def get_section_tutorial(OurFaceNet):
st.write()
'''
# Tutorial
In this section we want to visualize how the face recognition is done. For this purpose we wil first select
a few images of people (IDs) which will serve as the anchor images. Those images are taken from the
'Large-scale CelebFaces Attributes' dataset [(CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html).
After that, we then use a couple example images of the same people but in different settings.
The list of variations includes the following:
* Different backgrounds
* Different lighting, bright, dark, color, etc.
* Facing different angles
* Wearing hats, accessories, etc.
* Covering face with eyeglasses, sun-glasses, mustache, etc.
* Different hair cuts, e.g. long hair, short hair, bangs, etc.
* Same person in different age (young vs old)
* Different facial expressions
'''
st.write()
st.write()
load_tutorial_anchor_images()
test_images = load_tutorial_test_images()
st.write()
st.write()
"""
In the inference phase, the test image is feed into the trained model. It calculates an embedding which is
a numerical vector that represents the person (face). This embedding should include all relevant facial features
in order to identify a person. The generated embedding is then used to compare it with all known people.
This is done pairwise (test person <-> known person) with a distance function which calculates a similarity
value. You are free to use any distance function that maps two vectors to one scalar value. In our case, we
decide to train our model with the use of the euclidean distance (L2 Norm).
"""
st.write()
st.write()
st.image(Image.open(Path('documentation/images/InferenceProcess.jpeg')),
caption=f"Inference Process",
use_column_width=True
)
st.write()
st.write()
'''
'''
if len(test_images) > 0:
st.subheader("Inference")
start_inference = st.button(label="Start Inference")
if start_inference:
col_1, col_2, col_3 = st.beta_columns(3)
visited = [False, False]
for img_path in test_images:
img_tensor = transforms.ToTensor()(Image.open(img_path).resize([224, 224]))
pred_id, msg = OurFaceNet.inference(
img_tensor, use_threshold=False, fuzzy_matches=False
)
msg = f"{msg.replace('!', ' -')} Person (ID): {pred_id}"
temp_test_img_label = img_path.stem.split(" ")[0]
if str(pred_id) == temp_test_img_label:
t = "<div><span class='highlight green'><span class='bold'>Passed</span> </span></div>"
else:
t = "<div> <span class='highlight red'><span class='bold'>Failed</span></span></div>"
if not visited[0]:
col_1.markdown(t, unsafe_allow_html=True)
col_1.image(Image.open(img_path), caption=f"{msg}", use_column_width=True)
visited[0] = True
elif not visited[1]:
col_2.markdown(t, unsafe_allow_html=True)
col_2.image(Image.open(img_path), caption=f"{msg}", use_column_width=True)
visited[1] = True
else:
col_3.markdown(t, unsafe_allow_html=True)
col_3.image(Image.open(img_path), caption=f"{msg}", use_column_width=True)
visited = [False, False]
def run(opt):
torch.manual_seed(opt.seed)
torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
print('Setting up data...')
Dataset = get_dataset(opt.dataset,
opt.task) # if opt.task==mot -> JointDataset
f = open(opt.data_cfg) # 选择哪一个数据集进行训练测试 '../src/lib/cfg/mot15.json',
data_config = json.load(f)
trainset_paths = data_config['train'] # 训练集路径
dataset_root = data_config['root'] # 数据集所在目录
print("Dataset root: %s" % dataset_root)
f.close()
# Image data transformations
transforms = T.Compose([T.ToTensor()])
# Dataset
dataset = Dataset(opt=opt,
root=dataset_root,
paths=trainset_paths,
img_size=(1088, 608),
augment=True,
transforms=transforms)
opt = opts().update_dataset_info_and_set_heads(opt, dataset)
print("opt:\n", opt)
logger = Logger(opt)
# os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
# os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str # 多GPU训练
# print("opt.gpus_str: ", opt.gpus_str)
# opt.device = torch.device('cuda:0' if opt.gpus[0] >= 0 else 'cpu') # 设置GPU
opt.device = device
opt.gpus = my_visible_devs
print('Creating model...')
model = create_model(opt.arch, opt.heads, opt.head_conv)
# 初始化优化器
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
start_epoch = 0
if opt.load_model != '':
model, optimizer, start_epoch = load_model(model, opt.load_model,
optimizer, opt.resume,
opt.lr, opt.lr_step)
# Get dataloader
if opt.is_debug:
train_loader = torch.utils.data.DataLoader(
dataset=dataset,
batch_size=opt.batch_size,
shuffle=True,
pin_memory=True,
drop_last=True) # debug时不设置线程数(即默认为0)
else:
train_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True)
print('Starting training...')
Trainer = train_factory[opt.task]
trainer = Trainer(opt=opt, model=model, optimizer=optimizer)
# trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
trainer.set_device(opt.gpus, opt.chunk_sizes, device)
best = 1e10
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
mark = epoch if opt.save_all else 'last'
# Train an epoch
log_dict_train, _ = trainer.train(epoch, train_loader)
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),
epoch, model, optimizer)
else: # mcmot_last_track or mcmot_last_det
if opt.id_weight > 0: # do tracking(detection and re-id)
save_model(
os.path.join(opt.save_dir,
'mcmot_last_track_' + opt.arch + '.pth'),
epoch, model, optimizer)
else: # only do detection
# save_model(os.path.join(opt.save_dir, 'mcmot_last_det_' + opt.arch + '.pth'),
# epoch, model, optimizer)
save_model(
os.path.join(opt.save_dir,
'mcmot_last_det_' + opt.arch + '.pth'), epoch,
model, optimizer)
logger.write('\n')
if epoch in opt.lr_step:
save_model(
os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
lr = opt.lr * (0.1**(opt.lr_step.index(epoch) + 1))
print('Drop LR to', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if epoch % 10 == 0:
save_model(
os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
logger.close()
def train(
cfg,
data_cfg,
weights_from="",
weights_to="",
save_every=10,
img_size=(1088, 608),
resume=False,
epochs=100,
batch_size=16,
accumulated_batches=1,
freeze_backbone=False,
opt=None,
):
# The function starts
timme = strftime("%Y-%d-%m %H:%M:%S", gmtime())
timme = timme[5:-3].replace('-', '_')
timme = timme.replace(' ', '_')
timme = timme.replace(':', '_')
weights_to = osp.join(weights_to, 'run' + timme)
mkdir_if_missing(weights_to)
if resume:
latest_resume = osp.join(weights_from, 'latest.pt')
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
f = open(data_cfg)
data_config = json.load(f)
trainset_paths = data_config['train']
dataset_root = data_config['root']
f.close()
transforms = T.Compose([T.ToTensor()])
# Get dataloader
dataset = JointDataset(dataset_root,
trainset_paths,
img_size,
augment=True,
transforms=transforms)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True,
drop_last=True,
collate_fn=collate_fn)
# Initialize model
model = Darknet(cfg, dataset.nID)
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
if resume:
checkpoint = torch.load(latest_resume, map_location='cpu')
# Load weights to resume from
model.load_state_dict(checkpoint['model'])
model.cuda().train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad,
model.parameters()),
lr=opt.lr,
momentum=.9)
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
del checkpoint # current, saved
else:
# Initialize model with backbone (optional)
if cfg.endswith('yolov3.cfg'):
load_darknet_weights(model,
osp.join(weights_from, 'darknet53.conv.74'))
cutoff = 75
elif cfg.endswith('yolov3-tiny.cfg'):
load_darknet_weights(model,
osp.join(weights_from, 'yolov3-tiny.conv.15'))
cutoff = 15
model.cuda().train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad,
model.parameters()),
lr=opt.lr,
momentum=.9,
weight_decay=1e-4)
model = torch.nn.DataParallel(model)
# Set scheduler
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[int(0.5 * opt.epochs),
int(0.75 * opt.epochs)],
gamma=0.1)
# An important trick for detection: freeze bn during fine-tuning
if not opt.unfreeze_bn:
for i, (name, p) in enumerate(model.named_parameters()):
p.requires_grad = False if 'batch_norm' in name else True
# model_info(model)
t0 = time.time()
for epoch in range(epochs):
epoch += start_epoch
logger.info(
('%8s%12s' + '%10s' * 6) % ('Epoch', 'Batch', 'box', 'conf', 'id',
'total', 'nTargets', 'time'))
# Freeze darknet53.conv.74 for first epoch
if freeze_backbone and (epoch < 2):
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[2]) < cutoff: # if layer < 75
p.requires_grad = False if (epoch == 0) else True
ui = -1
rloss = defaultdict(float) # running loss
optimizer.zero_grad()
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
if sum([len(x) for x in targets]) < 1: # if no targets continue
continue
# SGD burn-in
burnin = min(1000, len(dataloader))
if (epoch == 0) & (i <= burnin):
lr = opt.lr * (i / burnin)**4
