code/OCT_RAE_main/src/eval/utils.py

from collections import namedtuple
import numpy as np
import torch
import math
import torch.nn as nn
from torchvision import models
from torch.utils.data import Dataset
import os
import sys
from pathlib import Path

# insert ../ as a module path
current_dir = Path(__file__).resolve().parent
parent_dir = current_dir.parent
if str(parent_dir) not in sys.path:
    sys.path.insert(0, str(parent_dir))
from disc.lpips_utils import get_ckpt_path

def normalize_tensor(x, eps=1e-10):
    norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
    return x / (norm_factor + eps)


def spatial_average(x, keepdim=True):
    return x.mean([2, 3],keepdim=keepdim)

class ScalingLayer(nn.Module):
    def __init__(self):
        super(ScalingLayer, self).__init__()
        self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
        self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])

    def forward(self, inp):
        return (inp - self.shift) / self.scale

class NetLinLayer(nn.Module):
    """ A single linear layer which does a 1x1 conv """
    def __init__(self, chn_in, chn_out=1, use_dropout=False):
        super(NetLinLayer, self).__init__()
        layers = [nn.Dropout(), ] if (use_dropout) else []
        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
        self.model = nn.Sequential(*layers)

class VGG16(torch.nn.Module):
    def __init__(self, requires_grad=False):
        super(VGG16, self).__init__()
        vgg_pretrained_features = models.vgg16(weights='DEFAULT').features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        self.N_slices = 5
        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, 16):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(16, 23):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(23, 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 = self.slice1(X)
        h_relu1_2 = h
        h = self.slice2(h)
        h_relu2_2 = h
        h = self.slice3(h)
        h_relu3_3 = h
        h = self.slice4(h)
        h_relu4_3 = h
        h = self.slice5(h)
        h_relu5_3 = h
        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
        return out

class LPIPS(nn.Module):
    # Learned perceptual metric
    def __init__(self, use_dropout=True):
        super().__init__()
        self.scaling_layer = ScalingLayer()
        self.chns = [64, 128, 256, 512, 512]  # vg16 features
        self.net = VGG16(requires_grad=False)
        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
        self.load_from_pretrained()
        for param in self.parameters():
            param.requires_grad = False

    def load_from_pretrained(self, name="vgg_lpips"):
        ckpt = get_ckpt_path(name)
        self.load_state_dict(
            torch.load(
                ckpt,
                map_location=torch.device("cpu")),
                strict=False
            )
        print("loaded pretrained LPIPS loss from {}".format(ckpt))

    def forward(self, input, target):
        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
        outs0, outs1 = self.net(in0_input), self.net(in1_input)
        feats0, feats1, diffs = {}, {}, {}
        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
        for kk in range(len(self.chns)):
            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2

        res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
        val = res[0]
        for l in range(1, len(self.chns)):
            val += res[l]
        return val

class ImgArrDataset(Dataset):
    """
    Serve as the wrapper purpose for `torch-fidelity` to calculate the metrics (FID),
    which expects [B, C, H, W] tensor in uint8. We are returning a [C, H, W] tensor.
    """
    def __init__(self, arr):
        self.arr = arr
    
    def __len__(self):
        return len(self.arr)

    def __getitem__(self, idx):
        return torch.from_numpy(self.arr[idx]).permute(2, 0, 1)


######################################################
# 1. Convert NumPy arrays to Torch tensors, if needed
######################################################
def to_torch_tensor(np_array):
    """
    Converts a NumPy array of shape (B, H, W, C) to a (B, C, H, W) float32 Torch tensor.
    Expects image to be in [0, 1] or [0, 255].
    Adjust if necessary.
    """
    tensor = torch.from_numpy(np_array).permute(0, 3, 1, 2)
    
    # If your data is in [0, 255], convert to [0, 1].
    if tensor.max() > 1.0:
        tensor = tensor.float() / 255.0
    else:
        tensor = tensor.float()
    return tensor