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"""Define all losses. When possible, as inheriting from nn.Module
To send predictions to target.device
"""
from random import random as rand
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
class GANLoss(nn.Module):
def __init__(
self,
use_lsgan=True,
target_real_label=1.0,
target_fake_label=0.0,
soft_shift=0.0,
flip_prob=0.0,
verbose=0,
):
"""Defines the GAN loss which uses either LSGAN or the regular GAN.
When LSGAN is used, it is basically same as MSELoss,
but it abstracts away the need to create the target label tensor
that has the same size as the input +
* label smoothing: target_real_label=0.75
* label flipping: flip_prob > 0.
source: https://github.com/sangwoomo/instagan/blob
/b67e9008fcdd6c41652f8805f0b36bcaa8b632d6/models/networks.py
Args:
use_lsgan (bool, optional): Use MSE or BCE. Defaults to True.
target_real_label (float, optional): Value for the real target.
Defaults to 1.0.
target_fake_label (float, optional): Value for the fake target.
Defaults to 0.0.
flip_prob (float, optional): Probability of flipping the label
(use for real target in Discriminator only). Defaults to 0.0.
"""
super().__init__()
self.soft_shift = soft_shift
self.verbose = verbose
self.register_buffer("real_label", torch.tensor(target_real_label))
self.register_buffer("fake_label", torch.tensor(target_fake_label))
if use_lsgan:
self.loss = nn.MSELoss()
else:
self.loss = nn.BCEWithLogitsLoss()
self.flip_prob = flip_prob
def get_target_tensor(self, input, target_is_real):
soft_change = torch.FloatTensor(1).uniform_(0, self.soft_shift)
if self.verbose > 0:
print("GANLoss sampled soft_change:", soft_change.item())
if target_is_real:
target_tensor = self.real_label - soft_change
else:
target_tensor = self.fake_label + soft_change
return target_tensor.expand_as(input)
def __call__(self, input, target_is_real, *args, **kwargs):
r = rand()
if isinstance(input, list):
loss = 0
for pred_i in input:
if isinstance(pred_i, list):
pred_i = pred_i[-1]
if r < self.flip_prob:
target_is_real = not target_is_real
target_tensor = self.get_target_tensor(pred_i, target_is_real)
loss_tensor = self.loss(pred_i, target_tensor.to(pred_i.device))
loss += loss_tensor
return loss / len(input)
else:
if r < self.flip_prob:
target_is_real = not target_is_real
target_tensor = self.get_target_tensor(input, target_is_real)
return self.loss(input, target_tensor.to(input.device))
class FeatMatchLoss(nn.Module):
def __init__(self):
super().__init__()
self.criterionFeat = nn.L1Loss()
def __call__(self, pred_real, pred_fake):
# pred_{real, fake} are lists of features
num_D = len(pred_fake)
GAN_Feat_loss = 0.0
for i in range(num_D): # for each discriminator
# last output is the final prediction, so we exclude it
num_intermediate_outputs = len(pred_fake[i]) - 1
for j in range(num_intermediate_outputs): # for each layer output
unweighted_loss = self.criterionFeat(
pred_fake[i][j], pred_real[i][j].detach()
)
GAN_Feat_loss += unweighted_loss / num_D
return GAN_Feat_loss
class CrossEntropy(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.CrossEntropyLoss()
def __call__(self, logits, target):
return self.loss(logits, target.to(logits.device).long())
class TravelLoss(nn.Module):
def __init__(self, eps=1e-12):
super().__init__()
self.eps = eps
def cosine_loss(self, real, fake):
norm_real = torch.norm(real, p=2, dim=1)[:, None]
norm_fake = torch.norm(fake, p=2, dim=1)[:, None]
mat_real = real / norm_real
mat_fake = fake / norm_fake
mat_real = torch.max(mat_real, self.eps * torch.ones_like(mat_real))
mat_fake = torch.max(mat_fake, self.eps * torch.ones_like(mat_fake))
# compute only the diagonal of the matrix multiplication
return torch.einsum("ij, ji -> i", mat_fake, mat_real).sum()
def __call__(self, S_real, S_fake):
self.v_real = []
self.v_fake = []
for i in range(len(S_real)):
for j in range(i):
self.v_real.append((S_real[i] - S_real[j])[None, :])
self.v_fake.append((S_fake[i] - S_fake[j])[None, :])
self.v_real_t = torch.cat(self.v_real, dim=0)
self.v_fake_t = torch.cat(self.v_fake, dim=0)
return self.cosine_loss(self.v_real_t, self.v_fake_t)
class TVLoss(nn.Module):
"""Total Variational Regularization: Penalizes differences in
neighboring pixel values
source:
https://github.com/jxgu1016/Total_Variation_Loss.pytorch/blob/master/TVLoss.py
"""
def __init__(self, tvloss_weight=1):
"""
Args:
TVLoss_weight (int, optional): [lambda i.e. weight for loss]. Defaults to 1.
