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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]