id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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2,660 | import cv2
import numpy as np
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `usm_sharp` function. Write a Python function `def usm_sharp(img, weight=0.5, radius=50, threshold=10)` to solve the following problem:
USM sharpening. Input image: I; Blurry image: B. 1. sharp = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * sharp + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int):
Here is the function:
def usm_sharp(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening.
Input image: I; Blurry image: B.
1. sharp = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * sharp + (1 - Mask) * I
Args:
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
weight (float): Sharp weight. Default: 1.
radius (float): Kernel size of Gaussian blur. Default: 50.
threshold (int):
"""
if radius % 2 == 0:
radius += 1
blur = cv2.GaussianBlur(img, (radius, radius), 0)
residual = img - blur
mask = np.abs(residual) * 255 > threshold
mask = mask.astype('float32')
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
sharp = img + weight * residual
sharp = np.clip(sharp, 0, 1)
return soft_mask * sharp + (1 - soft_mask) * img | USM sharpening. Input image: I; Blurry image: B. 1. sharp = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * sharp + (1 - Mask) * I Args: img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. weight (float): Sharp weight. Default: 1. radius (float): Kernel size of Gaussian blur. Default: 50. threshold (int): |
2,661 | import itertools
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `diff_round` function. Write a Python function `def diff_round(x)` to solve the following problem:
Differentiable rounding function
Here is the function:
def diff_round(x):
""" Differentiable rounding function
"""
return torch.round(x) + (x - torch.round(x))**3 | Differentiable rounding function |
2,662 | import itertools
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `quality_to_factor` function. Write a Python function `def quality_to_factor(quality)` to solve the following problem:
Calculate factor corresponding to quality Args: quality(float): Quality for jpeg compression. Returns: float: Compression factor.
Here is the function:
def quality_to_factor(quality):
""" Calculate factor corresponding to quality
Args:
quality(float): Quality for jpeg compression.
Returns:
float: Compression factor.
"""
if quality < 50:
quality = 5000. / quality
else:
quality = 200. - quality * 2
return quality / 100. | Calculate factor corresponding to quality Args: quality(float): Quality for jpeg compression. Returns: float: Compression factor. |
2,663 | import numpy as np
import torch
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
conversion functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
Returns:
(ndarray): The converted image with type of np.float32 and range of
[0, 1].
"""
img_type = img.dtype
img = img.astype(np.float32)
if img_type == np.float32:
pass
elif img_type == np.uint8:
img /= 255.
else:
raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}')
return img
def _convert_output_type_range(img, dst_type):
"""Convert the type and range of the image according to dst_type.
It converts the image to desired type and range. If `dst_type` is np.uint8,
images will be converted to np.uint8 type with range [0, 255]. If
`dst_type` is np.float32, it converts the image to np.float32 type with
range [0, 1].
It is mainly used for post-processing images in colorspace conversion
functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The image to be converted with np.float32 type and
range [0, 255].
dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
converts the image to np.uint8 type with range [0, 255]. If
dst_type is np.float32, it converts the image to np.float32 type
with range [0, 1].
Returns:
(ndarray): The converted image with desired type and range.
"""
if dst_type not in (np.uint8, np.float32):
raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}')
if dst_type == np.uint8:
img = img.round()
else:
img /= 255.
return img.astype(dst_type)
The provided code snippet includes necessary dependencies for implementing the `rgb2ycbcr` function. Write a Python function `def rgb2ycbcr(img, y_only=False)` to solve the following problem:
Convert a RGB image to YCbCr image. This function produces the same results as Matlab's `rgb2ycbcr` function. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. y_only (bool): Whether to only return Y channel. Default: False. Returns: ndarray: The converted YCbCr image. The output image has the same type and range as input image.
Here is the function:
def rgb2ycbcr(img, y_only=False):
"""Convert a RGB image to YCbCr image.
This function produces the same results as Matlab's `rgb2ycbcr` function.
It implements the ITU-R BT.601 conversion for standard-definition
television. See more details in
https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`.
In OpenCV, it implements a JPEG conversion. See more details in
https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
y_only (bool): Whether to only return Y channel. Default: False.
Returns:
ndarray: The converted YCbCr image. The output image has the same type
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img)
if y_only:
out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0
else:
out_img = np.matmul(
img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128]
out_img = _convert_output_type_range(out_img, img_type)
return out_img | Convert a RGB image to YCbCr image. This function produces the same results as Matlab's `rgb2ycbcr` function. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. y_only (bool): Whether to only return Y channel. Default: False. Returns: ndarray: The converted YCbCr image. The output image has the same type and range as input image. |
2,664 | import numpy as np
import torch
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
conversion functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
Returns:
(ndarray): The converted image with type of np.float32 and range of
[0, 1].
"""
img_type = img.dtype
img = img.astype(np.float32)
if img_type == np.float32:
pass
elif img_type == np.uint8:
img /= 255.
else:
raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}')
return img
def _convert_output_type_range(img, dst_type):
"""Convert the type and range of the image according to dst_type.
It converts the image to desired type and range. If `dst_type` is np.uint8,
images will be converted to np.uint8 type with range [0, 255]. If
`dst_type` is np.float32, it converts the image to np.float32 type with
range [0, 1].
It is mainly used for post-processing images in colorspace conversion
functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The image to be converted with np.float32 type and
range [0, 255].
dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
converts the image to np.uint8 type with range [0, 255]. If
dst_type is np.float32, it converts the image to np.float32 type
with range [0, 1].
Returns:
(ndarray): The converted image with desired type and range.
"""
if dst_type not in (np.uint8, np.float32):
raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}')
if dst_type == np.uint8:
img = img.round()
else:
img /= 255.
return img.astype(dst_type)
The provided code snippet includes necessary dependencies for implementing the `bgr2ycbcr` function. Write a Python function `def bgr2ycbcr(img, y_only=False)` to solve the following problem:
Convert a BGR image to YCbCr image. The bgr version of rgb2ycbcr. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. y_only (bool): Whether to only return Y channel. Default: False. Returns: ndarray: The converted YCbCr image. The output image has the same type and range as input image.
Here is the function:
def bgr2ycbcr(img, y_only=False):
"""Convert a BGR image to YCbCr image.
The bgr version of rgb2ycbcr.
It implements the ITU-R BT.601 conversion for standard-definition
television. See more details in
https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`.
In OpenCV, it implements a JPEG conversion. See more details in
https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
y_only (bool): Whether to only return Y channel. Default: False.
Returns:
ndarray: The converted YCbCr image. The output image has the same type
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img)
if y_only:
out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
else:
out_img = np.matmul(
img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
out_img = _convert_output_type_range(out_img, img_type)
return out_img | Convert a BGR image to YCbCr image. The bgr version of rgb2ycbcr. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. y_only (bool): Whether to only return Y channel. Default: False. Returns: ndarray: The converted YCbCr image. The output image has the same type and range as input image. |
2,665 | import numpy as np
import torch
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
conversion functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
Returns:
(ndarray): The converted image with type of np.float32 and range of
[0, 1].
"""
img_type = img.dtype
img = img.astype(np.float32)
if img_type == np.float32:
pass
elif img_type == np.uint8:
img /= 255.
else:
raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}')
return img
def _convert_output_type_range(img, dst_type):
"""Convert the type and range of the image according to dst_type.
It converts the image to desired type and range. If `dst_type` is np.uint8,
images will be converted to np.uint8 type with range [0, 255]. If
`dst_type` is np.float32, it converts the image to np.float32 type with
range [0, 1].
It is mainly used for post-processing images in colorspace conversion
functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The image to be converted with np.float32 type and
range [0, 255].
dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
converts the image to np.uint8 type with range [0, 255]. If
dst_type is np.float32, it converts the image to np.float32 type
with range [0, 1].
Returns:
(ndarray): The converted image with desired type and range.
