import os import sys import torch.nn as nn from torch.utils.checkpoint import checkpoint, checkpoint_sequential import torch.nn.functional as F import torch import torch.distributed as dist import re import math from collections.abc import Iterable from itertools import repeat from torchvision import transforms as T import random from PIL import Image def _ntuple(n): def parse(x): if isinstance(x, Iterable) and not isinstance(x, str): return x return tuple(repeat(x, n)) return parse to_1tuple = _ntuple(1) to_2tuple = _ntuple(2) def set_grad_checkpoint(model, use_fp32_attention=False, gc_step=1): assert isinstance(model, nn.Module) def set_attr(module): module.grad_checkpointing = True module.fp32_attention = use_fp32_attention module.grad_checkpointing_step = gc_step model.apply(set_attr) def auto_grad_checkpoint(module, *args, **kwargs): if getattr(module, 'grad_checkpointing', False): if isinstance(module, Iterable): gc_step = module[0].grad_checkpointing_step return checkpoint_sequential(module, gc_step, *args, **kwargs) else: return checkpoint(module, *args, **kwargs) return module(*args, **kwargs) def checkpoint_sequential(functions, step, input, *args, **kwargs): # Hack for keyword-only parameter in a python 2.7-compliant way preserve = kwargs.pop('preserve_rng_state', True) if kwargs: raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)) def run_function(start, end, functions): def forward(input): for j in range(start, end + 1): input = functions[j](input, *args) return input return forward if isinstance(functions, torch.nn.Sequential): functions = list(functions.children()) # the last chunk has to be non-volatile end = -1 segment = len(functions) // step for start in range(0, step * (segment - 1), step): end = start + step - 1 input = checkpoint(run_function(start, end, functions), input, preserve_rng_state=preserve) return run_function(end + 1, len(functions) - 1, functions)(input) def window_partition(x, window_size): """ Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition """ B, H, W, C = x.shape pad_h = (window_size - H % window_size) % window_size pad_w = (window_size - W % window_size) % window_size if pad_h > 0 or pad_w > 0: x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) Hp, Wp = H + pad_h, W + pad_w x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows, (Hp, Wp) def window_unpartition(windows, window_size, pad_hw, hw): """ Window unpartition into original sequences and removing padding. Args: x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned sequences with [B, H, W, C]. """ Hp, Wp = pad_hw H, W = hw B = windows.shape[0] // (Hp * Wp // window_size // window_size) x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) if Hp > H or Wp > W: x = x[:, :H, :W, :].contiguous() return x def get_rel_pos(q_size, k_size, rel_pos): """ Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of query q. k_size (int): size of key k. rel_pos (Tensor): relative position embeddings (L, C). Returns: Extracted positional embeddings according to relative positions. """ max_rel_dist = int(2 * max(q_size, k_size) - 1) # Interpolate rel pos if needed. if rel_pos.shape[0] != max_rel_dist: # Interpolate rel pos. rel_pos_resized = F.interpolate( rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), size=max_rel_dist, mode="linear", ) rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) else: rel_pos_resized = rel_pos # Scale the coords with short length if shapes for q and k are different. q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) return rel_pos_resized[relative_coords.long()] def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size): """ Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 Args: attn (Tensor): attention map. q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. q_size (Tuple): spatial sequence size of query q with (q_h, q_w). k_size (Tuple): spatial sequence size of key k with (k_h, k_w). Returns: attn (Tensor): attention map with added relative positional embeddings. """ q_h, q_w = q_size k_h, k_w = k_size Rh = get_rel_pos(q_h, k_h, rel_pos_h) Rw = get_rel_pos(q_w, k_w, rel_pos_w) B, _, dim = q.shape r_q = q.reshape(B, q_h, q_w, dim) rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) attn = ( attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] ).view(B, q_h * q_w, k_h * k_w) return attn def mean_flat(tensor): return tensor.mean(dim=list(range(1, tensor.ndim))) ################################################################################# # Token Masking and Unmasking # ################################################################################# def get_mask(batch, length, mask_ratio, device, mask_type=None, data_info=None, extra_len=0): """ Get the binary mask for the input sequence. Args: - batch: batch size - length: sequence length - mask_ratio: ratio of tokens to mask - data_info: dictionary with info for reconstruction return: mask_dict with following keys: - mask: binary mask, 0 is keep, 1 is remove - ids_keep: indices of tokens to keep - ids_restore: indices to restore the original order """ assert mask_type in ['random', 'fft', 'laplacian', 'group'] mask = torch.ones([batch, length], device=device) len_keep = int(length * (1 - mask_ratio)) - extra_len if mask_type == 'random' or mask_type == 'group': noise = torch.rand(batch, length, device=device) # noise in [0, 1] ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove ids_restore = torch.argsort(ids_shuffle, dim=1) # keep the first subset ids_keep = ids_shuffle[:, :len_keep] ids_removed = ids_shuffle[:, len_keep:] elif mask_type in ['fft', 'laplacian']: if 'strength' in data_info: strength = data_info['strength'] else: N = data_info['N'][0] img = data_info['ori_img'] # 获取原图的尺寸信息 _, C, H, W = img.shape if mask_type == 'fft': # 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N) reshaped_image = img.reshape((batch, -1, H // N, N, W // N, N)) fft_image = torch.fft.fftn(reshaped_image, dim=(3, 5)) # 取绝对值并求和获取频率强度 strength = torch.sum(torch.abs(fft_image), dim=(1, 3, 5)).reshape((batch, -1,)) elif type == 'laplacian': laplacian_kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32).reshape(1, 1, 3, 3) laplacian_kernel = laplacian_kernel.repeat(C, 1, 1, 1) # 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N) reshaped_image = img.reshape(-1, C, H // N, N, W // N, N).permute(0, 2, 4, 1, 3, 5).reshape(-1, C, N, N) laplacian_response = F.conv2d(reshaped_image, laplacian_kernel, padding=1, groups=C) strength = laplacian_response.sum(dim=[1, 2, 3]).reshape((batch, -1,)) # 对频率强度进行归一化，然后使用torch.multinomial进行采样 probabilities = strength / (strength.max(dim=1)[0][:, None]+1e-5) ids_shuffle = torch.multinomial(probabilities.clip(1e-5, 1), length, replacement=False) ids_keep = ids_shuffle[:, :len_keep] ids_restore = torch.argsort(ids_shuffle, dim=1) ids_removed = ids_shuffle[:, len_keep:] mask[:, :len_keep] = 0 mask = torch.gather(mask, dim=1, index=ids_restore) return {'mask': mask, 'ids_keep': ids_keep, 'ids_restore': ids_restore, 'ids_removed': ids_removed} def mask_out_token(x, ids_keep, ids_removed=None): """ Mask out the tokens specified by ids_keep. Args: - x: input sequence, [N, L, D] - ids_keep: indices of tokens to keep return: - x_masked: masked sequence """ N, L, D = x.shape # batch, length, dim x_remain = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) if ids_removed is not None: x_masked = torch.gather(x, dim=1, index=ids_removed.unsqueeze(-1).repeat(1, 1, D)) return x_remain, x_masked else: return x_remain def mask_tokens(x, mask_ratio): """ Perform per-sample random masking by per-sample shuffling. Per-sample shuffling is done by argsort random noise. x: [N, L, D], sequence """ N, L, D = x.shape # batch, length, dim len_keep = int(L * (1 - mask_ratio)) noise = torch.rand(N, L, device=x.device) # noise in [0, 1] # sort noise for each sample ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove ids_restore = torch.argsort(ids_shuffle, dim=1) # keep the first subset ids_keep = ids_shuffle[:, :len_keep] x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) # generate the binary mask: 0 is keep, 1 is remove mask = torch.ones([N, L], device=x.device) mask[:, :len_keep] = 0 mask = torch.gather(mask, dim=1, index=ids_restore) return x_masked, mask, ids_restore def unmask_tokens(x, ids_restore, mask_token): # x: [N, T, D] if extras == 0 (i.e., no cls token) else x: [N, T+1, D] mask_tokens = mask_token.repeat(x.shape[0], ids_restore.shape[1] - x.shape[1], 1) x = torch.cat([x, mask_tokens], dim=1) x = torch.gather(x, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle return x # Parse 'None' to None and others to float value def parse_float_none(s): assert isinstance(s, str) return None if s == 'None' else float(s) #---------------------------------------------------------------------------- # Parse a comma separated list of numbers or ranges and return a list of ints. # Example: '1,2,5-10' returns [1, 2, 5, 6, 7, 8, 9, 10] def parse_int_list(s): if isinstance(s, list): return s ranges = [] range_re = re.compile(r'^(\d+)-(\d+)$') for p in s.split(','): m = range_re.match(p) if m: ranges.extend(range(int(m.group(1)), int(m.group(2))+1)) else: ranges.append(int(p)) return ranges def init_processes(fn, args): """ Initialize the distributed environment. """ os.environ['MASTER_ADDR'] = args.master_address os.environ['MASTER_PORT'] = str(random.randint(2000, 6000)) print(f'MASTER_ADDR = {os.environ["MASTER_ADDR"]}') print(f'MASTER_PORT = {os.environ["MASTER_PORT"]}') torch.cuda.set_device(args.local_rank) dist.init_process_group(backend='nccl', init_method='env://', rank=args.global_rank, world_size=args.global_size) fn(args) if