losses

Time_TravelRephotography/losses/contextual_loss_pytorch/.gitignore

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+*.py[cod]
+*$py.class
+
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+
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+
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+# Unit test / coverage reports
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+.cache
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+*.cover
+.hypothesis/
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+
+# Translations
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+
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+/site
+
+# mypy
+.mypy_cache/

Time_TravelRephotography/losses/contextual_loss_pytorch/LICENSE

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+MIT License
+
+Copyright (c) 2019 Sou Uchida
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Time_TravelRephotography/losses/contextual_loss_pytorch/__init__.py

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+from .modules import *

Time_TravelRephotography/losses/contextual_loss_pytorch/config.py

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+# TODO: add supports for L1, L2 etc.
+LOSS_TYPES = ['cosine', 'l1', 'l2']

Time_TravelRephotography/losses/contextual_loss_pytorch/functional.py

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+import torch
+import torch.nn.functional as F
+
+from .config import LOSS_TYPES
+
+__all__ = ['contextual_loss', 'contextual_bilateral_loss']
+
+
+def contextual_loss(x: torch.Tensor,
+                    y: torch.Tensor,
+                    band_width: float = 0.5,
+                    loss_type: str = 'cosine',
+                    all_dist: bool = False):
+    """
+    Computes contextual loss between x and y.
+    The most of this code is copied from
+        https://gist.github.com/yunjey/3105146c736f9c1055463c33b4c989da.
+
+    Parameters
+    ---
+    x : torch.Tensor
+        features of shape (N, C, H, W).
+    y : torch.Tensor
+        features of shape (N, C, H, W).
+    band_width : float, optional
+        a band-width parameter used to convert distance to similarity.
+        in the paper, this is described as :math:`h`.
+    loss_type : str, optional
+        a loss type to measure the distance between features.
+        Note: `l1` and `l2` frequently raises OOM.
+
+    Returns
+    ---
+    cx_loss : torch.Tensor
+        contextual loss between x and y (Eq (1) in the paper)
+    """
+
+    assert x.size() == y.size(), 'input tensor must have the same size.'
+    assert loss_type in LOSS_TYPES, f'select a loss type from {LOSS_TYPES}.'
+
+    N, C, H, W = x.size()
+
+    if loss_type == 'cosine':
+        dist_raw = compute_cosine_distance(x, y)
+    elif loss_type == 'l1':
+        dist_raw = compute_l1_distance(x, y)
+    elif loss_type == 'l2':
+        dist_raw = compute_l2_distance(x, y)
+
+    dist_tilde = compute_relative_distance(dist_raw)
+    cx = compute_cx(dist_tilde, band_width)
+    if all_dist:
+        return cx
+
+    cx = torch.mean(torch.max(cx, dim=1)[0], dim=1)  # Eq(1)
+    cx_loss = torch.mean(-torch.log(cx + 1e-5))  # Eq(5)
+
+    return cx_loss
+
+
+# TODO: Operation check
+def contextual_bilateral_loss(x: torch.Tensor,
+                              y: torch.Tensor,
+                              weight_sp: float = 0.1,
+                              band_width: float = 1.,
+                              loss_type: str = 'cosine'):
+    """
+    Computes Contextual Bilateral (CoBi) Loss between x and y,
+        proposed in https://arxiv.org/pdf/1905.05169.pdf.
+
+    Parameters
+    ---
+    x : torch.Tensor
+        features of shape (N, C, H, W).
+    y : torch.Tensor
+        features of shape (N, C, H, W).