"""
super(TVLoss, self).__init__()
self.tvloss_weight = tvloss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self._tensor_size(x[:, :, 1:, :])
count_w = self._tensor_size(x[:, :, :, 1:])
h_tv = torch.pow((x[:, :, 1:, :] - x[:, :, : h_x - 1, :]), 2).sum()
w_tv = torch.pow((x[:, :, :, 1:] - x[:, :, :, : w_x - 1]), 2).sum()
return self.tvloss_weight * 2 * (h_tv / count_h + w_tv / count_w) / batch_size
def _tensor_size(self, t):
return t.size()[1] * t.size()[2] * t.size()[3]
class MinentLoss(nn.Module):
"""
Loss for the minimization of the entropy map
Source for version 1: https://github.com/valeoai/ADVENT
Version 2 adds the variance of the entropy map in the computation of the loss
"""
def __init__(self, version=1, lambda_var=0.1):
super().__init__()
self.version = version
self.lambda_var = lambda_var
def __call__(self, pred):
assert pred.dim() == 4
n, c, h, w = pred.size()
entropy_map = -torch.mul(pred, torch.log2(pred + 1e-30)) / np.log2(c)
if self.version == 1:
return torch.sum(entropy_map) / (n * h * w)
else:
entropy_map_demean = entropy_map - torch.sum(entropy_map) / (n * h * w)
entropy_map_squ = torch.mul(entropy_map_demean, entropy_map_demean)
return torch.sum(entropy_map + self.lambda_var * entropy_map_squ) / (
n * h * w
)
class MSELoss(nn.Module):
"""
Creates a criterion that measures the mean squared error
(squared L2 norm) between each element in the input x and target y .
"""
def __init__(self):
super().__init__()
self.loss = nn.MSELoss()
def __call__(self, prediction, target):
return self.loss(prediction, target.to(prediction.device))
class L1Loss(MSELoss):
"""
Creates a criterion that measures the mean absolute error
(MAE) between each element in the input x and target y
"""
def __init__(self):
super().__init__()
self.loss = nn.L1Loss()
class SIMSELoss(nn.Module):
"""Scale invariant MSE Loss"""
def __init__(self):
super(SIMSELoss, self).__init__()
def __call__(self, prediction, target):
d = prediction - target
diff = torch.mean(d * d)
relDiff = torch.mean(d) * torch.mean(d)
return diff - relDiff
class SIGMLoss(nn.Module):
"""loss from MiDaS paper
MiDaS did not specify how the gradients were computed but we use Sobel
filters which approximate the derivative of an image.