"""
if dst_type not in (np.uint8, np.float32):
raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}')
if dst_type == np.uint8:
img = img.round()
else:
img /= 255.
return img.astype(dst_type)
The provided code snippet includes necessary dependencies for implementing the `ycbcr2rgb` function. Write a Python function `def ycbcr2rgb(img)` to solve the following problem:
Convert a YCbCr image to RGB image. This function produces the same results as Matlab's ycbcr2rgb function. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. Returns: ndarray: The converted RGB image. The output image has the same type and range as input image.
Here is the function:
def ycbcr2rgb(img):
"""Convert a YCbCr image to RGB image.
This function produces the same results as Matlab's ycbcr2rgb function.
It implements the ITU-R BT.601 conversion for standard-definition
television. See more details in
https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`.
In OpenCV, it implements a JPEG conversion. See more details in
https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
Returns:
ndarray: The converted RGB image. The output image has the same type
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img) * 255
out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
[0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] # noqa: E126
out_img = _convert_output_type_range(out_img, img_type)
return out_img | Convert a YCbCr image to RGB image. This function produces the same results as Matlab's ycbcr2rgb function. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. Returns: ndarray: The converted RGB image. The output image has the same type and range as input image. |
2,666 | import numpy as np
import torch
def _convert_input_type_range(img):
"""Convert the type and range of the input image.
It converts the input image to np.float32 type and range of [0, 1].
It is mainly used for pre-processing the input image in colorspace
conversion functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
Returns:
(ndarray): The converted image with type of np.float32 and range of
[0, 1].
"""
img_type = img.dtype
img = img.astype(np.float32)
if img_type == np.float32:
pass
elif img_type == np.uint8:
img /= 255.
else:
raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}')
return img
def _convert_output_type_range(img, dst_type):
"""Convert the type and range of the image according to dst_type.
It converts the image to desired type and range. If `dst_type` is np.uint8,
images will be converted to np.uint8 type with range [0, 255]. If
`dst_type` is np.float32, it converts the image to np.float32 type with
range [0, 1].
It is mainly used for post-processing images in colorspace conversion
functions such as rgb2ycbcr and ycbcr2rgb.
Args:
img (ndarray): The image to be converted with np.float32 type and
range [0, 255].
dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it
converts the image to np.uint8 type with range [0, 255]. If
dst_type is np.float32, it converts the image to np.float32 type
with range [0, 1].
Returns:
(ndarray): The converted image with desired type and range.
"""
if dst_type not in (np.uint8, np.float32):
raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}')
if dst_type == np.uint8:
img = img.round()
else:
img /= 255.
return img.astype(dst_type)
The provided code snippet includes necessary dependencies for implementing the `ycbcr2bgr` function. Write a Python function `def ycbcr2bgr(img)` to solve the following problem:
Convert a YCbCr image to BGR image. The bgr version of ycbcr2rgb. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. Returns: ndarray: The converted BGR image. The output image has the same type and range as input image.
Here is the function:
def ycbcr2bgr(img):
"""Convert a YCbCr image to BGR image.
The bgr version of ycbcr2rgb.
It implements the ITU-R BT.601 conversion for standard-definition
television. See more details in
https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion.
It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`.
In OpenCV, it implements a JPEG conversion. See more details in
https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion.
Args:
img (ndarray): The input image. It accepts:
1. np.uint8 type with range [0, 255];
2. np.float32 type with range [0, 1].
Returns:
ndarray: The converted BGR image. The output image has the same type
and range as input image.
"""
img_type = img.dtype
img = _convert_input_type_range(img) * 255
out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0],
[0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921] # noqa: E126
out_img = _convert_output_type_range(out_img, img_type)
return out_img | Convert a YCbCr image to BGR image. The bgr version of ycbcr2rgb. It implements the ITU-R BT.601 conversion for standard-definition television. See more details in https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. In OpenCV, it implements a JPEG conversion. See more details in https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. Args: img (ndarray): The input image. It accepts: 1. np.uint8 type with range [0, 255]; 2. np.float32 type with range [0, 1]. Returns: ndarray: The converted BGR image. The output image has the same type and range as input image. |
2,667 | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
def init_tb_logger(log_dir):
from torch.utils.tensorboard import SummaryWriter
tb_logger = SummaryWriter(log_dir=log_dir)
return tb_logger | null |
2,668 | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
"""Get the root logger.
The logger will be initialized if it has not been initialized. By default a
StreamHandler will be added. If `log_file` is specified, a FileHandler will
also be added.
Args:
logger_name (str): root logger name. Default: 'basicsr'.
log_file (str | None): The log filename. If specified, a FileHandler
will be added to the root logger.
log_level (int): The root logger level. Note that only the process of
rank 0 is affected, while other processes will set the level to
"Error" and be silent most of the time.
Returns:
logging.Logger: The root logger.
"""
logger = logging.getLogger(logger_name)
# if the logger has been initialized, just return it
if logger_name in initialized_logger:
return logger
format_str = '%(asctime)s %(levelname)s: %(message)s'
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(logging.Formatter(format_str))
logger.addHandler(stream_handler)
logger.propagate = False
rank, _ = get_dist_info()
if rank != 0:
logger.setLevel('ERROR')
elif log_file is not None:
logger.setLevel(log_level)
# add file handler
file_handler = logging.FileHandler(log_file, 'w')
file_handler.setFormatter(logging.Formatter(format_str))
file_handler.setLevel(log_level)
logger.addHandler(file_handler)
initialized_logger[logger_name] = True
return logger
The provided code snippet includes necessary dependencies for implementing the `init_wandb_logger` function. Write a Python function `def init_wandb_logger(opt)` to solve the following problem:
We now only use wandb to sync tensorboard log.
Here is the function:
def init_wandb_logger(opt):
"""We now only use wandb to sync tensorboard log."""
import wandb
logger = get_root_logger()
project = opt['logger']['wandb']['project']
resume_id = opt['logger']['wandb'].get('resume_id')
if resume_id:
wandb_id = resume_id
resume = 'allow'
logger.warning(f'Resume wandb logger with id={wandb_id}.')
else:
wandb_id = wandb.util.generate_id()
resume = 'never'
wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True)
logger.info(f'Use wandb logger with id={wandb_id}; project={project}.') | We now only use wandb to sync tensorboard log. |
2,669 | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
The provided code snippet includes necessary dependencies for implementing the `get_env_info` function. Write a Python function `def get_env_info()` to solve the following problem:
Get environment information. Currently, only log the software version.
Here is the function:
def get_env_info():
"""Get environment information.
Currently, only log the software version.
"""
import torch
import torchvision
from basicsr.version import __version__
msg = r"""
____ _ _____ ____
/ __ ) ____ _ _____ (_)_____/ ___/ / __ \
/ __ |/ __ `// ___// // ___/\__ \ / /_/ /
/ /_/ // /_/ /(__ )/ // /__ ___/ // _, _/
/_____/ \__,_//____//_/ \___//____//_/ |_|
______ __ __ __ __
/ ____/____ ____ ____/ / / / __ __ _____ / /__ / /
/ / __ / __ \ / __ \ / __ / / / / / / // ___// //_/ / /
/ /_/ // /_/ // /_/ // /_/ / / /___/ /_/ // /__ / /< /_/
\____/ \____/ \____/ \____/ /_____/\____/ \___//_/|_| (_)
"""
msg += ('\nVersion Information: '
f'\n\tBasicSR: {__version__}'
f'\n\tPyTorch: {torch.__version__}'
f'\n\tTorchVision: {torchvision.__version__}')
return msg | Get environment information. Currently, only log the software version. |
2,670 | import re
The provided code snippet includes necessary dependencies for implementing the `read_data_from_tensorboard` function. Write a Python function `def read_data_from_tensorboard(log_path, tag)` to solve the following problem:
Get raw data (steps and values) from tensorboard events. Args: log_path (str): Path to the tensorboard log. tag (str): tag to be read.
