id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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2,760 | 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 `linear` function. Write a Python function `def linear(*args, **kwargs)` to solve the following problem:
Create a linear module.
Here is the function:
def linear(*args, **kwargs):
"""
Create a linear module.
"""
return nn.Linear(*args, **kwargs) | Create a linear module. |
2,761 | 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 `avg_pool_nd` function. Write a Python function `def avg_pool_nd(dims, *args, **kwargs)` to solve the following problem:
Create a 1D, 2D, or 3D average pooling module.
Here is the function:
def avg_pool_nd(dims, *args, **kwargs):
"""
Create a 1D, 2D, or 3D average pooling module.
"""
if dims == 1:
return nn.AvgPool1d(*args, **kwargs)
elif dims == 2:
return nn.AvgPool2d(*args, **kwargs)
elif dims == 3:
return nn.AvgPool3d(*args, **kwargs)
raise ValueError(f"unsupported dimensions: {dims}") | Create a 1D, 2D, or 3D average pooling module. |
2,762 | 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 noise_like(shape, device, repeat=False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise() | null |
2,763 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
from basicsr.archs.arch_util import default_init_weights, make_layer, pixel_unshuffle
from basicsr.archs.rrdbnet_arch import RRDB
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. |
2,764 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
from basicsr.archs.arch_util import default_init_weights, make_layer, pixel_unshuffle
from basicsr.archs.rrdbnet_arch import RRDB
The provided code snippet includes necessary dependencies for implementing the `get_timestep_embedding` function. Write a Python function `def get_timestep_embedding(timesteps, embedding_dim)` to solve the following problem:
This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need".
Here is the function:
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
assert len(timesteps.shape) == 1
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
emb = emb.to(device=timesteps.device)
emb = timesteps.float()[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0,1,0,0))
return emb | This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of "Attention Is All You Need". |
2,765 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
from basicsr.archs.arch_util import default_init_weights, make_layer, pixel_unshuffle
from basicsr.archs.rrdbnet_arch import RRDB
def nonlinearity(x):
# swish
return x*torch.sigmoid(x) | null |
2,766 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
from basicsr.archs.arch_util import default_init_weights, make_layer, pixel_unshuffle
from basicsr.archs.rrdbnet_arch import RRDB
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) | null |
2,767 | import math
import torch
import torch.nn as nn
import numpy as np
from einops import rearrange
from ldm.util import instantiate_from_config
from ldm.modules.attention import LinearAttention
from basicsr.archs.arch_util import default_init_weights, make_layer, pixel_unshuffle
from basicsr.archs.rrdbnet_arch import RRDB
class LinAttnBlock(LinearAttention):
"""to match AttnBlock usage"""
def __init__(self, in_channels):
super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
class AttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = q.reshape(b,c,h*w)
q = q.permute(0,2,1) # b,hw,c
k = k.reshape(b,c,h*w) # b,c,hw
w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
w_ = w_ * (int(c)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = v.reshape(b,c,h*w)
w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = h_.reshape(b,c,h,w)
h_ = self.proj_out(h_)
return x+h_
class MemoryEfficientAttnBlock(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.attention_op: Optional[Any] = None
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q, k, v = map(
lambda t:t.reshape(b, t.shape[1], t.shape[2]*t.shape[3], 1)
.squeeze(3)
.permute(0,2,1)
.contiguous(),
(q, k, v),
)
# actually compute the attention, what we cannot get enough of
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, scale=(int(c)**(-0.5)), op=self.attention_op)
h_ = (
out.permute(0,2,1)
.unsqueeze(3)
.reshape(b, c, h, w)
)
h_ = self.proj_out(h_)
return x+h_
def make_attn(in_channels, attn_type="vanilla"):
assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
if attn_type == "vanilla":
if XFORMERS_IS_AVAILBLE:
return MemoryEfficientAttnBlock(in_channels)
else:
return AttnBlock(in_channels)
elif attn_type == "none":
return nn.Identity(in_channels)
else:
return LinAttnBlock(in_channels) | null |
2,768 | from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import torch
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer, SpatialTransformerV2
from ldm.modules.spade import SPADE
from basicsr.archs.stylegan2_arch import ConvLayer, EqualConv2d
def convert_module_to_f16(x):
pass | null |
2,769 | from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import torch
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer, SpatialTransformerV2
from ldm.modules.spade import SPADE
from basicsr.archs.stylegan2_arch import ConvLayer, EqualConv2d
def convert_module_to_f32(x):
pass | null |
2,770 | from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import torch
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer, SpatialTransformerV2
from ldm.modules.spade import SPADE
from basicsr.archs.stylegan2_arch import ConvLayer, EqualConv2d
def exists(val):
return val is not None | null |
2,771 | from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import torch
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer, SpatialTransformerV2
from ldm.modules.spade import SPADE
from basicsr.archs.stylegan2_arch import ConvLayer, EqualConv2d
def cal_fea_cossim(fea_1, fea_2, save_dir=None):
cossim_fuc = nn.CosineSimilarity(dim=-1, eps=1e-6)
if save_dir is None:
save_dir_1 = './cos_sim64_1_not.txt'
save_dir_2 = './cos_sim64_2_not.txt'
b, c, h, w = fea_1.size()
fea_1 = fea_1.reshape(b, c, h*w)
fea_2 = fea_2.reshape(b, c, h*w)
cos_sim = cossim_fuc(fea_1, fea_2)
cos_sim = cos_sim.data.cpu().numpy()
with open(save_dir_1, "a") as my_file:
my_file.write(str(np.mean(cos_sim[0])) + "\n")
# with open(save_dir_2, "a") as my_file:
# my_file.write(str(np.mean(cos_sim[1])) + "\n") | null |
2,772 | from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import torch
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from ldm.modules.attention import SpatialTransformer, SpatialTransformerV2
from ldm.modules.spade import SPADE
from basicsr.archs.stylegan2_arch import ConvLayer, EqualConv2d
The provided code snippet includes necessary dependencies for implementing the `count_flops_attn` function. Write a Python function `def count_flops_attn(model, _x, y)` to solve the following problem:
A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.profile( model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops}, )
Here is the function:
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
matmul_ops = 2 * b * (num_spatial ** 2) * c
model.total_ops += th.DoubleTensor([matmul_ops]) | A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.profile( model, inputs=(inputs, timestamps), custom_ops={QKVAttention: QKVAttention.count_flops}, ) |
2,773 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
import os
def uniq(arr):
return{el: True for el in arr}.keys() | null |
2,774 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
import os
def exists(val):
return val is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d | null |
2,775 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
import os
def max_neg_value(t):
return -torch.finfo(t.dtype).max | null |
2,776 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
import os
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor | null |
