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trainer.py
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# %%
import importlib
import os
import pickle
import shutil
from os.path import dirname, exists, join
import h5py
import faiss
import numpy as np
import torch
import torch.nn as nn
import wandb
from torch.utils.data import DataLoader
from tqdm import tqdm
from datetime import datetime
import json
import torch.optim as optim
from torchsummary import summary
from PIL import Image
os.sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
import time
import random
from options import FixRandom
from util import *
import torchvision.transforms as transforms
from torchvision.transforms import InterpolationMode
from script.feature.options_nerf import config_parser
from script.models.nerfw import create_nerf
from color import rgb_to_yuv
from utils.util import get_image_to_tensor_balanced, coordinate_transformation
import torch.nn.functional as F
from nerf_init import create_nerf_init
def input_transform(opt=None):
return transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
CustomTo01Transform(),
transforms.Resize((opt.height, opt.width), interpolation=InterpolationMode.BILINEAR),
])
class CustomTo01Transform:
def __call__(self, images):
return (images + 1.0) * 0.5
class CKD_loss(nn.Module):
def __init__(self, margin) -> None:
super().__init__()
self.margin = margin
def forward(self, embs_a, embs_p, embs_n, mu_tea_a, mu_tea_p, mu_tea_n): # (1, D)
SaTp = torch.norm(embs_a - mu_tea_p, p=2).pow(2)
SpTa = torch.norm(embs_p - mu_tea_a, p=2).pow(2)
SaTn = torch.norm(embs_a - mu_tea_n, p=2).pow(2)
SnTa = torch.norm(embs_n - mu_tea_a, p=2).pow(2)
SaTa = torch.norm(embs_a - mu_tea_a, p=2).pow(2)
SpTp = torch.norm(embs_p - mu_tea_p, p=2).pow(2)
SnTn = torch.norm(embs_n - mu_tea_n, p=2).pow(2)
dis_D = SaTp + SpTa + SaTa + SpTp + SnTn
# dis_D=SaTp+SpTa
loss = 0.5 * (torch.clamp(self.margin + dis_D, min=0).pow(2))
return loss
class Trainer:
def __init__(self, options) -> None:
self.opt = options
self.input_transform = input_transform(self.opt)
self.image_to_tensor = get_image_to_tensor_balanced()
self.model_name = self.opt.model_name
self.data_transform = transforms.Compose([
transforms.ToTensor(),
])
# r variables
self.step = 0
self.epoch = 0
self.current_lr = 0
self.best_recalls = [0, 0, 0]
# seed
fix_random = FixRandom(self.opt.seed)
self.seed_worker = fix_random.seed_worker()
self.time_stamp = datetime.now().strftime('%m%d_%H%M%S')
# nerfh
self.args_nerf = self.opt
bds_dict = {
'near': 0,
'far': 10,
}
if self.args_nerf.Datasetname == "Cambridge":
# load NeRF
_, render_kwargs_test_GreatCourt, start_GreatCourt, _, _ = create_nerf(self.args_nerf, scenes="GreatCourt")
# _, render_kwargs_test_GreatCourt, start_GreatCourt, _, _ = create_nerf_init(self.args_nerf, scenes="GreatCourt")
global_step_GreatCourt = start_GreatCourt
# render_kwargs_train.update(bds_dict)
render_kwargs_test_GreatCourt.update(bds_dict)
if self.args_nerf.reduce_embedding == 2:
render_kwargs_test_GreatCourt['i_epoch'] = start_GreatCourt
self.render_kwargs_test_GreatCourt = render_kwargs_test_GreatCourt
_, render_kwargs_test_KingsCollege, start_KingsCollege, _, _ = create_nerf(self.args_nerf,
scenes="KingsCollege")
# _, render_kwargs_test_KingsCollege, start_KingsCollege, _, _ = create_nerf_init(self.args_nerf, scenes="KingsCollege")
global_step_KingsCollege = start_KingsCollege
# render_kwargs_train.update(bds_dict)
render_kwargs_test_KingsCollege.update(bds_dict)
if self.args_nerf.reduce_embedding == 2:
render_kwargs_test_KingsCollege['i_epoch'] = start_KingsCollege
self.render_kwargs_test_KingsCollege = render_kwargs_test_KingsCollege
_, render_kwargs_test_OldHospital, start_OldHospital, _, _ = create_nerf(self.args_nerf,
scenes="OldHospital")
# _, render_kwargs_test_OldHospital, start_OldHospital, _, _ = create_nerf_init(self.args_nerf, scenes="OldHospital")
global_step_OldHospital = start_OldHospital
# render_kwargs_train.update(bds_dict)
render_kwargs_test_OldHospital.update(bds_dict)
if self.args_nerf.reduce_embedding == 2:
render_kwargs_test_OldHospital['i_epoch'] = start_OldHospital
self.render_kwargs_test_OldHospital = render_kwargs_test_OldHospital
_, render_kwargs_test_ShopFacade, start_ShopFacade, _, _ = create_nerf(self.args_nerf, scenes="ShopFacade")
# _, render_kwargs_test_ShopFacade, start_ShopFacade, _, _ = create_nerf_init(self.args_nerf, scenes="ShopFacade")
global_step_ShopFacade = start_ShopFacade
# render_kwargs_train.update(bds_dict)
render_kwargs_test_ShopFacade.update(bds_dict)
if self.args_nerf.reduce_embedding == 2:
render_kwargs_test_ShopFacade['i_epoch'] = start_ShopFacade