+    band_width : float, optional
+        a band-width parameter used to convert distance to similarity.
+        in the paper, this is described as :math:`h`.
+    loss_type : str, optional
+        a loss type to measure the distance between features.
+        Note: `l1` and `l2` frequently raises OOM.
+
+    Returns
+    ---
+    cx_loss : torch.Tensor
+        contextual loss between x and y (Eq (1) in the paper).
+    k_arg_max_NC : torch.Tensor
+        indices to maximize similarity over channels.
+    """
+
+    assert x.size() == y.size(), 'input tensor must have the same size.'
+    assert loss_type in LOSS_TYPES, f'select a loss type from {LOSS_TYPES}.'
+
+    # spatial loss
+    grid = compute_meshgrid(x.shape).to(x.device)
+    dist_raw = compute_l2_distance(grid, grid)
+    dist_tilde = compute_relative_distance(dist_raw)
+    cx_sp = compute_cx(dist_tilde, band_width)
+
+    # feature loss
+    if loss_type == 'cosine':
+        dist_raw = compute_cosine_distance(x, y)
+    elif loss_type == 'l1':
+        dist_raw = compute_l1_distance(x, y)
+    elif loss_type == 'l2':
+        dist_raw = compute_l2_distance(x, y)
+    dist_tilde = compute_relative_distance(dist_raw)
+    cx_feat = compute_cx(dist_tilde, band_width)
+
+    # combined loss
+    cx_combine = (1. - weight_sp) * cx_feat + weight_sp * cx_sp
+
+    k_max_NC, _ = torch.max(cx_combine, dim=2, keepdim=True)
+
+    cx = k_max_NC.mean(dim=1)
+    cx_loss = torch.mean(-torch.log(cx + 1e-5))
+
+    return cx_loss
+
+
+def compute_cx(dist_tilde, band_width):
+    w = torch.exp((1 - dist_tilde) / band_width)  # Eq(3)
+    cx = w / torch.sum(w, dim=2, keepdim=True)  # Eq(4)
+    return cx
+
+
+def compute_relative_distance(dist_raw):
+    dist_min, _ = torch.min(dist_raw, dim=2, keepdim=True)
+    dist_tilde = dist_raw / (dist_min + 1e-5)
+    return dist_tilde
+
+
+def compute_cosine_distance(x, y):
+    # mean shifting by channel-wise mean of `y`.
+    y_mu = y.mean(dim=(0, 2, 3), keepdim=True)
+    x_centered = x - y_mu
+    y_centered = y - y_mu
+
+    # L2 normalization
+    x_normalized = F.normalize(x_centered, p=2, dim=1)
+    y_normalized = F.normalize(y_centered, p=2, dim=1)
+
+    # channel-wise vectorization
+    N, C, *_ = x.size()
+    x_normalized = x_normalized.reshape(N, C, -1)  # (N, C, H*W)
+    y_normalized = y_normalized.reshape(N, C, -1)  # (N, C, H*W)
+
+    # consine similarity
+    cosine_sim = torch.bmm(x_normalized.transpose(1, 2),
+                           y_normalized)  # (N, H*W, H*W)
+
+    # convert to distance
+    dist = 1 - cosine_sim
+
+    return dist
+
+
+# TODO: Considering avoiding OOM.
+def compute_l1_distance(x: torch.Tensor, y: torch.Tensor):
+    N, C, H, W = x.size()
+    x_vec = x.view(N, C, -1)
+    y_vec = y.view(N, C, -1)
+
+    dist = x_vec.unsqueeze(2) - y_vec.unsqueeze(3)
+    dist = dist.abs().sum(dim=1)
+    dist = dist.transpose(1, 2).reshape(N, H*W, H*W)
+    dist = dist.clamp(min=0.)
+
+    return dist
+
+
+# TODO: Considering avoiding OOM.
+def compute_l2_distance(x, y):
+    N, C, H, W = x.size()
+    x_vec = x.view(N, C, -1)
+    y_vec = y.view(N, C, -1)
+    x_s = torch.sum(x_vec ** 2, dim=1)
+    y_s = torch.sum(y_vec ** 2, dim=1)
+
+    A = y_vec.transpose(1, 2) @ x_vec
+    dist = y_s - 2 * A + x_s.transpose(0, 1)
+    dist = dist.transpose(1, 2).reshape(N, H*W, H*W)
+    dist = dist.clamp(min=0.)
+
+    return dist
+
+
+def compute_meshgrid(shape):
+    N, C, H, W = shape
+    rows = torch.arange(0, H, dtype=torch.float32) / (H + 1)
+    cols = torch.arange(0, W, dtype=torch.float32) / (W + 1)
+
+    feature_grid = torch.meshgrid(rows, cols)
+    feature_grid = torch.stack(feature_grid).unsqueeze(0)
+    feature_grid = torch.cat([feature_grid for _ in range(N)], dim=0)
+
+    return feature_grid