2,777 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
import os
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. |
2,778 | from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
import os
def Normalize(in_channels):
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) | null |
2,779 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
The provided code snippet includes necessary dependencies for implementing the `modcrop_np` function. Write a Python function `def modcrop_np(img, sf)` to solve the following problem:
Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image
Here is the function:
def modcrop_np(img, sf):
'''
Args:
img: numpy image, WxH or WxHxC
sf: scale factor
Return:
cropped image
'''
w, h = img.shape[:2]
im = np.copy(img)
return im[:w - w % sf, :h - h % sf, ...] | Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image |
2,780 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
The provided code snippet includes necessary dependencies for implementing the `analytic_kernel` function. Write a Python function `def analytic_kernel(k)` to solve the following problem:
Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)
Here is the function:
def analytic_kernel(k):
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
k_size = k.shape[0]
# Calculate the big kernels size
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
# Loop over the small kernel to fill the big one
for r in range(k_size):
for c in range(k_size):
big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
crop = k_size // 2
cropped_big_k = big_k[crop:-crop, crop:-crop]
# Normalize to 1
return cropped_big_k / cropped_big_k.sum() | Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper) |
2,781 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
The provided code snippet includes necessary dependencies for implementing the `gen_kernel` function. Write a Python function `def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0)` to solve the following problem:
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf
Here is the function:
def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
""""
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
# Kai Zhang
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
# max_var = 2.5 * sf
"""
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
theta = np.random.rand() * np.pi # random theta
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
# Set COV matrix using Lambdas and Theta
LAMBDA = np.diag([lambda_1, lambda_2])
Q = np.array([[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]])
SIGMA = Q @ LAMBDA @ Q.T
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
# Set expectation position (shifting kernel for aligned image)
MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
MU = MU[None, None, :, None]
# Create meshgrid for Gaussian
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
Z = np.stack([X, Y], 2)[:, :, :, None]
# Calcualte Gaussian for every pixel of the kernel
ZZ = Z - MU
ZZ_t = ZZ.transpose(0, 1, 3, 2)
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
# shift the kernel so it will be centered
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
# Normalize the kernel and return
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
kernel = raw_kernel / np.sum(raw_kernel)
return kernel | # modified version of https://github.com/assafshocher/BlindSR_dataset_generator # Kai Zhang # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var # max_var = 2.5 * sf |
2,782 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
The provided code snippet includes necessary dependencies for implementing the `srmd_degradation` function. Write a Python function `def srmd_degradation(x, k, sf=3)` to solve the following problem:
blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} }
Here is the function:
def srmd_degradation(x, k, sf=3):
''' blur + bicubic downsampling
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2018learning,
title={Learning a single convolutional super-resolution network for multiple degradations},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={3262--3271},
year={2018}
}
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x | blur + bicubic downsampling Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2018learning, title={Learning a single convolutional super-resolution network for multiple degradations}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={3262--3271}, year={2018} } |
2,783 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def bicubic_degradation(x, sf=3):
'''
Args:
x: HxWxC image, [0, 1]
sf: down-scale factor
Return:
bicubicly downsampled LR image
'''
x = util.imresize_np(x, scale=1 / sf)
return x
The provided code snippet includes necessary dependencies for implementing the `dpsr_degradation` function. Write a Python function `def dpsr_degradation(x, k, sf=3)` to solve the following problem:
bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} }
Here is the function:
def dpsr_degradation(x, k, sf=3):
''' bicubic downsampling + blur
Args:
x: HxWxC image, [0, 1]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
Reference:
@inproceedings{zhang2019deep,
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
pages={1671--1681},
year={2019}
}
'''
x = bicubic_degradation(x, sf=sf)
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
return x | bicubic downsampling + blur Args: x: HxWxC image, [0, 1] k: hxw, double sf: down-scale factor Return: downsampled LR image Reference: @inproceedings{zhang2019deep, title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels}, author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, pages={1671--1681}, year={2019} } |
2,784 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
The provided code snippet includes necessary dependencies for implementing the `classical_degradation` function. Write a Python function `def classical_degradation(x, k, sf=3)` to solve the following problem:
blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image
Here is the function:
def classical_degradation(x, k, sf=3):
''' blur + downsampling
Args:
x: HxWxC image, [0, 1]/[0, 255]
k: hxw, double
sf: down-scale factor
Return:
downsampled LR image
'''
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
st = 0
return x[st::sf, st::sf, ...] | blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsampled LR image |
2,785 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def blur(x, k):
'''
x: image, NxcxHxW
k: kernel, Nx1xhxw
'''
n, c = x.shape[:2]
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
k = k.repeat(1, c, 1, 1)
k = k.view(-1, 1, k.shape[2], k.shape[3])
x = x.view(1, -1, x.shape[2], x.shape[3])
x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
x = x.view(n, c, x.shape[2], x.shape[3])
return x
The provided code snippet includes necessary dependencies for implementing the `add_sharpening` function. Write a Python function `def add_sharpening(img, weight=0.5, radius=50, threshold=10)` to solve the following problem:
USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (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 add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (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)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img | USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > threshold, else: 0 3. Blur mask: 4. Out = Mask * K + (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): |
2,786 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
return img | null |
2,787 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img | null |
2,788 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img | null |
2,789 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
wd2 = wd2/4
wd = wd/4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