self.render_kwargs_test_ShopFacade = render_kwargs_test_ShopFacade
_, render_kwargs_test_StMarysChurch, start_StMarysChurch, _, _ = create_nerf(self.args_nerf,
scenes="StMarysChurch")
# _, render_kwargs_test_StMarysChurch, start_StMarysChurch, _, _ = create_nerf_init(self.args_nerf, scenes="StMarysChurch")
global_step_StMarysChurch = start_StMarysChurch
# render_kwargs_train.update(bds_dict)
render_kwargs_test_StMarysChurch.update(bds_dict)
if self.args_nerf.reduce_embedding == 2:
render_kwargs_test_StMarysChurch['i_epoch'] = start_StMarysChurch
self.render_kwargs_test_StMarysChurch = render_kwargs_test_StMarysChurch
if self.args_nerf.Datasetname == "NEU":
self.NEU_folders = ["NEU_scan01", "NEU_scan02", "NEU_scan03", "NEU_scan04", "NEU_scan05"]
render_kwargs_test = [None] * 5
start = [None] * 5
global_step = [None] * 5
self.render_kwargs_test = [None] * 5
for i in range(5):
_, render_kwargs_test[i], start[i], _, _ = create_nerf(self.args_nerf, scenes=self.NEU_folders[i])
global_step[i] = start[i]
# render_kwargs_train.update(bds_dict)
render_kwargs_test[i].update(bds_dict)
if self.args_nerf.reduce_embedding == 2:
render_kwargs_test[i]['i_epoch'] = start[i]
self.render_kwargs_test[i] = render_kwargs_test[i]
if self.args_nerf.Datasetname == "SIASUN":
self.SIASUN_folders = ["sia_scan01", "sia_scan02", "sia_scan03", "sia_scan04", "sia_scan05"]
render_kwargs_test = [None] * 5
start = [None] * 5
global_step = [None] * 5
self.render_kwargs_test = [None] * 5
for i in range(5):
_, render_kwargs_test[i], start[i], _, _ = create_nerf(self.args_nerf, scenes=self.SIASUN_folders[i])
global_step[i] = start[i]
# render_kwargs_train.update(bds_dict)
render_kwargs_test[i].update(bds_dict)
if self.args_nerf.reduce_embedding == 2:
render_kwargs_test[i]['i_epoch'] = start[i]
self.render_kwargs_test[i] = render_kwargs_test[i]
# set device
if self.opt.phase == 'train_tea':
self.opt.cGPU = schedule_device()
if self.opt.cuda and not torch.cuda.is_available():
raise Exception("No GPU found, please run with --nocuda :(")
torch.cuda.set_device(self.opt.cGPU)
self.device = torch.device("cuda")
print('{}:{}{}'.format('device', self.device, torch.cuda.current_device()))
# CKD_loss
self.CKD_loss = CKD_loss(margin=torch.tensor(self.opt.margin, device=self.device))
# make model
if self.opt.phase == 'train_tea':
self.model, self.optimizer, self.scheduler, self.criterion = self.make_model()
elif self.opt.phase == 'train_stu':
self.teacher_net, self.student_net, self.optimizer, self.scheduler, self.criterion = self.make_model()
self.model = self.teacher_net
elif self.opt.phase in ['test_tea', 'test_stu']:
self.model = self.make_model()
else:
raise Exception('Undefined phase :(')
# make folders
self.make_folders()
# make dataset
self.make_dataset()
# online logs
if self.opt.phase in ['train_tea', 'train_stu']:
wandb.init(project="TSCM", config=vars(self.opt),
name=f"{self.opt.loss}_{self.opt.phase}_{self.time_stamp}")
def make_folders(self):
''' create folders to store tensorboard files and a copy of networks files
'''
if self.opt.phase in ['train_tea', 'train_stu']:
self.opt.runsPath = join(self.opt.logsPath, f"{self.opt.loss}_{self.opt.phase}_{self.time_stamp}")
if not os.path.exists(join(self.opt.runsPath, 'models')):
os.makedirs(join(self.opt.runsPath, 'models'))
if not os.path.exists(join(self.opt.runsPath, 'transformed')):
os.makedirs(join(self.opt.runsPath, 'transformed'))
for file in [__file__, 'datasets/{}.py'.format(self.opt.dataset), 'networks/{}.py'.format(self.opt.net)]:
shutil.copyfile(file, os.path.join(self.opt.runsPath, 'models', file.split('/')[-1]))
with open(join(self.opt.runsPath, 'flags.json'), 'w') as f:
f.write(json.dumps({k: v for k, v in vars(self.opt).items()}, indent=''))
def make_dataset(self):
''' make dataset
'''
if self.opt.phase in ['train_tea', 'train_stu']:
assert os.path.exists(f'datasets/{self.opt.dataset}.py'), 'Cannot find ' + f'{self.opt.dataset}.py :('
self.dataset = importlib.import_module('datasets.' + self.opt.dataset)
elif self.opt.phase in ['test_tea', 'test_stu']:
self.dataset = importlib.import_module('tmp.models.{}'.format(self.opt.dataset))
# for emb cache
self.whole_train_set = self.dataset.get_whole_training_set(self.opt)
self.whole_training_data_loader = DataLoader(dataset=self.whole_train_set, num_workers=self.opt.threads,
batch_size=self.opt.cacheBatchSize, shuffle=False,
pin_memory=self.opt.cuda, worker_init_fn=self.seed_worker)
self.whole_val_set = self.dataset.get_whole_val_set(self.opt)
self.whole_val_data_loader = DataLoader(dataset=self.whole_val_set, num_workers=self.opt.threads,
batch_size=self.opt.cacheBatchSize, shuffle=False,
pin_memory=self.opt.cuda, worker_init_fn=self.seed_worker)
