def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print("Using {} device training.".format(device.type))
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
results_file = "results{}.txt".format(
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
VOC_root = parser_data.data_path
# check voc root
if os.path.exists(os.path.join(VOC_root, "VOCdevkit")) is False:
raise FileNotFoundError(
"VOCdevkit dose not in path:'{}'.".format(VOC_root))
train_dataset = VOC2012DataSet(VOC_root,
data_transform['train'],
train_set='train.txt')
# 注意训练时，batch_size必须大于1
batch_size = parser_data.batch_size
assert batch_size > 1, "batch size must be greater than 1"
# 防止最后一个batch_size=1，如果最后一个batch_size=1就舍去
drop_last = True if len(train_dataset) % batch_size == 1 else False
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using %g dataloader workers' % nw)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=nw,
collate_fn=train_dataset.collate_fn,
drop_last=drop_last)
val_dataset = VOC2012DataSet(VOC_root,
data_transform['val'],
train_set='val.txt')
val_data_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=nw,
collate_fn=train_dataset.collate_fn)
model = create_model(num_classes=args.num_classes + 1, device=device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.0005,
momentum=0.9,
weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.3)
# 如果指定了上次训练保存的权重文件地址，则接着上次结果接着训练
if parser_data.resume != "":
checkpoint = torch.load(parser_data.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
parser_data.start_epoch = checkpoint['epoch'] + 1
print("the training process from epoch{}...".format(
parser_data.start_epoch))
train_loss = []
learning_rate = []
val_map = []
# 提前加载验证集数据，以免每次验证时都要重新加载一次数据，节省时间
val_data = get_coco_api_from_dataset(val_data_loader.dataset)
for epoch in range(parser_data.start_epoch, parser_data.epochs):
mean_loss, lr = utils.train_one_epoch(model=model,
optimizer=optimizer,
data_loader=train_data_loader,
device=device,
epoch=epoch,
print_freq=50)
train_loss.append(mean_loss.item())
learning_rate.append(lr)
# update learning rate
lr_scheduler.step()
coco_info = utils.evaluate(model=model,
data_loader=val_data_loader,
device=device,
data_set=val_data)
# write into txt
with open(results_file, "a") as f:
result_info = [
str(round(i, 4)) for i in coco_info + [mean_loss.item(), lr]
]
txt = "epoch:{} {}".format(epoch, ' '.join(result_info))
f.write(txt + "\n")
val_map.append(coco_info[1]) # pascal mAP
# save weights
save_files = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch
}
torch.save(save_files, "./save_weights/ssd300-{}.pth".format(epoch))
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
def main(args):
print(args)
# mp.spawn(main_worker, args=(args,), nprocs=args.world_size, join=True)
utils.init_distributed_mode(args)
device = torch.device(args.device)
# Data loading code
print("Loading data")
data_transform = {
"train": transform.Compose([transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()]),
"val": transform.Compose([transform.Resize(),
transform.ToTensor(),
transform.Normalization()])
}
VOC_root = args.data_path
# load train data set
train_data_set = VOC2012DataSet(VOC_root, data_transform["train"], train_set='train.txt')
# load validation data set
val_data_set = VOC2012DataSet(VOC_root, data_transform["val"], train_set='val.txt')
print("Creating data loaders")
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_data_set)
test_sampler = torch.utils.data.distributed.DistributedSampler(val_data_set)
else:
train_sampler = torch.utils.data.RandomSampler(train_data_set)
test_sampler = torch.utils.data.SequentialSampler(val_data_set)
if args.aspect_ratio_group_factor >= 0:
# 统计所有图像比例在bins区间中的位置索引
group_ids = create_aspect_ratio_groups(train_data_set, k=args.aspect_ratio_group_factor)
train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids, args.batch_size)
else:
train_batch_sampler = torch.utils.data.BatchSampler(
train_sampler, args.batch_size, drop_last=True)
data_loader = torch.utils.data.DataLoader(
train_data_set, batch_sampler=train_batch_sampler, num_workers=args.workers,
collate_fn=utils.collate_fn)
data_loader_test = torch.utils.data.DataLoader(
val_data_set, batch_size=4,
sampler=test_sampler, num_workers=args.workers,
collate_fn=utils.collate_fn)
print("Creating model")
model = create_model(num_classes=21)
model.to(device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(
params, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
# lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma)
# 如果传入resume参数，即上次训练的权重地址，则接着上次的参数训练
if args.resume:
# If map_location is missing, torch.load will first load the module to CPU
# and then copy each parameter to where it was saved,
# which would result in all processes on the same machine using the same set of devices.
checkpoint = torch.load(args.resume, map_location='cpu') # 读取之前保存的权重文件(包括优化器以及学习率策略)
model_without_ddp.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.test_only:
utils.evaluate(model, data_loader_test, device=device)
return
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
utils.train_one_epoch(model, optimizer, data_loader, device, epoch, args.print_freq)
lr_scheduler.step()
if args.output_dir:
# 只在主节点上执行保存权重操作
utils.save_on_master({
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'args': args,
'epoch': epoch},
os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
# evaluate after every epoch
utils.evaluate(model, data_loader_test, device=device)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main():
# get devices
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# create model
# 目标检测数 + 背景
num_classes = 20 + 1
model = create_model(num_classes=num_classes)
# load train weights
train_weights = "./save_weights/ssd300-14.pth"
train_weights_dict = torch.load(train_weights,
map_location=device)['model']
model.load_state_dict(train_weights_dict)
model.to(device)
# read class_indict
json_path = "./pascal_voc_classes.json"
assert os.path.exists(json_path), "file '{}' dose not exist.".format(
json_path)
json_file = open(json_path, 'r')
class_dict = json.load(json_file)
category_index = {v: k for k, v in class_dict.items()}
# load image
original_img = Image.open("./test.jpg")
# from pil image to tensor, do not normalize image
data_transform = transform.Compose(
[transform.Resize(),
transform.ToTensor(),
transform.Normalization()])
img, _ = data_transform(original_img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
model.eval()
with torch.no_grad():
# initial model
init_img = torch.zeros((1, 3, 300, 300), device=device)
model(init_img)
time_start = time_synchronized()
predictions = model(
img.to(device))[0] # bboxes_out, labels_out, scores_out
time_end = time_synchronized()
print("inference+NMS time: {}".format(time_end - time_start))
predict_boxes = predictions[0].to("cpu").numpy()
predict_boxes[:,
[0, 2]] = predict_boxes[:, [0, 2]] * original_img.size[0]
predict_boxes[:,
[1, 3]] = predict_boxes[:, [1, 3]] * original_img.size[1]
predict_classes = predictions[1].to("cpu").numpy()
predict_scores = predictions[2].to("cpu").numpy()
if len(predict_boxes) == 0:
print("没有检测到任何目标!")
draw_box(original_img,
predict_boxes,
predict_classes,
predict_scores,
category_index,
thresh=0.5,
line_thickness=5)
plt.imshow(original_img)
plt.show()
# read class_indict
category_index = {}
try:
json_file = open('./pascal_voc_classes.json', 'r')
class_dict = json.load(json_file)
category_index = {v: k for k, v in class_dict.items()}
except Exception as e:
print(e)
exit(-1)
# load image
original_img = Image.open("./timg.jpg")
# from pil image to tensor, do not normalize image
data_transform = transform.Compose(
[transform.Resize(),
transform.ToTensor(),
transform.Normalization()])
img, _ = data_transform(original_img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
model.eval()
with torch.no_grad():
predictions = model(
img.to(device))[0] # bboxes_out, labels_out, scores_out
predict_boxes = predictions[0].to("cpu").numpy()
predict_boxes[:, [0, 2]] = predict_boxes[:, [0, 2]] * original_img.size[0]
predict_boxes[:, [1, 3]] = predict_boxes[:, [1, 3]] * original_img.size[1]
predict_classes = predictions[1].to("cpu").numpy()
predict_scores = predictions[2].to("cpu").numpy()
def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print(device)
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
night_root = parser_data.data_path
train_dataset = NightDataSet(night_root,
data_transform['train'],
train_set='train.txt')
# aa = train_dataset[1]
# 注意训练时，batch_size必须大于1
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=8,
shuffle=True,
num_workers=4,
collate_fn=utils.collate_fn)
val_dataset = NightDataSet(night_root,
data_transform['val'],
train_set='val.txt')
# bb = val_dataset[2]
val_data_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=4,
shuffle=False,
num_workers=0,
collate_fn=utils.collate_fn)
model = create_model(num_classes=3, device=device)
print(model)
model.to(device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.005,
momentum=0.9,
weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.5)
# 如果指定了上次训练保存的权重文件地址，则接着上次结果接着训练
if parser_data.resume != "":
checkpoint = torch.load(parser_data.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
parser_data.start_epoch = checkpoint['epoch'] + 1
print("the training process from epoch{}...".format(