The provided code snippet includes necessary dependencies for implementing the `degradation_bsrgan` function. Write a Python function `def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None)` to solve the following problem:
This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
Here is the function:
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq | This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
2,790 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
wd2 = wd2/4
wd = wd/4
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
The provided code snippet includes necessary dependencies for implementing the `degradation_bsrgan_variant` function. Write a Python function `def degradation_bsrgan_variant(image, sf=4, isp_model=None)` to solve the following problem:
This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
Here is the function:
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = image.shape[:2]
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
# elif i == 1:
# image = add_blur(image, sf=sf)
if i == 0:
pass
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.8:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
#
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image": image}
return example | This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
2,791 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def imshow(x, title=None, cbar=False, figsize=None):
plt.figure(figsize=figsize)
plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
if title:
plt.title(title)
if cbar:
plt.colorbar()
plt.show() | null |
2,792 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def surf(Z, cmap='rainbow', figsize=None):
plt.figure(figsize=figsize)
ax3 = plt.axes(projection='3d')
w, h = Z.shape[:2]
xx = np.arange(0,w,1)
yy = np.arange(0,h,1)
X, Y = np.meshgrid(xx, yy)
ax3.plot_surface(X,Y,Z,cmap=cmap)
#ax3.contour(X,Y,Z, zdim='z',offset=-2，cmap=cmap)
plt.show() | null |
2,793 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
def get_image_paths(dataroot):
paths = None # return None if dataroot is None
if dataroot is not None:
paths = sorted(_get_paths_from_images(dataroot))
return paths
def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
w, h = img.shape[:2]
patches = []
if w > p_max and h > p_max:
w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
w1.append(w-p_size)
h1.append(h-p_size)
# print(w1)
# print(h1)
for i in w1:
for j in h1:
patches.append(img[i:i+p_size, j:j+p_size,:])
else:
patches.append(img)
return patches
def imssave(imgs, img_path):
"""
imgs: list, N images of size WxHxC
"""
img_name, ext = os.path.splitext(os.path.basename(img_path))
for i, img in enumerate(imgs):
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
cv2.imwrite(new_path, img)
def imread_uint(path, n_channels=3):
# input: path
# output: HxWx3(RGB or GGG), or HxWx1 (G)
if n_channels == 1:
img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
img = np.expand_dims(img, axis=2) # HxWx1
elif n_channels == 3:
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
return img
The provided code snippet includes necessary dependencies for implementing the `split_imageset` function. Write a Python function `def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000)` to solve the following problem:
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) will be splitted. Args: original_dataroot: taget_dataroot: p_size: size of small images p_overlap: patch size in training is a good choice p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
Here is the function:
def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
"""
split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
will be splitted.
Args:
original_dataroot:
taget_dataroot:
p_size: size of small images
p_overlap: patch size in training is a good choice
p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
"""
paths = get_image_paths(original_dataroot)
for img_path in paths:
# img_name, ext = os.path.splitext(os.path.basename(img_path))
img = imread_uint(img_path, n_channels=n_channels)
patches = patches_from_image(img, p_size, p_overlap, p_max)
imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
#if original_dataroot == taget_dataroot:
#del img_path | split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size), and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max) will be splitted. Args: original_dataroot: taget_dataroot: p_size: size of small images p_overlap: patch size in training is a good choice p_max: images with smaller size than (p_max)x(p_max) keep unchanged. |
2,794 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdirs(paths):
if isinstance(paths, str):
mkdir(paths)
else:
for path in paths:
mkdir(path) | null |
2,795 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
def get_timestamp():
def mkdir_and_rename(path):
if os.path.exists(path):
new_name = path + '_archived_' + get_timestamp()
print('Path already exists. Rename it to [{:s}]'.format(new_name))
os.rename(path, new_name)
os.makedirs(path) | null |
2,796 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def imwrite(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img)
def imsave(img, img_path):
img = np.squeeze(img)
if img.ndim == 3:
img = img[:, :, [2, 1, 0]]
cv2.imwrite(img_path, img) | null |
2,797 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def read_img(path):
# read image by cv2
# return: Numpy float32, HWC, BGR, [0,1]
img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
img = img.astype(np.float32) / 255.
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# some images have 4 channels
if img.shape[2] > 3:
img = img[:, :, :3]
return img | null |
2,798 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def uint162single(img):
return np.float32(img/65535.) | null |
2,799 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def single2uint16(img):
return np.uint16((img.clip(0, 1)*65535.).round()) | null |
2,800 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def uint2tensor4(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0) | null |
2,801 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def uint2tensor3(img):
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.) | null |
2,802 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def tensor2uint(img):
img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return np.uint8((img*255.0).round()) | null |
2,803 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def single2tensor3(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float() | null |
2,804 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def single2tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0) | null |
2,805 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def tensor2single(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
return img | null |
2,806 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def tensor2single3(img):
img = img.data.squeeze().float().cpu().numpy()
if img.ndim == 3:
img = np.transpose(img, (1, 2, 0))
elif img.ndim == 2:
img = np.expand_dims(img, axis=2)
return img | null |
2,807 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def single2tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0) | null |
2,808 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def single32tensor5(img):
return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0) | null |
2,809 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def single42tensor4(img):
return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float() | null |
2,810 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
The provided code snippet includes necessary dependencies for implementing the `tensor2img` function. Write a Python function `def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1))` to solve the following problem:
Converts a torch Tensor into an image Numpy array of BGR channel order Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
Here is the function:
def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
'''
Converts a torch Tensor into an image Numpy array of BGR channel order
Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
'''
tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
n_dim = tensor.dim()
if n_dim == 4:
n_img = len(tensor)
img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 3:
img_np = tensor.numpy()
img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
elif n_dim == 2:
img_np = tensor.numpy()
else:
raise TypeError(
'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
if out_type == np.uint8:
img_np = (img_np * 255.0).round()
# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
return img_np.astype(out_type) | Converts a torch Tensor into an image Numpy array of BGR channel order Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default) |
2,811 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
The provided code snippet includes necessary dependencies for implementing the `augment_img_tensor4` function. Write a Python function `def augment_img_tensor4(img, mode=0)` to solve the following problem:
Kai Zhang (github: https://github.com/cszn)
Here is the function:
def augment_img_tensor4(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return img.rot90(1, [2, 3]).flip([2])
elif mode == 2:
return img.flip([2])
elif mode == 3:
return img.rot90(3, [2, 3])
elif mode == 4:
return img.rot90(2, [2, 3]).flip([2])
elif mode == 5:
return img.rot90(1, [2, 3])
elif mode == 6:
return img.rot90(2, [2, 3])
elif mode == 7:
return img.rot90(3, [2, 3]).flip([2]) | Kai Zhang (github: https://github.com/cszn) |
2,812 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def augment_img(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
if mode == 0:
return img
elif mode == 1:
return np.flipud(np.rot90(img))
elif mode == 2:
return np.flipud(img)
elif mode == 3:
return np.rot90(img, k=3)
elif mode == 4:
return np.flipud(np.rot90(img, k=2))
elif mode == 5:
return np.rot90(img)
elif mode == 6:
return np.rot90(img, k=2)
elif mode == 7:
return np.flipud(np.rot90(img, k=3))
The provided code snippet includes necessary dependencies for implementing the `augment_img_tensor` function. Write a Python function `def augment_img_tensor(img, mode=0)` to solve the following problem:
Kai Zhang (github: https://github.com/cszn)
Here is the function:
def augment_img_tensor(img, mode=0):
'''Kai Zhang (github: https://github.com/cszn)
'''
img_size = img.size()
img_np = img.data.cpu().numpy()
if len(img_size) == 3:
img_np = np.transpose(img_np, (1, 2, 0))
elif len(img_size) == 4:
img_np = np.transpose(img_np, (2, 3, 1, 0))
img_np = augment_img(img_np, mode=mode)
img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
if len(img_size) == 3:
img_tensor = img_tensor.permute(2, 0, 1)
elif len(img_size) == 4:
img_tensor = img_tensor.permute(3, 2, 0, 1)
return img_tensor.type_as(img) | Kai Zhang (github: https://github.com/cszn) |
2,813 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def augment_img_np3(img, mode=0):
if mode == 0:
return img
elif mode == 1:
return img.transpose(1, 0, 2)
elif mode == 2:
return img[::-1, :, :]
elif mode == 3:
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 4:
return img[:, ::-1, :]
elif mode == 5:
img = img[:, ::-1, :]
img = img.transpose(1, 0, 2)
return img
elif mode == 6:
img = img[:, ::-1, :]
img = img[::-1, :, :]
return img
elif mode == 7:
img = img[:, ::-1, :]
img = img[::-1, :, :]
img = img.transpose(1, 0, 2)
return img | null |
2,814 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def augment_imgs(img_list, hflip=True, rot=True):
# horizontal flip OR rotate
hflip = hflip and random.random() < 0.5
vflip = rot and random.random() < 0.5
rot90 = rot and random.random() < 0.5
def _augment(img):
if hflip:
img = img[:, ::-1, :]
if vflip:
img = img[::-1, :, :]
if rot90:
img = img.transpose(1, 0, 2)
return img
return [_augment(img) for img in img_list] | null |
2,815 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def modcrop(img_in, scale):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
if img.ndim == 2:
H, W = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r]
elif img.ndim == 3:
H, W, C = img.shape
H_r, W_r = H % scale, W % scale
img = img[:H - H_r, :W - W_r, :]
else:
raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
return img | null |
2,816 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def shave(img_in, border=0):
# img_in: Numpy, HWC or HW
img = np.copy(img_in)
h, w = img.shape[:2]
img = img[border:h-border, border:w-border]
return img | null |
2,817 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
The provided code snippet includes necessary dependencies for implementing the `rgb2ycbcr` function. Write a Python function `def rgb2ycbcr(img, only_y=True)` to solve the following problem:
same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1]
Here is the function:
def rgb2ycbcr(img, only_y=True):
'''same as matlab rgb2ycbcr
only_y: only return Y channel
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else:
rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
[24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type) | same as matlab rgb2ycbcr only_y: only return Y channel Input: uint8, [0, 255] float, [0, 1] |
2,818 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
The provided code snippet includes necessary dependencies for implementing the `ycbcr2rgb` function. Write a Python function `def ycbcr2rgb(img)` to solve the following problem:
same as matlab ycbcr2rgb Input: uint8, [0, 255] float, [0, 1]
Here is the function:
def ycbcr2rgb(img):
'''same as matlab ycbcr2rgb
Input:
uint8, [0, 255]
float, [0, 1]
'''
in_img_type = img.dtype
img.astype(np.float32)
if in_img_type != np.uint8:
img *= 255.
# convert
rlt = 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]
if in_img_type == np.uint8:
rlt = rlt.round()
else:
rlt /= 255.
return rlt.astype(in_img_type) | same as matlab ycbcr2rgb Input: uint8, [0, 255] float, [0, 1] |
2,819 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def bgr2ycbcr(img, only_y=True):
def channel_convert(in_c, tar_type, img_list):
# conversion among BGR, gray and y
if in_c == 3 and tar_type == 'gray': # BGR to gray
gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
return [np.expand_dims(img, axis=2) for img in gray_list]
elif in_c == 3 and tar_type == 'y': # BGR to y
y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
return [np.expand_dims(img, axis=2) for img in y_list]
elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
else:
return img_list | null |
2,820 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def calculate_psnr(img1, img2, border=0):
# img1 and img2 have range [0, 255]
#img1 = img1.squeeze()
#img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
img1 = img1[border:h-border, border:w-border]
img2 = img2[border:h-border, border:w-border]
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
mse = np.mean((img1 - img2)**2)
if mse == 0:
return float('inf')
return 20 * math.log10(255.0 / math.sqrt(mse)) | null |
2,821 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def ssim(img1, img2):
C1 = (0.01 * 255)**2
C2 = (0.03 * 255)**2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1**2
mu2_sq = mu2**2
mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
(sigma1_sq + sigma2_sq + C2))
return ssim_map.mean()
The provided code snippet includes necessary dependencies for implementing the `calculate_ssim` function. Write a Python function `def calculate_ssim(img1, img2, border=0)` to solve the following problem:
calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255]
Here is the function:
def calculate_ssim(img1, img2, border=0):
'''calculate SSIM
the same outputs as MATLAB's
img1, img2: [0, 255]
'''
#img1 = img1.squeeze()
#img2 = img2.squeeze()
if not img1.shape == img2.shape:
raise ValueError('Input images must have the same dimensions.')