Time_TravelRephotography/losses/contextual_loss_pytorch/modules/__init__.py

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+from .contextual import ContextualLoss
+from .contextual_bilateral import ContextualBilateralLoss
+from .vgg import VGG19
+
+__all__ = ['ContextualLoss', 'ContextualBilateralLoss', 'VGG19']

Time_TravelRephotography/losses/contextual_loss_pytorch/modules/contextual.py

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+import random
+from typing import (
+    Iterable,
+    List,
+    Optional,
+)
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from .vgg import VGG19
+from .. import functional as F
+from ..config import LOSS_TYPES
+
+
+class ContextualLoss(nn.Module):
+    """
+    Creates a criterion that measures the contextual loss.
+
+    Parameters
+    ---
+    band_width : int, optional
+        a band_width parameter described as :math:`h` in the paper.
+    use_vgg : bool, optional
+        if you want to use VGG feature, set this `True`.
+    vgg_layer : str, optional
+        intermidiate layer name for VGG feature.
+        Now we support layer names:
+            `['relu1_2', 'relu2_2', 'relu3_4', 'relu4_4', 'relu5_4']`
+    """
+
+    def __init__(
+            self,
+            band_width: float = 0.5,
+            loss_type: str = 'cosine',
+            use_vgg: bool = False,
+            vgg_model: nn.Module = None,
+            vgg_layers: List[str] = ['relu3_4'],
+            feature_1d_size: int = 64,
+    ):
+
+        super().__init__()
+
+        assert band_width > 0, 'band_width parameter must be positive.'
+        assert loss_type in LOSS_TYPES,\
+            f'select a loss type from {LOSS_TYPES}.'
+
+        self.loss_type = loss_type
+        self.band_width = band_width
+        self.feature_1d_size = feature_1d_size
+
+        if use_vgg:
+            self.vgg_model = VGG19() if vgg_model is None else vgg_model
+            self.vgg_layers = vgg_layers
+            self.register_buffer(
+                name='vgg_mean',
+                tensor=torch.tensor(
+                    [[[0.485]], [[0.456]], [[0.406]]], requires_grad=False)
+            )
+            self.register_buffer(
+                name='vgg_std',
+                tensor=torch.tensor(
+                    [[[0.229]], [[0.224]], [[0.225]]], requires_grad=False)
+            )
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor, all_dist: bool = False):
+        if not hasattr(self, 'vgg_model'):
+            return self.contextual_loss(x, y, self.feature_1d_size, self.band_width, all_dist=all_dist)
+
+
+        x = self.forward_vgg(x)
+        y = self.forward_vgg(y)
+
+        loss = 0
+        for layer in self.vgg_layers:
+            # picking up vgg feature maps
+            fx = getattr(x, layer)
+            fy = getattr(y, layer)
+            loss = loss + self.contextual_loss(
+                fx, fy, self.feature_1d_size, self.band_width, all_dist=all_dist, loss_type=self.loss_type
+            )
+        return loss
+
+    def forward_vgg(self, x: torch.Tensor):
+        assert x.shape[1] == 3, 'VGG model takes 3 chennel images.'
+        # [-1, 1] -> [0, 1]
+        x = (x + 1) * 0.5
+
+        # normalization
+        x = x.sub(self.vgg_mean.detach()).div(self.vgg_std)
+        return self.vgg_model(x)
+
+    @classmethod
+    def contextual_loss(
+            cls,
+            x: torch.Tensor, y: torch.Tensor,
+            feature_1d_size: int,
+            band_width: int,
+            all_dist: bool = False,
+            loss_type: str = 'cosine',
+    ) -> torch.Tensor:
+        feature_size = feature_1d_size ** 2
+        if np.prod(x.shape[2:]) > feature_size or np.prod(y.shape[2:]) > feature_size:
+            x, indices = cls.random_sampling(x, feature_1d_size=feature_1d_size)
+            y, _ = cls.random_sampling(y, feature_1d_size=feature_1d_size, indices=indices)
+
+        return F.contextual_loss(x, y, band_width, all_dist=all_dist, loss_type=loss_type)
+
+    @staticmethod
+    def random_sampling(
+            tensor_NCHW: torch.Tensor, feature_1d_size: int, indices: Optional[List] = None
+    ):
+        N, C, H, W = tensor_NCHW.shape
+        S = H * W
+        tensor_NCS = tensor_NCHW.reshape([N, C, S])
+        if indices is None:
+            all_indices = list(range(S))
+            random.shuffle(all_indices)
+            indices = all_indices[:feature_1d_size**2]
+        res = tensor_NCS[:, :, indices].reshape(N, -1, feature_1d_size, feature_1d_size)
+        return res, indices