for g in optimizer.param_groups:
g['lr'] = lr
# Compute loss, compute gradient, update parameters
loss, components = model(imgs.cuda(), targets.cuda(),
targets_len.cuda())
components = torch.mean(components.view(-1, 5), dim=0)
loss = torch.mean(loss)
loss.backward()
# accumulate gradient for x batches before optimizing
if ((i + 1) % accumulated_batches
== 0) or (i == len(dataloader) - 1):
optimizer.step()
optimizer.zero_grad()
# Running epoch-means of tracked metrics
ui += 1
for ii, key in enumerate(model.module.loss_names):
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
# rloss indicates running loss values with mean updated at every epoch
s = ('%8s%12s' + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1), '%g/%g' %
(i, len(dataloader) - 1), rloss['box'], rloss['conf'],
rloss['id'], rloss['loss'], rloss['nT'], time.time() - t0)
t0 = time.time()
if i % opt.print_interval == 0:
logger.info(s)
# Save latest checkpoint
checkpoint = {
'epoch': epoch,
'model': model.module.state_dict(),
'optimizer': optimizer.state_dict()
}
#copyfile(cfg, weights_to + '/cfg/yolo3.cfg')
#copyfile(data_cfg, weights_to + '/cfg/ccmcpe.json')
latest = osp.join(weights_to, 'latest.pt')
torch.save(checkpoint, latest)
if epoch % save_every == 0 and epoch != 0:
# making the checkpoint lite
checkpoint["optimizer"] = []
torch.save(
checkpoint,
osp.join(weights_to, "weights_epoch_" + str(epoch) + ".pt"))
# Calculate mAP
if epoch % opt.test_interval == 0:
with torch.no_grad():
mAP, R, P = test.test(cfg,
data_cfg,
weights=latest,
batch_size=batch_size)
test.test_emb(cfg,
data_cfg,
weights=latest,
batch_size=batch_size)
# Call scheduler.step() after opimizer.step() with pytorch > 1.1.0
scheduler.step()
def train_one_epoch(
model,
arch,
optimizer,
lr_scheduler,
data_loader,
device,
epoch,
print_freq,
ngpus_per_node,
model_without_ddp,
args
):
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value:.6f}"))
# header = "Epoch: [{}]".format(epoch)
for images, targets in metric_logger.log_every(
iterable=data_loader,
print_freq=print_freq,
# header=header,
iter_num=args.iter_num
):
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
"""
[{"boxes": tensor([], device="cuda:0"), "labels": tensor([], device="cuda:0", dtype=torch.int64), "masks": tensor([], device="cuda:0", dtype=torch.uint8), "iscrowd": tensor([], device="cuda:0", dtype=torch.int64)}]
"""
try:
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
loss_value = losses_reduced.item()
if not math.isfinite(loss_value):
logger.fatal("Loss is {}, stopping training".format(loss_value))
logger.fatal(loss_dict_reduced)
sys.exit(1)
optimizer.zero_grad()
losses.backward()
optimizer.step()
lr_scheduler.step()
metric_logger.update(loss=losses_reduced, **loss_dict_reduced)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
except Exception as e:
logger.warning(e, exc_info=True)
# logger.info("print target for debug")
# print(targets)
args.iter_num += 1
# save checkpoint here
if not args.multiprocessing_distributed or (args.multiprocessing_distributed and args.rank % ngpus_per_node == 0):
if args.iter_num % 1000 == 0:
utils.save_on_master({
"model": model_without_ddp.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"epoch": epoch,
"iter_num": args.iter_num,
"args": args,
},
"{}/{}_{}.pth".format(checkpoint_dir, arch.__name__, args.iter_num)
)
os.makedirs("{}/debug_image/".format(checkpoint_dir), exist_ok=True)
if args.iter_num < 5000:
continue
model.eval()
# from barez import overlay_ann
debug_image = None
debug_image_list = []
cnt = 0
for image_path in glob.glob("./table_test/*"):
cnt += 1
image_name = os.path.basename(image_path)
# print(image_name)
image = cv2.imread(image_path)
rat = 1300 / image.shape[0]
image = cv2.resize(image, None, fx=rat, fy=rat)
transform = transforms.Compose([transforms.ToTensor()])
image = transform(image)
# put the model in evaluation mode
with torch.no_grad():
tensor = [image.to(device)]
prediction = model(tensor)
image = torch.squeeze(image, 0).permute(1, 2, 0).mul(255).numpy().astype(np.uint8)
for pred in prediction:
for idx, mask in enumerate(pred['masks']):
if pred['scores'][idx].item() < 0.5:
continue
m = mask[0].mul(255).byte().cpu().numpy()
box = list(map(int, pred["boxes"][idx].tolist()))
score = pred["scores"][idx].item()
# image = overlay_ann(image, m, box, "", score)
# if debug_image is None:
# debug_image = image
# else:
# debug_image = np.concatenate((debug_image, image), axis=1)
# if cnt == 10:
# cnt = 0
# debug_image_list.append(debug_image)
# debug_image = None
avg_length = np.mean([i.shape[1] for i in debug_image_list])
di = None
for debug_image in debug_image_list:
rat = avg_length / debug_image.shape[1]
debug_image = cv2.resize(debug_image, None, fx=rat, fy=rat)
if di is None:
di = debug_image
else:
di = np.concatenate((di, debug_image), axis=0)
di = cv2.resize(di, None, fx=0.4, fy=0.4)
cv2.imwrite("{}/debug_image/{}.jpg".format(checkpoint_dir, args.iter_num), di)
model.train()
# hard stop
if args.iter_num == 50000:
logger.info("ITER NUM == 50k, training successfully!")
raise SystemExit
def simple_augment():
simple_augment_ = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
return simple_augment_
def test_emb(
opt,
batch_size=16,
img_size=(1088, 608),
print_interval=40,
):
"""
:param opt:
:param batch_size:
:param img_size:
:param print_interval:
:return:
"""
data_cfg = opt.data_cfg
f = open(data_cfg)
data_cfg_dict = json.load(f)
f.close()
nC = 1
test_paths = data_cfg_dict['test_emb']
dataset_root = data_cfg_dict['root']
if opt.gpus[0] >= 0:
opt.device = torch.device('cuda')
else:
opt.device = torch.device('cpu')
print('Creating model...')
model = create_model(opt.arch, opt.heads, opt.head_conv)
model = load_model(model, opt.load_model)
# model = torch.nn.DataParallel(model)
model = model.to(opt.device)
model.eval()
# Get data loader
transforms = T.Compose([T.ToTensor()])
dataset = JointDataset(opt,
dataset_root,
test_paths,
img_size,
augment=False,
transforms=transforms)
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=False,
num_workers=8,
drop_last=False)
embedding, id_labels = [], []
print('Extracting features...')
for batch_i, batch in enumerate(data_loader):
t = time.time()
output = model(batch['input'].cuda())[-1]
id_head = _tranpose_and_gather_feat(output['id'], batch['ind'].cuda())
id_head = id_head[batch['reg_mask'].cuda() > 0].contiguous()
emb_scale = math.sqrt(2) * math.log(opt.nID - 1)
id_head = emb_scale * F.normalize(id_head)
id_target = batch['ids'].cuda()[batch['reg_mask'].cuda() > 0]
for i in range(0, id_head.shape[0]):
if len(id_head.shape) == 0:
continue
else:
feat, label = id_head[i], id_target[i].long()
if label != -1:
embedding.append(feat)
id_labels.append(label)
if batch_i % print_interval == 0:
print(
'Extracting {}/{}, # of instances {}, time {:.2f} sec.'.format(
batch_i, len(data_loader), len(id_labels),
time.time() - t))
print('Computing pairwise similarity...')
if len(embedding) < 1:
return None
embedding = torch.stack(embedding, dim=0).cuda()
id_labels = torch.LongTensor(id_labels)
n = len(id_labels)
print(n, len(embedding))
assert len(embedding) == n
embedding = F.normalize(embedding, dim=1)
p_dist = torch.mm(embedding, embedding.t()).cpu().numpy()
gt = id_labels.expand(n, n).eq(id_labels.expand(n, n).t()).numpy()
up_triangle = np.where(np.triu(p_dist) - np.eye(n) * p_dist != 0)
p_dist = p_dist[up_triangle]
gt = gt[up_triangle]
far_levels = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]
far, tar, threshold = metrics.roc_curve(gt, p_dist)
interp = interpolate.interp1d(far, tar)
tar_at_far = [interp(x) for x in far_levels]
for f, fa in enumerate(far_levels):
print('TPR@FAR={:.7f}: {:.4f}'.format(fa, tar_at_far[f]))
return tar_at_far
def main(opt):
torch.manual_seed(opt.seed)
torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
print('Setting up data...')
Dataset = get_dataset(opt.dataset, opt.task)
f = open(opt.data_cfg)
data_config = json.load(f)
trainset_paths = data_config['train']
dataset_root = data_config['root']
f.close()
transforms = T.Compose([T.ToTensor()])
dataset = Dataset(opt, dataset_root, trainset_paths, (1088, 608), augment=True, transforms=transforms)
opt = opts().update_dataset_info_and_set_heads(opt, dataset)
print(opt)
logger = Logger(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
print('Creating model...')
model = create_model(opt.arch, opt.heads, opt.head_conv)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
start_epoch = 0
if opt.load_model != '':
model, optimizer, start_epoch = load_model(
model, opt.load_model, optimizer, opt.resume, opt.lr, opt.lr_step)
# Get dataloader
train_loader = torch.utils.data.DataLoader(
dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True
)
print('Starting training...')