h, w = img1.shape[:2]
img1 = img1[border:h-border, border:w-border]
img2 = img2[border:h-border, border:w-border]
if img1.ndim == 2:
return ssim(img1, img2)
elif img1.ndim == 3:
if img1.shape[2] == 3:
ssims = []
for i in range(3):
ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
return np.array(ssims).mean()
elif img1.shape[2] == 1:
return ssim(np.squeeze(img1), np.squeeze(img2))
else:
raise ValueError('Wrong input image dimensions.') | calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255] |
2,822 | import os
import math
import random
import numpy as np
import torch
import cv2
from torchvision.utils import make_grid
from datetime import datetime
def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
if (scale < 1) and (antialiasing):
# Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
kernel_width = kernel_width / scale
# Output-space coordinates
x = torch.linspace(1, out_length, out_length)
# Input-space coordinates. Calculate the inverse mapping such that 0.5
# in output space maps to 0.5 in input space, and 0.5+scale in output
# space maps to 1.5 in input space.
u = x / scale + 0.5 * (1 - 1 / scale)
# What is the left-most pixel that can be involved in the computation?
left = torch.floor(u - kernel_width / 2)
# What is the maximum number of pixels that can be involved in the
# computation? Note: it's OK to use an extra pixel here; if the
# corresponding weights are all zero, it will be eliminated at the end
# of this function.
P = math.ceil(kernel_width) + 2
# The indices of the input pixels involved in computing the k-th output
# pixel are in row k of the indices matrix.
indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
1, P).expand(out_length, P)
# The weights used to compute the k-th output pixel are in row k of the
# weights matrix.
distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
# apply cubic kernel
if (scale < 1) and (antialiasing):
weights = scale * cubic(distance_to_center * scale)
else:
weights = cubic(distance_to_center)
# Normalize the weights matrix so that each row sums to 1.
weights_sum = torch.sum(weights, 1).view(out_length, 1)
weights = weights / weights_sum.expand(out_length, P)
# If a column in weights is all zero, get rid of it. only consider the first and last column.
weights_zero_tmp = torch.sum((weights == 0), 0)
if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
indices = indices.narrow(1, 1, P - 2)
weights = weights.narrow(1, 1, P - 2)
if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
indices = indices.narrow(1, 0, P - 2)
weights = weights.narrow(1, 0, P - 2)
weights = weights.contiguous()
indices = indices.contiguous()
sym_len_s = -indices.min() + 1
sym_len_e = indices.max() - in_length
indices = indices + sym_len_s - 1
return weights, indices, int(sym_len_s), int(sym_len_e)
def imresize(img, scale, antialiasing=True):
# Now the scale should be the same for H and W
# input: img: pytorch tensor, CHW or HW [0,1]
# output: CHW or HW [0,1] w/o round
need_squeeze = True if img.dim() == 2 else False
if need_squeeze:
img.unsqueeze_(0)
in_C, in_H, in_W = img.size()
out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
kernel_width = 4
kernel = 'cubic'
# Return the desired dimension order for performing the resize. The
# strategy is to perform the resize first along the dimension with the
# smallest scale factor.
# Now we do not support this.
# get weights and indices
weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
in_H, out_H, scale, kernel, kernel_width, antialiasing)
weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
in_W, out_W, scale, kernel, kernel_width, antialiasing)
# process H dimension
# symmetric copying
img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
sym_patch = img[:, :sym_len_Hs, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
sym_patch = img[:, -sym_len_He:, :]
inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(1, inv_idx)
img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
out_1 = torch.FloatTensor(in_C, out_H, in_W)
kernel_width = weights_H.size(1)
for i in range(out_H):
idx = int(indices_H[i][0])
for j in range(out_C):
out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
# process W dimension
# symmetric copying
out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
sym_patch = out_1[:, :, :sym_len_Ws]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
sym_patch = out_1[:, :, -sym_len_We:]
inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
sym_patch_inv = sym_patch.index_select(2, inv_idx)
out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
out_2 = torch.FloatTensor(in_C, out_H, out_W)
kernel_width = weights_W.size(1)
for i in range(out_W):
idx = int(indices_W[i][0])
for j in range(out_C):
out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
if need_squeeze:
out_2.squeeze_()
return out_2 | null |
2,829 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
The provided code snippet includes necessary dependencies for implementing the `degradation_bsrgan` function. Write a Python function `def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None)` to solve the following problem:
This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
Here is the function:
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
hq = img.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
img = util.imresize_np(img, 1 / 2, True)
img = np.clip(img, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_blur(img, sf=sf)
elif i == 2:
a, b = img.shape[1], img.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
elif i == 3:
# downsample3
img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
img = add_JPEG_noise(img)
elif i == 6:
# add processed camera sensor noise
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
return img, hq | This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
2,830 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def shift_pixel(x, sf, upper_left=True):
"""shift pixel for super-resolution with different scale factors
Args:
x: WxHxC or WxH
sf: scale factor
upper_left: shift direction
"""
h, w = x.shape[:2]
shift = (sf - 1) * 0.5
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
if upper_left:
x1 = xv + shift
y1 = yv + shift
else:
x1 = xv - shift
y1 = yv - shift
x1 = np.clip(x1, 0, w - 1)
y1 = np.clip(y1, 0, h - 1)
if x.ndim == 2:
x = interp2d(xv, yv, x)(x1, y1)
if x.ndim == 3:
for i in range(x.shape[-1]):
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
return x
def fspecial(filter_type, *args, **kwargs):
'''
python code from:
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
'''
if filter_type == 'gaussian':
return fspecial_gaussian(*args, **kwargs)
if filter_type == 'laplacian':
return fspecial_laplacian(*args, **kwargs)
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
The provided code snippet includes necessary dependencies for implementing the `degradation_bsrgan_variant` function. Write a Python function `def degradation_bsrgan_variant(image, sf=4, isp_model=None)` to solve the following problem:
This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
Here is the function:
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
"""
This is the degradation model of BSRGAN from the paper
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
----------
sf: scale factor
isp_model: camera ISP model
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
image = util.uint2single(image)
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
sf_ori = sf
h1, w1 = image.shape[:2]
image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = image.shape[:2]
hq = image.copy()
if sf == 4 and random.random() < scale2_prob: # downsample1
if np.random.rand() < 0.5:
image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
image = util.imresize_np(image, 1 / 2, True)
image = np.clip(image, 0.0, 1.0)
sf = 2
shuffle_order = random.sample(range(7), 7)
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
if idx1 > idx2: # keep downsample3 last
shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
for i in shuffle_order:
if i == 0:
image = add_blur(image, sf=sf)
elif i == 1:
image = add_blur(image, sf=sf)
elif i == 2:
a, b = image.shape[1], image.shape[0]
# downsample2
if random.random() < 0.75:
sf1 = random.uniform(1, 2 * sf)
image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
interpolation=random.choice([1, 2, 3]))
else:
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
elif i == 3:
# downsample3
image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
image = np.clip(image, 0.0, 1.0)
elif i == 4:
# add Gaussian noise
image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
elif i == 5:
# add JPEG noise
if random.random() < jpeg_prob:
image = add_JPEG_noise(image)
# elif i == 6:
# # add processed camera sensor noise
# if random.random() < isp_prob and isp_model is not None:
# with torch.no_grad():
# img, hq = isp_model.forward(img.copy(), hq)
# add final JPEG compression noise
image = add_JPEG_noise(image)
image = util.single2uint(image)
example = {"image":image}
return example | This is the degradation model of BSRGAN from the paper "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution" ---------- sf: scale factor isp_model: camera ISP model Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
2,831 | import numpy as np
import cv2
import torch
from functools import partial
import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth
import albumentations
import ldm.modules.image_degradation.utils_image as util
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
"""USM sharpening. borrowed from real-ESRGAN
Input image: I; Blurry image: B.