self.whole_test_set = self.dataset.get_whole_test_set(self.opt)
self.whole_test_data_loader = DataLoader(dataset=self.whole_test_set, num_workers=self.opt.threads,
batch_size=self.opt.cacheBatchSize, shuffle=False,
pin_memory=self.opt.cuda, worker_init_fn=self.seed_worker)
self.train_set = self.dataset.get_training_query_set(self.opt, self.opt.margin)
self.training_data_loader = DataLoader(dataset=self.train_set, num_workers=8, batch_size=self.opt.batchSize,
shuffle=True, collate_fn=self.dataset.collate_fn,
worker_init_fn=self.seed_worker)
print('{}:{}, {}:{}, {}:{}, {}:{}, {}:{}'.format('dataset', self.opt.dataset, 'database',
self.whole_train_set.dbStruct.numDb, 'train_set',
self.whole_train_set.dbStruct.numQ, 'val_set',
self.whole_val_set.dbStruct.numQ, 'test_set',
self.whole_test_set.dbStruct.numQ))
print('{}:{}, {}:{}'.format('cache_bs', self.opt.cacheBatchSize, 'tuple_bs', self.opt.batchSize))
def make_model(self):
'''build model
'''
if self.opt.phase == 'train_tea':
# build teacher net
assert os.path.exists(f'networks/{self.opt.net}.py'), 'Cannot find ' + f'{self.opt.net}.py :('
network = importlib.import_module('networks.' + self.opt.net)
model = network.deliver_model(self.opt, 'tea')
model = model.to(self.device)
outputs = model(torch.rand((2, 3, self.opt.height, self.opt.width), device=self.device))
self.opt.output_dim = \
model(torch.rand((2, 3, self.opt.height, self.opt.width), device=self.device))[0].shape[-1]
self.opt.sigma_dim = \
model(torch.rand((2, 3, self.opt.height, self.opt.width), device=self.device))[1].shape[
-1] # place holder
elif self.opt.phase == 'train_stu': # load teacher net
assert self.opt.resume != '', 'You need to define the teacher/resume path :('
if exists('tmp'):
shutil.rmtree('tmp')
os.mkdir('tmp')
shutil.copytree(join(dirname(self.opt.resume), 'models'), join('tmp', 'models'))
network = importlib.import_module(f'tmp.models.{self.opt.net}')
model_tea = network.deliver_model(self.opt, 'tea').to(self.device)
checkpoint = torch.load(self.opt.resume)
model_tea.load_state_dict(checkpoint['state_dict'])
# build student net
assert os.path.exists(f'networks/{self.opt.net}.py'), 'Cannot find ' + f'{self.opt.net}.py :('
network = importlib.import_module('networks.' + self.opt.net)
model = network.deliver_model(self.opt, 'stu').to(self.device)
# checkpointS=torch.load('logs/tri_train_stu_0804_180109/ckpt_e_1.pth.tar')
# model.load_state_dict(checkpointS['state_dict'])
self.opt.output_dim = \
model(torch.rand((2, 3, self.opt.height, self.opt.width), device=self.device))[0].shape[-1]
self.opt.sigma_dim = \
model(torch.rand((2, 3, self.opt.height, self.opt.width), device=self.device))[1].shape[-1]
elif self.opt.phase in ['test_tea', 'test_stu']:
# load teacher or student net
assert self.opt.resume != '', 'You need to define a teacher/resume path :('
if exists('tmp'):
shutil.rmtree('tmp')
os.mkdir('tmp')
shutil.copytree(join(dirname(self.opt.resume), 'models'), join('tmp', 'models'))
network = importlib.import_module('tmp.models.{}'.format(self.opt.net))
model = network.deliver_model(self.opt, self.opt.phase[-3:]).to(self.device)
checkpoint = torch.load(self.opt.resume)
model.load_state_dict(checkpoint['state_dict'])
print('{}:{}, {}:{}, {}:{}'.format(model.id, self.opt.net, 'loss', self.opt.loss, 'mu_dim', self.opt.output_dim,
'sigma_dim', self.opt.sigma_dim if self.opt.phase[-3:] == 'stu' else '-'))
if self.opt.phase in ['train_tea', 'train_stu']:
# optimizer
if self.opt.optim == 'adam':
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), self.opt.lr,
weight_decay=self.opt.weightDecay)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, self.opt.lrGamma, last_epoch=-1, verbose=False)
elif self.opt.optim == 'sgd':
optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=self.opt.lr,
momentum=self.opt.momentum, weight_decay=self.opt.weightDecay)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=self.opt.lrStep, gamma=self.opt.lrGamma)
else:
raise NameError('Undefined optimizer :(')
criterion = nn.TripletMarginLoss(margin=self.opt.margin, p=2, reduction='sum').to(self.device)
if self.opt.nGPU > 1:
model = nn.DataParallel(model)
if self.opt.phase == 'train_tea':
return model, optimizer, scheduler, criterion
elif self.opt.phase == 'train_stu':
return model_tea, model, optimizer, scheduler, criterion
elif self.opt.phase in ['test_tea', 'test_stu']:
return model
else:
raise NameError('Undefined phase :(')
def build_embedding_cache(self):
'''build embedding cache, such that we can find the corresponding (p) and (n) with respect to (a) in embedding space
'''
self.train_set.cache = os.path.join(self.opt.runsPath, self.train_set.whichSet + '_feat_cache.hdf5')
with h5py.File(self.train_set.cache, mode='w') as h5:
h5feat = h5.create_dataset("features", [len(self.whole_train_set), self.opt.output_dim], dtype=np.float32)
with torch.no_grad():
for iteration, (input, indices) in enumerate(tqdm(self.whole_training_data_loader), 1):
input = input.to(self.device) # torch.Size([32, 3, 154, 154]) ([32, 5, 3, 200, 200])
emb, _ = self.model(input)
h5feat[indices.detach().numpy(), :] = emb.detach().cpu().numpy()
del input, emb
def build_embedding_cache_stu(self):
'''build embedding cache, such that we can find the corresponding (p) and (n) with respect to (a) in embedding space
'''
self.train_set.cache = os.path.join(self.opt.runsPath, self.train_set.whichSet + '_feat_cache.hdf5')
with h5py.File(self.train_set.cache, mode='w') as h5:
h5feat = h5.create_dataset("features", [len(self.whole_train_set), self.opt.output_dim], dtype=np.float32)
with torch.no_grad():
for iteration, (input, indices) in enumerate(tqdm(self.whole_training_data_loader), 1):
input = input.to(self.device) # torch.Size([32, 3, 154, 154]) ([32, 5, 3, 200, 200])
emb, _ = self.student_net(input)
h5feat[indices.detach().numpy(), :] = emb.detach().cpu().numpy()
del input, emb
def process_batch(self, batch_inputs):
'''
process a batch of input
'''
anchor, positives, negatives, neg_counts, indices = batch_inputs
# in case we get an empty batch
if anchor is None:
return None, None
# some reshaping to put query, pos, negs in a single (N, 3, H, W) tensor, where N = batchSize * (nQuery + nPos + n_neg)
B = anchor.shape[0] # ([8, 1, 3, 200, 200])
n_neg = torch.sum(neg_counts) # tensor(80) = torch.sum(torch.Size([8]))
input = torch.cat([anchor, positives, negatives]) # ([B, C, H, 200])
input = input.to(self.device) # ([96, 1, C, H, W])
embs, vars = self.model(input) # ([96, D])
tuple_loss = 0
# Standard triplet loss (via PyTorch library)
if self.opt.loss == 'tri':
embs_a, embs_p, embs_n = torch.split(embs, [B, B, n_neg])
for i, neg_count in enumerate(neg_counts):
for n in range(neg_count):
negIx = (torch.sum(neg_counts[:i]) + n).item()
tuple_loss += self.criterion(embs_a[i:i + 1], embs_p[i:i + 1], embs_n[negIx:negIx + 1])
tuple_loss /= n_neg.float().to(self.device)
del input, embs, embs_a, embs_p, embs_n
del anchor, positives, negatives
return tuple_loss, n_neg
def process_batch_stu(self, batch_inputs):
'''
process a batch of input
'''
anchor, positives, negatives, neg_counts, indices = batch_inputs
# in case we get an empty batch
if anchor is None:
return None, None
# some reshaping to put query, pos, negs in a single (N, 3, H, W) tensor, where N = batchSize * (nQuery + nPos + n_neg)
B = anchor.shape[0] # ([8, 1, 3, 200, 200])
n_neg = torch.sum(neg_counts) # tensor(80) = torch.sum(torch.Size([8]))
input = torch.cat([anchor, positives, negatives]) # ([B, C, H, 200])
input = input.to(self.device) # ([96, 1, C, H, W])
embs, vars = self.student_net(input) # ([96, D])
anchor = anchor.to(self.device)
with torch.no_grad():
mu_tea, _ = self.teacher_net(input) # ([B, D])
# mu_stu, log_sigma_sq = self.student_net(anchor) # ([B, D]), ([B, D])
tuple_loss = 0
CKDloss = 0
# Standard triplet loss (via PyTorch library)
if self.opt.loss == 'tri':
embs_a, embs_p, embs_n = torch.split(embs, [B, B, n_neg])
vars_a, vars_p, vars_n = torch.split(vars, [B, B, n_neg])
mu_tea_a, mu_tea_p, mu_tea_n = torch.split(mu_tea, [B, B, n_neg])
for i, neg_count in enumerate(neg_counts):
for n in range(neg_count):
negIx = (torch.sum(neg_counts[:i]) + n).item()
tuple_loss += self.criterion(embs_a[i:i + 1], embs_p[i:i + 1], embs_n[negIx:negIx + 1])
CKDloss += self.CKD_loss(embs_a[i:i + 1], embs_p[i:i + 1], embs_n[negIx:negIx + 1],
mu_tea_a[i:i + 1], mu_tea_p[i:i + 1], mu_tea_n[negIx:negIx + 1])
tuple_loss /= n_neg.float().to(self.device)
CKDloss /= n_neg.float().to(self.device)
del input, embs, embs_a, embs_p, embs_n
del anchor, positives, negatives
return tuple_loss + CKDloss, n_neg
def train(self):
not_improved = 0
for epoch in range(self.opt.nEpochs):
# make dataset
self.make_dataset()
self.epoch = epoch
self.current_lr = self.optimizer.state_dict()['param_groups'][0]['lr']
# build embedding cache
if self.epoch % self.opt.cacheRefreshEvery == 0:
self.model.eval()
self.build_embedding_cache()
self.model.train()
# train
tuple_loss_sum = 0
for _, batch_inputs in enumerate(tqdm(self.training_data_loader)):
self.step += 1
self.optimizer.zero_grad()
tuple_loss, n_neg = self.process_batch(batch_inputs)
if tuple_loss is None:
continue
tuple_loss.backward()
self.optimizer.step()
tuple_loss_sum += tuple_loss.item()
if self.step % 10 == 0:
wandb.log({'train_tuple_loss': tuple_loss.item()}, step=self.step)
wandb.log({'train_batch_num_neg': n_neg}, step=self.step)
n_batches = len(self.training_data_loader)