args.global_size > 1: cleanup() def mprint(*args, **kwargs): """ Print only from rank 0. """ if dist.get_rank() == 0: print(*args, **kwargs) def cleanup(): """ End DDP training. """ dist.barrier() mprint("Done!") dist.barrier() dist.destroy_process_group() #---------------------------------------------------------------------------- # logging info. class Logger(object): """ Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file. """ def __init__(self, file_name=None, file_mode="w", should_flush=True): self.file = None if file_name is not None: self.file = open(file_name, file_mode) self.should_flush = should_flush self.stdout = sys.stdout self.stderr = sys.stderr sys.stdout = self sys.stderr = self def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def write(self, text): """Write text to stdout (and a file) and optionally flush.""" if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash return if self.file is not None: self.file.write(text) self.stdout.write(text) if self.should_flush: self.flush() def flush(self): """Flush written text to both stdout and a file, if open.""" if self.file is not None: self.file.flush() self.stdout.flush() def close(self): """Flush, close possible files, and remove stdout/stderr mirroring.""" self.flush() # if using multiple loggers, prevent closing in wrong order if sys.stdout is self: sys.stdout = self.stdout if sys.stderr is self: sys.stderr = self.stderr if self.file is not None: self.file.close() class StackedRandomGenerator: def __init__(self, device, seeds): super().__init__() self.generators = [torch.Generator(device).manual_seed(int(seed) % (1 << 32)) for seed in seeds] def randn(self, size, **kwargs): assert size[0] == len(self.generators) return torch.stack([torch.randn(size[1:], generator=gen, **kwargs) for gen in self.generators]) def randn_like(self, input): return self.randn(input.shape, dtype=input.dtype, layout=input.layout, device=input.device) def randint(self, *args, size, **kwargs): assert size[0] == len(self.generators) return torch.stack([torch.randint(*args, size=size[1:], generator=gen, **kwargs) for gen in self.generators]) def prepare_prompt_ar(prompt, ratios, device='cpu', show=True): # get aspect_ratio or ar aspect_ratios = re.findall(r"--aspect_ratio\s+(\d+:\d+)", prompt) ars = re.findall(r"--ar\s+(\d+:\d+)", prompt) custom_hw = re.findall(r"--hw\s+(\d+:\d+)", prompt) if show: print("aspect_ratios:", aspect_ratios, "ars:", ars, "hws:", custom_hw) prompt_clean = prompt.split("--aspect_ratio")[0].split("--ar")[0].split("--hw")[0] if len(aspect_ratios) + len(ars) + len(custom_hw) == 0 and show: print("Wrong prompt format. Set to default ar: 1. change your prompt into format '--ar h:w or --hw h:w' for correct generating") if len(aspect_ratios) != 0: ar = float(aspect_ratios[0].split(':')[0]) / float(aspect_ratios[0].split(':')[1]) elif len(ars) != 0: ar = float(ars[0].split(':')[0]) / float(ars[0].split(':')[1]) else: ar = 1. closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar)) if len(custom_hw) != 0: custom_hw = [float(custom_hw[0].split(':')[0]), float(custom_hw[0].split(':')[1])] else: custom_hw = ratios[closest_ratio] default_hw = ratios[closest_ratio] prompt_show = f'prompt: {prompt_clean.strip()}\nSize: --ar {closest_ratio}, --bin hw {ratios[closest_ratio]}, --custom hw {custom_hw}' return prompt_clean, prompt_show, torch.tensor(default_hw, device=device)[None], torch.tensor([float(closest_ratio)], device=device)[None], torch.tensor(custom_hw, device=device)[None] def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int): orig_hw = torch.tensor([samples.shape[2], samples.shape[3]], dtype=torch.int) custom_hw = torch.tensor([int(new_height), int(new_width)], dtype=torch.int) if (orig_hw != custom_hw).all(): ratio = max(custom_hw[0] / orig_hw[0], custom_hw[1] / orig_hw[1]) resized_width = int(orig_hw[1] * ratio) resized_height = int(orig_hw[0] * ratio) transform = T.Compose([ T.Resize((resized_height, resized_width)), T.CenterCrop(custom_hw.tolist()) ]) return transform(samples) else: return samples def resize_and_crop_img(img: Image, new_width, new_height): orig_width, orig_height = img.size ratio = max(new_width/orig_width, new_height/orig_height) resized_width = int(orig_width * ratio) resized_height = int(orig_height * ratio) img = img.resize((resized_width, resized_height), Image.LANCZOS) left = (resized_width - new_width)/2 top = (resized_height - new_height)/2 right = (resized_width + new_width)/2 bottom = (resized_height + new_height)/2 img = img.crop((left, top, right, bottom)) return img def mask_feature(emb, mask): if emb.shape[0] == 1: keep_index = mask.sum().item() return emb[:, :, :keep_index, :], keep_index else: masked_feature = emb * mask[:, None, :, None] return masked_feature, emb.shape[2]