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from math import comb, ceil
import time
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset
from LPNet import LPNet
from dciknn_cuda import DCI, MDCI
from torch.optim import AdamW
from helpers.utils import ZippedDataset
from models import parse_layer_string
class Sampler:
def __init__(self, H, sz, preprocess_fn):
self.pool_size = ceil(int(H.force_factor * sz) / H.imle_db_size) * H.imle_db_size
self.preprocess_fn = preprocess_fn
self.l2_loss = torch.nn.MSELoss(reduce=False).cuda()
self.H = H
self.latent_lr = H.latent_lr
self.entire_ds = torch.arange(sz)
self.selected_latents = torch.empty([sz, H.latent_dim], dtype=torch.float32)
self.selected_latents_tmp = torch.empty([sz, H.latent_dim], dtype=torch.float32)
blocks = parse_layer_string(H.dec_blocks)
self.block_res = [s[0] for s in blocks]
self.res = sorted(set([s[0] for s in blocks if s[0] <= H.max_hierarchy]))
self.neutral_snoise = [torch.zeros([self.H.imle_db_size, 1, s, s], dtype=torch.float32) for s in self.res]
if(H.use_snoise == True):
self.snoise_tmp = [torch.randn([self.H.imle_db_size, 1, s, s], dtype=torch.float32) for s in self.res]
self.selected_snoise = [torch.randn([sz, 1, s, s,], dtype=torch.float32) for s in self.res]
self.snoise_pool = [torch.randn([self.pool_size, 1, s, s], dtype=torch.float32) for s in self.res]
else:
self.snoise_tmp = [torch.zeros([self.H.imle_db_size, 1, s, s], dtype=torch.float32) for s in self.res]
self.selected_snoise = [torch.zeros([sz, 1, s, s,], dtype=torch.float32) for s in self.res]
self.snoise_pool = [torch.zeros([self.pool_size, 1, s, s], dtype=torch.float32) for s in self.res]
self.selected_dists = torch.empty([sz], dtype=torch.float32).cuda()
self.selected_dists[:] = np.inf
self.selected_dists_tmp = torch.empty([sz], dtype=torch.float32).cuda()
self.selected_dists_lpips = torch.empty([sz], dtype=torch.float32).cuda()
self.selected_dists_lpips[:] = np.inf
self.selected_dists_l2 = torch.empty([sz], dtype=torch.float32).cuda()
self.selected_dists_l2[:] = np.inf
self.temp_latent_rnds = torch.empty([self.H.imle_db_size, self.H.latent_dim], dtype=torch.float32)
self.temp_samples = torch.empty([self.H.imle_db_size, H.image_channels, self.H.image_size, self.H.image_size],
dtype=torch.float32)
self.pool_latents = torch.randn([self.pool_size, H.latent_dim], dtype=torch.float32)
self.sample_pool_usage = torch.ones([sz], dtype=torch.bool)
self.projections = []
self.lpips_net = LPNet(pnet_type=H.lpips_net, path=H.lpips_path).cuda()
self.l2_projection = None
fake = torch.zeros(1, 3, H.image_size, H.image_size).cuda()
out, shapes = self.lpips_net(fake)
sum_dims = 0
if(H.search_type == 'lpips'):
dims = [int(H.proj_dim * 1. / len(out)) for _ in range(len(out))]
if H.proj_proportion:
sm = sum([dim.shape[1] for dim in out])
dims = [int(out[feat_ind].shape[1] * (H.proj_dim / sm)) for feat_ind in range(len(out) - 1)]
dims.append(H.proj_dim - sum(dims))
for ind, feat in enumerate(out):
self.projections.append(F.normalize(torch.randn(feat.shape[1], dims[ind]), p=2, dim=1).cuda())
sum_dims = sum(dims)
elif(H.search_type == 'l2'):
interpolated = F.interpolate(fake,scale_factor = H.l2_search_downsample)
interpolated = interpolated.reshape(interpolated.shape[0],-1)
self.l2_projection = F.normalize(torch.randn(interpolated.shape[1], H.proj_dim), p=2, dim=1).cuda()
sum_dims = H.proj_dim
else:
projection_dim = H.proj_dim // 2
dims = [int(projection_dim * 1. / len(out)) for _ in range(len(out))]
if H.proj_proportion:
sm = sum([dim.shape[1] for dim in out])
dims = [int(out[feat_ind].shape[1] * (projection_dim / sm)) for feat_ind in range(len(out) - 1)]
dims.append(projection_dim - sum(dims))
for ind, feat in enumerate(out):
self.projections.append(F.normalize(torch.randn(feat.shape[1], dims[ind]), p=2, dim=1).cuda())
interpolated = F.interpolate(fake,scale_factor = H.l2_search_downsample)
interpolated = interpolated.reshape(interpolated.shape[0],-1)
self.l2_projection = F.normalize(torch.randn(interpolated.shape[1], H.proj_dim // 2), p=2, dim=1).cuda()
sum_dims = H.proj_dim
self.dci_dim = sum_dims
print('dci_dim', self.dci_dim)
self.temp_samples_proj = torch.empty([self.H.imle_db_size, sum_dims], dtype=torch.float32).cuda()
self.dataset_proj = torch.empty([sz, sum_dims], dtype=torch.float32)
self.pool_samples_proj = torch.empty([self.pool_size, sum_dims], dtype=torch.float32)
self.snoise_pool_samples_proj = torch.empty([sz * H.snoise_factor, sum_dims], dtype=torch.float32)
self.knn_ignore = H.knn_ignore
self.ignore_radius = H.ignore_radius
self.total_excluded = 0
self.total_excluded_percentage = 0
self.dataset_size = sz
self.db_iter = 0
def get_projected(self, inp, permute=True):
if permute:
out, _ = self.lpips_net(inp.permute(0, 3, 1, 2).cuda())
else:
out, _ = self.lpips_net(inp.cuda())
gen_feat = []
for i in range(len(out)):
gen_feat.append(torch.mm(out[i], self.projections[i]))
# TODO divide?
lpips_feat = torch.cat(gen_feat, dim=1)
lpips_feat = F.normalize(lpips_feat, p=2, dim=1)
return lpips_feat.cuda()
def get_l2_feature(self, inp, permute=True):
if(permute):
inp = inp.permute(0, 3, 1, 2)
interpolated = F.interpolate(inp,scale_factor = self.H.l2_search_downsample)
interpolated = interpolated.reshape(interpolated.shape[0],-1)
interpolated = torch.mm(interpolated, self.l2_projection)
interpolated = F.normalize(interpolated, p=2, dim=1)
return interpolated.cuda()
def get_combined_feature(self, inp, permute=True):
lpips_feat = self.get_projected(inp, permute)
l2_feat = self.get_l2_feature(inp, permute)
return torch.cat([lpips_feat, l2_feat], dim=1)