"""
def __init__(self, gmweight=0.5, scale=4, device="cuda"):
super(SIGMLoss, self).__init__()
self.gmweight = gmweight
self.sobelx = torch.Tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).to(device)
self.sobely = torch.Tensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]).to(device)
self.scale = scale
def __call__(self, prediction, target):
# get disparities
# align both the prediction and the ground truth to have zero
# translation and unit scale
t_pred = torch.median(prediction)
t_targ = torch.median(target)
s_pred = torch.mean(torch.abs(prediction - t_pred))
s_targ = torch.mean(torch.abs(target - t_targ))
pred = (prediction - t_pred) / s_pred
targ = (target - t_targ) / s_targ
R = pred - targ
# get gradient map with sobel filters
batch_size = prediction.size()[0]
num_pix = prediction.size()[-1] * prediction.size()[-2]
sobelx = (self.sobelx).expand((batch_size, 1, -1, -1))
sobely = (self.sobely).expand((batch_size, 1, -1, -1))
gmLoss = 0 # gradient matching term
for k in range(self.scale):
R_ = F.interpolate(R, scale_factor=1 / 2 ** k)
Rx = F.conv2d(R_, sobelx, stride=1)
Ry = F.conv2d(R_, sobely, stride=1)
gmLoss += torch.sum(torch.abs(Rx) + torch.abs(Ry))
gmLoss = self.gmweight / num_pix * gmLoss
# scale invariant MSE
simseLoss = 0.5 / num_pix * torch.sum(torch.abs(R))
loss = simseLoss + gmLoss
return loss
class ContextLoss(nn.Module):
"""
Masked L1 loss on non-water
"""
def __call__(self, input, target, mask):
return torch.mean(torch.abs(torch.mul((input - target), 1 - mask)))
class ReconstructionLoss(nn.Module):
"""
Masked L1 loss on water
"""
def __call__(self, input, target, mask):
return torch.mean(torch.abs(torch.mul((input - target), mask)))
##################################################################################
# VGG network definition
##################################################################################
# Source: https://github.com/NVIDIA/pix2pixHD
class Vgg19(nn.Module):
def __init__(self, requires_grad=False):
super(Vgg19, self).__init__()
vgg_pretrained_features = models.vgg19(pretrained=True).features
self.slice1 = nn.Sequential()
self.slice2 = nn.Sequential()
self.slice3 = nn.Sequential()
self.slice4 = nn.Sequential()
self.slice5 = nn.Sequential()
for x in range(2):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(2, 7):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(7, 12):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(12, 21):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
for x in range(21, 30):
self.slice5.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h_relu1 = self.slice1(X)
h_relu2 = self.slice2(h_relu1)
h_relu3 = self.slice3(h_relu2)
h_relu4 = self.slice4(h_relu3)
h_relu5 = self.slice5(h_relu4)
out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
return out
# Source: https://github.com/NVIDIA/pix2pixHD
class VGGLoss(nn.Module):
def __init__(self, device):
super().__init__()
self.vgg = Vgg19().to(device).eval()
self.criterion = nn.L1Loss()
self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0]
def forward(self, x, y):
x_vgg, y_vgg = self.vgg(x), self.vgg(y)
loss = 0
for i in range(len(x_vgg)):
loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())
return loss
def get_losses(opts, verbose, device=None):
"""Sets the loss functions to be used by G, D and C, as specified
in the opts and returns a dictionnary of losses:
losses = {
"G": {
"gan": {"a": ..., "t": ...},
"cycle": {"a": ..., "t": ...}
"auto": {"a": ..., "t": ...}
"tasks": {"h": ..., "d": ..., "s": ..., etc.}
},
"D": GANLoss,
"C": ...