Here is the function:
def read_data_from_tensorboard(log_path, tag):
"""Get raw data (steps and values) from tensorboard events.
Args:
log_path (str): Path to the tensorboard log.
tag (str): tag to be read.
"""
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
# tensorboard event
event_acc = EventAccumulator(log_path)
event_acc.Reload()
scalar_list = event_acc.Tags()['scalars']
print('tag list: ', scalar_list)
steps = [int(s.step) for s in event_acc.Scalars(tag)]
values = [s.value for s in event_acc.Scalars(tag)]
return steps, values | Get raw data (steps and values) from tensorboard events. Args: log_path (str): Path to the tensorboard log. tag (str): tag to be read. |
2,671 | import re
The provided code snippet includes necessary dependencies for implementing the `read_data_from_txt_2v` function. Write a Python function `def read_data_from_txt_2v(path, pattern, step_one=False)` to solve the following problem:
Read data from txt with 2 returned values (usually [step, value]). Args: path (str): path to the txt file. pattern (str): re (regular expression) pattern. step_one (bool): add 1 to steps. Default: False.
Here is the function:
def read_data_from_txt_2v(path, pattern, step_one=False):
"""Read data from txt with 2 returned values (usually [step, value]).
Args:
path (str): path to the txt file.
pattern (str): re (regular expression) pattern.
step_one (bool): add 1 to steps. Default: False.
"""
with open(path) as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
steps = []
values = []
pattern = re.compile(pattern)
for line in lines:
match = pattern.match(line)
if match:
steps.append(int(match.group(1)))
values.append(float(match.group(2)))
if step_one:
steps = [v + 1 for v in steps]
return steps, values | Read data from txt with 2 returned values (usually [step, value]). Args: path (str): path to the txt file. pattern (str): re (regular expression) pattern. step_one (bool): add 1 to steps. Default: False. |
2,672 | import re
The provided code snippet includes necessary dependencies for implementing the `read_data_from_txt_1v` function. Write a Python function `def read_data_from_txt_1v(path, pattern)` to solve the following problem:
Read data from txt with 1 returned values. Args: path (str): path to the txt file. pattern (str): re (regular expression) pattern.
Here is the function:
def read_data_from_txt_1v(path, pattern):
"""Read data from txt with 1 returned values.
Args:
path (str): path to the txt file.
pattern (str): re (regular expression) pattern.
"""
with open(path) as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
data = []
pattern = re.compile(pattern)
for line in lines:
match = pattern.match(line)
if match:
data.append(float(match.group(1)))
return data | Read data from txt with 1 returned values. Args: path (str): path to the txt file. pattern (str): re (regular expression) pattern. |
2,673 | import re
The provided code snippet includes necessary dependencies for implementing the `smooth_data` function. Write a Python function `def smooth_data(values, smooth_weight)` to solve the following problem:
Smooth data using 1st-order IIR low-pass filter (what tensorflow does). Reference: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 # noqa: E501 Args: values (list): A list of values to be smoothed. smooth_weight (float): Smooth weight.
Here is the function:
def smooth_data(values, smooth_weight):
""" Smooth data using 1st-order IIR low-pass filter (what tensorflow does).
Reference: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 # noqa: E501
Args:
values (list): A list of values to be smoothed.
smooth_weight (float): Smooth weight.
"""
values_sm = []
last_sm_value = values[0]
for value in values:
value_sm = last_sm_value * smooth_weight + (1 - smooth_weight) * value
values_sm.append(value_sm)
last_sm_value = value_sm
return values_sm | Smooth data using 1st-order IIR low-pass filter (what tensorflow does). Reference: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 # noqa: E501 Args: values (list): A list of values to be smoothed. smooth_weight (float): Smooth weight. |
2,674 | import os
import PIL
import numpy as np
import copy
import torch
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
from itertools import islice
from einops import rearrange, repeat
from torch import autocast
from pytorch_lightning import seed_everything
import torch.nn.functional as F
from ldm.util import instantiate_from_config
from scripts.wavelet_color_fix import (
wavelet_reconstruction,
adaptive_instance_normalization,
)
from cog import BasePredictor, Input, Path
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,675 | import os
import PIL
import numpy as np
import copy
import torch
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
from itertools import islice
from einops import rearrange, repeat
from torch import autocast
from pytorch_lightning import seed_everything
import torch.nn.functional as F
from ldm.util import instantiate_from_config
from scripts.wavelet_color_fix import (
wavelet_reconstruction,
adaptive_instance_normalization,
)
from cog import BasePredictor, Input, Path
def read_image(im_path):
im = np.array(Image.open(im_path).convert("RGB"))
im = im.astype(np.float32) / 255.0
im = im[None].transpose(0, 3, 1, 2)
im = (torch.from_numpy(im) - 0.5) / 0.5
return im.cuda() | null |
2,676 | import os
import PIL
import numpy as np
import copy
import torch
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
from itertools import islice
from einops import rearrange, repeat
from torch import autocast
from pytorch_lightning import seed_everything
import torch.nn.functional as F
from ldm.util import instantiate_from_config
from scripts.wavelet_color_fix import (
wavelet_reconstruction,
adaptive_instance_normalization,
)
from cog import BasePredictor, Input, Path
def space_timesteps(num_timesteps, section_counts):
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim") :])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] # [250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | null |
2,677 | import os
import PIL
import numpy as np
import copy
import torch
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
from itertools import islice
from einops import rearrange, repeat
from torch import autocast
from pytorch_lightning import seed_everything
import torch.nn.functional as F
from ldm.util import instantiate_from_config
from scripts.wavelet_color_fix import (
wavelet_reconstruction,
adaptive_instance_normalization,
)
from cog import BasePredictor, Input, Path
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,678 | import os
import PIL
import numpy as np
import copy
import torch
from omegaconf import OmegaConf
from PIL import Image
from tqdm import trange
from itertools import islice
from einops import rearrange, repeat
from torch import autocast
from pytorch_lightning import seed_everything
import torch.nn.functional as F
from ldm.util import instantiate_from_config
from scripts.wavelet_color_fix import (
wavelet_reconstruction,
adaptive_instance_normalization,
)
from cog import BasePredictor, Input, Path
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.0 * image - 1.0 | null |
2,679 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.realesrgan_dataset import RealESRGANDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def calc_mean_std(feat, eps=1e-5):
"""Calculate mean and std for adaptive_instance_normalization.
Args:
feat (Tensor): 4D tensor.
eps (float): A small value added to the variance to avoid
divide-by-zero. Default: 1e-5.
"""
size = feat.size()
assert len(size) == 4, 'The input feature should be 4D tensor.'
b, c = size[:2]
feat_var = feat.view(b, c, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(b, c, 1, 1)
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
return feat_mean, feat_std
The provided code snippet includes necessary dependencies for implementing the `adaptive_instance_normalization` function. Write a Python function `def adaptive_instance_normalization(content_feat, style_feat)` to solve the following problem:
Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features.
Here is the function:
def adaptive_instance_normalization(content_feat, style_feat):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size) | Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. |
2,680 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.realesrgan_dataset import RealESRGANDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,681 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.realesrgan_dataset import RealESRGANDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,682 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.realesrgan_dataset import RealESRGANDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,683 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.realesrgan_dataset import RealESRGANDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,684 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.ffhq_degradation_dataset import FFHQDegradationDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def calc_mean_std(feat, eps=1e-5):
"""Calculate mean and std for adaptive_instance_normalization.
Args:
feat (Tensor): 4D tensor.
eps (float): A small value added to the variance to avoid
divide-by-zero. Default: 1e-5.