wandb.log({'train_avg_tuple_loss': tuple_loss_sum / n_batches}, step=self.step)
torch.cuda.empty_cache()
self.scheduler.step()
# val every x epochs
if (self.epoch % self.opt.evalEvery) == 0:
recalls = self.val(self.model, self.epoch)
if recalls[0] > self.best_recalls[0]:
self.best_recalls = recalls
not_improved = 0
else:
if recalls[0] == self.best_recalls[0]:
self.save_model(self.model, is_best=False, save_every_epoch=True)
not_improved += self.opt.evalEvery
# light log
vars_to_log = [
'e={:>2d},'.format(self.epoch),
'lr={:>.8f},'.format(self.current_lr),
'tl={:>.4f},'.format(tuple_loss_sum / n_batches),
'r@1/5/10={:.2f}/{:.2f}/{:.2f}'.format(recalls[0], recalls[1], recalls[2]),
'\n' if not_improved else ' *\n',
]
light_log(self.opt.runsPath, vars_to_log)
else:
recalls = None
self.save_model(self.model, is_best=not not_improved)
# stop when not improving for a period
if self.opt.phase == 'train_tea':
if self.opt.patience > 0 and not_improved > self.opt.patience:
print('terminated because performance has not improve for', self.opt.patience, 'epochs')
break
self.save_model(self.model, is_best=False)
print('best r@1/5/10={:.2f}/{:.2f}/{:.2f}'.format(self.best_recalls[0], self.best_recalls[1],
self.best_recalls[2]))
return self.best_recalls
def train_student(self):
not_improved = 0
for epoch in range(self.opt.nEpochs):
self.epoch = epoch
self.current_lr = self.optimizer.state_dict()['param_groups'][0]['lr']
# build embedding cache
if self.epoch % self.opt.cacheRefreshEvery == 0:
self.student_net.eval()
self.build_embedding_cache()
self.student_net.train()
# train
tuple_loss_sum = 0
for _, batch_inputs in enumerate(tqdm(self.training_data_loader)):
self.step += 1
self.optimizer.zero_grad()
tuple_loss, n_neg = self.process_batch_stu(batch_inputs)
if tuple_loss is None:
continue
tuple_loss.backward()
self.optimizer.step()
tuple_loss_sum += tuple_loss.item()
loss_sum = tuple_loss_sum
if self.step % 10 == 0:
wandb.log({'train_tuple_loss': tuple_loss.item()}, step=self.step)
wandb.log({'train_batch_num_neg': n_neg}, step=self.step)
n_batches = len(self.training_data_loader)
wandb.log({'train_avg_tuple_loss': tuple_loss_sum / n_batches}, step=self.step)
wandb.log({'student/epoch_loss': loss_sum / n_batches}, step=self.step)
torch.cuda.empty_cache()
self.scheduler.step()
# val
if (self.epoch % self.opt.evalEvery) == 0:
recalls = self.val(self.student_net)
if recalls[0] > self.best_recalls[0]:
self.best_recalls = recalls
not_improved = 0
else:
not_improved += self.opt.evalEvery
light_log(self.opt.runsPath, [
f'e={self.epoch:>2d},',
f'lr={self.current_lr:>.8f},',
f'tl={loss_sum / n_batches:>.4f},',
f'r@1/5/10={recalls[0]:.2f}/{recalls[1]:.2f}/{recalls[2]:.2f}',
'\n' if not_improved else ' *\n',
])
else:
recalls = None
self.save_model(self.student_net, is_best=False, save_every_epoch=True)
if self.opt.patience > 0 and not_improved > self.opt.patience:
print('terminated because performance has not improve for', self.opt.patience, 'epochs')
break
print('best r@1/5/10={:.2f}/{:.2f}/{:.2f}'.format(self.best_recalls[0], self.best_recalls[1],
self.best_recalls[2]))
return self.best_recalls
def val(self, model, epoch):
mode = "val"
recalls, _ = self.get_recall(model, mode, epoch)
for i, n in enumerate([1, 5, 10]):
wandb.log({'{}/{}_r@{}'.format(model.id, self.opt.split, n): recalls[i]}, step=self.step)
# self.writer.add_scalar('{}/{}_r@{}'.format(model.id, self.opt.split, n), recalls[i], self.epoch)
return recalls
def test(self):
mode = "test"
epoch = None
recalls, _ = self.get_recall(self.model, mode, epoch, save_embs=True)
print('best r@1/5/10={:.2f}/{:.2f}/{:.2f}'.format(recalls[0], recalls[1], recalls[2]))
# summary(self.model, input_size=(3, 224, 224))
return recalls
def save_model(self, model, is_best=False, save_every_epoch=False):
if is_best:
torch.save({
'epoch': self.epoch,
'step': self.step,
'state_dict': model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict(),
}, os.path.join(self.opt.runsPath, 'ckpt_best.pth.tar'))
if save_every_epoch:
torch.save({
'epoch': self.epoch,
'step': self.step,
'state_dict': model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict(),
}, os.path.join(self.opt.runsPath, 'ckpt_e_{}.pth.tar'.format(self.epoch)))
def get_recall(self, model, mode, epoch, save_embs=False):
model.eval()
if self.opt.split == 'val':
eval_dataloader = self.whole_val_data_loader
eval_set = self.whole_val_set
eval_dataloader_train = self.whole_training_data_loader
eval_set_train = self.whole_train_set
elif self.opt.split == 'test':
eval_dataloader = self.whole_test_data_loader
eval_set = self.whole_test_set
# print(f"{self.opt.split} len:{len(eval_set)}")
# print(len(eval_set))
# val
whole_mu = torch.zeros((len(eval_set), self.opt.output_dim), device=self.device) # (N, D)