Trainer = train_factory[opt.task]
trainer = Trainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
best = 1e10
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
mark = epoch if opt.save_all else 'last'
log_dict_train, _ = trainer.train(epoch, train_loader)
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),
epoch, model, optimizer)
else:
save_model(os.path.join(opt.save_dir, 'model_last.pth'),
epoch, model, optimizer)
logger.write('\n')
if epoch in opt.lr_step:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1))
print('Drop LR to', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if epoch % 5 == 0:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
logger.close()
import torch
from dataset import Dataset
from torch.utils.data import DataLoader
import numpy as np
import os, sys
from osgeo import gdal_array
import gdal
import argparse
from torchvision.transforms import transforms as T
sys.path.append(r'/home/zhoudengji/ghx/data/code/mycnn')
from model import MyCNN
x_transform = T.Compose([
T.ToTensor(),
# 标准化至[-1,1],规定均值和标准差
# T.Normalize([0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5]) # torchvision.transforms.Normalize(mean, std, inplace=False)
])
# mask只需要转换为tensor
y_transform = T.ToTensor()
def nomlize(array):
return np.divide(array, np.max(array))
# 测试
def test():
model = MyCNN(4, 512, 512)
# model = models.UNet(2)
model.load_state_dict(torch.load(args.weight, map_location='cpu'))
model.eval()
def train():
start_time = time.time()
learning_rate = 0.001
Epoch_num = 50
Batch_size = 1024
Resolution = 36
save_path = 'save/'
# https://tutorials.pytorch.kr/intermediate/tensorboard_tutorial.html
# The direction for tensorboard
writer = SummaryWriter(save_path)
writer.add_scalar('epoch', Epoch_num)
writer.add_scalar('batch size', Batch_size)
writer.add_scalar('learning_rate', learning_rate)
writer.add_scalar('resolution', Resolution)
tr_transform = transforms.Compose([
transforms.Resize((Resolution, Resolution)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
te_transform = transforms.Compose([
transforms.Resize((Resolution, Resolution)),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010))
])
# https://gldmg.tistory.com/43
# This reference is to store and get .npy file
# https://wikidocs.net/14304
# Check if the file is located.
train_path = 'data/pre_train'
train_file = 'data/train.npy'
test_path = 'data/pre_test'
test_file = 'data/test.npy'
if os.path.isfile(train_file):
load = np.load(train_file, allow_pickle=True)
tr_x, tr_y = load[0], load[1]
else:
tr_x, tr_y = make_tensor(train_path)
np.save(train_file, np.array([tr_x, tr_y]))
if os.path.isfile(test_file):
load = np.load(test_file, allow_pickle=True)
te_x, te_y = load[0], load[1]
else:
te_x, te_y = make_tensor(test_path)
np.save(test_file, np.array([te_x, te_y]))
processing_time = time.time()
time_check('processing', start_time, processing_time)
tr_data = CustomDataset(tr_x, tr_y, tr_transform)
tr_loader = DataLoader(tr_data, batch_size=Batch_size, shuffle=True)
te_data = CustomDataset(te_x, te_y, te_transform)
te_loader = DataLoader(te_data, batch_size=Batch_size, shuffle=False)
loader_time = time.time()
time_check('loading', processing_time, loader_time)
# model
model = models.resnet18()
model.fc = nn.Linear(512, 4)
model = model.cuda()
# Summary Model
summary(model, input_size=(3, Resolution, Resolution))
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Start training
model.train()
for epoch in range(Epoch_num):
train_loss = 0
correct = 0
total = 0
for inputs, targets in tr_loader:
inputs = inputs.cuda()
targets = targets.cuda()
outputs = model(inputs)
# Ground True vs Prediction value
# print(outputs)
# print(targets)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
loss = train_loss / len(tr_loader)
acc = 100 * correct / total
if epoch % 5 == 0:
print('Train - Epoch : %d, Loss : %.2f, Accuracy : %.2f' %
(epoch, loss, acc))
writer.add_scalar('Training loss', loss, epoch)
writer.add_scalar('Accuracy', acc, epoch)
train_time = time.time()
time_check('train', loader_time, train_time)
# https://tutorials.pytorch.kr/beginner/saving_loading_models.html
torch.save(model, save_path + '/model.pt')
# Evaluation
model.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(te_loader):
inputs = inputs.cuda()
targets = targets.cuda()
outputs = model(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
loss = test_loss / len(te_loader)
acc = 100 * correct / total
print('Evaluation - Loss : %.2f, Accuracy : %.2f' % (loss, acc))
test_time = time.time()
time_check('test', train_time, test_time)
writer.close()
def pixelshuffle_upsample(lr, scale, upsample_model):
lr = transforms.ToTensor()(lr).unsqueeze(0)
lr = lr.cuda()
lr = upsample_model(lr)
return lr.squeeze(0)
def bicubic_upsample(lr, scale):
lr = lr.resize([lr.size[0] * scale, lr.size[1] * scale],
resample=Image.BICUBIC)
return transforms.ToTensor()(lr)
def __init__(self):
"""Initialize ResMaskNet
@misc{luanresmaskingnet2020,
Author = {Luan Pham & Tuan Anh Tran},
Title = {Facial Expression Recognition using Residual Masking Network},
url = {https://github.com/phamquiluan/ResidualMaskingNetwork},
Year = {2020}
}
"""
self.transform = transforms.Compose(
[transforms.ToPILImage(),
transforms.ToTensor()])
self.FER_2013_EMO_DICT = {
0: "angry",
1: "disgust",
2: "fear",
3: "happy",
4: "sad",
5: "surprise",
6: "neutral",
}
# load configs and set random seed
configs = json.load(
open(
os.path.join(get_resource_path(),
"ResMaskNet_fer2013_config.json")))
self.image_size = (configs["image_size"], configs["image_size"])
self.use_gpu = torch.cuda.is_available()
# if self.use_gpu:
# self.state = torch.load(
# os.path.join(
# get_resource_path(), "ResMaskNet_Z_resmasking_dropout1_rot30.pth"
# )
# )
# else:
# self.state = torch.load(
# os.path.join(
# get_resource_path(), "ResMaskNet_Z_resmasking_dropout1_rot30.pth"
# ),
# map_location={"cuda:0": "cpu"},
# )
self.model = resmasking_dropout1(in_channels=3, num_classes=7)
if self.use_gpu:
self.model.load_state_dict(
torch.load(
os.path.join(
get_resource_path(),
"ResMaskNet_Z_resmasking_dropout1_rot30.pth"))['net'])
self.model.cuda()
else:
self.model.load_state_dict(
torch.load(
os.path.join(get_resource_path(),
"ResMaskNet_Z_resmasking_dropout1_rot30.pth"),
map_location={"cuda:0": "cpu"},
)['net'])
self.model.eval()
def main():
# img_folders = ["../coco_img/bg2_0/", "../coco_img/bg2_127/", "../coco_img/bg2_255/",
# "../coco_img/obj2_0/", "../coco_img/obj2_127/", "../coco_img/obj2_255/"]
img_folders = [
"../coco_img/merged_bg2_0/", "../coco_img/merged_bg2_127/",
"../coco_img/merged_bg2_255/", "../coco_img/merged_obj2_0/",
"../coco_img/merged_obj2_127/", "../coco_img/merged_obj2_255/"
]
img_folders = ["../coco_img/org/"]
model_name = "MLCCOCO"
model = MultilabelObject(None, 80).cuda()
log_dir = "./"
checkpoint = torch.load(os.path.join(log_dir, 'model_best.pth.tar'),
encoding='bytes')
new_checkpoint = OrderedDict()
for k in checkpoint[b'state_dict']:
new_checkpoint[k.decode('utf-8')] = checkpoint[b'state_dict'][k]
model.load_state_dict(new_checkpoint)
model.eval()
with open("classes_list.pickle", "rb") as f:
classes_list = pickle.load(f)
for img_folder in img_folders:
crop_size = 224
image_size = 256
batch_size = 64
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_transform = transforms.Compose([
transforms.Scale(image_size),
transforms.CenterCrop(crop_size),
transforms.ToTensor(), normalize
])
cocomlc = COCO_MLC(img_folder, val_transform)
test_loader = torch.utils.data.DataLoader(cocomlc,
batch_size=batch_size,
shuffle=False,
num_workers=8,
pin_memory=True)
t = tqdm(test_loader, desc='testing {}'.format(img_folder))
result = {}
for batch_idx, (imgs, paths) in enumerate(t):
images = imgs.cuda()
# print(images.shape)
probs, labels, labels_probs = infer_batch(model,
classes_list,
inputs=images,
threshold=0.5)
for i in range(0, len(paths)):
path = paths[i]
result[path] = {
"prob": probs[i],
"labels": labels[i],
"labels_probs": labels_probs[i]
}
pickle_file_name = "{}_{}.pickle".format(
model_name, os.path.basename(os.path.normpath(img_folder)))
pickle_path = os.path.join(".", "result_pickle", pickle_file_name)
with open(pickle_path, 'wb') as handle:
pickle.dump(result, handle)
print("Done, Saved to {}".format(pickle_path))
def get_section_one_shot_learning(OurFaceNet):
# Sidebar
st.sidebar.header("One-Shot Learning")
st.sidebar.write("You can add a new person (ID) to the model.\
Afterwards, you can test the verification with some test images.")
st.sidebar.subheader("Add Person (ID)")
person_name = st.sidebar.text_input("Enter name of person (ID):", max_chars=40)
anchor_file = st.sidebar.file_uploader("Select anchor image:", type=['jpg', 'png', 'jpeg'])
# Main page
'''
# One-Shot Learning
'''
st.subheader(f"Step 1: Add new Person!")
'''
Check the sidebar, in order to add a new person (ID) to
the database. After you entered a name and uploaded the image, it will be added and preprocessed
automatically into the database.
'''
if person_name is not None and anchor_file is not None:
# add anchor to known IDs
OurFaceNet.create_anchor_embeddings(anchor_dict={person_name: anchor_file})
st.subheader("Done. New Person Added!")
anchor_image = Image.open(anchor_file)
st.image(anchor_image, caption=f"Person (ID): {person_name}", use_column_width=True)
st.subheader("Step 2: Select Test Images")
st.write("Now, after getting setup you can select some random images to test out the verification system. \
Select a directory with all (face) images you want to verify."
)
test_images_path = st.text_input("Relative Path to test directory: ")
if test_images_path == "":
st.warning("Path to directory is missing! Please add path to test images.")
return
test_image_dir = Path(test_images_path)
if test_image_dir.is_dir():
test_images = load_tutorial_test_images(test_image_dir)
else:
st.warning("Path to directory is missing! Please add path to test images.")
