1,f-2*num,1,1,1,dtype=dtype, device=device)
        mask_last = torch.zeros_like(mask_one)
        mask = torch.cat([mask_one]*num + [mask_mid] + [mask_last]*num, dim=1)
        mask = mask.expand(b, -1, c, h, w)
    else:
        raise ValueError(f"Invalid mask type: {mask_type}")
    return mask