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"""This module contains simple helper functions """
from __future__ import print_function
import torch
import numbers
import torch.nn as nn
import torchvision
import torch.nn.functional as F
import math
import numpy as np
from PIL import Image
import os
import importlib
import argparse
from argparse import Namespace
from sklearn.decomposition import PCA as PCA
def normalize(v):
if type(v) == list:
return [normalize(vv) for vv in v]
return v * torch.rsqrt((torch.sum(v ** 2, dim=1, keepdim=True) + 1e-8))
def slerp(a, b, r):
d = torch.sum(a * b, dim=-1, keepdim=True)
p = r * torch.acos(d * (1 - 1e-4))
c = normalize(b - d * a)
d = a * torch.cos(p) + c * torch.sin(p)
return normalize(d)
def lerp(a, b, r):
if type(a) == list or type(a) == tuple:
return [lerp(aa, bb, r) for aa, bb in zip(a, b)]
return a * (1 - r) + b * r
def madd(a, b, r):
if type(a) == list or type(a) == tuple:
return [madd(aa, bb, r) for aa, bb in zip(a, b)]
return a + b * r
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def copyconf(default_opt, **kwargs):
conf = Namespace(**vars(default_opt))
for key in kwargs:
setattr(conf, key, kwargs[key])
return conf
def find_class_in_module(target_cls_name, module):
target_cls_name = target_cls_name.replace('_', '').lower()
clslib = importlib.import_module(module)
cls = None
for name, clsobj in clslib.__dict__.items():
if name.lower() == target_cls_name:
cls = clsobj
assert cls is not None, "In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module, target_cls_name)
return cls
def tile_images(imgs, picturesPerRow=4):
""" Code borrowed from
https://stackoverflow.com/questions/26521365/cleanly-tile-numpy-array-of-images-stored-in-a-flattened-1d-format/26521997
"""
# Padding
if imgs.shape[0] % picturesPerRow == 0:
rowPadding = 0
else:
rowPadding = picturesPerRow - imgs.shape[0] % picturesPerRow
if rowPadding > 0:
imgs = np.concatenate([imgs, np.zeros((rowPadding, *imgs.shape[1:]), dtype=imgs.dtype)], axis=0)
# Tiling Loop (The conditionals are not necessary anymore)
tiled = []
for i in range(0, imgs.shape[0], picturesPerRow):
tiled.append(np.concatenate([imgs[j] for j in range(i, i + picturesPerRow)], axis=1))
tiled = np.concatenate(tiled, axis=0)
return tiled
# Converts a Tensor into a Numpy array
# |imtype|: the desired type of the converted numpy array
def tensor2im(image_tensor, imtype=np.uint8, normalize=True, tile=2):
if isinstance(image_tensor, list):
image_numpy = []
for i in range(len(image_tensor)):
image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
return image_numpy
if len(image_tensor.shape) == 4:
# transform each image in the batch
images_np = []
for b in range(image_tensor.shape[0]):
one_image = image_tensor[b]
one_image_np = tensor2im(one_image)
images_np.append(one_image_np.reshape(1, *one_image_np.shape))
images_np = np.concatenate(images_np, axis=0)
if tile is not False:
tile = max(min(images_np.shape[0] // 2, 4), 1) if tile is True else tile
images_tiled = tile_images(images_np, picturesPerRow=tile)
return images_tiled
else:
return images_np
if len(image_tensor.shape) == 2:
assert False
#imagce_tensor = image_tensor.unsqueeze(0)
image_numpy = image_tensor.detach().cpu().numpy() if type(image_tensor) is not np.ndarray else image_tensor
if normalize:
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
else:
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
image_numpy = np.clip(image_numpy, 0, 255)
if image_numpy.shape[2] == 1:
image_numpy = np.repeat(image_numpy, 3, axis=2)
return image_numpy.astype(imtype)
def toPILImage(images, tile=None):
if isinstance(images, list):
if all(['tensor' in str(type(image)).lower() for image in images]):
return toPILImage(torch.cat([im.cpu() for im in images], dim=0), tile)
return [toPILImage(image, tile=tile) for image in images]
if 'ndarray' in str(type(images)).lower():
return toPILImage(torch.from_numpy(images))