1. K = I + weight * (I - B)
2. Mask = 1 if abs(I - B) > threshold, else: 0
3. Blur mask:
4. Out = Mask * K + (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)
K = img + weight * residual
K = np.clip(K, 0, 1)
return soft_mask * K + (1 - soft_mask) * img
def add_blur(img, sf=4):
wd2 = 4.0 + sf
wd = 2.0 + 0.2 * sf
if random.random() < 0.5:
l1 = wd2 * random.random()
l2 = wd2 * random.random()
k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
else:
k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
return img
def add_resize(img, sf=4):
rnum = np.random.rand()
if rnum > 0.8: # up
sf1 = random.uniform(1, 2)
elif rnum < 0.7: # down
sf1 = random.uniform(0.5 / sf, 1)
else:
sf1 = 1.0
img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
img = np.clip(img, 0.0, 1.0)
return img
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
def add_Poisson_noise(img):
img = np.clip((img * 255.0).round(), 0, 255) / 255.
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
if random.random() < 0.5:
img = np.random.poisson(img * vals).astype(np.float32) / vals
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
def random_crop(lq, hq, sf=4, lq_patchsize=64):
h, w = lq.shape[:2]
rnd_h = random.randint(0, h - lq_patchsize)
rnd_w = random.randint(0, w - lq_patchsize)
lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
return lq, hq
The provided code snippet includes necessary dependencies for implementing the `degradation_bsrgan_plus` function. Write a Python function `def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None)` to solve the following problem:
This is an extended degradation model by combining the degradation models of BSRGAN and Real-ESRGAN ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor use_shuffle: the degradation shuffle use_sharp: sharpening the img Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
Here is the function:
def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
"""
This is an extended degradation model by combining
the degradation models of BSRGAN and Real-ESRGAN
----------
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
sf: scale factor
use_shuffle: the degradation shuffle
use_sharp: sharpening the img
Returns
-------
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
"""
h1, w1 = img.shape[:2]
img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
h, w = img.shape[:2]
if h < lq_patchsize * sf or w < lq_patchsize * sf:
raise ValueError(f'img size ({h1}X{w1}) is too small!')
if use_sharp:
img = add_sharpening(img)
hq = img.copy()
if random.random() < shuffle_prob:
shuffle_order = random.sample(range(13), 13)
else:
shuffle_order = list(range(13))
# local shuffle for noise, JPEG is always the last one
shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
for i in shuffle_order:
if i == 0:
img = add_blur(img, sf=sf)
elif i == 1:
img = add_resize(img, sf=sf)
elif i == 2:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 3:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 4:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 5:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
elif i == 6:
img = add_JPEG_noise(img)
elif i == 7:
img = add_blur(img, sf=sf)
elif i == 8:
img = add_resize(img, sf=sf)
elif i == 9:
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
elif i == 10:
if random.random() < poisson_prob:
img = add_Poisson_noise(img)
elif i == 11:
if random.random() < speckle_prob:
img = add_speckle_noise(img)
elif i == 12:
if random.random() < isp_prob and isp_model is not None:
with torch.no_grad():
img, hq = isp_model.forward(img.copy(), hq)
else:
print('check the shuffle!')
# resize to desired size
img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
interpolation=random.choice([1, 2, 3]))
# add final JPEG compression noise
img = add_JPEG_noise(img)
# random crop
img, hq = random_crop(img, hq, sf, lq_patchsize)
return img, hq | This is an extended degradation model by combining the degradation models of BSRGAN and Real-ESRGAN ---------- img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf) sf: scale factor use_shuffle: the degradation shuffle use_sharp: sharpening the img Returns ------- img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1] hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1] |
2,832 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def exists(val):
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d | null |
2,833 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def always(val):
def inner(*args, **kwargs):
return val
return inner | null |
2,834 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def not_equals(val):
def inner(x):
return x != val
return inner | null |
2,835 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def equals(val):
def inner(x):
return x == val
return inner | null |
2,836 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def max_neg_value(tensor):
return -torch.finfo(tensor.dtype).max | null |
2,837 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def pick_and_pop(keys, d):
values = list(map(lambda key: d.pop(key), keys))
return dict(zip(keys, values)) | null |
2,838 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def group_dict_by_key(cond, d):
return_val = [dict(), dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def string_begins_with(prefix, str):
return str.startswith(prefix)
def group_by_key_prefix(prefix, d):
return group_dict_by_key(partial(string_begins_with, prefix), d) | null |
2,839 | import torch
from torch import nn, einsum
import torch.nn.functional as F
from functools import partial
from inspect import isfunction
from collections import namedtuple
from einops import rearrange, repeat, reduce
def group_dict_by_key(cond, d):
return_val = [dict(), dict()]
for key in d.keys():
match = bool(cond(key))
ind = int(not match)
return_val[ind][key] = d[key]
return (*return_val,)
def string_begins_with(prefix, str):
return str.startswith(prefix)
def groupby_prefix_and_trim(prefix, d):
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
return kwargs_without_prefix, kwargs | null |
2,840 | import torch
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `normal_kl` function. Write a Python function `def normal_kl(mean1, logvar1, mean2, logvar2)` to solve the following problem:
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases.