}
"""
losses = {
"G": {"a": {}, "p": {}, "tasks": {}},
"D": {"default": {}, "advent": {}},
"C": {},
}
# ------------------------------
# ----- Generator Losses -----
# ------------------------------
# painter losses
if "p" in opts.tasks:
losses["G"]["p"]["gan"] = (
HingeLoss()
if opts.gen.p.loss == "hinge"
else GANLoss(
use_lsgan=False,
soft_shift=opts.dis.soft_shift,
flip_prob=opts.dis.flip_prob,
)
)
losses["G"]["p"]["dm"] = MSELoss()
losses["G"]["p"]["vgg"] = VGGLoss(device)
losses["G"]["p"]["tv"] = TVLoss()
losses["G"]["p"]["context"] = ContextLoss()
losses["G"]["p"]["reconstruction"] = ReconstructionLoss()
losses["G"]["p"]["featmatch"] = FeatMatchLoss()
# depth losses
if "d" in opts.tasks:
if not opts.gen.d.classify.enable:
if opts.gen.d.loss == "dada":
depth_func = DADADepthLoss()
else:
depth_func = SIGMLoss(opts.train.lambdas.G.d.gml)
else:
depth_func = CrossEntropy()
losses["G"]["tasks"]["d"] = depth_func
# segmentation losses
if "s" in opts.tasks:
losses["G"]["tasks"]["s"] = {}
losses["G"]["tasks"]["s"]["crossent"] = CrossEntropy()
losses["G"]["tasks"]["s"]["minent"] = MinentLoss()
losses["G"]["tasks"]["s"]["advent"] = ADVENTAdversarialLoss(
opts, gan_type=opts.dis.s.gan_type
)
# masker losses
if "m" in opts.tasks:
losses["G"]["tasks"]["m"] = {}
losses["G"]["tasks"]["m"]["bce"] = nn.BCEWithLogitsLoss()
if opts.gen.m.use_minent_var:
losses["G"]["tasks"]["m"]["minent"] = MinentLoss(
version=2, lambda_var=opts.train.lambdas.advent.ent_var
)
else:
losses["G"]["tasks"]["m"]["minent"] = MinentLoss()
losses["G"]["tasks"]["m"]["tv"] = TVLoss()
losses["G"]["tasks"]["m"]["advent"] = ADVENTAdversarialLoss(
opts, gan_type=opts.dis.m.gan_type
)
losses["G"]["tasks"]["m"]["gi"] = GroundIntersectionLoss()
# ----------------------------------
# ----- Discriminator Losses -----
# ----------------------------------
if "p" in opts.tasks:
losses["D"]["p"] = losses["G"]["p"]["gan"]
if "m" in opts.tasks or "s" in opts.tasks:
losses["D"]["advent"] = ADVENTAdversarialLoss(opts)
return losses
class GroundIntersectionLoss(nn.Module):
"""
Penalize areas in ground seg but not in flood mask
"""
def __call__(self, pred, pseudo_ground):
return torch.mean(1.0 * ((pseudo_ground - pred) > 0.5))
def prob_2_entropy(prob):
"""
convert probabilistic prediction maps to weighted self-information maps
"""
n, c, h, w = prob.size()
return -torch.mul(prob, torch.log2(prob + 1e-30)) / np.log2(c)
class CustomBCELoss(nn.Module):
"""
The first argument is a tensor and the second argument is an int.
There is no need to take sigmoid before calling this function.
"""
def __init__(self):
super().__init__()
self.loss = nn.BCEWithLogitsLoss()
def __call__(self, prediction, target):
return self.loss(
prediction,
torch.FloatTensor(prediction.size())
.fill_(target)
.to(prediction.get_device()),
)
class ADVENTAdversarialLoss(nn.Module):
"""
The class is for calculating the advent loss.
It is used to indirectly shrink the domain gap between sim and real
_call_ function:
prediction: torch.tensor with shape of [bs,c,h,w]
target: int; domain label: 0 (sim) or 1 (real)
discriminator: the discriminator model tells if a tensor is from sim or real
output: the loss value of GANLoss
"""
def __init__(self, opts, gan_type="GAN"):
super().__init__()
self.opts = opts
if gan_type == "GAN":
self.loss = CustomBCELoss()
elif gan_type == "WGAN" or "WGAN_gp" or "WGAN_norm":
self.loss = lambda x, y: -torch.mean(y * x + (1 - y) * (1 - x))
else:
raise NotImplementedError
def __call__(self, prediction, target, discriminator, depth_preds=None):
"""
Compute the GAN loss from the Advent Discriminator given
normalized (softmaxed) predictions (=pixel-wise class probabilities),
and int labels (target).