# return torch.cat([lpips_feat, l2_feat], dim=1)
# if(permute):
# inp = inp.permute(0, 3, 1, 2)
# out, _ = self.lpips_net(inp.cuda())
# gen_feat = []
# for i in range(len(out)):
# gen_feat.append(torch.mm(out[i], self.projections[i]))
# # TODO divide?
# gen_feat = torch.cat(gen_feat, dim=1)
# interpolated = F.interpolate(inp,scale_factor = self.H.l2_search_downsample)
# interpolated = interpolated.reshape(interpolated.shape[0],-1)
# interpolated = torch.mm(interpolated, self.l2_projection)
# return gen_feat + interpolated.cuda()
def init_projection(self, dataset):
for proj_mat in self.projections:
proj_mat[:] = F.normalize(torch.randn(proj_mat.shape), p=2, dim=1)
for ind, x in enumerate(DataLoader(TensorDataset(dataset), batch_size=self.H.n_batch)):
batch_slice = slice(ind * self.H.n_batch, ind * self.H.n_batch + x[0].shape[0])
if(self.H.search_type == 'lpips'):
self.dataset_proj[batch_slice] = self.get_projected(self.preprocess_fn(x)[1])
elif(self.H.search_type == 'l2'):
self.dataset_proj[batch_slice] = self.get_l2_feature(self.preprocess_fn(x)[1])
else:
self.dataset_proj[batch_slice] = self.get_combined_feature(self.preprocess_fn(x)[1])
def sample(self, latents, gen, snoise=None):
with torch.no_grad():
nm = latents.shape[0]
if snoise is None:
for i in range(len(self.res)):
if(self.H.use_snoise == True):
self.snoise_tmp[i].normal_()
snoise = [s[:nm] for s in self.snoise_tmp]
px_z = gen(latents, snoise).permute(0, 2, 3, 1)
xhat = (px_z + 1.0) * 127.5
xhat = xhat.detach().cpu().numpy()
xhat = np.minimum(np.maximum(0.0, xhat), 255.0).astype(np.uint8)
return xhat
def sample_from_out(self, px_z):
with torch.no_grad():
px_z = px_z.permute(0, 2, 3, 1)
xhat = (px_z + 1.0) * 127.5
xhat = xhat.detach().cpu().numpy()
xhat = np.minimum(np.maximum(0.0, xhat), 255.0).astype(np.uint8)
return xhat
def calc_loss_projected(self, inp, tar):
inp_feat = self.get_projected(inp,False)
tar_feat = self.get_projected(tar,False)
res = torch.linalg.norm(inp_feat - tar_feat, dim=1)
return res
def calc_loss_l2(self, inp, tar):
inp_feat = self.get_l2_feature(inp,False)
tar_feat = self.get_l2_feature(tar,False)
res = torch.linalg.norm(inp_feat - tar_feat, dim=1)
return res
def calc_loss(self, inp, tar, use_mean=True, logging=False):
# inp_feat, inp_shape = self.lpips_net(inp)
# tar_feat, _ = self.lpips_net(tar)
# res = 0
# for i, g_feat in enumerate(inp_feat):
# res += torch.sum((g_feat - tar_feat[i]) ** 2, dim=1) / (inp_shape[i] ** 2)
# if use_mean:
# l2_loss = self.l2_loss(inp, tar)
# loss = self.H.lpips_coef * res.mean() + self.H.l2_coef * l2_loss.mean()
# if logging:
# return loss, res.mean(), l2_loss.mean()
# else:
# return loss
# else:
# l2_loss = torch.mean(self.l2_loss(inp, tar), dim=[1, 2, 3])
# loss = self.H.lpips_coef * res + self.H.l2_coef * l2_loss
# if logging:
# return loss, res.mean(), l2_loss
# else:
# return loss
inp_feat, inp_shape = self.lpips_net(inp)
tar_feat, _ = self.lpips_net(tar)
if use_mean:
l2_loss = torch.mean(self.l2_loss(inp, tar), dim=[1, 2, 3])
res = 0
for i, g_feat in enumerate(inp_feat):
lpips_feature_loss = (g_feat - tar_feat[i]) ** 2
res += torch.sum(lpips_feature_loss, dim=1) / (inp_shape[i] ** 2)
loss = self.H.lpips_coef * res.mean() + self.H.l2_coef * l2_loss.mean()
if logging:
return loss, res.mean(), l2_loss.mean()
else:
return loss
else:
res = 0
for i, g_feat in enumerate(inp_feat):
res += torch.sum((g_feat - tar_feat[i]) ** 2, dim=1) / (inp_shape[i] ** 2)
