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import pickle
import numpy as np
import torch
import time
from pathlib import Path
from dp2 import utils
import tops
from .lpips import SampleSimilarityLPIPS
from torch_fidelity.defaults import DEFAULTS as trf_defaults
from torch_fidelity.metric_fid import fid_features_to_statistics, fid_statistics_to_metric
from torch_fidelity.utils import create_feature_extractor
lpips_model = None
fid_model = None
@torch.no_grad()
def mse(images1: torch.Tensor, images2: torch.Tensor) -> torch.Tensor:
se = (images1 - images2) ** 2
se = se.view(images1.shape[0], -1).mean(dim=1)
return se
@torch.no_grad()
def psnr(images1: torch.Tensor, images2: torch.Tensor) -> torch.Tensor:
mse_ = mse(images1, images2)
psnr = 10 * torch.log10(1 / mse_)
return psnr
@torch.no_grad()
def lpips(images1: torch.Tensor, images2: torch.Tensor) -> torch.Tensor:
return _lpips_w_grad(images1, images2)
def _lpips_w_grad(images1: torch.Tensor, images2: torch.Tensor) -> torch.Tensor:
global lpips_model
if lpips_model is None:
lpips_model = tops.to_cuda(SampleSimilarityLPIPS())
images1 = images1.mul(255)
images2 = images2.mul(255)
with torch.cuda.amp.autocast(tops.AMP()):
dists = lpips_model(images1, images2)[0].view(-1)
return dists
@torch.no_grad()
def compute_metrics_iteratively(
dataloader, generator,
cache_directory,
data_len=None,
truncation_value: float = None,
) -> dict:
"""
Args:
n_samples (int): Creates N samples from same image to calculate stats
dataset_percentage (float): The percentage of the dataset to compute metrics on.
"""
global lpips_model, fid_model
if lpips_model is None:
lpips_model = tops.to_cuda(SampleSimilarityLPIPS())
if fid_model is None:
fid_model = create_feature_extractor(
trf_defaults["feature_extractor"], [trf_defaults["feature_layer_fid"]], cuda=False)
fid_model = tops.to_cuda(fid_model)
cache_directory = Path(cache_directory)
start_time = time.time()
lpips_total = torch.tensor(0, dtype=torch.float32, device=tops.get_device())
diversity_total = torch.zeros_like(lpips_total)
fid_cache_path = cache_directory.joinpath("fid_stats.pkl")
has_fid_cache = fid_cache_path.is_file()
if data_len is None:
data_len = len(dataloader)*dataloader.batch_size
if not has_fid_cache:
fid_features_real = torch.zeros(data_len, 2048, dtype=torch.float32, device=tops.get_device())
fid_features_fake = torch.zeros(data_len, 2048, dtype=torch.float32, device=tops.get_device())
n_samples_seen = torch.tensor([0], dtype=torch.int32, device=tops.get_device())
eidx = 0
for batch in utils.tqdm_(iter(dataloader), desc="Computing FID, LPIPS and LPIPS Diversity"):
sidx = eidx
eidx = sidx + batch["img"].shape[0]
n_samples_seen += batch["img"].shape[0]
with torch.cuda.amp.autocast(tops.AMP()):
fakes1 = generator.sample(**batch, truncation_value=truncation_value)["img"]
fakes2 = generator.sample(**batch, truncation_value=truncation_value)["img"]
fakes1 = utils.denormalize_img(fakes1).mul(255)
fakes2 = utils.denormalize_img(fakes2).mul(255)
real_data = utils.denormalize_img(batch["img"]).mul(255)
lpips_1, real_lpips_feats, fake1_lpips_feats = lpips_model(real_data, fakes1)
fake2_lpips_feats = lpips_model.get_feats(fakes2)
lpips_2 = lpips_model.lpips_from_feats(real_lpips_feats, fake2_lpips_feats)
lpips_total += lpips_1.sum().add(lpips_2.sum()).div(2)
diversity_total += lpips_model.lpips_from_feats(fake1_lpips_feats, fake2_lpips_feats).sum()
if not has_fid_cache:
fid_features_real[sidx:eidx] = fid_model(real_data.byte())[0]