"""
size = feat.size()
assert len(size) == 4, 'The input feature should be 4D tensor.'
b, c = size[:2]
feat_var = feat.view(b, c, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(b, c, 1, 1)
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
return feat_mean, feat_std
The provided code snippet includes necessary dependencies for implementing the `adaptive_instance_normalization` function. Write a Python function `def adaptive_instance_normalization(content_feat, style_feat)` to solve the following problem:
Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features.
Here is the function:
def adaptive_instance_normalization(content_feat, style_feat):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size) | Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. |
2,685 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.ffhq_degradation_dataset import FFHQDegradationDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,686 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.ffhq_degradation_dataset import FFHQDegradationDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,687 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.ffhq_degradation_dataset import FFHQDegradationDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,688 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from basicsr.utils import DiffJPEG
from basicsr.data.ffhq_degradation_dataset import FFHQDegradationDataset
from torch.utils.data import random_split, DataLoader, Dataset, Subset
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,689 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
from util_image import ImageSpliterTh
from pathlib import Path
def calc_mean_std(feat, eps=1e-5):
"""Calculate mean and std for adaptive_instance_normalization.
Args:
feat (Tensor): 4D tensor.
eps (float): A small value added to the variance to avoid
divide-by-zero. Default: 1e-5.
"""
size = feat.size()
assert len(size) == 4, 'The input feature should be 4D tensor.'
b, c = size[:2]
feat_var = feat.view(b, c, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(b, c, 1, 1)
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
return feat_mean, feat_std
The provided code snippet includes necessary dependencies for implementing the `adaptive_instance_normalization` function. Write a Python function `def adaptive_instance_normalization(content_feat, style_feat)` to solve the following problem:
Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features.
Here is the function:
def adaptive_instance_normalization(content_feat, style_feat):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size) | Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. |
2,690 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
from util_image import ImageSpliterTh
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,691 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
from util_image import ImageSpliterTh
from pathlib import Path
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,692 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
from util_image import ImageSpliterTh
from pathlib import Path
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
print('>>>>>>>>>>>>>>>>>>>load results>>>>>>>>>>>>>>>>>>>>>>>')
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,693 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
from util_image import ImageSpliterTh
from pathlib import Path
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,694 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
from util_image import ImageSpliterTh
from pathlib import Path
def read_image(im_path):
im = np.array(Image.open(im_path).convert("RGB"))
im = im.astype(np.float32)/255.0
im = im[None].transpose(0,3,1,2)
im = (torch.from_numpy(im) - 0.5) / 0.5
return im.cuda() | null |
2,695 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def calc_mean_std(feat, eps=1e-5):
"""Calculate mean and std for adaptive_instance_normalization.
Args:
feat (Tensor): 4D tensor.
eps (float): A small value added to the variance to avoid
divide-by-zero. Default: 1e-5.
"""
size = feat.size()
assert len(size) == 4, 'The input feature should be 4D tensor.'
b, c = size[:2]
feat_var = feat.view(b, c, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(b, c, 1, 1)
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
return feat_mean, feat_std
The provided code snippet includes necessary dependencies for implementing the `adaptive_instance_normalization` function. Write a Python function `def adaptive_instance_normalization(content_feat, style_feat)` to solve the following problem:
Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features.
Here is the function:
def adaptive_instance_normalization(content_feat, style_feat):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size) | Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. |
2,696 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,697 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,698 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
print('>>>>>>>>>>>>>>>>>>>load results>>>>>>>>>>>>>>>>>>>>>>>')
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,699 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
import torch.nn.functional as F
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,700 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from util_image import ImageSpliterTh
from pathlib import Path
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,701 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from util_image import ImageSpliterTh
from pathlib import Path
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,702 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from util_image import ImageSpliterTh
from pathlib import Path
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,703 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from util_image import ImageSpliterTh
from pathlib import Path
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,704 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from util_image import ImageSpliterTh
from pathlib import Path
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def read_image(im_path):
im = np.array(Image.open(im_path).convert("RGB"))
im = im.astype(np.float32)/255.0
im = im[None].transpose(0,3,1,2)
im = (torch.from_numpy(im) - 0.5) / 0.5
return im.cuda() | null |
2,705 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
def calc_mean_std(feat, eps=1e-5):
"""Calculate mean and std for adaptive_instance_normalization.
Args:
feat (Tensor): 4D tensor.
eps (float): A small value added to the variance to avoid
divide-by-zero. Default: 1e-5.
"""
size = feat.size()
assert len(size) == 4, 'The input feature should be 4D tensor.'
b, c = size[:2]
feat_var = feat.view(b, c, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(b, c, 1, 1)
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
return feat_mean, feat_std
The provided code snippet includes necessary dependencies for implementing the `adaptive_instance_normalization` function. Write a Python function `def adaptive_instance_normalization(content_feat, style_feat)` to solve the following problem:
Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features.
Here is the function:
def adaptive_instance_normalization(content_feat, style_feat):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size) | Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. |
2,706 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,707 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,708 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
print('>>>>>>>>>>>>>>>>>>>load results>>>>>>>>>>>>>>>>>>>>>>>')
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,709 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
from basicsr.metrics import calculate_niqe
import math
import copy
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,710 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,711 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,712 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,713 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
import torch.nn.functional as F
import cv2
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,714 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,715 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,716 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,717 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,718 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def calc_mean_std(feat, eps=1e-5):
"""Calculate mean and std for adaptive_instance_normalization.
Args:
feat (Tensor): 4D tensor.
eps (float): A small value added to the variance to avoid
divide-by-zero. Default: 1e-5.
"""
size = feat.size()
assert len(size) == 4, 'The input feature should be 4D tensor.'
b, c = size[:2]
feat_var = feat.view(b, c, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(b, c, 1, 1)
feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
return feat_mean, feat_std
The provided code snippet includes necessary dependencies for implementing the `adaptive_instance_normalization` function. Write a Python function `def adaptive_instance_normalization(content_feat, style_feat)` to solve the following problem:
Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features.
Here is the function:
def adaptive_instance_normalization(content_feat, style_feat):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size) | Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate features. Args: content_feat (Tensor): The reference feature. style_feat (Tensor): The degradate features. |
2,719 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
The provided code snippet includes necessary dependencies for implementing the `space_timesteps` function. Write a Python function `def space_timesteps(num_timesteps, section_counts)` to solve the following problem:
Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use.
Here is the function:
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim"):])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")] #[250,]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps) | Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portions of the original process. For example, if there's 300 timesteps and the section counts are [10,15,20] then the first 100 timesteps are strided to be 10 timesteps, the second 100 are strided to be 15 timesteps, and the final 100 are strided to be 20. If the stride is a string starting with "ddim", then the fixed striding from the DDIM paper is used, and only one section is allowed. :param num_timesteps: the number of diffusion steps in the original process to divide up. :param section_counts: either a list of numbers, or a string containing comma-separated numbers, indicating the step count per section. As a special case, use "ddimN" where N is a number of steps to use the striding from the DDIM paper. :return: a set of diffusion steps from the original process to use. |
2,720 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def chunk(it, size):
it = iter(it)
return iter(lambda: tuple(islice(it, size)), ()) | null |
2,721 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def instantiate_from_config(config):
if not "target" in config:
if config == '__is_first_stage__':
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, ckpt, verbose=False):
print(f"Loading model from {ckpt}")
pl_sd = torch.load(ckpt, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
sd = pl_sd["state_dict"]
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
print('>>>>>>>>>>>>>>>>>>>load results>>>>>>>>>>>>>>>>>>>>>>>')
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model | null |
2,722 | import argparse, os, sys, glob
import PIL
import torch
import numpy as np
import torchvision
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from itertools import islice
from einops import rearrange, repeat
from torchvision.utils import make_grid
from torch import autocast
from contextlib import nullcontext
import time
from pytorch_lightning import seed_everything
from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.metrics import calculate_niqe
import math
import copy
from scripts.wavelet_color_fix import wavelet_reconstruction, adaptive_instance_normalization
def load_img(path):
image = Image.open(path).convert("RGB")
w, h = image.size
print(f"loaded input image of size ({w}, {h}) from {path}")
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
image = image.resize((w, h), resample=PIL.Image.LANCZOS)
image = np.array(image).astype(np.float32) / 255.0
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return 2.*image - 1. | null |
2,723 | import torch
from PIL import Image
from torch import Tensor
from torch.nn import functional as F
from torchvision.transforms import ToTensor, ToPILImage
def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tensor):
"""Adaptive instance normalization.