Here is the function:
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().
logvar1, logvar2 = [
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ torch.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
) | source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other use cases. |
2,841 | import torch
from torch import nn
from ldm.data.personalized import per_img_token_list
from transformers import CLIPTokenizer
from functools import partial
def get_clip_token_for_string(tokenizer, string):
batch_encoding = tokenizer(string, truncation=True, max_length=77, return_length=True,
return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
tokens = batch_encoding["input_ids"]
assert torch.count_nonzero(tokens - 49407) == 2, f"String '{string}' maps to more than a single token. Please use another string"
return tokens[0, 1] | null |
2,842 | import torch
from torch import nn
from ldm.data.personalized import per_img_token_list
from transformers import CLIPTokenizer
from functools import partial
def get_bert_token_for_string(tokenizer, string):
token = tokenizer(string)
assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
token = token[0, 1]
return token | null |
2,843 | import torch
from torch import nn
from ldm.data.personalized import per_img_token_list
from transformers import CLIPTokenizer
from functools import partial
def get_embedding_for_clip_token(embedder, token):
return embedder(token.unsqueeze(0))[0, 0] | null |
2,844 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def log_txt_as_img(wh, xc, size=10):
# wh a tuple of (width, height)
# xc a list of captions to plot
b = len(xc)
txts = list()
for bi in range(b):
txt = Image.new("RGB", wh, color="white")
draw = ImageDraw.Draw(txt)
font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
nc = int(40 * (wh[0] / 256))
lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
try:
draw.text((0, 0), lines, fill="black", font=font)
except UnicodeEncodeError:
print("Cant encode string for logging. Skipping.")
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
txts = np.stack(txts)
txts = torch.tensor(txts)
return txts | null |
2,845 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def ismap(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] > 3) | null |
2,846 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def isimage(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) | null |
2,847 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
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:
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all non-batch dimensions.
Here is the function:
def mean_flat(tensor):
"""
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape)))) | https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all non-batch dimensions. |
2,848 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
print(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
return total_params | null |
2,849 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def instantiate_from_config_sr(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())) | null |
2,850 | import importlib
import torch
import numpy as np
from collections import abc
from einops import rearrange
from functools import partial
import multiprocessing as mp
from threading import Thread
from queue import Queue
from inspect import isfunction
from PIL import Image, ImageDraw, ImageFont
def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
# create dummy dataset instance
# run prefetching
if idx_to_fn:
res = func(data, worker_id=idx)
else:
res = func(data)
Q.put([idx, res])
Q.put("Done")
def parallel_data_prefetch(
func: callable, data, n_proc, target_data_type="ndarray", cpu_intensive=True, use_worker_id=False
):
# if target_data_type not in ["ndarray", "list"]:
# raise ValueError(
# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
# )
if isinstance(data, np.ndarray) and target_data_type == "list":
raise ValueError("list expected but function got ndarray.")
elif isinstance(data, abc.Iterable):
if isinstance(data, dict):
print(
f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
)
data = list(data.values())
if target_data_type == "ndarray":
data = np.asarray(data)
else:
data = list(data)
else:
raise TypeError(
f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
)
if cpu_intensive:
Q = mp.Queue(1000)
proc = mp.Process
else:
Q = Queue(1000)
proc = Thread
# spawn processes
if target_data_type == "ndarray":
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(np.array_split(data, n_proc))
]
else:
step = (
int(len(data) / n_proc + 1)
if len(data) % n_proc != 0
else int(len(data) / n_proc)
)
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate(
[data[i: i + step] for i in range(0, len(data), step)]
)
]
processes = []
for i in range(n_proc):
p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
processes += [p]
# start processes
print(f"Start prefetching...")
import time
start = time.time()
gather_res = [[] for _ in range(n_proc)]
try:
for p in processes:
p.start()
k = 0
while k < n_proc:
# get result
res = Q.get()
if res == "Done":
k += 1
else:
gather_res[res[0]] = res[1]
except Exception as e:
print("Exception: ", e)
for p in processes:
p.terminate()
raise e
finally:
for p in processes:
p.join()
print(f"Prefetching complete. [{time.time() - start} sec.]")
if target_data_type == 'ndarray':
if not isinstance(gather_res[0], np.ndarray):
return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
# order outputs
return np.concatenate(gather_res, axis=0)
elif target_data_type == 'list':
out = []
for r in gather_res:
out.extend(r)
return out
else:
return gather_res | null |
2,851 | import os, yaml, pickle, shutil, tarfile, glob
import cv2
import albumentations
import PIL
import numpy as np
import torchvision.transforms.functional as TF
from omegaconf import OmegaConf
from functools import partial
from PIL import Image
from tqdm import tqdm
from torch.utils.data import Dataset, Subset
import taming.data.utils as tdu
from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
from taming.data.imagenet import ImagePaths
from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
def synset2idx(path_to_yaml="data/index_synset.yaml"):
with open(path_to_yaml) as f:
di2s = yaml.load(f)
return dict((v,k) for k,v in di2s.items()) | null |
2,852 | import numpy as np
import torch as th
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. |
2,853 | import os
import torch
import pytorch_lightning as pl
from omegaconf import OmegaConf
from torch.nn import functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from copy import deepcopy
from einops import rearrange
from glob import glob
from natsort import natsorted
from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel
from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config
The provided code snippet includes necessary dependencies for implementing the `disabled_train` function. Write a Python function `def disabled_train(self, mode=True)` to solve the following problem:
Overwrite model.train with this function to make sure train/eval mode does not change anymore.
Here is the function:
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
2,854 | import torch
import torch.nn as nn
import os
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
The provided code snippet includes necessary dependencies for implementing the `disabled_train` function. Write a Python function `def disabled_train(self, mode=True)` to solve the following problem:
Overwrite model.train with this function to make sure train/eval mode does not change anymore.