Time_TravelRephotography/losses/contextual_loss_pytorch/modules/contextual_bilateral.py

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+import torch
+import torch.nn as nn
+
+from .vgg import VGG19
+from .. import functional as F
+from ..config import LOSS_TYPES
+
+
+class ContextualBilateralLoss(nn.Module):
+    """
+    Creates a criterion that measures the contextual bilateral loss.
+
+    Parameters
+    ---
+    weight_sp : float, optional
+        a balancing weight between spatial and feature loss.
+    band_width : int, optional
+        a band_width parameter described as :math:`h` in the paper.
+    use_vgg : bool, optional
+        if you want to use VGG feature, set this `True`.
+    vgg_layer : str, optional
+        intermidiate layer name for VGG feature.
+        Now we support layer names:
+            `['relu1_2', 'relu2_2', 'relu3_4', 'relu4_4', 'relu5_4']`
+    """
+
+    def __init__(self,
+                 weight_sp: float = 0.1,
+                 band_width: float = 0.5,
+                 loss_type: str = 'cosine',
+                 use_vgg: bool = False,
+                 vgg_layer: str = 'relu3_4'):
+
+        super(ContextualBilateralLoss, self).__init__()
+
+        assert band_width > 0, 'band_width parameter must be positive.'
+        assert loss_type in LOSS_TYPES,\
+            f'select a loss type from {LOSS_TYPES}.'
+
+        self.band_width = band_width
+
+        if use_vgg:
+            self.vgg_model = VGG19()
+            self.vgg_layer = vgg_layer
+            self.register_buffer(
+                name='vgg_mean',
+                tensor=torch.tensor(
+                    [[[0.485]], [[0.456]], [[0.406]]], requires_grad=False)
+            )
+            self.register_buffer(
+                name='vgg_std',
+                tensor=torch.tensor(
+                    [[[0.229]], [[0.224]], [[0.225]]], requires_grad=False)
+            )
+
+    def forward(self, x, y):
+        if hasattr(self, 'vgg_model'):
+            assert x.shape[1] == 3 and y.shape[1] == 3,\
+                'VGG model takes 3 chennel images.'
+
+            # normalization
+            x = x.sub(self.vgg_mean.detach()).div(self.vgg_std.detach())
+            y = y.sub(self.vgg_mean.detach()).div(self.vgg_std.detach())
+
+            # picking up vgg feature maps
+            x = getattr(self.vgg_model(x), self.vgg_layer)
+            y = getattr(self.vgg_model(y), self.vgg_layer)
+
+        return F.contextual_bilateral_loss(x, y, self.band_width)

Time_TravelRephotography/losses/contextual_loss_pytorch/modules/vgg.py

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+from collections import namedtuple
+
+import torch.nn as nn
+import torchvision.models.vgg as vgg
+
+
+class VGG19(nn.Module):
+    def __init__(self, requires_grad=False):
+        super(VGG19, self).__init__()
+        vgg_pretrained_features = vgg.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(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 18):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(18, 27):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(27, 36):
+            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 = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_4 = h
+        h = self.slice4(h)
+        h_relu4_4 = h
+        h = self.slice5(h)
+        h_relu5_4 = h
+
+        vgg_outputs = namedtuple(
+            "VggOutputs", ['relu1_2', 'relu2_2',
+                           'relu3_4', 'relu4_4', 'relu5_4'])
+        out = vgg_outputs(h_relu1_2, h_relu2_2,
+                          h_relu3_4, h_relu4_4, h_relu5_4)
+
+        return out