fid_features_fake[sidx:eidx] = fid_model(fakes1.byte())[0]
fid_features_fake = fid_features_fake[:n_samples_seen]
if has_fid_cache:
if tops.rank() == 0:
with open(fid_cache_path, "rb") as fp:
fid_stat_real = pickle.load(fp)
else:
fid_features_real = fid_features_real[:n_samples_seen]
fid_features_real = tops.all_gather_uneven(fid_features_real).cpu()
if tops.rank() == 0:
fid_stat_real = fid_features_to_statistics(fid_features_real)
cache_directory.mkdir(exist_ok=True, parents=True)
with open(fid_cache_path, "wb") as fp:
pickle.dump(fid_stat_real, fp)
fid_features_fake = tops.all_gather_uneven(fid_features_fake).cpu()
if tops.rank() == 0:
print("Starting calculation of fid from features of shape:", fid_features_fake.shape)
fid_stat_fake = fid_features_to_statistics(fid_features_fake)
fid_ = fid_statistics_to_metric(fid_stat_real, fid_stat_fake, verbose=False)["frechet_inception_distance"]
tops.all_reduce(n_samples_seen, torch.distributed.ReduceOp.SUM)
tops.all_reduce(lpips_total, torch.distributed.ReduceOp.SUM)
tops.all_reduce(diversity_total, torch.distributed.ReduceOp.SUM)
lpips_total = lpips_total / n_samples_seen
diversity_total = diversity_total / n_samples_seen
to_return = dict(lpips=lpips_total, lpips_diversity=diversity_total)
if tops.rank() == 0:
to_return["fid"] = fid_
else:
to_return["fid"] = -1
to_return["validation_time_s"] = time.time() - start_time
return to_return
@torch.no_grad()
def compute_lpips(
dataloader, generator,
truncation_value: float = None,
data_len=None,
) -> dict:
"""
Args:
n_samples (int): Creates N samples from same image to calculate stats
dataset_percentage (float): The percentage of the dataset to compute metrics on.
"""
global lpips_model, fid_model
if lpips_model is None:
lpips_model = tops.to_cuda(SampleSimilarityLPIPS())
start_time = time.time()
lpips_total = torch.tensor(0, dtype=torch.float32, device=tops.get_device())
diversity_total = torch.zeros_like(lpips_total)
if data_len is None:
data_len = len(dataloader) * dataloader.batch_size
eidx = 0
n_samples_seen = torch.tensor([0], dtype=torch.int32, device=tops.get_device())
for batch in utils.tqdm_(dataloader, desc="Validating on dataset."):
sidx = eidx
eidx = sidx + batch["img"].shape[0]
n_samples_seen += batch["img"].shape[0]
with torch.cuda.amp.autocast(tops.AMP()):
fakes1 = generator.sample(**batch, truncation_value=truncation_value)["img"]
fakes2 = generator.sample(**batch, truncation_value=truncation_value)["img"]
real_data = batch["img"]
fakes1 = utils.denormalize_img(fakes1).mul(255)
fakes2 = utils.denormalize_img(fakes2).mul(255)
real_data = utils.denormalize_img(real_data).mul(255)
lpips_1, real_lpips_feats, fake1_lpips_feats = lpips_model(real_data, fakes1)
fake2_lpips_feats = lpips_model.get_feats(fakes2)
lpips_2 = lpips_model.lpips_from_feats(real_lpips_feats, fake2_lpips_feats)
lpips_total += lpips_1.sum().add(lpips_2.sum()).div(2)
diversity_total += lpips_model.lpips_from_feats(fake1_lpips_feats, fake2_lpips_feats).sum()
tops.all_reduce(n_samples_seen, torch.distributed.ReduceOp.SUM)
tops.all_reduce(lpips_total, torch.distributed.ReduceOp.SUM)
tops.all_reduce(diversity_total, torch.distributed.ReduceOp.SUM)
lpips_total = lpips_total / n_samples_seen
diversity_total = diversity_total / n_samples_seen
to_return = dict(lpips=lpips_total, lpips_diversity=diversity_total)
to_return = {k: v.cpu().item() for k, v in to_return.items()}
to_return["validation_time_s"] = time.time() - start_time
return to_return
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