parser_data.start_epoch))
train_loss = []
learning_rate = []
val_map = []
train_val_map = []
val_data = None
# 如果电脑内存充裕，可提前加载验证集数据，以免每次验证时都要重新加载一次数据，节省时间
# val_data = get_coco_api_from_dataset(val_data_loader.dataset)
for epoch in range(parser_data.start_epoch, parser_data.epochs):
utils.train_one_epoch(model=model,
optimizer=optimizer,
data_loader=train_data_loader,
device=device,
epoch=epoch,
print_freq=50,
train_loss=train_loss,
train_lr=learning_rate)
lr_scheduler.step()
if epoch >= 20 or epoch == 10:
utils.evaluate(model=model,
data_loader=val_data_loader,
device=device,
data_set=val_data,
mAP_list=val_map)
# save weights
save_files = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch
}
torch.save(save_files, "./save_weights/ssd512-{}.pth".format(epoch))
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
n_gpu = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
args.n_gpu = n_gpu
args.distributed = n_gpu > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='gloo',
init_method='env://')
synchronize()
device = 'cuda'
train_trans = transform.Compose([
transform.RandomResize(args.train_min_size_range, args.train_max_size),
transform.RandomHorizontalFlip(0.5),
transform.ToTensor(),
transform.Normalize(args.pixel_mean, args.pixel_std)
])
valid_trans = transform.Compose([
transform.Resize(args.test_min_size, args.test_max_size),
transform.ToTensor(),
transform.Normalize(args.pixel_mean, args.pixel_std)
])
train_set = COCODataset(args.path, 'train', train_trans)
valid_set = COCODataset(args.path, 'val', valid_trans)
# backbone = vovnet39(pretrained=True)
# backbone = vovnet57(pretrained=True)
# backbone = resnet18(pretrained=True)
def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print(device)
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
data_transform = {
"test":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
night_root = parser_data.data_path
test_dataset = NightDataSet(night_root,
data_transform['test'],
train_set='test.txt')
test_data_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=4,
shuffle=False,
num_workers=0,
collate_fn=utils.collate_fn)
model = create_model(num_classes=3, device=device)
print(model)
model.to(device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.005,
momentum=0.9,
weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.5)
# 如果指定了上次训练保存的权重文件地址，则接着上次结果接着训练
if parser_data.resume != "":
checkpoint = torch.load(parser_data.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
parser_data.start_epoch = checkpoint['epoch'] + 1
print("the training process from epoch{}...".format(
parser_data.start_epoch))
test_val_map = []
val_data = None
for epoch in range(parser_data.start_epoch, parser_data.epochs):
utils.evaluate(model=model,
data_loader=test_data_loader,
device=device,
data_set=val_data,
mAP_list=test_val_map)
def main(args):
init_distributed_mode(args)
print(args)
device = torch.device(args.device)
results_file = "results{}.txt".format(
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
# Data loading code
print("Loading data")
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
VOC_root = args.data_path
# check voc root
if os.path.exists(os.path.join(VOC_root, "VOCdevkit")) is False:
raise FileNotFoundError(
"VOCdevkit dose not in path:'{}'.".format(VOC_root))
# load train data set
train_data_set = VOC2012DataSet(VOC_root,
data_transform["train"],
train_set='train.txt')
# load validation data set
val_data_set = VOC2012DataSet(VOC_root,
data_transform["val"],
train_set='val.txt')
print("Creating data loaders")
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_data_set)
test_sampler = torch.utils.data.distributed.DistributedSampler(
val_data_set)
else:
train_sampler = torch.utils.data.RandomSampler(train_data_set)
test_sampler = torch.utils.data.SequentialSampler(val_data_set)
if args.aspect_ratio_group_factor >= 0:
# 统计所有图像比例在bins区间中的位置索引
group_ids = create_aspect_ratio_groups(
train_data_set, k=args.aspect_ratio_group_factor)
train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids,
args.batch_size)
else:
train_batch_sampler = torch.utils.data.BatchSampler(train_sampler,
args.batch_size,
drop_last=True)
data_loader = torch.utils.data.DataLoader(
train_data_set,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
collate_fn=train_data_set.collate_fn)
data_loader_test = torch.utils.data.DataLoader(
val_data_set,
batch_size=1,
sampler=test_sampler,
num_workers=args.workers,
collate_fn=train_data_set.collate_fn)
print("Creating model")
model = create_model(num_classes=args.num_classes + 1, device=device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=args.lr_step_size,
gamma=args.lr_gamma)
# lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma)
# 如果传入resume参数，即上次训练的权重地址，则接着上次的参数训练
if args.resume:
# If map_location is missing, torch.load will first load the module to CPU
# and then copy each parameter to where it was saved,
# which would result in all processes on the same machine using the same set of devices.
checkpoint = torch.load(
args.resume, map_location='cpu') # 读取之前保存的权重文件(包括优化器以及学习率策略)
model_without_ddp.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.test_only:
utils.evaluate(model, data_loader_test, device=device)
return
train_loss = []
learning_rate = []
val_map = []
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
mean_loss, lr = utils.train_one_epoch(model,
optimizer,
data_loader,
device,
epoch,
args.print_freq,
warmup=True)
# only first process to save training info
if args.rank in [-1, 0]:
train_loss.append(mean_loss.item())
learning_rate.append(lr)
# update learning rate
lr_scheduler.step()
# evaluate after every epoch
coco_info = utils.evaluate(model, data_loader_test, device=device)
if args.rank in [-1, 0]:
# write into txt
with open(results_file, "a") as f:
# 写入的数据包括coco指标还有loss和learning rate
result_info = [
str(round(i, 4))
for i in coco_info + [mean_loss.item(), lr]
]
txt = "epoch:{} {}".format(epoch, ' '.join(result_info))
f.write(txt + "\n")
val_map.append(coco_info[1]) # pascal mAP
if args.output_dir:
# 只在主节点上执行保存权重操作
save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'args': args,
'epoch': epoch
}, os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if args.rank in [-1, 0]:
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
args = get_args()
n_gpu = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
args.distributed = n_gpu > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='gloo',
init_method='env://')
synchronize()
device = 'cuda'
valid_trans = transform.Compose([
transform.Resize(args.test_min_size, args.test_max_size),
transform.ToTensor(),
transform.Normalize(args.pixel_mean, args.pixel_std)
])
valid_set = COCODataset("/data/COCO_17/", 'val', valid_trans)
# backbone = vovnet39(pretrained=False)
# backbone = resnet18(pretrained=False)
backbone = resnet50(pretrained=False)
model = ATSS(args, backbone)
# load weight
model_file = "./training_dir/epoch-12.pt"
chkpt = torch.load(model_file, map_location='cpu') # load checkpoint
model.load_state_dict(chkpt['model'])
print('load weights from ' + model_file)
return 0
if __name__ == '__main__':
# 使用局部对齐模型
model = ReIDNet(num_classes=751, loss={'softmax, metric'}, aligned=True)
# 加载局部对齐模型最优参数
model.load_state_dict(
torch.load('./model/param/aligned_trihard_net_params_best.pth'))
# 指定数据集
dataset = Market1501()
# query数据与gallery数据处理器
transform = T.Compose([
T.Resize((height, width)), # 尺度统一
T.ToTensor(), # 图片转张量
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]), # 归一化，参数固定
])
# query集吞吐器
query_data_loader = DataLoader(
ImageDataset(dataset.query, transform=transform), # 自定义的数据集，指定使用数据处理器
batch_size=batch_size, # 一个批次的大小（一个批次有多少个图片张量）
drop_last=True, # 丢弃最后无法称为一整个批次的数据
)
# gallery集吞吐器
gallery_data_loader = DataLoader(
ImageDataset(dataset.gallery,
transform=transform), # 自定义的数据集，指定使用数据处理器
batch_size=batch_size, # 一个批次的大小（一个批次有多少个图片张量）
drop_last=True, # 丢弃最后无法称为一整个批次的数据
)
import engine as eng
import eval
import models
import transform
import utils
from datasets import CocoSingleKPS
IMAGE_SIZE = 256, 256
data_path, remaining_args = utils.get_args()
engine = eng.Engine.command_line_init(args=remaining_args)
data_transform = transform.Compose([
transform.ResizeKPS(IMAGE_SIZE),
transform.extract_keypoints,
transform.ToTensor(),
transform.ImageTargetWrapper(T.Normalize(CocoSingleKPS.MEAN, CocoSingleKPS.STD))
])
selected_kps = ['left_eye', 'right_eye']
coco_train = CocoSingleKPS.from_data_path(data_path, train=True, transforms=data_transform, keypoints=selected_kps)
coco_val = CocoSingleKPS.from_data_path(data_path, train=False, transforms=data_transform, keypoints=selected_kps)
num_instructions = len(selected_kps)
model = models.resnet50(td_outplanes=1, num_instructions=num_instructions)
if len(selected_kps) == 1:
model.one_iteration()
model = models.SequentialInstructor(model, num_instructions)
train_eval = eval.Evaluator()
val_eval = eval.Evaluator()