# Step 3: Inference
if len(test_images) > 0:
st.subheader("Step 3: Inference")
st.write(f"Now we will verify the given test images w.r.t. given Person (ID): {person_name}.")
st.write("")
start_inference = st.button(label="Start Inference")
if start_inference:
col_1, col_2, col_3 = st.beta_columns(3)
visited = [False, False]
for img_path in test_images:
test_image = Image.open(img_path).resize([224, 224])
img_tensor = transforms.ToTensor()(test_image)
# Prediction
pred_id, msg = OurFaceNet.inference(
img_tensor, use_threshold=False, fuzzy_matches=False
)
temp_test_img_label = ' '.join([img_path.stem.split(" ")[0], img_path.stem.split(" ")[1]])
if str(pred_id) == temp_test_img_label:
t = "<div><span class='highlight green'><span class='bold'>Passed</span> </span></div>"
msg = f"{msg} Person (ID): {pred_id}"
else:
t = "<div> <span class='highlight red'><span class='bold'>Failed</span></span></div>"
msg = "Unknown person. No identity match found!"
if not visited[0]:
col_1.markdown(t, unsafe_allow_html=True)
col_1.image(test_image, caption=f"{msg}", use_column_width=True)
visited[0] = True
elif not visited[1]:
col_2.markdown(t, unsafe_allow_html=True)
col_2.image(test_image, caption=f"{msg}", use_column_width=True)
visited[1] = True
else:
col_3.markdown(t, unsafe_allow_html=True)
col_3.image(test_image, caption=f"{msg}", use_column_width=True)
visited = [False, False]
def test():
transform_test = transforms.Compose([
transforms.ToTensor(),
# transforms.Normalize(0.41,2)
])
# testset1 = datasets.ImageFolder(args.test_root1, transform=transform_test)
#
# testset2 = datasets.ImageFolder(args.test_root2, transform=transform_test)
#
# test_loader1 = DataLoader(dataset=testset1,
# batch_size=1,
# shuffle=False)
#
# test_loader2 = DataLoader(dataset=testset2,
# batch_size=1,
# shuffle=False)
# transform = transforms.Compose([transforms.ToTensor(),
# # transforms.Lambda(lambda x: global_contrast_normalization(x, scale='l1')),
# transforms.Normalize([0.5], [0.5])])
encoder, decoder = Encoder(args), Decoder(args)
# model_dict = torch.load(args.model)
# encoder_dict = model_dict['encoder']
# decoder_dict = model_dict['decoder']
# encoder.load_state_dict(encoder_dict)
# decoder.load_state_dict(decoder_dict)
encooder, decoder = load_model(encoder, decoder, args.model)
encoder.to('cuda')
decoder.to('cuda')
encoder.eval()
decoder.eval()
# all_data=torch.zeros((3,128,128))
thr = 0.09000 #0.0908
import numpy as np
Tensor = torch.cuda.FloatTensor
from PIL import Image
cnt = 0
# thr=0.06 for part1
for img in tqdm(img_list1):
points = {}
img_path = os.path.join(args.test_root1, img)
image = Image.open(img_path)
image = transform_test(image)
image = image.unsqueeze(0)
if image.shape[1] != 3:
image = image.repeat(1, 3, 1, 1)
image = image.type(Tensor)
data_rebuild1 = encoder.forward(image)
data_rebuild1 = decoder.forward(data_rebuild1)
# criterion=nn.MSELoss()
# recon_loss = criterion(data_rebuild1,image)
# recon_loss=recon_loss.detach().to('cpu').numpy()
residual = torch.abs(data_rebuild1.squeeze()[0, :, :] -
image.squeeze()[0, :, :])
point_set = residual.ge(thr)
# point_set=point_set.detach().to('cpu').numpy()
point = point_set.nonzero().cpu().numpy()
points['points'] = ['{},{}'.format(p[0], p[1]) for p in point]
if (point.shape[0] > 5):
save_json(img, points, args.json_part1)
# print(points)
# all_data+=residual.detach().cpu().squeeze()
if args.show:
data_rebuild1 = data_rebuild1.detach().to('cpu').squeeze().permute(
1, 2, 0).numpy()
data = image.to('cpu')
data = data.squeeze().detach().permute(1, 2, 0)
data = data.numpy()
points = point_set.to('cpu')
im_show = data.copy()
im_show[points, :] = 1
###########
muti_img = np.hstack([data, data_rebuild1, im_show])
#cv2.imshow('flaw_locate',im_show)
#cv2.imshow('ori',data)
#cv2.imshow('rebuild1', data_rebuild1)
cv2.imshow("muti_img", muti_img)
#cv2.waitKey(0)
# cv2.destoryAllWindows()
cnt = cnt + 1
# cv2.imwrite('/home/zww/cv/defect/auto-encoder-net/image/%s.jpg'%str(cnt),data_rebuild1*255)
cv2.waitKey(0)
def do_marking_run(class_marking_percentage,
experiment_directory,
tensorboard_log_directory,
augment=True):
# Setup experiment directory
if os.path.isdir(experiment_directory):
error_message = f"Directory {experiment_directory} already exists. By default we assume you don't want to "\
"repeat the marking stage."
logger.info(error_message)
return
logfile_path = os.path.join(experiment_directory, 'marking.log')
setup_logger_tqdm(filepath=logfile_path)
# Load randomly sampled images from random class along with list of original indexes
training_set = torchvision.datasets.CIFAR10(root="experiments/datasets",
download=True)
class_id, images, original_indexes = get_images_for_marking_cifar10(
training_set,
class_marking_percentage=class_marking_percentage,
tensorboard_log_directory=tensorboard_log_directory)
# Marking network is the resnet18 we trained on CIFAR10
marking_network = torchvision.models.resnet18(pretrained=False,
num_classes=10)
checkpoint_path = "experiments/table1/step1/checkpoint.pth"
marking_network_checkpoint = torch.load(checkpoint_path)
marking_network.load_state_dict(
marking_network_checkpoint["model_state_dict"])
# Carriers
marking_network_fc_feature_size = 512
carriers = torch.randn(len(training_set.classes),
marking_network_fc_feature_size)
carriers /= torch.norm(carriers, dim=1, keepdim=True)
torch.save(carriers, os.path.join(experiment_directory, "carriers.pth"))
# Run!
optimizer = lambda x: torch.optim.AdamW(x, lr=0.1)
epochs = 100
batch_size = 32
output_directory = os.path.join(experiment_directory, "marked_images")
if not augment:
augmentation = None
marked_images = do_marking(output_directory,
marking_network,
images,
original_indexes,
carriers,
class_id,
NORMALIZE_CIFAR10,
optimizer,
tensorboard_log_directory,
epochs=epochs,
batch_size=batch_size,
overwrite=True,
augmentation=augmentation)
# Show marked images in Tensorboard
tensorboard_summary_writer = SummaryWriter(
log_dir=tensorboard_log_directory)
images_for_tensorboard = [transforms.ToTensor()(x) for x in marked_images]
img_grid = torchvision.utils.make_grid(images_for_tensorboard, nrow=16)
tensorboard_summary_writer.add_image('marked_images', img_grid)
# Record marking completion
with open(os.path.join(experiment_directory, "marking.complete"),
"w") as fh:
fh.write("1")
def main():
parser = argparse.ArgumentParser(description='Chainer example: MNIST')
parser.add_argument('--batchsize',
'-b',
type=int,
default=100,
help='Number of images in each mini-batch')
parser.add_argument('--epoch',
'-e',
type=int,
default=20,
help='Number of sweeps over the dataset to train')
parser.add_argument('--frequency',
'-f',
type=int,
default=-1,
help='Frequency of taking a snapshot')
parser.add_argument('--device',
'-d',
type=str,
default='-1',
help='Device specifier. Either ChainerX device '
'specifier or an integer. If non-negative integer, '
'CuPy arrays with specified device id are used. If '
'negative integer, NumPy arrays are used')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--resume',
'-r',
type=str,
help='Resume the training from snapshot')
parser.add_argument('--autoload',
action='store_true',
help='Automatically load trainer snapshots in case'
' of preemption or other temporary system failure')
parser.add_argument('--unit',
'-u',
type=int,
default=1000,
help='Number of units')
group = parser.add_argument_group('deprecated arguments')
group.add_argument('--gpu',
'-g',
dest='device',
type=int,
nargs='?',
const=0,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
device = torch.device(args.device)
print('Device: {}'.format(device))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
print('')
# Set up a neural network to train
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
model = Classifier(MLP(784, args.unit, 10))
model.to(device)
# Setup an optimizer
optimizer = torch.optim.Adam(model.parameters())
# Load the MNIST dataset
transform = transforms.ToTensor()
train = datasets.MNIST('data',
train=True,
download=True,
transform=transform)
test = datasets.MNIST('data', train=False, transform=transform)
train_iter = pytorch_trainer.iterators.SerialIterator(
train, args.batchsize)
test_iter = pytorch_trainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
# Set up a trainer
updater = training.updaters.StandardUpdater(train_iter,
optimizer,
model,
device=device)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
# Evaluate the model with the test dataset for each epoch
trainer.extend(extensions.Evaluator(test_iter, model, device=device),
call_before_training=True)