assert 'tensor' in str(type(images)).lower(), "input of type %s cannot be handled." % str(type(images))
if tile is None:
max_width = 2560
tile = min(images.size(0), int(max_width / images.size(3)))
return Image.fromarray(tensor2im(images, tile=tile))
def diagnose_network(net, name='network'):
"""Calculate and print the mean of average absolute(gradients)
Parameters:
net (torch network) -- Torch network
name (str) -- the name of the network
"""
mean = 0.0
count = 0
for param in net.parameters():
if param.grad is not None:
mean += torch.mean(torch.abs(param.grad.data))
count += 1
if count > 0:
mean = mean / count
print(name)
print(mean)
def save_image(image_numpy, image_path, aspect_ratio=1.0):
"""Save a numpy image to the disk
Parameters:
image_numpy (numpy array) -- input numpy array
image_path (str) -- the path of the image
"""
image_pil = Image.fromarray(image_numpy)
h, w, _ = image_numpy.shape
if aspect_ratio is None:
pass
elif aspect_ratio > 1.0:
image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
elif aspect_ratio < 1.0:
image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
image_pil.save(image_path)
def print_numpy(x, val=True, shp=False):
"""Print the mean, min, max, median, std, and size of a numpy array
Parameters:
val (bool) -- if print the values of the numpy array
shp (bool) -- if print the shape of the numpy array
"""
x = x.astype(np.float64)
if shp:
print('shape,', x.shape)
if val:
x = x.flatten()
print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
def mkdirs(paths):
"""create empty directories if they don't exist
Parameters:
paths (str list) -- a list of directory paths
"""
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
"""create a single empty directory if it didn't exist
Parameters:
path (str) -- a single directory path
"""
if not os.path.exists(path):
os.makedirs(path)
def visualize_spatial_code(sp):
device = sp.device
#sp = (sp - sp.min()) / (sp.max() - sp.min() + 1e-7)
if sp.size(1) <= 2:
sp = sp.repeat([1, 3, 1, 1])[:, :3, :, :]
if sp.size(1) == 3:
pass
else:
sp = sp.detach().cpu().numpy()
X = np.transpose(sp, (0, 2, 3, 1))
B, H, W = X.shape[0], X.shape[1], X.shape[2]
X = np.reshape(X, (-1, X.shape[3]))
X = X - X.mean(axis=0, keepdims=True)
try:
Z = PCA(3).fit_transform(X)
except ValueError:
print("Running PCA on the structure code has failed.")
print("This is likely a bug of scikit-learn in version 0.18.1.")
print("https://stackoverflow.com/a/42764378")
print("The visualization of the structure code on visdom won't work.")
return torch.zeros(B, 3, H, W, device=device)
sp = np.transpose(np.reshape(Z, (B, H, W, -1)), (0, 3, 1, 2))
sp = (sp - sp.min()) / (sp.max() - sp.min()) * 2 - 1
sp = torch.from_numpy(sp).to(device)
return sp
def blank_tensor(w, h):
return torch.ones(1, 3, h, w)
class RandomSpatialTransformer:
def __init__(self, opt, bs):
self.opt = opt
#self.resample_transformation(bs)
def create_affine_transformation(self, ref, rot, sx, sy, tx, ty):
return torch.stack([-ref * sx * torch.cos(rot), -sy * torch.sin(rot), tx,
-ref * sx * torch.sin(rot), sy * torch.cos(rot), ty], axis=1)
def resample_transformation(self, bs, device, reflection=None, rotation=None, scale=None, translation=None):
dev = device
zero = torch.zeros((bs), device=dev)
if reflection is None:
#if "ref" in self.opt.random_transformation_mode:
ref = torch.round(torch.rand((bs), device=dev)) * 2 - 1
#else:
# ref = 1.0
else:
ref = reflection
if rotation is None:
#if "rot" in self.opt.random_transformation_mode:
max_rotation = 30 * math.pi / 180
rot = torch.rand((bs), device=dev) * (2 * max_rotation) - max_rotation
#else:
# rot = 0.0
else:
rot = rotation
if scale is None:
#if "scale" in self.opt.random_transformation_mode:
min_scale = 1.0
max_scale = 1.0
sx = torch.rand((bs), device=dev) * (max_scale - min_scale) + min_scale
sy = torch.rand((bs), device=dev) * (max_scale - min_scale) + min_scale
#else:
# sx, sy = 1.0, 1.0
else:
sx, sy = scale
tx, ty = zero, zero
A = torch.stack([ref * sx * torch.cos(rot), -sy * torch.sin(rot), tx,