whole_var = torch.zeros((len(eval_set), self.opt.sigma_dim), device=self.device) # (N, D)
gt = eval_set.get_positives() # (N_q, n_pos)
if self.opt.split == 'val':
# train
whole_mu_train = torch.zeros((len(eval_set_train), self.opt.output_dim), device=self.device) # (N, D)
whole_var_train = torch.zeros((len(eval_set_train), self.opt.sigma_dim), device=self.device) # (N, D)
gt_train = eval_set_train.get_positives() # (N_q, n_pos)
# print(len(gt))
start_time = time.time()
with torch.no_grad():
for iteration, (input, indices) in enumerate(tqdm(eval_dataloader), 1):
input = input.to(self.device)
# print(iteration)
# print(input.shape)
# print(indices)
mu, _ = model(input) # (B, D)
# summary(self.model, input_size=input.shape[1:])
# print(input.shape)
# var = torch.exp(var)
whole_mu[indices, :] = mu
# whole_var[indices, :] = var
del input, mu
end_time = time.time()
elapsed_time = end_time - start_time
print("Elapsed Time:", elapsed_time)
if self.opt.split == 'val':
with torch.no_grad():
for iteration, (input, indices) in enumerate(tqdm(eval_dataloader_train), 1):
input = input.to(self.device)
# print(iteration)
# print(input.shape)
# print(indices)
mu, _ = model(input) # (B, D)
# summary(self.model, input_size=input.shape[1:])
# print(input.shape)
# var = torch.exp(var)
whole_mu_train[indices, :] = mu
# whole_var[indices, :] = var
del input, mu
n_values = [1, 5, 10]
whole_mu = whole_mu.cpu().numpy()
mu_q = whole_mu[eval_set.dbStruct.numDb:].astype('float32')
mu_db = whole_mu[:eval_set.dbStruct.numDb].astype('float32')
faiss_index = faiss.IndexFlatL2(mu_q.shape[1])
faiss_index.add(mu_db)
dists, preds = faiss_index.search(mu_q, max(n_values)) # the results is sorted
# cull queries without any ground truth positives in the database
val_inds = [True if len(gt[ind]) != 0 else False for ind in range(len(gt))]
val_inds = np.array(val_inds)
mu_q = mu_q[val_inds]
preds = preds[val_inds]
dists = dists[val_inds]
gt = gt[val_inds]
recall_at_k, q_id = cal_recall(preds, gt, n_values)
if self.opt.split == 'val':
# train result for augmentation
whole_mu_train = whole_mu_train.cpu().numpy()
mu_q_train = whole_mu_train[eval_set_train.dbStruct.numDb:].astype('float32')
mu_db_train = whole_mu_train[:eval_set_train.dbStruct.numDb].astype('float32')
faiss_index_train = faiss.IndexFlatL2(mu_q_train.shape[1])
faiss_index_train.add(mu_db_train)
dists_train, preds_train = faiss_index_train.search(mu_q_train, max(n_values)) # the results is sorted
# cull queries without any ground truth positives in the database
val_inds_train = [True if len(gt_train[ind]) != 0 else False for ind in range(len(gt_train))]
val_inds_train = np.array(val_inds_train)
mu_q_train = mu_q_train[val_inds_train]
preds_train = preds_train[val_inds_train] # (n_q, 10)
dists_train = dists_train[val_inds_train] # (n_q, 10) 从近到远
gt_train = gt_train[val_inds_train]
recall_at_k_train, q_id_train = cal_recall(preds_train, gt_train, n_values)
if mode == "val" and self.opt.Datasetname == "Cambridge" and len(q_id_train) > 0 and epoch > 5:
device = torch.device("cuda")
dir_q = os.path.join(self.opt.imgDir, "train", "query")
# dir_q = os.path.join("E:/shujuji/nerfCambridge4VPR/CambridgeNerf_train1_4", "val", "query")
poseq, scenesq = gen_pose(q_id_train, preds_train, dir_q) # 需要增加数据的pose，使用的q_id_train筛选
print(poseq.shape) # [58, 4, 4]
print(len(scenesq)) # 58
# print(q_id_train)
# todo [58, 4, 4]->[58, n, 4, 4]
# determine bounding box
b_min = [poseq[:, 0, 3].min() - self.args_nerf.d_max, poseq[:, 1, 3].min() - self.args_nerf.d_max,
poseq[:, 2, 3].min() - self.args_nerf.d_max]
b_max = [poseq[:, 0, 3].max() + self.args_nerf.d_max, poseq[:, 1, 3].max() + self.args_nerf.d_max,
poseq[:, 2, 3].max() + self.args_nerf.d_max]
poses_target = perturb_single_render_pose(poseq, self.args_nerf.rvs_trans, self.args_nerf.rvs_rotation, 10)
# poses_target = poseq.unsqueeze(1) #测试需要删去
# poses_target = poses_target.repeat(1, 3, 1, 1) #测试需要删去
poses_target = poses_target.to("cuda")
for i in range(poses_target.shape[0]):
for j in range(poses_target.shape[1]):
for k in range(3):
if poses_target[i, j, k, 3] < b_min[k]:
poses_target[i, j, k, 3] = b_min[k]
elif poses_target[i, j, k, 3] > b_max[k]:
poses_target[i, j, k, 3] = b_max[k]
# print(poses_target)
dir_d = os.path.join(self.opt.imgDir, "train", "database")
imageq = find_image(dir_q)
imaged = find_image(dir_d)
dir_d_pose = os.path.join(dir_d, "poses4")
pose_database, _ = read_pose(dir_d_pose) # (1342,4,4)
# print(pose_database.shape)
image_database = []
img_idx_list = []
# print(len(imaged))
for idx in range(len(imaged)):
img = Image.open(imaged[idx])