Adjust the reference features to have the similar color and illuminations
as those in the degradate features.
Args:
content_feat (Tensor): The reference feature.
style_feat (Tensor): The degradate features.
"""
size = content_feat.size()
style_mean, style_std = calc_mean_std(style_feat)
content_mean, content_std = calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
def adain_color_fix(target: Image, source: Image):
# Convert images to tensors
to_tensor = ToTensor()
target_tensor = to_tensor(target).unsqueeze(0)
source_tensor = to_tensor(source).unsqueeze(0)
# Apply adaptive instance normalization
result_tensor = adaptive_instance_normalization(target_tensor, source_tensor)
# Convert tensor back to image
to_image = ToPILImage()
result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
return result_image | null |
2,724 | import torch
from PIL import Image
from torch import Tensor
from torch.nn import functional as F
from torchvision.transforms import ToTensor, ToPILImage
def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):
def wavelet_color_fix(target: Image, source: Image):
# Convert images to tensors
to_tensor = ToTensor()
target_tensor = to_tensor(target).unsqueeze(0)
source_tensor = to_tensor(source).unsqueeze(0)
# Apply wavelet reconstruction
result_tensor = wavelet_reconstruction(target_tensor, source_tensor)
# Convert tensor back to image
to_image = ToPILImage()
result_image = to_image(result_tensor.squeeze(0).clamp_(0.0, 1.0))
return result_image | null |
2,725 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
def calculate_psnr(im1, im2, border=0, ycbcr=False):
def rgb2ycbcrTorch(im, only_y=True):
def batch_PSNR(img, imclean, border=0, ycbcr=False):
if ycbcr:
img = rgb2ycbcrTorch(img, True)
imclean = rgb2ycbcrTorch(imclean, True)
Img = img.data.cpu().numpy()
Iclean = imclean.data.cpu().numpy()
Img = img_as_ubyte(Img)
Iclean = img_as_ubyte(Iclean)
PSNR = 0
h, w = Iclean.shape[2:]
for i in range(Img.shape[0]):
PSNR += calculate_psnr(Iclean[i,:,].transpose((1,2,0)), Img[i,:,].transpose((1,2,0)), border)
return PSNR | null |
2,726 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
def calculate_ssim(im1, im2, border=0, ycbcr=False):
'''
SSIM the same outputs as MATLAB's
im1, im2: h x w x , [0, 255], uint8
'''
if not im1.shape == im2.shape:
raise ValueError('Input images must have the same dimensions.')
if ycbcr:
im1 = rgb2ycbcr(im1, True)
im2 = rgb2ycbcr(im2, True)
h, w = im1.shape[:2]
im1 = im1[border:h-border, border:w-border]
im2 = im2[border:h-border, border:w-border]
if im1.ndim == 2:
return ssim(im1, im2)
elif im1.ndim == 3:
if im1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(im1[:,:,i], im2[:,:,i]))
return np.array(ssims).mean()
elif im1.shape[2] == 1:
return ssim(np.squeeze(im1), np.squeeze(im2))
else:
raise ValueError('Wrong input image dimensions.')
def rgb2ycbcrTorch(im, only_y=True):
'''
same as matlab rgb2ycbcr
Input:
im: float [0,1], N x 3 x H x W
only_y: only return Y channel
'''
# transform to range [0,255.0]
im_temp = im.permute([0,2,3,1]) * 255.0 # N x H x W x C --> N x H x W x C
# convert
if only_y:
rlt = torch.matmul(im_temp, torch.tensor([65.481, 128.553, 24.966],
device=im.device, dtype=im.dtype).view([3,1])/ 255.0) + 16.0
else:
rlt = torch.matmul(im_temp, torch.tensor([[65.481, -37.797, 112.0 ],
[128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]],
device=im.device, dtype=im.dtype)/255.0) + \
torch.tensor([16, 128, 128]).view([-1, 1, 1, 3])
rlt /= 255.0
rlt.clamp_(0.0, 1.0)
return rlt.permute([0, 3, 1, 2])
def batch_SSIM(img, imclean, border=0, ycbcr=False):
if ycbcr:
img = rgb2ycbcrTorch(img, True)
imclean = rgb2ycbcrTorch(imclean, True)
Img = img.data.cpu().numpy()
Iclean = imclean.data.cpu().numpy()
Img = img_as_ubyte(Img)
Iclean = img_as_ubyte(Iclean)
SSIM = 0
for i in range(Img.shape[0]):
SSIM += calculate_ssim(Iclean[i,:,].transpose((1,2,0)), Img[i,:,].transpose((1,2,0)), border)
return SSIM | null |
2,727 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `normalize_np` function. Write a Python function `def normalize_np(im, mean=0.5, std=0.5, reverse=False)` to solve the following problem:
Input: im: h x w x c, numpy array Normalize: (im - mean) / std Reverse: im * std + mean
Here is the function:
def normalize_np(im, mean=0.5, std=0.5, reverse=False):
'''
Input:
im: h x w x c, numpy array
Normalize: (im - mean) / std
Reverse: im * std + mean
'''
if not isinstance(mean, (list, tuple)):
mean = [mean, ] * im.shape[2]
mean = np.array(mean).reshape([1, 1, im.shape[2]])
if not isinstance(std, (list, tuple)):
std = [std, ] * im.shape[2]
std = np.array(std).reshape([1, 1, im.shape[2]])
if not reverse:
out = (im.astype(np.float32) - mean) / std
else:
out = im.astype(np.float32) * std + mean
return out | Input: im: h x w x c, numpy array Normalize: (im - mean) / std Reverse: im * std + mean |
2,728 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `normalize_th` function. Write a Python function `def normalize_th(im, mean=0.5, std=0.5, reverse=False)` to solve the following problem:
Input: im: b x c x h x w, torch tensor Normalize: (im - mean) / std Reverse: im * std + mean
Here is the function:
def normalize_th(im, mean=0.5, std=0.5, reverse=False):
'''
Input:
im: b x c x h x w, torch tensor
Normalize: (im - mean) / std
Reverse: im * std + mean
'''
if not isinstance(mean, (list, tuple)):
mean = [mean, ] * im.shape[1]
mean = torch.tensor(mean, device=im.device).view([1, im.shape[1], 1, 1])
if not isinstance(std, (list, tuple)):
std = [std, ] * im.shape[1]
std = torch.tensor(std, device=im.device).view([1, im.shape[1], 1, 1])
if not reverse:
out = (im - mean) / std
else:
out = im * std + mean
return out | Input: im: b x c x h x w, torch tensor Normalize: (im - mean) / std Reverse: im * std + mean |
2,729 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `tensor2img` function. Write a Python function `def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1))` to solve the following problem:
Convert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tensor or list[Tensor]): Accept shapes: 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); 2) 3D Tensor of shape (3/1 x H x W); 3) 2D Tensor of shape (H x W). Tensor channel should be in RGB order. rgb2bgr (bool): Whether to change rgb to bgr. out_type (numpy type): output types. If ``np.uint8``, transform outputs to uint8 type with range [0, 255]; otherwise, float type with range [0, 1]. Default: ``np.uint8``. min_max (tuple[int]): min and max values for clamp. Returns: (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of shape (H x W). The channel order is BGR.
Here is the function:
def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
"""Convert torch Tensors into image numpy arrays.