# Dump a computational graph from 'loss' variable at the first iteration
# The "main" refers to the target link of the "main" optimizer.
# trainer.extend(extensions.DumpGraph('main/loss'))
# Take a snapshot for each specified epoch
frequency = args.epoch if args.frequency == -1 else max(1, args.frequency)
# Take a snapshot each ``frequency`` epoch, delete old stale
# snapshots and automatically load from snapshot files if any
# files are already resident at result directory.
trainer.extend(extensions.snapshot(n_retains=1, autoload=args.autoload),
trigger=(frequency, 'epoch'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport(), call_before_training=True)
# Save two plot images to the result dir
trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch',
file_name='loss.png'),
call_before_training=True)
trainer.extend(extensions.PlotReport(
['main/accuracy', 'validation/main/accuracy'],
'epoch',
file_name='accuracy.png'),
call_before_training=True)
# Print selected entries of the log to stdout
# Here "main" refers to the target link of the "main" optimizer again, and
# "validation" refers to the default name of the Evaluator extension.
# Entries other than 'epoch' are reported by the Classifier link, called by
# either the updater or the evaluator.
trainer.extend(extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy', 'elapsed_time'
]),
call_before_training=True)
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
if args.resume is not None:
# Resume from a snapshot (Note: this loaded model is to be
# overwritten by --autoload option, autoloading snapshots, if
# any snapshots exist in output directory)
trainer.load_state_dict(torch.load(args.resume))
# Run the training
trainer.run()
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
# //TODO: Custom Transforms
transform = transforms.Compose([
transforms.Resize((30, 30)),
transforms.ToTensor(), # //* 0-255 to 0-1, numpy to tensors
transforms.Normalize(
[0.5, 0.5, 0.5], # //* 0-1 to [-1,1] , formula (x-mean)/std
[0.5, 0.5, 0.5])
])
# //! custom Dataloader function
def load_data(train_path, test_path):
train_loader = DataLoader(datasets.ImageFolder(train_path,
transform=transform),
batch_size=64,
shuffle=True)
test_loader = DataLoader(datasets.ImageFolder(test_path,
transform=transform),
batch_size=32,
shuffle=True)
# //TODO: load all classes(43)
classes = datasets.ImageFolder(train_path, transform=transform).classes
return train_loader, test_loader, classes
class NetShell:
default_scale = 224
default_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
class _Path:
def __init__(self):
self.root = '../..'
self.dataset_root = 'cookie/data'
self.dataset_dir = 'CIFAR10'
self.save_root = 'cookie/save'
self.save_dir = None
def dataset_path(self):
dataset_path = f'{self.root}/{self.dataset_root}'
if self.dataset_dir:
dataset_path = dataset_path + '/' + self.dataset_dir
if not os.path.exists(dataset_path):
os.makedirs(dataset_path)
return dataset_path
def save_path(self):
save_path = f'{self.root}/{self.save_root}'
if self.save_dir:
save_path = save_path + '/' + self.save_dir
if not os.path.exists(save_path):
os.makedirs(save_path)
return save_path
class _Train:
def __init__(self):
self.transform = NetShell.default_transform
self.shuffle = True
self.max_epoch = 200
self.save_checkpoint = -1
self.save_epoch = -1
self.early_stop_max = 5
class _Test:
def __init__(self):
self.transform = NetShell.default_transform
self.shuffle = True
class _Save:
def __init__(self):
self.optimizer = True
self.state = True
class _Options:
def __init__(self):
self.train = NetShell._Train()
self.test = NetShell._Test()
self.save = NetShell._Save()
def __init__(self,
net,
Dataset=None,
criterion=torch.nn.CrossEntropyLoss()):
self.net = net
self.cuda = True
self.options = NetShell._Options()
self.path = NetShell._Path()
self.criterion = criterion
self.Dataset = Dataset if Dataset else torchvision.datasets.CIFAR10
self.optimizer = optim.SGD(self.net.parameters(),
lr=0.001,
momentum=0.9)
self.state = {
'epoch': 0,
'iter': 0,
'special_mark': None,
}
@staticmethod
def sav_loader(path=None,
auto_load_path=None,
Dataset=None,
load_optimizer=False):
if auto_load_path:
files = os.listdir(auto_load_path)
files.sort()
path = f'{auto_load_path}/{files[-1]}'
with open(path, 'rb') as f:
sav = torch.load(f)
net = sav['net']
ns = NetShell(net, Dataset=Dataset)
state = sav.get('state')
if state:
ns.state = state
optimizer = sav.get('optimizer')
if load_optimizer and optimizer:
ns.optimizer.load_state_dict(optimizer)
return ns
def train(self,
batch_size=8,
num_workers=2,
checkpoint=2000,
sampling_test=True,
early_stop=False):
if self.Dataset:
dataset = self.Dataset(root=self.path.dataset_path(),
transform=self.options.train.transform,
train=True)
else:
dataset = torchvision.datasets.CIFAR10(
root=self.path.dataset_path(),
transform=self.options.train.transform,
download=True)
trainloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=self.options.train.shuffle,
num_workers=num_workers)
base_epoch = self.state['epoch']
cuda = self.cuda
net = self.net
optimizer = self.optimizer
criterion = self.criterion
checkpoint_timer = 0
checkpoint_save = self.options.train.save_checkpoint
epoch_save = self.options.train.save_epoch
if cuda:
net.cuda()
es_history = 0
es_flag = 0
for epoch in range(base_epoch, self.options.train.max_epoch):
then = datetime.now()
running_loss = 0
total_itr = 0
for itr, data in enumerate(trainloader, 0):
xs, truth = data
if cuda:
xs, truth = Variable(xs.cuda()), Variable(truth.cuda())
else:
xs, truth = Variable(xs), Variable(truth)
optimizer.zero_grad()
ys = net(xs)
loss = criterion(ys, truth)
loss.backward()
optimizer.step()
running_loss += loss.data.item()
if itr != 0 and itr % checkpoint == 0:
now = datetime.now()
average_loss = running_loss / checkpoint
print(
f'[{epoch + 1}, {itr:5d}] loss:{average_loss:.10f} | {(now - then).seconds}s'
)
running_loss = 0.0
then = now
checkpoint_timer += 1
if checkpoint_save > 0 and checkpoint_timer % checkpoint_save == 0:
self.state['iter'] = itr
self.save()
total_itr = itr
self.state['iter'] = total_itr
self.state['epoch'] = epoch + 1
if epoch_save > 0 and (epoch + 1) % epoch_save == 0:
self.save()
if sampling_test:
accuracy = self.test(batch_size=batch_size,
num_workers=num_workers)
if early_stop:
if accuracy <= es_history:
es_flag += 1
if es_flag >= self.options.train.early_stop_max:
return
else:
es_flag = 0
es_history = accuracy
def test(self, batch_size=8, num_workers=2):
if self.Dataset:
dataset = self.Dataset(root=self.path.dataset_path(),
transform=self.options.test.transform,
train=False)
else:
dataset = torchvision.datasets.CIFAR10(
root=self.path.dataset_root,
train=False,
transform=self.options.test.transform,
download=True)
testloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=self.options.test.shuffle,
num_workers=num_workers)
net = self.net
cuda = self.cuda
if cuda:
net.cuda()
correct = 0
total = 0
for data in testloader:
xs, truth = data
if cuda:
xs, truth = Variable(xs.cuda()), Variable(truth.cuda())
else:
xs, truth = Variable(xs), Variable(truth)
ys = net(xs)
_, predicted = torch.max(ys.data, 1)
total += truth.size(0)
correct += (predicted == truth).sum()
acc = 100.0 * correct.float() / total
print(f'Accuracy of the network on the test dataset_root: {acc:.2f} %')
return acc
def save(self):
pkg = {'net': self.net}
suffix = 'n'
if self.options.save.optimizer:
pkg['optimizer'] = self.optimizer.state_dict()
suffix += 'o'
if self.options.save.state:
pkg['state'] = self.state
suffix += 's'
suffix += '.sav'
nhash = self.net_hash()
fp = f"{self.path.save_path()}/{nhash}.{suffix}"
torch.save(pkg, fp)
print(f'saved as {fp}')
def net_hash(self):
special_mark = self.state.get('special_mark')
if special_mark:
special_mark = f'x{special_mark}'
else:
special_mark = ''
return f"{self.net._get_name()}{special_mark}_ep{self.state['epoch']}_{self.state['iter']}"
def train(
cfg,
data_cfg,
resume=False,
epochs=100,
batch_size=16,
accumulated_batches=1,
freeze_backbone=False,
opt=None,
):
weights = '../weights' # 改到上一层， 这样方便文件夹复制
mkdir_if_missing(weights)
latest = osp.join(weights, 'latest.pt') # 这个是为了resume上次存好的checkpoint，注意不要覆盖！
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
print("loading data")
sys.stdout.flush()
f = open(data_cfg)
data_config = json.load(f)
trainset_paths = data_config['train']
dataset_root = data_config['root']
f.close()
cfg_dict = parse_model_cfg(cfg)
img_size = [int(cfg_dict[0]['width']), int(cfg_dict[0]['height'])]
# Get dataloader
transforms = T.Compose([T.ToTensor()])
dataset = JointDataset(dataset_root, trainset_paths, img_size, augment=True, transforms=transforms)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,
num_workers=8, pin_memory=False, drop_last=True, collate_fn=collate_fn)
# Initialize model
print("building model")
sys.stdout.flush()
model = Darknet(cfg_dict, dataset.nID)
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
if resume:
if opt.latest:
latest_resume = "/home/master/kuanzi/weights/72_epoch_arcface.pt"
print("Loading the latest weight...", latest_resume)
checkpoint = torch.load(latest_resume, map_location='cpu')
# Load weights to resume from
model.load_state_dict(checkpoint['model'])
model.cuda().train()
# Set optimizer
classifer_param_value = list(map(id, model.classifier.parameters()))
classifer_param = model.classifier.parameters()
base_params = filter(lambda p: id(p) not in classifer_param_value, model.parameters())
print("classifer_param\n", classifer_param) # [2218660649072]
print("classifer_param_value\n", classifer_param_value) # [2218660649072]
print("base_params\n", base_params) # <filter object at 0x0000020493D95048>
sys.stdout.flush()
# optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr * 0.1, momentum=.9)
optimizer = torch.optim.SGD([
{'params': filter(lambda x: x.requires_grad, base_params), 'lr': opt.lr * 0.01},
{'params': classifer_param, 'lr': opt.lr}],
momentum=.9)
# optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9)
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None:
# Anyway, if you’re “freezing” any part of your network, and your optimizer is only passed “unfrozen” model parameters
# (i.e. your optimizer filters out model parameters whose requires_grad is False),
# then when resuming, you’ll need to unfreeze the network again and re-instantiate the optimizer afterwards.
optimizer.load_state_dict(checkpoint['optimizer'])
del checkpoint # current, saved
else:
# pretrain = "/home/master/kuanzi/weights/jde_1088x608_uncertainty.pt"
pretrain = "/home/master/kuanzi/weights/jde_864x480_uncertainty.pt" #576x320
print("Loading jde finetune weight...", pretrain)
sys.stdout.flush()
checkpoint = torch.load(pretrain, map_location='cpu')
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in checkpoint['model'].items() if not k.startswith("classifier")} # 去掉全连接层
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model.cuda().train()
print ("model weight loaded")
sys.stdout.flush()
classifer_param_value = list(map(id, model.classifier.parameters()))
classifer_param = model.classifier.parameters()
base_params = filter(lambda p: id(p) not in classifer_param_value, model.parameters())
print("classifer_param\n", classifer_param) # [2218660649072]
print("classifer_param_value\n", classifer_param_value) # [2218660649072]
print("base_params\n", base_params) # <filter object at 0x0000020493D95048>
sys.stdout.flush()
# optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr * 0.1, momentum=.9)
optimizer = torch.optim.SGD([
{'params': filter(lambda x: x.requires_grad, base_params), 'lr': opt.lr * 0.01},
{'params': classifer_param, 'lr': opt.lr}],
momentum=.9)
print("chk epoch:\n", checkpoint['epoch'])
sys.stdout.flush()
start_epoch = checkpoint['epoch'] + 1
else:
# Initialize model with backbone (optional)
print("Loading backbone...")
sys.stdout.flush()
if cfg.endswith('yolov3.cfg'):
load_darknet_weights(model, osp.join(weights ,'darknet53.conv.74'))
cutoff = 75
elif cfg.endswith('yolov3-tiny.cfg'):
load_darknet_weights(model, osp.join(weights , 'yolov3-tiny.conv.15'))
cutoff = 15
model.cuda().train()
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda x: x.requires_grad, model.parameters()), lr=opt.lr, momentum=.9, weight_decay=1e-4)
model = torch.nn.DataParallel(model)
# Set scheduler
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[int(0.5*opt.epochs), int(0.75*opt.epochs)], gamma=0.1)
# An important trick for detection: freeze bn during fine-tuning
if not opt.unfreeze_bn:
for i, (name, p) in enumerate(model.named_parameters()):
p.requires_grad = False if 'batch_norm' in name else True
model_info(model)
t0 = time.time()
print("begin training...")
sys.stdout.flush()
for epoch in range(epochs):
epoch += start_epoch
logger.info(('%8s%12s' + '%10s' * 6) % (
'Epoch', 'Batch', 'box', 'conf', 'id', 'total', 'nTargets', 'time'))
# Freeze darknet53.conv.74 for first epoch
if freeze_backbone and (epoch < 2):
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[2]) < cutoff: # if layer < 75
p.requires_grad = False if (epoch == 0) else True
ui = -1
rloss = defaultdict(float) # running loss
optimizer.zero_grad()
for i, (imgs, targets, _, _, targets_len) in enumerate(dataloader):
if sum([len(x) for x in targets]) < 1: # if no targets continue
continue
# SGD burn-in
burnin = min(1000, len(dataloader))
if (epoch == 0) & (i <= burnin):
lr = opt.lr * (i / burnin) **4
for g in optimizer.param_groups:
g['lr'] = lr
# Compute loss, compute gradient, update parameters
loss, components = model(imgs.cuda(), targets.cuda(), targets_len.cuda())
components = torch.mean(components.view(-1, 5),dim=0)
loss = torch.mean(loss)
loss.backward()
# accumulate gradient for x batches before optimizing
if ((i + 1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
optimizer.step()
optimizer.zero_grad()
# Running epoch-means of tracked metrics
ui += 1
for ii, key in enumerate(model.module.loss_names):
rloss[key] = (rloss[key] * ui + components[ii]) / (ui + 1)
s = ('%8s%12s' + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1),
'%g/%g' % (i, len(dataloader) - 1),
rloss['box'], rloss['conf'],
rloss['id'],rloss['loss'],
rloss['nT'], time.time() - t0)
t0 = time.time()
if i % opt.print_interval == 0:
logger.info(s)
# # Save latest checkpoint
# checkpoint = {'epoch': epoch,
# 'model': model.module.state_dict(),
# 'optimizer': optimizer.state_dict()}
# torch.save(checkpoint, latest)
# Calculate mAP
if epoch % opt.test_interval ==0 and epoch != 0:
epoch_chk = osp.join(weights, str(epoch) + '_epoch_arcface.pt')
checkpoint = {'epoch': epoch,
'model': model.module.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(checkpoint, epoch_chk)
# """ 训练与测试解耦，以下工作单独进行 """
# with torch.no_grad():
# # mAP, R, P = test.test(cfg, data_cfg, weights=latest, batch_size=batch_size, print_interval=40)
# # print ("test.test:\t", mAP, "\t", R, "\t", P)
# test_mapgiou.test_giou(cfg, data_cfg, weights=latest, batch_size=batch_size, print_interval=40)
# test_mapgiou.test_emb(cfg, data_cfg, weights=latest, batch_size=batch_size, print_interval=40)
# Call scheduler.step() after opimizer.step() with pytorch > 1.1.0
scheduler.step()
def train_single_task(t_id, task, tasks, vis_p, learner, config,
transfer_matrix, total_steps):
training_params = config.pop('training-params')
learner_params = config.pop('learner-params', {})
assert 'model-params' not in config, "Can't have model-specific " \
"parameters while tuning at the " \
"stream level."
if learner_params:
learner.set_h_params(**learner_params)
batch_sizes = training_params.pop('batch_sizes')
# optim_func = training_params.pop('optim_func')
optim_func = learner.optim_func
optim_params = config.pop('optim')
schedule_mode = training_params.pop('schedule_mode')
split_optims = training_params.pop('split_optims')
dropout = config.pop('dropout') if 'dropout' in config else None
stream_setting = training_params.pop('stream_setting')
plot_all = training_params.pop('plot_all')
normalize = training_params.pop('normalize')
augment_data = training_params.pop('augment_data')
transformations = []
if augment_data:
transformations.extend([
transforms.ToPILImage(),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor()
])
lca_n = training_params.pop('lca')
if plot_all:
vis_p = get_training_vis_conf(vis_p, tune.get_trial_dir())
# print('NEW vis: ', vis_p)
task_vis = visdom.Visdom(**vis_p)
# env = [env[0], env[-1]]
# vis_p['env'] = '_'.join(env)
# vis_p['log_to_filename'] = os.path.join(vis_logdir, vis_p['env'])
# g_task_vis = visdom.Visdom(**vis_p)
logger.info(get_env_url(task_vis))
else:
task_vis = None
t_trans = [[] for _ in range(len(task['split_names']))]
t_trans[0] = transformations.copy()
datasets_p = dict(task=task, transforms=t_trans, normalize=normalize)
datasets = _load_datasets(**datasets_p)
train_loader, eval_loaders = get_classic_dataloaders(datasets, batch_sizes)
assert t_id == task['id']
start1 = time.time()
model = learner.get_model(task['id'],
x_dim=task['x_dim'],
n_classes=task['n_classes'],
descriptor=task['descriptor'],
dataset=eval_loaders[:2])
model_creation_time = time.time() - start1
loss_fn = task['loss_fn']
training_params['loss_fn'] = loss_fn
prepare_batch = _prepare_batch
if hasattr(model, 'prepare_batch_wrapper'):
prepare_batch = model.prepare_batch_wrapper(prepare_batch, t_id)
if hasattr(model, 'loss_wrapper'):
training_params['loss_fn'] = \
model.loss_wrapper(training_params['loss_fn'])
# if hasattr(model, 'backward_hook'):
# training_params[]
# optim = set_optim_params(optim_func, optim_params, model, split_optims)
optim_fact = partial(set_optim_params,
optim_func=optim_func,
optim_params=optim_params,
split_optims=split_optims)
# if schedule_mode == 'steps':
# lr_scheduler = torch.optim.lr_scheduler.\
# MultiStepLR(optim[0], milestones=[25, 40])
# elif schedule_mode == 'cos':
# lr_scheduler = torch.optim.lr_scheduler.\
# CosineAnnealingLR(optim[0], T_max=200, eta_min=0.001)
# elif schedule_mode is None:
# lr_scheduler = None
# else:
# raise NotImplementedError()
if dropout is not None:
set_dropout(model, dropout)
assert not config, config
start2 = time.time()
rescaled, t, metrics, b_state_dict = train_model(
model, datasets_p, batch_sizes, optim_fact, prepare_batch, task,
train_loader, eval_loaders, training_params, config)
training_time = time.time() - start2
start3 = time.time()
if not isinstance(model, ExhaustiveSearch):
#todo Handle the state dict loading uniformly for all learners RN only
# the exhaustive search models load the best state dict after training
model.load_state_dict(b_state_dict['state_dict'])
iterations = list(metrics.pop('training_iteration').values())
epochs = list(metrics.pop('training_epoch').values())
assert len(iterations) == len(epochs)
index = dict(epochs=epochs, iterations=iterations)
update_summary(index, task_vis, 'index', 0.5)
grouped_xs = dict()
grouped_metrics = defaultdict(list)
grouped_legends = defaultdict(list)
for metric_n, metric_v in metrics.items():
split_n = metric_n.split()
if len(split_n) < 2:
continue
name = ' '.join(split_n[:-1])
grouped_metrics[split_n[-1]].append(list(metric_v.values()))
grouped_legends[split_n[-1]].append(name)
if split_n[-1] in grouped_xs:
if len(metric_v) > len(grouped_xs[split_n[-1]]):
longer_xs = list(metric_v.keys())
assert all(a == b
for a, b in zip(longer_xs, grouped_xs[split_n[-1]]))
grouped_xs[split_n[-1]] = longer_xs
else:
grouped_xs[split_n[-1]] = list(metric_v.keys())
for (plot_name, val), (_, legends) in sorted(
zip(grouped_metrics.items(), grouped_legends.items())):
assert plot_name == _
val = fill_matrix(val)
if len(val) == 1:
val = np.array(val[0])
else:
val = np.array(val).transpose()
x = grouped_xs[plot_name]
task_vis.line(val,
X=x,
win=plot_name,
opts={
'title': plot_name,