Args:
prediction (torch.Tensor): pixel-wise probability distribution over classes
target (torch.Tensor): pixel wise int target labels
discriminator (torch.nn.Module): Discriminator to get the loss
Returns:
torch.Tensor: float 0-D loss
"""
d_out = prob_2_entropy(prediction)
if depth_preds is not None:
d_out = d_out * depth_preds
d_out = discriminator(d_out)
if self.opts.dis.m.architecture == "OmniDiscriminator":
d_out = multiDiscriminatorAdapter(d_out, self.opts)
loss_ = self.loss(d_out, target)
return loss_
def multiDiscriminatorAdapter(d_out: list, opts: dict) -> torch.tensor:
"""
Because the OmniDiscriminator does not directly return a tensor
(but a list of tensor).
Since there is no multilevel masker, the 0th tensor in the list is all we want.
This Adapter returns the first element(tensor) of the list that OmniDiscriminator
returns.
"""
if (
isinstance(d_out, list) and len(d_out) == 1
): # adapt the multi-scale OmniDiscriminator
if not opts.dis.p.get_intermediate_features:
d_out = d_out[0][0]
else:
d_out = d_out[0]
else:
raise Exception(
"Check the setting of OmniDiscriminator! "
+ "For now, we don't support multi-scale OmniDiscriminator."
)
return d_out
class HingeLoss(nn.Module):
"""
Adapted from https://github.com/NVlabs/SPADE/blob/master/models/networks/loss.py
for the painter
"""
def __init__(self, tensor=torch.FloatTensor):
super().__init__()
self.zero_tensor = None
self.Tensor = tensor
def get_zero_tensor(self, input):
if self.zero_tensor is None:
self.zero_tensor = self.Tensor(1).fill_(0)
self.zero_tensor.requires_grad_(False)
self.zero_tensor = self.zero_tensor.to(input.device)
return self.zero_tensor.expand_as(input)
def loss(self, input, target_is_real, for_discriminator=True):
if for_discriminator:
if target_is_real:
minval = torch.min(input - 1, self.get_zero_tensor(input))
loss = -torch.mean(minval)
else:
minval = torch.min(-input - 1, self.get_zero_tensor(input))
loss = -torch.mean(minval)
else:
assert target_is_real, "The generator's hinge loss must be aiming for real"
loss = -torch.mean(input)
return loss
def __call__(self, input, target_is_real, for_discriminator=True):
# computing loss is a bit complicated because |input| may not be
# a tensor, but list of tensors in case of multiscale discriminator
if isinstance(input, list):
loss = 0
for pred_i in input:
if isinstance(pred_i, list):
pred_i = pred_i[-1]
loss_tensor = self.loss(pred_i, target_is_real, for_discriminator)
loss += loss_tensor
return loss / len(input)
else:
return self.loss(input, target_is_real, for_discriminator)
class DADADepthLoss:
"""Defines the reverse Huber loss from DADA paper for depth prediction
- Samples with larger residuals are penalized more by l2 term
- Samples with smaller residuals are penalized more by l1 term
From https://github.com/valeoai/DADA/blob/master/dada/utils/func.py
"""
def loss_calc_depth(self, pred, label):
n, c, h, w = pred.size()
assert c == 1
pred = pred.squeeze()
label = label.squeeze()
adiff = torch.abs(pred - label)
batch_max = 0.2 * torch.max(adiff).item()
t1_mask = adiff.le(batch_max).float()
t2_mask = adiff.gt(batch_max).float()
t1 = adiff * t1_mask
t2 = (adiff * adiff + batch_max * batch_max) / (2 * batch_max)
t2 = t2 * t2_mask
return (torch.sum(t1) + torch.sum(t2)) / torch.numel(pred.data)
def __call__(self, pred, label):
return self.loss_calc_depth(pred, label)
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