# img = self.input_transform(img)
img = self.image_to_tensor(img)
image_database.append(img)
for idx in range(len(imageq)):
img = load_image(imageq[idx])
img = self.data_transform(img)
yuv = rgb_to_yuv(img)
y_img = yuv[0] # extract y channel only
hist = torch.histc(y_img, bins=10, min=0., max=1.) # compute intensity histogram
hist = hist / (hist.sum()) * 100 # convert to histogram density, in terms of percentage per bin
hist = torch.round(hist)
hist = hist.unsqueeze(0)
# print(hist.shape)
img_idx_list.append(hist.cpu())
img_idxs = torch.stack(img_idx_list).detach()
# print(img_idxs.shape)
selected_img_idxs = img_idxs[q_id_train]
img_idxs_repeated = torch.repeat_interleave(selected_img_idxs, repeats=3, dim=0)
# print(img_idxs_repeated.shape)
img_idxs = img_idxs_repeated
# print(image_database.shape)
imgs_database = torch.stack(image_database, dim=0) # (n_database, 3, 224, 224)
imgs_database = F.interpolate(imgs_database, size=[224, 224], mode="area")
imgs = torch.zeros((len(q_id_train), 5, 3, 224, 224))
# 截取每个数组的前5个元素
gt_train = gt_train = np.array([
arr[:5] if len(arr) >= 5 else np.pad(arr, (0, 5 - len(arr)), mode='wrap')
for arr in gt_train
])
# 转换为二维数组，形状为 [len(gt), 5]
gt_train = np.array(gt_train)
index_array = gt_train[q_id_train, :5]
# print(index_array.shape)
for i, indices in enumerate(index_array):
imgs[i] = imgs_database[indices]
# imgs = imgs_database[preds_train[q_id_train, :5]] # [58, 5, 3, 224, 224]
images_ref = imgs
print(images_ref.shape)
# images_ref = imgs[:20] # [20, 5, 3, 224, 224]
pose = torch.zeros((len(q_id_train), 5, 4, 4))
index_array = gt_train[q_id_train, :5]
for i, indices in enumerate(index_array):
pose[i] = pose_database[indices]
# pose = pose_database[preds_train[q_id_train, :5]] # [58, 5, 4, 4]
poses_ref = pose
# poses_ref = pose[:20] # [20, 5, 4, 4]
print("img shape")
# print(imgs.shape)
print(images_ref.shape)
print("pose shape")
# print(pose.shape)
print(poses_ref.shape)
predict = NBP_Cam(self.model_name, images_ref, poses_ref, poses_target) #[58 3]
predict = predict.to(self.device)
del images_ref, poses_ref
expanded_predict = predict.unsqueeze(-1).unsqueeze(-1).expand(-1, 3, 4, 4)
selected_poses = torch.gather(poses_target, 1, expanded_predict) # (58,n,4,4)->(58,3,4,4)
selected_scene = [item for sublist in scenesq for item in [sublist] * 3] # scenes重塑为了后续fixcoord
# print(selected_poses.shape)
hwf = [480, 854, 744.0]
world_setup_dict = {
'pose_scale': 0.3027,
'pose_scale2': 0.2,
'move_all_cam_vec': [0.0, 0.0, 0.0],
}
world_setup_dict_OldHospital = {
'pose_scale': 0.3027,
'pose_scale2': 0.2,
'move_all_cam_vec': [0.0, 0.0, 5.0],
}
world_setup_dict_ShopFacade = {
'pose_scale': 0.3027,
'pose_scale2': 0.32,
'move_all_cam_vec': [0.0, 0.0, 2.5],
}
# print(selected_poses[0,0])
# print("after_fixcoord")
# print(selected_scene)
selected_poses, bounds, idx = fix_coord(selected_poses, selected_scene,
pose_avg_stats_file='data/poses_avg_stats')
save_folder_rgb="Cambridge/CambridgeNerf_train1_4/train/database/rgb"
save_folder_poses = "Cambridge/CambridgeNerf_train1_4/train/database/poses"
save_folder_poses4 = "Cambridge/CambridgeNerf_train1_4/train/database/poses4"
os.makedirs(save_folder_rgb, exist_ok=True)
os.makedirs(save_folder_poses, exist_ok=True)
os.makedirs(save_folder_poses4, exist_ok=True)
import glob
image_remove_files = glob.glob(os.path.join(save_folder_rgb, 'zadd*.png'))
poses_remove_files = glob.glob(os.path.join(save_folder_poses, 'zadd*.txt'))
poses4_remove_files = glob.glob(os.path.join(save_folder_poses4, 'zadd*.txt'))
# 合并所有要删除的文件列表
files_to_delete = image_remove_files + poses_remove_files + poses4_remove_files
for file in files_to_delete:
try:
os.remove(file)
print(f'Successfully deleted: {file}')
except Exception as e:
print(f'Error deleting file {file}: {e}')
# print(selected_poses[0])
# print(img_idxs[0])
if (idx[0] > 0):
# GreatCourt
render_virtual_Cam_imgs(self.args_nerf, selected_poses[:idx[0]], img_idxs[:idx[0]], hwf, self.device,
self.render_kwargs_test_GreatCourt, world_setup_dict, epoch, scene="GreatCourt")
if ((idx[1] - idx[0]) > 0):
# KingsCollege
render_virtual_Cam_imgs(self.args_nerf, selected_poses[idx[0]:idx[1]], img_idxs[idx[0]:idx[1]], hwf,
self.device,
self.render_kwargs_test_KingsCollege, world_setup_dict, epoch,
scene="KingsCollege")
if ((idx[2] - idx[1]) > 0):
# OldHospital
render_virtual_Cam_imgs(self.args_nerf, selected_poses[idx[1]:idx[2]], img_idxs[idx[1]:idx[2]], hwf,
self.device,
self.render_kwargs_test_OldHospital, world_setup_dict_OldHospital, epoch,
scene="OldHospital")
if ((idx[3] - idx[2]) > 0):
# ShopFacade
render_virtual_Cam_imgs(self.args_nerf, selected_poses[idx[2]:idx[3]], img_idxs[idx[2]:idx[3]], hwf,
self.device,