After clamping to [min, max], values will be normalized to [0, 1].
Args:
tensor (Tensor or list[Tensor]): Accept shapes:
1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
2) 3D Tensor of shape (3/1 x H x W);
3) 2D Tensor of shape (H x W).
Tensor channel should be in RGB order.
rgb2bgr (bool): Whether to change rgb to bgr.
out_type (numpy type): output types. If ``np.uint8``, transform outputs
to uint8 type with range [0, 255]; otherwise, float type with
range [0, 1]. Default: ``np.uint8``.
min_max (tuple[int]): min and max values for clamp.
Returns:
(Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of
shape (H x W). The channel order is BGR.
"""
if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')
flag_tensor = torch.is_tensor(tensor)
if flag_tensor:
tensor = [tensor]
result = []
for _tensor in tensor:
_tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
_tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0])
n_dim = _tensor.dim()
if n_dim == 4:
img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy()
img_np = img_np.transpose(1, 2, 0)
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 3:
img_np = _tensor.numpy()
img_np = img_np.transpose(1, 2, 0)
if img_np.shape[2] == 1: # gray image
img_np = np.squeeze(img_np, axis=2)
else:
if rgb2bgr:
img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
elif n_dim == 2:
img_np = _tensor.numpy()
else:
raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}')
if out_type == np.uint8:
# Unlike MATLAB, numpy.unit8() WILL NOT round by default.
img_np = (img_np * 255.0).round()
img_np = img_np.astype(out_type)
result.append(img_np)
if len(result) == 1 and flag_tensor:
result = result[0]
return result | Convert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tensor or list[Tensor]): Accept shapes: 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); 2) 3D Tensor of shape (3/1 x H x W); 3) 2D Tensor of shape (H x W). Tensor channel should be in RGB order. rgb2bgr (bool): Whether to change rgb to bgr. out_type (numpy type): output types. If ``np.uint8``, transform outputs to uint8 type with range [0, 255]; otherwise, float type with range [0, 1]. Default: ``np.uint8``. min_max (tuple[int]): min and max values for clamp. Returns: (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of shape (H x W). The channel order is BGR. |
2,730 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `img2tensor` function. Write a Python function `def img2tensor(imgs, out_type=torch.float32)` to solve the following problem:
Convert image numpy arrays into torch tensor. Args: imgs (Array or list[array]): Accept shapes: 3) list of numpy arrays 1) 3D numpy array of shape (H x W x 3/1); 2) 2D Tensor of shape (H x W). Tensor channel should be in RGB order. Returns: (array or list): 4D ndarray of shape (1 x C x H x W)
Here is the function:
def img2tensor(imgs, out_type=torch.float32):
"""Convert image numpy arrays into torch tensor.
Args:
imgs (Array or list[array]): Accept shapes:
3) list of numpy arrays
1) 3D numpy array of shape (H x W x 3/1);
2) 2D Tensor of shape (H x W).
Tensor channel should be in RGB order.
Returns:
(array or list): 4D ndarray of shape (1 x C x H x W)
"""
def _img2tensor(img):
if img.ndim == 2:
tensor = torch.from_numpy(img[None, None,]).type(out_type)
elif img.ndim == 3:
tensor = torch.from_numpy(rearrange(img, 'h w c -> c h w')).type(out_type).unsqueeze(0)
else:
raise TypeError(f'2D or 3D numpy array expected, got{img.ndim}D array')
return tensor
if not (isinstance(imgs, np.ndarray) or (isinstance(imgs, list) and all(isinstance(t, np.ndarray) for t in imgs))):
raise TypeError(f'Numpy array or list of numpy array expected, got {type(imgs)}')
flag_numpy = isinstance(imgs, np.ndarray)
if flag_numpy:
imgs = [imgs,]
result = []
for _img in imgs:
result.append(_img2tensor(_img))
if len(result) == 1 and flag_numpy:
result = result[0]
return result | Convert image numpy arrays into torch tensor. Args: imgs (Array or list[array]): Accept shapes: 3) list of numpy arrays 1) 3D numpy array of shape (H x W x 3/1); 2) 2D Tensor of shape (H x W). Tensor channel should be in RGB order. Returns: (array or list): 4D ndarray of shape (1 x C x H x W) |
2,731 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
def bgr2rgb(im): return cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
The provided code snippet includes necessary dependencies for implementing the `imread` function. Write a Python function `def imread(path, chn='rgb', dtype='float32')` to solve the following problem:
Read image. chn: 'rgb', 'bgr' or 'gray' out: im: h x w x c, numpy tensor
Here is the function:
def imread(path, chn='rgb', dtype='float32'):
'''
Read image.
chn: 'rgb', 'bgr' or 'gray'
out:
im: h x w x c, numpy tensor
'''
im = cv2.imread(str(path), cv2.IMREAD_UNCHANGED) # BGR, uint8
try:
if chn.lower() == 'rgb':
if im.ndim == 3:
im = bgr2rgb(im)
else:
im = np.stack((im, im, im), axis=2)
elif chn.lower() == 'gray':
assert im.ndim == 2
except:
print(str(path))
if dtype == 'float32':
im = im.astype(np.float32) / 255.
elif dtype == 'float64':
im = im.astype(np.float64) / 255.
elif dtype == 'uint8':
pass
else:
sys.exit('Please input corrected dtype: float32, float64 or uint8!')
return im | Read image. chn: 'rgb', 'bgr' or 'gray' out: im: h x w x c, numpy tensor |
2,732 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
def rgb2bgr(im): return cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
The provided code snippet includes necessary dependencies for implementing the `imwrite` function. Write a Python function `def imwrite(im_in, path, chn='rgb', dtype_in='float32', qf=None)` to solve the following problem:
Save image. Input: im: h x w x c, numpy tensor path: the saving path chn: the channel order of the im,
Here is the function:
def imwrite(im_in, path, chn='rgb', dtype_in='float32', qf=None):
'''
Save image.