l2_loss = torch.mean(self.l2_loss(inp, tar), dim=[1, 2, 3])
loss = self.H.lpips_coef * res + self.H.l2_coef * l2_loss
if logging:
return loss, res.mean(), l2_loss
else:
return loss
def calc_dists_existing(self, dataset_tensor, gen, dists=None, dists_lpips = None, dists_l2 = None, latents=None, to_update=None, snoise=None, logging=False):
if dists is None:
dists = self.selected_dists
if dists_lpips is None:
dists_lpips = self.selected_dists_lpips
if dists_l2 is None:
dists_l2 = self.selected_dists_l2
if latents is None:
latents = self.selected_latents
if snoise is None:
snoise = self.selected_snoise
if to_update is not None:
latents = latents[to_update]
dists = dists[to_update]
dataset_tensor = dataset_tensor[to_update]
snoise = [s[to_update] for s in snoise]
for ind, x in enumerate(DataLoader(TensorDataset(dataset_tensor), batch_size=self.H.n_batch)):
_, target = self.preprocess_fn(x)
batch_slice = slice(ind * self.H.n_batch, ind * self.H.n_batch + target.shape[0])
cur_latents = latents[batch_slice]
cur_snoise = [s[batch_slice] for s in snoise]
with torch.no_grad():
out = gen(cur_latents, cur_snoise)
if(logging):
dist, dist_lpips, dist_l2 = self.calc_loss(target.permute(0, 3, 1, 2), out, use_mean=False, logging=True)
dists[batch_slice] = torch.squeeze(dist)
dists_lpips[batch_slice] = torch.squeeze(dist_lpips)
dists_l2[batch_slice] = torch.squeeze(dist_l2)
else:
dist = self.calc_loss(target.permute(0, 3, 1, 2), out, use_mean=False)
dists[batch_slice] = torch.squeeze(dist)
if(logging):
return dists, dists_lpips, dists_l2
else:
return dists
def calc_dists_existing_nn(self, dataset_tensor, gen, dists=None, latents=None, to_update=None, snoise=None):
if dists is None:
dists = self.selected_dists
if latents is None:
latents = self.selected_latents
if snoise is None:
snoise = self.selected_snoise
if to_update is not None:
latents = latents[to_update]
dists = dists[to_update]
dataset_tensor = dataset_tensor[to_update]
snoise = [s[to_update] for s in snoise]
for ind, x in enumerate(DataLoader(TensorDataset(dataset_tensor), batch_size=self.H.n_batch)):
_, target = self.preprocess_fn(x)
batch_slice = slice(ind * self.H.n_batch, ind * self.H.n_batch + target.shape[0])
cur_latents = latents[batch_slice]
cur_snoise = [s[batch_slice] for s in snoise]
with torch.no_grad():
out = gen(cur_latents, cur_snoise)
if(self.H.search_type == 'lpips'):
dist = self.calc_loss_projected(target.permute(0, 3, 1, 2), out)
else:
dist = self.calc_loss_l2(target.permute(0, 3, 1, 2), out)
dists[batch_slice] = torch.squeeze(dist)
return dists
def imle_sample(self, dataset, gen, factor=None):
if factor is None:
factor = self.H.imle_factor
imle_pool_size = int(len(dataset) * factor)
t1 = time.time()
self.selected_dists_tmp[:] = self.selected_dists[:]
for i in range((imle_pool_size // self.H.imle_db_size)+1):
self.temp_latent_rnds.normal_()
for j in range(len(self.res)):
if(self.H.use_snoise == True):
self.snoise_tmp[j].normal_()
for j in range(self.H.imle_db_size // self.H.imle_batch):
batch_slice = slice(j * self.H.imle_batch, (j + 1) * self.H.imle_batch)
cur_latents = self.temp_latent_rnds[batch_slice]
cur_snoise = [x[batch_slice] for x in self.snoise_tmp]
with torch.no_grad():
self.temp_samples[batch_slice] = gen(cur_latents, cur_snoise)
if(self.H.search_type == 'lpips'):
self.temp_samples_proj[batch_slice] = self.get_projected(self.temp_samples[batch_slice], False)