self.render_kwargs_test_ShopFacade, world_setup_dict_ShopFacade, epoch,
scene="ShopFacade")
if ((idx[4] - idx[3]) > 0):
# StMarysChurch
render_virtual_Cam_imgs(self.args_nerf, selected_poses[idx[3]:idx[4]], img_idxs[idx[3]:idx[4]], hwf,
self.device,
self.render_kwargs_test_StMarysChurch, world_setup_dict, epoch,
scene="StMarysChurch")
del poses_target, imgs_database
torch.cuda.empty_cache()
if mode == "val" and self.opt.Datasetname == "NEU" and len(q_id_train) > 0:
device = torch.device("cuda")
dir_q = os.path.join(self.opt.imgDir, "train", "query")
# dir_q = os.path.join("E:/shujuji/nerfCambridge4VPR/CambridgeNerf_train1_4", "val", "query")
poseq, scenesq = gen_pose(q_id_train, preds_train, dir_q) # 需要增加数据的pose，使用的q_id_train筛选
print(poseq.shape) # [58, 4, 4]
print(len(scenesq)) # 58
# print(q_id_train)
# todo [58, 4, 4]->[58, n, 4, 4]
# determine bounding box
b_min = [poseq[:, 0, 3].min() - self.args_nerf.d_max, poseq[:, 1, 3].min() - self.args_nerf.d_max,
poseq[:, 2, 3].min() - self.args_nerf.d_max]
b_max = [poseq[:, 0, 3].max() + self.args_nerf.d_max, poseq[:, 1, 3].max() + self.args_nerf.d_max,
poseq[:, 2, 3].max() + self.args_nerf.d_max]
poses_target = perturb_single_render_pose(poseq, self.args_nerf.rvs_trans, self.args_nerf.rvs_rotation, 10)
# poses_target = poseq.unsqueeze(1) #测试需要删去
# poses_target = poses_target.repeat(1, 3, 1, 1) #测试需要删1
poses_target = poses_target.to("cuda")
for i in range(poses_target.shape[0]):
for j in range(poses_target.shape[1]):
for k in range(3):
if poses_target[i, j, k, 3] < b_min[k]:
poses_target[i, j, k, 3] = b_min[k]
elif poses_target[i, j, k, 3] > b_max[k]:
poses_target[i, j, k, 3] = b_max[k]
# print(poses_target)
dir_d = os.path.join(self.opt.imgDir, "train", "database")
imageq = find_image(dir_q)
imaged = find_image(dir_d)
dir_d_pose = os.path.join(dir_d, "poses4")
pose_database, _ = read_pose(dir_d_pose) # (1342,4,4)
# print(pose_database.shape)
image_database = []
img_idx_list = []
# print(len(imaged))
for idx in range(len(imaged)):
img = Image.open(imaged[idx])
# img = self.input_transform(img)
img = self.image_to_tensor(img)
image_database.append(img)
for idx in range(len(imageq)):
img = load_image(imageq[idx])
img = self.data_transform(img)
yuv = rgb_to_yuv(img)
y_img = yuv[0] # extract y channel only
hist = torch.histc(y_img, bins=10, min=0., max=1.) # compute intensity histogram
hist = hist / (hist.sum()) * 100 # convert to histogram density, in terms of percentage per bin
hist = torch.round(hist)
hist = hist.unsqueeze(0)
# print(hist.shape)
img_idx_list.append(hist.cpu())
img_idxs = torch.stack(img_idx_list).detach()
selected_img_idxs = img_idxs[q_id_train]
img_idxs_repeated = torch.repeat_interleave(selected_img_idxs, repeats=3, dim=0)
# print(img_idxs_repeated.shape)
img_idxs = img_idxs_repeated
# print(image_database.shape)
imgs_database = torch.stack(image_database, dim=0) # (n_database, 3, 224, 224)
imgs_database = F.interpolate(imgs_database, size=[224, 224], mode="area")
imgs = torch.zeros((len(q_id_train), 5, 3, 224, 224))
# 截取每个数组的前5个元素
gt_train = gt_train = np.array([
arr[:5] if len(arr) >= 5 else np.pad(arr, (0, 5 - len(arr)), mode='wrap')
for arr in gt_train
])
# 转换为二维数组，形状为 [len(gt), 5]
gt_train = np.array(gt_train)
index_array = gt_train[q_id_train, :5]
# print(index_array.shape)
for i, indices in enumerate(index_array):
imgs[i] = imgs_database[indices]
# imgs = imgs_database[preds_train[q_id_train, :5]] # [58, 5, 3, 224, 224]
images_ref = imgs
print(images_ref.shape)
# images_ref = imgs[:20] # [20, 5, 3, 224, 224]
pose = torch.zeros((len(q_id_train), 5, 4, 4))
index_array = gt_train[q_id_train, :5]
for i, indices in enumerate(index_array):
pose[i] = pose_database[indices]
# pose = pose_database[preds_train[q_id_train, :5]] # [58, 5, 4, 4]
poses_ref = pose
# poses_ref = pose[:20] # [20, 5, 4, 4]
print("img shape")
# print(imgs.shape)
print(images_ref.shape)
print("pose shape")
# print(pose.shape)
print(poses_ref.shape)
predict = NBP_NEU(self.model_name, images_ref, poses_ref, poses_target, self.device) # [58 3]
predict = predict.to(self.device)
del images_ref, poses_ref
expanded_predict = predict.unsqueeze(-1).unsqueeze(-1).expand(-1, 3, 4, 4)
selected_poses = torch.gather(poses_target, 1, expanded_predict) # (58,n,4,4)->(58,3,4,4)
selected_scene = [item for sublist in scenesq for item in [sublist] * 3] # scenes重塑为了后续fixcoord
# print(selected_scene)
hwf = [270, 480, 211.]
world_setup_dict = {
'pose_scale': 1,
'pose_scale2': 1,
'move_all_cam_vec': [0.0, 0.0, 0.0],
}
selected_poses, bounds, idx = fix_coord_NEU(selected_poses, selected_scene)
save_folder_rgb = "Cambridge/NEUnightNerf/train/database/rgb"
save_folder_poses = "Cambridge/NEUnightNerf/train/database/poses"