Here is the function:
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
2,855 | import torch
import torch.nn as nn
import os
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2 | null |
2,856 | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.transforms import paired_random_crop, triplet_random_crop
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
import random
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import make_ddim_timesteps
import copy
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def torch2img(input):
input_ = input[0]
input_ = input_.permute(1,2,0)
input_ = input_.data.cpu().numpy()
input_ = (input_ + 1.0) / 2
cv2.imwrite('./test.png', input_[:,:,::-1]*255.0) | null |
2,857 | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.transforms import paired_random_crop, triplet_random_crop
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
import random
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import make_ddim_timesteps
import copy
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def cal_pca_components(input, n_components=3):
pca = PCA(n_components=n_components)
c, h, w = input.size()
pca_data = input.permute(1,2,0)
pca_data = pca_data.reshape(h*w, c)
pca_data = pca.fit_transform(pca_data.data.cpu().numpy())
pca_data = pca_data.reshape((h, w, n_components))
return pca_data | null |
2,858 | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.transforms import paired_random_crop, triplet_random_crop
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
import random
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import make_ddim_timesteps
import copy
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def visualize_fea(save_path, fea_img):
fig = plt.figure(figsize = (fea_img.shape[1]/10, fea_img.shape[0]/10)) # Your image (W)idth and (H)eight in inches
plt.subplots_adjust(left = 0, right = 1.0, top = 1.0, bottom = 0)
im = plt.imshow(fea_img, vmin=0.0, vmax=1.0, cmap='jet', aspect='auto') # Show the image
plt.savefig(save_path)
plt.clf() | null |
2,859 | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.transforms import paired_random_crop, triplet_random_crop
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
import random
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import make_ddim_timesteps
import copy
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
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. |
2,860 | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.transforms import paired_random_crop, triplet_random_crop
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
import random
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import make_ddim_timesteps
import copy
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
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. |
2,861 | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.transforms import paired_random_crop, triplet_random_crop
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
import random
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import make_ddim_timesteps
import copy
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
The provided code snippet includes necessary dependencies for implementing the `disabled_train` function. Write a Python function `def disabled_train(self, mode=True)` to solve the following problem:
Overwrite model.train with this function to make sure train/eval mode does not change anymore.
Here is the function:
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self | Overwrite model.train with this function to make sure train/eval mode does not change anymore. |
2,862 | import torch
import torch.nn as nn
import numpy as np
import pytorch_lightning as pl
from torch.optim.lr_scheduler import LambdaLR
from einops import rearrange, repeat
from contextlib import contextmanager
from functools import partial
from tqdm import tqdm
from torchvision.utils import make_grid
from pytorch_lightning.utilities.distributed import rank_zero_only
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
from ldm.modules.ema import LitEma
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
from ldm.models.diffusion.ddim import DDIMSampler
from basicsr.utils import DiffJPEG, USMSharp
from basicsr.utils.img_process_util import filter2D
from basicsr.data.transforms import paired_random_crop, triplet_random_crop
from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt, random_add_speckle_noise_pt, random_add_saltpepper_noise_pt, bivariate_Gaussian
import random
import torch.nn.functional as F
from ldm.modules.diffusionmodules.util import make_ddim_timesteps
import copy
import os
import cv2
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
def uniform_on_device(r1, r2, shape, device):
return (r1 - r2) * torch.rand(*shape, device=device) + r2 | null |
2,863 | import torch
import numpy as np
from tqdm import tqdm
from functools import partial
from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
extract_into_tensor
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
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. |
2,864 | from huggingface_hub import model_info
from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
from accelerate import init_empty_weights
from accelerate.commands.utils import CustomArgumentParser
from accelerate.utils import (
calculate_maximum_sizes,
convert_bytes,
is_timm_available,
is_transformers_available,
)
def estimate_command(args):
class CustomArgumentParser(argparse.ArgumentParser):
def add_argument(self, *args, **kwargs):
def add_argument_group(self, *args, **kwargs):
def estimate_command_parser(subparsers=None):
if subparsers is not None:
parser = subparsers.add_parser("estimate-memory")
else:
parser = CustomArgumentParser(description="Model size estimator for fitting a model onto CUDA memory.")
parser.add_argument("model_name", type=str, help="The model name on the Hugging Face Hub.")
parser.add_argument(
"--library_name",
type=str,
help="The library the model has an integration with, such as `transformers`, needed only if this information is not stored on the Hub.",
choices=["timm", "transformers"],
)
parser.add_argument(
"--dtypes",
type=str,
nargs="+",
default=["float32", "float16", "int8", "int4"],
help="The dtypes to use for the model, must be one (or many) of `float32`, `float16`, `int8`, and `int4`",
choices=["float32", "float16", "int8", "int4"],
)
parser.add_argument(
"--trust_remote_code",
action="store_true",
help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. This flag
should only be used for repositories you trust and in which you have read the code, as it will execute
code present on the Hub on your local machine.""",
)
if subparsers is not None:
parser.set_defaults(func=estimate_command)
return parser | null |
2,865 | import os
import string
import sys
ARROW_KEY_FLAG = 1 << 8
KEYMAP = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
KEYMAP["arrow_begin"] = KEYMAP["up"]
KEYMAP["arrow_end"] = KEYMAP["left"]
def get_raw_chars():
"Gets raw characters from inputs"
if os.name == "nt":
import msvcrt
encoding = "mbcs"
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(WIN_CH_BUFFER) == 0:
# Read the keystroke
ch = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
ch2 = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
chx = chr(WIN_KEYMAP[ch2])
WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"]))
WIN_CH_BUFFER.append(chx)
if ord(chx) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126))
ch = chr(KEYMAP["esc"])
except KeyError:
ch = ch2[1]
else:
ch = ch.decode(encoding)
else:
ch = WIN_CH_BUFFER.pop(0)
elif os.name == "posix":
import termios
import tty
fd = sys.stdin.fileno()
old_settings = termios.tcgetattr(fd)
try:
tty.setraw(fd)
ch = sys.stdin.read(1)
finally:
termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
return ch
The provided code snippet includes necessary dependencies for implementing the `get_character` function. Write a Python function `def get_character()` to solve the following problem:
Gets a character from the keyboard and returns the key code
Here is the function:
def get_character():
"Gets a character from the keyboard and returns the key code"
char = get_raw_chars()
if ord(char) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(char) == KEYMAP["esc"]:
combo = get_raw_chars()
if ord(combo) == KEYMAP["mod_int"]:
key = get_raw_chars()
if ord(key) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(key) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(key) + ARROW_KEY_FLAG)
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"] | Gets a character from the keyboard and returns the key code |
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