elif(self.H.search_type == 'l2'):
self.temp_samples_proj[batch_slice] = self.get_l2_feature(self.temp_samples[batch_slice], False)
else:
self.temp_samples_proj[batch_slice] = self.get_combined_feature(self.temp_samples[batch_slice], False)
if not gen.module.dci_db:
device_count = torch.cuda.device_count()
gen.module.dci_db = MDCI(self.temp_samples_proj.shape[1], num_comp_indices=self.H.num_comp_indices,
num_simp_indices=self.H.num_simp_indices, devices=[i for i in range(device_count)], ts=device_count)
# gen.module.dci_db = DCI(self.temp_samples_proj.shape[1], num_comp_indices=self.H.num_comp_indices,
# num_simp_indices=self.H.num_simp_indices)
gen.module.dci_db.add(self.temp_samples_proj)
t0 = time.time()
for ind, y in enumerate(DataLoader(dataset, batch_size=self.H.imle_batch)):
# t2 = time.time()
_, target = self.preprocess_fn(y)
x = self.dataset_proj[ind * self.H.imle_batch:ind * self.H.imle_batch + target.shape[0]]
cur_batch_data_flat = x.float()
nearest_indices, _ = gen.module.dci_db.query(cur_batch_data_flat, num_neighbours=1)
nearest_indices = nearest_indices.long()[:, 0]
batch_slice = slice(ind * self.H.imle_batch, ind * self.H.imle_batch + x.size()[0])
actual_selected_dists = self.calc_loss(target.permute(0, 3, 1, 2),
self.temp_samples[nearest_indices].cuda(), use_mean=False)
# actual_selected_dists = torch.squeeze(actual_selected_dists)
to_update = torch.nonzero(actual_selected_dists < self.selected_dists[batch_slice], as_tuple=False)
to_update = torch.squeeze(to_update)
self.selected_dists[ind * self.H.imle_batch + to_update] = actual_selected_dists[to_update].clone()
self.selected_latents[ind * self.H.imle_batch + to_update] = self.temp_latent_rnds[nearest_indices[to_update]].clone()
for k in range(len(self.res)):
self.selected_snoise[k][ind * self.H.imle_batch + to_update] = self.snoise_tmp[k][nearest_indices[to_update]].clone()
del cur_batch_data_flat
gen.module.dci_db.clear()
# adding perturbation
changed = torch.sum(self.selected_dists_tmp != self.selected_dists).item()
print("Samples and NN are calculated, time: {}, mean: {} # changed: {}, {}%".format(time.time() - t1,
self.selected_dists.mean(),
changed, (changed / len(
dataset)) * 100))
def resample_pool(self, gen, ds):
# self.init_projection(ds)
self.pool_latents.normal_()
for i in range(len(self.res)):
if(self.H.use_snoise == True):
self.snoise_pool[i].normal_()
for j in range(self.pool_size // self.H.imle_batch):
batch_slice = slice(j * self.H.imle_batch, (j + 1) * self.H.imle_batch)
cur_latents = self.pool_latents[batch_slice]
cur_snosie = [s[batch_slice] for s in self.snoise_pool]
with torch.no_grad():
if(self.H.search_type == 'lpips'):
self.pool_samples_proj[batch_slice] = self.get_projected(gen(cur_latents, cur_snosie), False)
elif(self.H.search_type == 'l2'):
self.pool_samples_proj[batch_slice] = self.get_l2_feature(gen(cur_latents, cur_snosie), False)
else:
self.pool_samples_proj[batch_slice] = self.get_combined_feature(gen(cur_latents, cur_snosie), False)
def imle_sample_force(self, dataset, gen, to_update=None):
if to_update is None:
to_update = self.entire_ds
if to_update.shape[0] == 0:
return
to_update = to_update.cpu()
t1 = time.time()
if torch.any(self.sample_pool_usage[to_update]):
self.resample_pool(gen, dataset)
self.sample_pool_usage[:] = False
print(f'resampling took {time.time() - t1}')
self.selected_dists_tmp[:] = np.inf
self.sample_pool_usage[to_update] = True