Input:
im: h x w x c, numpy tensor
path: the saving path
chn: the channel order of the im,
'''
im = im_in.copy()
if isinstance(path, str):
path = Path(path)
if dtype_in != 'uint8':
im = img_as_ubyte(im)
if chn.lower() == 'rgb' and im.ndim == 3:
im = rgb2bgr(im)
if qf is not None and path.suffix.lower() in ['.jpg', '.jpeg']:
flag = cv2.imwrite(str(path), im, [int(cv2.IMWRITE_JPEG_QUALITY), int(qf)])
else:
flag = cv2.imwrite(str(path), im)
return flag | Save image. Input: im: h x w x c, numpy tensor path: the saving path chn: the channel order of the im, |
2,733 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
def bgr2rgb(im): return cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
def rgb2bgr(im): return cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
The provided code snippet includes necessary dependencies for implementing the `jpeg_compress` function. Write a Python function `def jpeg_compress(im, qf, chn_in='rgb')` to solve the following problem:
Input: im: h x w x 3 array qf: compress factor, (0, 100] chn_in: 'rgb' or 'bgr' Return: Compressed Image with channel order: chn_in
Here is the function:
def jpeg_compress(im, qf, chn_in='rgb'):
'''
Input:
im: h x w x 3 array
qf: compress factor, (0, 100]
chn_in: 'rgb' or 'bgr'
Return:
Compressed Image with channel order: chn_in
'''
# transform to BGR channle and uint8 data type
im_bgr = rgb2bgr(im) if chn_in.lower() == 'rgb' else im
if im.dtype != np.dtype('uint8'): im_bgr = img_as_ubyte(im_bgr)
# JPEG compress
flag, encimg = cv2.imencode('.jpg', im_bgr, [int(cv2.IMWRITE_JPEG_QUALITY), qf])
assert flag
im_jpg_bgr = cv2.imdecode(encimg, 1) # uint8, BGR
# transform back to original channel and the original data type
im_out = bgr2rgb(im_jpg_bgr) if chn_in.lower() == 'rgb' else im_jpg_bgr
if im.dtype != np.dtype('uint8'): im_out = img_as_float32(im_out).astype(im.dtype)
return im_out | Input: im: h x w x 3 array qf: compress factor, (0, 100] chn_in: 'rgb' or 'bgr' Return: Compressed Image with channel order: chn_in |
2,734 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `data_aug_np` function. Write a Python function `def data_aug_np(image, mode)` to solve the following problem:
Performs data augmentation of the input image Input: image: a cv2 (OpenCV) image mode: int. Choice of transformation to apply to the image 0 - no transformation 1 - flip up and down 2 - rotate counterwise 90 degree 3 - rotate 90 degree and flip up and down 4 - rotate 180 degree 5 - rotate 180 degree and flip 6 - rotate 270 degree 7 - rotate 270 degree and flip
Here is the function:
def data_aug_np(image, mode):
'''
Performs data augmentation of the input image
Input:
image: a cv2 (OpenCV) image
mode: int. Choice of transformation to apply to the image
0 - no transformation
1 - flip up and down
2 - rotate counterwise 90 degree
3 - rotate 90 degree and flip up and down
4 - rotate 180 degree
5 - rotate 180 degree and flip
6 - rotate 270 degree
7 - rotate 270 degree and flip
'''
if mode == 0:
# original
out = image
elif mode == 1:
# flip up and down
out = np.flipud(image)
elif mode == 2:
# rotate counterwise 90 degree
out = np.rot90(image)
elif mode == 3:
# rotate 90 degree and flip up and down
out = np.rot90(image)
out = np.flipud(out)
elif mode == 4:
# rotate 180 degree
out = np.rot90(image, k=2)
elif mode == 5:
# rotate 180 degree and flip
out = np.rot90(image, k=2)
out = np.flipud(out)
elif mode == 6:
# rotate 270 degree
out = np.rot90(image, k=3)
elif mode == 7:
# rotate 270 degree and flip
out = np.rot90(image, k=3)
out = np.flipud(out)
else:
raise Exception('Invalid choice of image transformation')
return out.copy() | Performs data augmentation of the input image Input: image: a cv2 (OpenCV) image mode: int. Choice of transformation to apply to the image 0 - no transformation 1 - flip up and down 2 - rotate counterwise 90 degree 3 - rotate 90 degree and flip up and down 4 - rotate 180 degree 5 - rotate 180 degree and flip 6 - rotate 270 degree 7 - rotate 270 degree and flip |
2,735 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `inverse_data_aug_np` function. Write a Python function `def inverse_data_aug_np(image, mode)` to solve the following problem:
Performs inverse data augmentation of the input image
Here is the function:
def inverse_data_aug_np(image, mode):
'''
Performs inverse data augmentation of the input image
'''
if mode == 0:
# original
out = image
elif mode == 1:
out = np.flipud(image)
elif mode == 2:
out = np.rot90(image, axes=(1,0))
elif mode == 3:
out = np.flipud(image)
out = np.rot90(out, axes=(1,0))
elif mode == 4:
out = np.rot90(image, k=2, axes=(1,0))
elif mode == 5:
out = np.flipud(image)
out = np.rot90(out, k=2, axes=(1,0))
elif mode == 6:
out = np.rot90(image, k=3, axes=(1,0))
elif mode == 7:
# rotate 270 degree and flip
out = np.flipud(image)
out = np.rot90(out, k=3, axes=(1,0))
else:
raise Exception('Invalid choice of image transformation')
return out | Performs inverse data augmentation of the input image |
2,736 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
def imshow(x, title=None, cbar=False):
import matplotlib.pyplot as plt
plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
if title:
plt.title(title)
if cbar:
plt.colorbar()
plt.show() | null |
2,737 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `imgrad` function. Write a Python function `def imgrad(im, pading_mode='mirror')` to solve the following problem:
Calculate image gradient. Input: im: h x w x c numpy array
Here is the function:
def imgrad(im, pading_mode='mirror'):
'''
Calculate image gradient.
Input:
im: h x w x c numpy array
'''
from scipy.ndimage import correlate # lazy import
wx = np.array([[0, 0, 0],
[-1, 1, 0],
[0, 0, 0]], dtype=np.float32)
wy = np.array([[0, -1, 0],
[0, 1, 0],
[0, 0, 0]], dtype=np.float32)
if im.ndim == 3:
gradx = np.stack(
[correlate(im[:,:,c], wx, mode=pading_mode) for c in range(im.shape[2])],
axis=2
)
grady = np.stack(
[correlate(im[:,:,c], wy, mode=pading_mode) for c in range(im.shape[2])],
axis=2
)
grad = np.concatenate((gradx, grady), axis=2)
else:
gradx = correlate(im, wx, mode=pading_mode)
grady = correlate(im, wy, mode=pading_mode)
grad = np.stack((gradx, grady), axis=2)
return {'gradx': gradx, 'grady': grady, 'grad':grad} | Calculate image gradient. Input: im: h x w x c numpy array |
2,738 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
def convfft(im, weight):
'''
Convolution with FFT
Input:
im: h1 x w1 x c numpy array
weight: h2 x w2 numpy array
Output:
out: h1 x w1 x c numpy array
'''
axes = (0,1)
otf = psf2otf(weight, im.shape[:2])
if im.ndim == 3:
otf = np.tile(otf[:, :, None], (1,1,im.shape[2]))
out = fft.ifft2(fft.fft2(im, axes=axes) * otf, axes=axes).real
return out
The provided code snippet includes necessary dependencies for implementing the `imgrad_fft` function. Write a Python function `def imgrad_fft(im)` to solve the following problem:
Calculate image gradient. Input: im: h x w x c numpy array
Here is the function:
def imgrad_fft(im):
'''
Calculate image gradient.
Input:
im: h x w x c numpy array
'''
wx = np.rot90(np.array([[0, 0, 0],
[-1, 1, 0],
[0, 0, 0]], dtype=np.float32), k=2)
gradx = convfft(im, wx)
wy = np.rot90(np.array([[0, -1, 0],
[0, 1, 0],
[0, 0, 0]], dtype=np.float32), k=2)
grady = convfft(im, wy)
grad = np.concatenate((gradx, grady), axis=2)
return {'gradx': gradx, 'grady': grady, 'grad':grad} | Calculate image gradient. Input: im: h x w x c numpy array |
2,739 | import sys
import cv2
import math
import torch
import random
import numpy as np
from scipy import fft
from pathlib import Path
from einops import rearrange
from skimage import img_as_ubyte, img_as_float32
The provided code snippet includes necessary dependencies for implementing the `random_crop` function. Write a Python function `def random_crop(im, pch_size)` to solve the following problem:
Randomly crop a patch from the give image.
Here is the function:
def random_crop(im, pch_size):
'''
Randomly crop a patch from the give image.