'showlegend': True,
'width': 500,
'legend': legends,
'xlabel': 'iterations',
'ylabel': plot_name
})
avg_data_time = list(metrics['data time_ps'].values())[-1]
avg_forward_time = list(metrics['forward time_ps'].values())[-1]
avg_epoch_time = list(metrics['epoch time_ps'].values())[-1]
avg_eval_time = list(metrics['eval time_ps'].values())[-1]
total_time = list(metrics['total time'].values())[-1]
entropies, ent_legend = [], []
for metric_n, metric_v in metrics.items():
if metric_n.startswith('Trainer entropy'):
entropies.append(list(metric_v.values()))
ent_legend.append(metric_n)
if entropies:
task_vis.line(np.array(entropies).transpose(),
X=iterations,
win='ENT',
opts={
'title': 'Arch entropy',
'showlegend': True,
'width': 500,
'legend': ent_legend,
'xlabel': 'Iterations',
'ylabel': 'Loss'
})
if hasattr(learner, 'arch_scores') and hasattr(learner, 'get_top_archs'):
update_summary(learner.arch_scores[t_id], task_vis, 'scores')
archs = model.get_top_archs(5)
list_top_archs(archs, task_vis)
if 'training_archs' in metrics:
plot_trajectory(model.ssn.graph, metrics['training_archs'],
model.ssn.stochastic_node_ids, task_vis)
postproc_time = time.time() - start3
start4 = time.time()
save_path = tune.get_trial_dir()
finish_res = learner.finish_task(datasets[0], t_id, task_vis, save_path)
finish_time = time.time() - start4
start5 = time.time()
eval_tasks = tasks
# eval_tasks = tasks[:t_id + 1] if stream_setting else tasks
evaluation = evaluate_on_tasks(eval_tasks,
learner,
batch_sizes[1],
training_params['device'], ['Val', 'Test'],
normalize,
cur_task=t_id)
assert evaluation['Val']['accuracy'][t_id] == b_state_dict['value']
stats = {}
eval_time = time.time() - start5
stats.update(finish_res)
test_accs = metrics['Test accuracy_0']
if not test_accs:
lca = np.float('nan')
else:
if len(test_accs) <= lca_n:
last_key = max(test_accs.keys())
assert len(test_accs) == last_key + 1,\
f"Can't compute LCA@{lca_n} if steps were skipped " \
f"(got {list(test_accs.keys())})"
test_accs = test_accs.copy()
last_acc = test_accs[last_key]
for i in range(last_key + 1, lca_n + 1):
test_accs[i] = last_acc
lca = np.mean([test_accs[i] for i in range(lca_n + 1)])
accs = {}
key = 'accuracy'
# logger.warning(evaluation)
for split in evaluation.keys():
transfer_matrix[split].append(evaluation[split][key])
for i in range(len(tasks)):
split_acc = evaluation[split][key]
if i < len(split_acc):
accs['{}_T{}'.format(split, i)] = split_acc[i]
else:
accs['{}_T{}'.format(split, i)] = float('nan')
plot_heatmaps(list(transfer_matrix.keys()),
list(map(fill_matrix, transfer_matrix.values())), task_vis)
# logger.warning(t_id)
# logger.warning(transfer_matrix)
avg_val = np.mean(evaluation['Val']['accuracy'])
avg_val_so_far = np.mean(evaluation['Val']['accuracy'][:t_id + 1])
avg_test = np.mean(evaluation['Test']['accuracy'])
avg_test_so_far = np.mean(evaluation['Test']['accuracy'][:t_id + 1])
step_time_s = time.time() - start1
step_sum = model_creation_time + training_time + postproc_time + \
finish_time + eval_time
best_it = b_state_dict.get('cum_best_iter', b_state_dict['iter'])
tune.report(
t=t_id,
best_val=b_state_dict['value'],
avg_acc_val=avg_val,
avg_acc_val_so_far=avg_val_so_far,
avg_acc_test_so_far=avg_test_so_far,
lca=lca,
avg_acc_test=avg_test,
test_acc=evaluation['Test']['accuracy'][t_id],
duration_seconds=step_time_s,
duration_iterations=t,
duration_best_it=best_it,
duration_finish=finish_time,
duration_model_creation=model_creation_time,
duration_training=training_time,
duration_postproc=postproc_time,
duration_eval=eval_time,
duration_sum=step_sum,
# entropy=stats.pop('entropy'),
new_params=learner.new_params(t_id),
total_params=learner.n_params(t_id),
total_steps=total_steps + t,
fw_t=round(avg_forward_time * 1000) / 1000,
data_t=round(avg_data_time * 1000) / 1000,
epoch_t=round(avg_epoch_time * 1000) / 1000,
eval_t=round(avg_eval_time * 1000) / 1000,
total_t=round(total_time * 1000) / 1000,
env_url=get_env_url(vis_p),
**accs,
**stats)
return rescaled, t, metrics, b_state_dict, stats
def __init__(self, clip_info):
self.list = clip_info
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
from torch.utils.data import DataLoader
from torch import nn, optim
from torchvision.transforms import transforms
from unet import Unet
from DataHelper import *
from tqdm import tqdm
import numpy as np
import skimage.io as io
PATH = './model/unet_model.pt'
# 是否使用cuda
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
x_transforms = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])])
# mask只需要转换为tensor
y_transforms = transforms.ToTensor()
def train_model(model, criterion, optimizer, dataload, num_epochs=10):
best_model = model
min_loss = 1000
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
# print('-' * 10)
dt_size = len(dataload.dataset)
epoch_loss = 0
step = 0
def __init__(self, body_regressor_checkpoint, hand_regressor_checkpoint, smpl_dir, device=torch.device('cuda'), use_smplx=True):
super().__init__('BodyMocap')
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print("Loading Body Pose Estimator")
self.__load_body_estimator()
self.visualizer = Visualizer('opengl')
self.frame_id = 0 #count frames
parser = argparse.ArgumentParser()
parser.add_argument("--rot90", default=False, type= bool, help="clockwise rotate 90 degrees")
#parser.add_argument("--camera_topic", default="/logi_c922_2/image_rect_color", help="choose a topic as input image")
parser.add_argument("--body_only", default=False, type= bool, help="detect only body and save its result")
parser.add_argument("--result_path", default="/home/student/result/", help="choose a topic as input image")
parser.add_argument("--save_result", default=False, help="save result or not")
args = parser.parse_args()
self.rot90 = args.rot90
#self.camera_topic = args.camera_topic
self.body_only = args.body_only
self.result_path = args.result_path
self.save_result = args.save_result
self.load = [0,0]
self.angle_leg = 0
self.angle_trunk = 0
self.start = 0
self.angles = np.empty((1,20),dtype = float)
self.body_side = np.empty((25,3),dtype = float)
# Load parametric model (SMPLX or SMPL)
if use_smplx:
smplModelPath = smpl_dir + '/SMPLX_NEUTRAL.pkl'
self.smpl = SMPLX(smpl_dir,
batch_size=1,
num_betas = 10,
use_pca = False,
create_transl=False).to(self.device)
self.use_smplx = True
else:
smplModelPath = smpl_dir + '/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl'
self.smpl = SMPL(smplModelPath, batch_size=1, create_transl=False).to(self.device)
self.use_smplx = False
#Load pre-trained neural network
SMPL_MEAN_PARAMS = '/home/student/frankmocap/extra_data/body_module/data_from_spin/smpl_mean_params.npz'
self.model_regressor = hmr(SMPL_MEAN_PARAMS).to(self.device)
body_checkpoint = torch.load(body_regressor_checkpoint)
self.model_regressor.load_state_dict(body_checkpoint['model'], strict=False)
self.model_regressor.eval()
#hand module init
transform_list = [ transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
self.normalize_transform = transforms.Compose(transform_list)
#Load Hand network
self.opt = TestOptions().parse([])
#Default options
self.opt.single_branch = True
self.opt.main_encoder = "resnet50"
# self.opt.data_root = "/home/hjoo/dropbox/hand_yu/data/"
self.opt.model_root = "/home/student/frankmocap/extra_data"
self.opt.smplx_model_file = os.path.join(smpl_dir,'SMPLX_NEUTRAL.pkl')
self.opt.batchSize = 1
self.opt.phase = "test"
self.opt.nThreads = 0
self.opt.which_epoch = -1
self.opt.checkpoint_path = hand_regressor_checkpoint
self.opt.serial_batches = True # no shuffle
self.opt.no_flip = True # no flip
self.opt.process_rank = -1
# self.opt.which_epoch = str(epoch)
self.hand_model_regressor = H3DWModel(self.opt)
# if there is no specified checkpoint, then skip
assert self.hand_model_regressor.success_load, "Specificed checkpoints does not exists: {}".format(self.opt.checkpoint_path)
self.hand_model_regressor.eval()
self.hand_bbox_detector = HandBboxDetector('third_view', self.device)
#subscriber and publisher initialization
#input subscriber
self.br = CvBridge()
self.subscription_img = self.create_subscription(Image, '/side_img', self.callback_side,10)
self.subscription_img = self.create_subscription(Image, '/front_img', self.callback_front,10)
#output publisher
self.publisher_pose = self.create_publisher(Image,'/pose',10) #images with keypoints annotation
#self.publisher_keypoints = self.create_publisher(Float32MultiArray,'/keypoints',10) #keypoints coordinates
self.publisher_risk = self.create_publisher(Int64,'/risk',10) #risk level
self.publisher_angles = self.create_publisher(Float32MultiArray,'/angles',10)
#!/usr/bin/python
# -*- coding:utf-8 -*-
"""
@author:fangpf
@time: 2020/11/30
"""
import torch
from torchvision.transforms import transforms
from model.inception_iccv import InceptionNet
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose(
[transforms.Resize(size=(256, 128)),
transforms.ToTensor(), normalize])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
classes = [
'Female', 'AgeLess16', 'Age17-30', 'Age31-45', 'BodyFat', 'BodyNormal',
'BodyThin', 'Customer', 'Clerk', 'BaldHead', 'LongHair', 'BlackHair',
'Hat', 'Glasses', 'Muffler', 'Shirt', 'Sweater', 'Vest', 'TShirt',
'Cotton', 'Jacket', 'Suit-Up', 'Tight', 'ShortSleeve', 'LongTrousers',
'Skirt', 'ShortSkirt', 'Dress', 'Jeans', 'TightTrousers', 'LeatherShoes',
'SportShoes', 'Boots', 'ClothShoes', 'CasualShoes', 'Backpack', 'SSBag',
'HandBag', 'Box', 'PlasticBag', 'PaperBag', 'HandTrunk', 'OtherAttchment',
'Calling', 'Talking', 'Gathering', 'Holding', 'Pusing', 'Pulling',
'CarryingbyArm', 'CarryingbyHand'
]
classes_name = [
def evaluate(args):
'''
This script is used to generate a evaluation figure.