ref * sx * torch.sin(rot), sy * torch.cos(rot), ty], axis=1)
return A.view(bs, 2, 3)
def forward_transform(self, x, size):
if type(x) == list:
return [self.forward_transform(xx) for xx in x]
affine_param = self.resample_transformation(x.size(0), x.device)
affine_grid = F.affine_grid(affine_param, (x.size(0), x.size(1), size[0], size[1]), align_corners=False)
x = F.grid_sample(x, affine_grid, padding_mode='reflection', align_corners=False)
return x
def apply_random_crop(x, target_size, scale_range, num_crops=1, return_rect=False):
# build grid
B = x.size(0) * num_crops
flip = torch.round(torch.rand(B, 1, 1, 1, device=x.device)) * 2 - 1.0
unit_grid_x = torch.linspace(-1.0, 1.0, target_size, device=x.device)[np.newaxis, np.newaxis, :, np.newaxis].repeat(B, target_size, 1, 1)
unit_grid_y = unit_grid_x.transpose(1, 2)
unit_grid = torch.cat([unit_grid_x * flip, unit_grid_y], dim=3)
#crops = []
x = x.unsqueeze(1).expand(-1, num_crops, -1, -1, -1).flatten(0, 1)
#for i in range(num_crops):
scale = torch.rand(B, 1, 1, 2, device=x.device) * (scale_range[1] - scale_range[0]) + scale_range[0]
offset = (torch.rand(B, 1, 1, 2, device=x.device) * 2 - 1) * (1 - scale)
sampling_grid = unit_grid * scale + offset
crop = F.grid_sample(x, sampling_grid, align_corners=False)
#crops.append(crop)
#crop = torch.stack(crops, dim=1)
crop = crop.view(B // num_crops, num_crops, crop.size(1), crop.size(2), crop.size(3))
return crop
def five_crop_noresize(A):
Y, X = A.size(2) // 3, A.size(3) // 3
H, W = Y * 2, X * 2
return torch.stack([A[:, :, 0:0+H, 0:0+W],
A[:, :, Y:Y+H, 0:0+W],
A[:, :, Y:Y+H, X:X+W],
A[:, :, 0:0+H, X:X+W],
A[:, :, Y//2:Y//2+H, X//2:X//2+W]],
dim=1) # return 5-dim tensor
def random_crop_noresize(A, crop_size):
offset_y = np.random.randint(A.size(2) - crop_size[0])
offset_x = np.random.randint(A.size(3) - crop_size[1])
return A[:, :, offset_y:offset_y + crop_size[0], offset_x:offset_x + crop_size[1]], (offset_y, offset_x)
def random_crop_with_resize(A, crop_size):
#size_y = np.random.randint(crop_size[0], A.size(2) + 1)
#size_x = np.random.randint(crop_size[1], A.size(3) + 1)
#size_y, size_x = crop_size
size_y = max(crop_size[0], np.random.randint(A.size(2) // 3, A.size(2) + 1))
size_x = max(crop_size[1], np.random.randint(A.size(3) // 3, A.size(3) + 1))
offset_y = np.random.randint(A.size(2) - size_y + 1)
offset_x = np.random.randint(A.size(3) - size_x + 1)
crop_rect = (offset_y, offset_x, size_y, size_x)
resized = crop_with_resize(A, crop_rect, crop_size)
#print('resized %s to %s' % (A.size(), resized.size()))
return resized, crop_rect
def crop_with_resize(A, crop_rect, return_size):
offset_y, offset_x, size_y, size_x = crop_rect
crop = A[:, :, offset_y:offset_y + size_y, offset_x:offset_x + size_x]
resized = F.interpolate(crop, size=return_size, mode='bilinear', align_corners=False)
#print('resized %s to %s' % (A.size(), resized.size()))
return resized
def compute_similarity_logit(x, y, p=1, compute_interdistances=True):
def compute_dist(x, y, p):
if p == 2:
return ((x - y) ** 2).sum(dim=-1).sqrt()
else:
return (x - y).abs().sum(dim=-1)
C = x.shape[-1]
if len(x.shape) == 2:
if compute_interdistances:
dist = torch.cdist(x[None, :, :], y[None, :, :], p)[0]
else:
dist = compute_dist(x, y, p)
if len(x.shape) == 3:
if compute_interdistances:
dist = torch.cdist(x, y, p)
else:
dist = compute_dist(x, y, p)
if p == 1:
dist = 1 - dist / math.sqrt(C)
elif p == 2:
dist = 1 - 0.5 * (dist ** 2)
return dist / 0.07
def set_diag_(x, value):
assert x.size(-2) == x.size(-1)
L = x.size(-2)
identity = torch.eye(L, dtype=torch.bool, device=x.device)
identity = identity.view([1] * (len(x.shape) - 2) + [L, L])
x.masked_fill_(identity, value)
def to_numpy(metric_dict):
new_dict = {}
for k, v in metric_dict.items():
if "numpy" not in str(type(v)):
v = v.detach().cpu().mean().numpy()
new_dict[k] = v
return new_dict
def is_custom_kernel_supported():
version_str = str(torch.version.cuda).split(".")