## removing samples too close
total_rejected = 0
if(self.H.use_rsimle):
with torch.no_grad():
for i in range(self.pool_size // self.H.imle_db_size):
pool_slice = slice(i * self.H.imle_db_size, (i + 1) * self.H.imle_db_size)
if not gen.module.dci_db:
device_count = torch.cuda.device_count()
gen.module.dci_db = MDCI(self.dci_dim, num_comp_indices=self.H.num_comp_indices,
num_simp_indices=self.H.num_simp_indices,
devices=[i for i in range(device_count)])
gen.module.dci_db.add(self.pool_samples_proj[pool_slice])
pool_latents = self.pool_latents[pool_slice]
snoise_pool = [b[pool_slice] for b in self.snoise_pool]
rejected_flag = torch.zeros(self.H.imle_db_size, dtype=torch.bool)
for ind, y in enumerate(DataLoader(TensorDataset(dataset[to_update]), batch_size=self.H.imle_batch)):
_, target = self.preprocess_fn(y)
batch_slice = slice(ind * self.H.imle_batch, ind * self.H.imle_batch + target.shape[0])
indices = to_update[batch_slice]
x = self.dataset_proj[indices]
nearest_indices, dci_dists = gen.module.dci_db.query(x.float(), num_neighbours=self.H.knn_ignore)
nearest_indices = nearest_indices.long()
check = dci_dists < self.H.eps_radius
easy_samples_list = torch.unique(nearest_indices[check])
self.pool_samples_proj[pool_slice][easy_samples_list] = torch.tensor(float('inf'))
rejected_flag[easy_samples_list] = 1
gen.module.dci_db.clear()
total_rejected += rejected_flag.sum().item()
self.total_excluded = total_rejected
self.total_excluded_percentage = (total_rejected * 1.0 / self.pool_size) * 100
with torch.no_grad():
for i in range(self.pool_size // self.H.imle_db_size):
pool_slice = slice(i * self.H.imle_db_size, (i + 1) * self.H.imle_db_size)
if not gen.module.dci_db:
device_count = torch.cuda.device_count()
gen.module.dci_db = MDCI(self.dci_dim, num_comp_indices=self.H.num_comp_indices,
num_simp_indices=self.H.num_simp_indices, devices=[i for i in range(device_count)])
gen.module.dci_db.add(self.pool_samples_proj[pool_slice])
pool_latents = self.pool_latents[pool_slice]
snoise_pool = [b[pool_slice] for b in self.snoise_pool]
t0 = time.time()
for ind, y in enumerate(DataLoader(TensorDataset(dataset[to_update]), batch_size=self.H.imle_batch)):
_, target = self.preprocess_fn(y)
batch_slice = slice(ind * self.H.imle_batch, ind * self.H.imle_batch + target.shape[0])
indices = to_update[batch_slice]
x = self.dataset_proj[indices]
nearest_indices, dci_dists = gen.module.dci_db.query(x.float(), num_neighbours=1)
nearest_indices = nearest_indices.long()[:, 0]
nearest_indices = nearest_indices.cpu()
dci_dists = dci_dists[:, 0]
need_update = dci_dists < self.selected_dists_tmp[indices]
need_update = need_update.cpu()
global_need_update = indices[need_update]
self.selected_dists_tmp[global_need_update] = dci_dists[need_update].clone()
self.selected_latents_tmp[global_need_update] = pool_latents[nearest_indices[need_update]].clone() + self.H.imle_perturb_coef * torch.randn((need_update.sum(), self.H.latent_dim))
for j in range(len(self.res)):
self.selected_snoise[j][global_need_update] = snoise_pool[j][nearest_indices[need_update]].clone()
gen.module.dci_db.clear()
if i % 100 == 0:
print("NN calculated for {} out of {} - {}".format((i + 1) * self.H.imle_db_size, self.pool_size, time.time() - t0))
self.selected_latents[to_update] = self.selected_latents_tmp[to_update]
print(f'Force resampling took {time.time() - t1}')
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