'''
use_gpu = torch.cuda.is_available()
if use_gpu:
print("GPU Enabled")
print(f"Current GPU Memory Usage: {torch.cuda.memory_allocated()}")
#Create the data
t = transforms.Compose([
transforms.Resize(args.input_size),
transforms.ToTensor(),
])
t2 = transforms.Compose([
transforms.Resize(args.mask_size),
transforms.ToTensor(),
])
data_set = landpy.MyDataLoader(args.data_dir,
args.mask_size,
image_transforms=t,
mask_transforms=t2)
#Set Batch Size
batch_size = 1
dataset_size = len(data_set)
print(f"Number of Images {dataset_size}")
train_loader, test_loader = landpy.create_data_loaders(
data_set, 0.8, batch_size)
unet_model = landpy.UNet(3, 7)
if use_gpu:
unet_model = unet_model.cuda()
unet_model.load_state_dict(torch.load(args.model_path))
else:
unet_model.load_state_dict(
torch.load(args.model_path, map_location=torch.device('cpu')))
unet_model.eval()
num_images = 3
figure_row = 0
sat_image, actual_mask, label = next(iter(test_loader))
fig, ax = plt.subplots(nrows=num_images, ncols=3, figsize=(20, 20))
for i, (test_image, labels, class_labels) in enumerate(test_loader):
if use_gpu:
test_image = test_image.cuda()
torch.cuda.empty_cache()
predictions = unet_model(test_image)
soft_max_output = torch.nn.LogSoftmax(dim=1)(predictions)
if use_gpu:
soft_max_output = soft_max_output.cpu()
numpy_output = soft_max_output.data.numpy()
final_prediction = np.argmax(numpy_output, axis=1)
prediction_img = landpy.construct_image(final_prediction[0],
args.mask_size)
ax[figure_row][0].imshow(
np.transpose(test_image[0].data.numpy(), (1, 2, 0)))
ax[figure_row][0].set_title("Real Image")
ax[figure_row][1].imshow(
np.transpose(labels[0].data.numpy(), (1, 2, 0)))
ax[figure_row][1].set_title("Actual Mask")
ax[figure_row][2].imshow(np.transpose(prediction_img, (1, 2, 0)))
ax[figure_row][2].set_title("Prediction Mask")
figure_row += 1
if figure_row == num_images:
break
fig_path = os.path.join(args.figure_path, 'evaluation_figure.png')
plt.savefig(fig_path)
x = ReLU(self.fc4(code))
x = ReLU(self.fc5(x))
x = self.fc6(x)
return x
def encoders(self, x):
x = ReLU(self.fc1(x))
x = ReLU(self.fc2(x))
code = ReLU(self.fc3(x))
return code
tr = transforms.Compose([transforms.ToTensor()])
train = MNIST('./data', transform=tr, download=True)
test = MNIST('./data', transform=tr, train=False, download=True)
epoch = 10
batch_size = 30
train = DataLoader(train, batch_size, True)
test = DataLoader(test, batch_size, False)
vae = VAE((1, 28, 28), 120)
sgd = tch.optim.RMSprop(vae.parameters())
mse = nn.MSELoss()
# dev = tch.device('cuda:0')
# vae.to(dev)
for j in range(epoch):
from torch.utils.data import DataLoader
import torchvision
from torchvision.transforms import transforms as tfs
from torchvision import models
from torchvision.models import ResNet
import numpy as np
from visdom import Visdom
import os
data_dir='./data/hymenoptera_data'
train_dataset=torchvision.datasets.ImageFolder(root=os.path.join(data_dir,'train'),
transform=tfs.Compose([
tfs.RandomResizedCrop(224),
tfs.RandomHorizontalFlip(),
tfs.ToTensor(),
tfs.Normalize(
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)
)
])
)
val_dataset=torchvision.datasets.ImageFolder(root=os.path.join(data_dir,'val'),
transform=tfs.Compose([
tfs.RandomResizedCrop(224),
tfs.RandomHorizontalFlip(),
tfs.ToTensor(),
tfs.Normalize(
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)
)
# -*- coding: utf-8 -*-
"""s15transform.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1aAmDlTSAc1ypmMIHgcHpYVlTSFQL3Dbo
"""
from torchvision.transforms import transforms
scale_transform = transforms.Compose([
transforms.Resize((128, 128)),
transforms.Grayscale(num_output_channels=3),
transforms.ToTensor(),
])
def __init__(self):
##The top config
#self.data_root = '/media/hhy/data/USdata/MergePhase1/test_0.3'
#self.log_dir = '/media/hhy/data/code_results/MILs/MIL_H_Attention'
self.root = '/remote-home/my/Ultrasound_CV/data/Ruijin/clean'
self.log_dir = '/remote-home/my/hhy/Ultrasound_MIL/experiments/PLN1/base/fold4'
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
##training config
self.lr = 1e-4
self.epoch = 50
self.resume = -1
self.batch_size = 1
self.net = Attention()
self.net.cuda()
self.optimizer = Adam(self.net.parameters(), lr=self.lr)
self.lrsch = torch.optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=[10, 30, 50, 70], gamma=0.5)
self.logger = Logger(self.log_dir)
self.train_transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomResizedCrop((224, 224)),
transforms.RandomHorizontalFlip(0.5),
transforms.RandomVerticalFlip(0.5),
transforms.ColorJitter(0.25, 0.25, 0.25, 0.25),
transforms.ToTensor()
])
self.test_transform = transforms.Compose(
[transforms.Resize((224, 224)),
transforms.ToTensor()])
self.label_name = "手术淋巴结情况（0未转移；1转移）"
self.trainbag = RuijinBags(self.root, [0, 1, 2, 3],
self.train_transform,
label_name=self.label_name)
self.testbag = RuijinBags(self.root, [4],
self.test_transform,
label_name=self.label_name)
train_label_list = list(
map(lambda x: int(x['label']), self.trainbag.patient_info))
pos_ratio = sum(train_label_list) / len(train_label_list)
print(pos_ratio)
train_weight = [(1 - pos_ratio) if x > 0 else pos_ratio
for x in train_label_list]
# self.train_sampler = WeightedRandomSampler(weights=train_weight, num_samples=len(self.trainbag))
self.train_loader = DataLoader(self.trainbag,
batch_size=self.batch_size,
num_workers=8,
shuffle=True)
self.val_loader = DataLoader(self.testbag,
batch_size=self.batch_size,
shuffle=False,
num_workers=8)
if self.resume > 0:
self.net, self.optimizer, self.lrsch, self.loss, self.global_step = self.logger.load(
self.net, self.optimizer, self.lrsch, self.loss, self.resume)
else:
self.global_step = 0
# self.trainer = MTTrainer(self.net, self.optimizer, self.lrsch, self.loss, self.train_loader, self.val_loader, self.logger, self.global_step, mode=2)
self.trainer = MILTrainer(self.net, self.optimizer, self.lrsch, None,
self.train_loader, self.val_loader,
self.logger, self.global_step)