major = version_str[0]
minor = version_str[1]
return int(major) >= 10 and int(minor) >= 1
def shuffle_batch(x):
B = x.size(0)
perm = torch.randperm(B, dtype=torch.long, device=x.device)
return x[perm]
def unravel_index(index, shape):
out = []
for dim in reversed(shape):
out.append(index % dim)
index = index // dim
return tuple(reversed(out))
def quantize_color(x, num=64):
return (x * num / 2).round() * (2 / num)
def resize2d_tensor(x, size_or_tensor_of_size):
if torch.is_tensor(size_or_tensor_of_size):
size = size_or_tensor_of_size.size()
elif isinstance(size_or_tensor_of_size, np.ndarray):
size = size_or_tensor_of_size.shape
else:
size = size_or_tensor_of_size
if isinstance(size, tuple) or isinstance(size, list):
return F.interpolate(x, size[-2:],
mode='bilinear', align_corners=False)
else:
raise ValueError("%s is unrecognized" % str(type(size)))
def correct_resize(t, size, mode=Image.BICUBIC):
device = t.device
t = t.detach().cpu()
resized = []
for i in range(t.size(0)):
one_t = t[i:i+1]
one_image = Image.fromarray(tensor2im(one_t, tile=1)).resize(size, Image.BICUBIC)
resized_t = torchvision.transforms.functional.to_tensor(one_image) * 2 - 1.0
resized.append(resized_t)
return torch.stack(resized, dim=0).to(device)
class GaussianSmoothing(nn.Module):
"""
Apply gaussian smoothing on a
1d, 2d or 3d tensor. Filtering is performed seperately for each channel
in the input using a depthwise convolution.
Arguments:
channels (int, sequence): Number of channels of the input tensors. Output will
have this number of channels as well.
kernel_size (int, sequence): Size of the gaussian kernel.
sigma (float, sequence): Standard deviation of the gaussian kernel.
dim (int, optional): The number of dimensions of the data.
Default value is 2 (spatial).
"""
def __init__(self, channels, kernel_size, sigma, dim=2):
super(GaussianSmoothing, self).__init__()
if isinstance(kernel_size, numbers.Number):
self.pad_size = kernel_size // 2
kernel_size = [kernel_size] * dim
else:
raise NotImplementedError()
if isinstance(sigma, numbers.Number):
sigma = [sigma] * dim
# The gaussian kernel is the product of the
# gaussian function of each dimension.
kernel = 1
meshgrids = torch.meshgrid(
[
torch.arange(size, dtype=torch.float32)
for size in kernel_size
]
)
for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
mean = (size - 1) / 2
kernel *= 1 / (std * math.sqrt(2 * math.pi)) * \
torch.exp(-((mgrid - mean) / std) ** 2 / 2)
# Make sure sum of values in gaussian kernel equals 1.
kernel = kernel / (torch.sum(kernel))
# Reshape to depthwise convolutional weight
kernel = kernel.view(1, 1, *kernel.size())
kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
self.register_buffer('weight', kernel)
self.groups = channels
if dim == 1:
self.conv = F.conv1d
elif dim == 2:
self.conv = F.conv2d
elif dim == 3:
self.conv = F.conv3d
else:
raise RuntimeError(
'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(dim)
)
def forward(self, input):
"""
Apply gaussian filter to input.
Arguments:
input (torch.Tensor): Input to apply gaussian filter on.
Returns:
filtered (torch.Tensor): Filtered output.
"""
x = F.pad(input, [self.pad_size] * 4, mode="reflect")
return self.conv(x, weight=self.weight, groups=self.groups)
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