'''
h, w = im.shape[:2]
if h == pch_size and w == pch_size:
im_pch = im
else:
assert h >= pch_size or w >= pch_size
ind_h = random.randint(0, h-pch_size)
ind_w = random.randint(0, w-pch_size)
im_pch = im[ind_h:ind_h+pch_size, ind_w:ind_w+pch_size,]
return im_pch | Randomly crop a patch from the give image. |
2,740 | import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3])
loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3])
loss_real = (weights * loss_real).sum() / weights.sum()
loss_fake = (weights * loss_fake).sum() / weights.sum()
d_loss = 0.5 * (loss_real + loss_fake)
return d_loss | null |
2,741 | import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def adopt_weight(weight, global_step, threshold=0, value=0.):
if global_step < threshold:
weight = value
return weight | null |
2,742 | import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def measure_perplexity(predicted_indices, n_embed):
# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
avg_probs = encodings.mean(0)
perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
cluster_use = torch.sum(avg_probs > 0)
return perplexity, cluster_use | null |
2,743 | import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def l1(x, y):
return torch.abs(x-y) | null |
2,744 | import torch
from torch import nn
import torch.nn.functional as F
from einops import repeat
from taming.modules.discriminator.model import NLayerDiscriminator, weights_init
from taming.modules.losses.lpips import LPIPS
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
def l2(x, y):
return torch.pow((x-y), 2) | null |
2,745 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
The provided code snippet includes necessary dependencies for implementing the `window_partition` function. Write a Python function `def window_partition(x, window_size)` to solve the following problem:
Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)
Here is the function:
def window_partition(x, window_size):
"""
Args:
x: (B, H, W, C)
window_size (int): window size
Returns:
windows: (num_windows*B, window_size, window_size, C)
"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows | Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C) |
2,746 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
The provided code snippet includes necessary dependencies for implementing the `window_reverse` function. Write a Python function `def window_reverse(windows, window_size, H, W)` to solve the following problem:
Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C)
Here is the function:
def window_reverse(windows, window_size, H, W):
"""
Args:
windows: (num_windows*B, window_size, window_size, C)
window_size (int): Window size
H (int): Height of image
W (int): Width of image
Returns:
x: (B, H, W, C)
"""
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x | Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image W (int): Width of image Returns: x: (B, H, W, C) |
2,747 | import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.spectral_norm as spectral_norm
from ldm.modules.diffusionmodules.util import normalization
def get_nonspade_norm_layer(opt, norm_type='instance'):
# helper function to get # output channels of the previous layer
def get_out_channel(layer):
if hasattr(layer, 'out_channels'):
return getattr(layer, 'out_channels')
return layer.weight.size(0)
# this function will be returned
def add_norm_layer(layer):
nonlocal norm_type
if norm_type.startswith('spectral'):
layer = spectral_norm(layer)
subnorm_type = norm_type[len('spectral'):]
if subnorm_type == 'none' or len(subnorm_type) == 0:
return layer
# remove bias in the previous layer, which is meaningless
# since it has no effect after normalization
if getattr(layer, 'bias', None) is not None:
delattr(layer, 'bias')
layer.register_parameter('bias', None)
if subnorm_type == 'batch':
norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True)
elif subnorm_type == 'sync_batch':
norm_layer = SynchronizedBatchNorm2d(get_out_channel(layer), affine=True)
elif subnorm_type == 'instance':
norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False)
else:
raise ValueError('normalization layer %s is not recognized' % subnorm_type)
return nn.Sequential(layer, norm_layer)
return add_norm_layer | null |
2,748 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
if schedule == "linear":
betas = (
torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
)
elif schedule == "cosine":
timesteps = (
torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
)
alphas = timesteps / (1 + cosine_s) * np.pi / 2
alphas = torch.cos(alphas).pow(2)
alphas = alphas / alphas[0]
betas = 1 - alphas[1:] / alphas[:-1]
betas = np.clip(betas, a_min=0, a_max=0.999)
elif schedule == "sqrt_linear":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
elif schedule == "sqrt":
betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
else:
raise ValueError(f"schedule '{schedule}' unknown.")
return betas.numpy() | null |
2,749 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
if ddim_discr_method == 'uniform':
c = num_ddpm_timesteps // num_ddim_timesteps
ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
elif ddim_discr_method == 'quad':
ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
else:
raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
# assert ddim_timesteps.shape[0] == num_ddim_timesteps
# add one to get the final alpha values right (the ones from first scale to data during sampling)
steps_out = ddim_timesteps
if verbose:
print(f'Selected timesteps for ddim sampler: {steps_out}')
return steps_out | null |
2,750 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
# select alphas for computing the variance schedule
alphas = alphacums[ddim_timesteps]
alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
# according the the formula provided in https://arxiv.org/abs/2010.02502
sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
if verbose:
print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
print(f'For the chosen value of eta, which is {eta}, '
f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
return sigmas, alphas, alphas_prev | null |
2,751 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
The provided code snippet includes necessary dependencies for implementing the `betas_for_alpha_bar` function. Write a Python function `def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999)` to solve the following problem:
Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities.
Here is the function:
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_timesteps: the number of betas to produce.
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
produces the cumulative product of (1-beta) up to that
part of the diffusion process.
:param max_beta: the maximum beta to use; use values lower than 1 to
prevent singularities.
"""
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas) | Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [0,1]. :param num_diffusion_timesteps: the number of betas to produce. :param alpha_bar: a lambda that takes an argument t from 0 to 1 and produces the cumulative product of (1-beta) up to that part of the diffusion process. :param max_beta: the maximum beta to use; use values lower than 1 to prevent singularities. |
2,752 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
def extract_into_tensor(a, t, x_shape):
b, *_ = t.shape
out = a.gather(-1, t)
return out.reshape(b, *((1,) * (len(x_shape) - 1))) | null |
2,753 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
class CheckpointFunction(torch.autograd.Function):
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with torch.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with torch.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = torch.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads
The provided code snippet includes necessary dependencies for implementing the `checkpoint` function. Write a Python function `def checkpoint(func, inputs, params, flag)` to solve the following problem:
Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing.
Here is the function:
def checkpoint(func, inputs, params, flag):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
"""
if flag:
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs) | Evaluate a function without caching intermediate activations, allowing for reduced memory at the expense of extra compute in the backward pass. :param func: the function to evaluate. :param inputs: the argument sequence to pass to `func`. :param params: a sequence of parameters `func` depends on but does not explicitly take as arguments. :param flag: if False, disable gradient checkpointing. |
2,754 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
The provided code snippet includes necessary dependencies for implementing the `timestep_embedding` function. Write a Python function `def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False)` to solve the following problem:
Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings.
Here is the function:
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
if not repeat_only:
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
else:
embedding = repeat(timesteps, 'b -> b d', d=dim)
return embedding | Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an [N x dim] Tensor of positional embeddings. |
2,755 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
The provided code snippet includes necessary dependencies for implementing the `zero_module` function. Write a Python function `def zero_module(module)` to solve the following problem:
Zero out the parameters of a module and return it.
Here is the function:
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module | Zero out the parameters of a module and return it. |
2,756 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
The provided code snippet includes necessary dependencies for implementing the `scale_module` function. Write a Python function `def scale_module(module, scale)` to solve the following problem:
Scale the parameters of a module and return it.
Here is the function:
def scale_module(module, scale):
"""
Scale the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().mul_(scale)
return module | Scale the parameters of a module and return it. |
2,757 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
The provided code snippet includes necessary dependencies for implementing the `mean_flat` function. Write a Python function `def mean_flat(tensor)` to solve the following problem:
Take the mean over all non-batch dimensions.
Here is the function:
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape)))) | Take the mean over all non-batch dimensions. |
2,758 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super().forward(x.float()).type(x.dtype)
The provided code snippet includes necessary dependencies for implementing the `normalization` function. Write a Python function `def normalization(channels, norm_channel=32)` to solve the following problem:
Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.
Here is the function:
def normalization(channels, norm_channel=32):
"""
Make a standard normalization layer.
:param channels: number of input channels.
:return: an nn.Module for normalization.
"""
return GroupNorm32(norm_channel, channels) | Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization. |
2,759 | import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
from ldm.util import instantiate_from_config
The provided code snippet includes necessary dependencies for implementing the `conv_nd` function. Write a Python function `def conv_nd(dims, *args, **kwargs)` to solve the following problem:
Create a 1D, 2D, or 3D convolution module.
Here is the function:
def conv_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D convolution module.
"""
if dims == 1:
return nn.Conv1d(*args, **kwargs)
elif dims == 2:
return nn.Conv2d(*args, **kwargs)
elif dims == 3:
return nn.Conv3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}") | Create a 1D, 2D, or 3D convolution module. |
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