def __init__(self, net_path, board_size=15, n=5):
# 游戏棋盘大小
self.board_size = board_size
# 连五子胜利
self.n = n
# 环境实例化
self.env = Game(board_size, n)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.number_playout = args.n_playout
# 记忆库大小
self.buffer_size = args.buffer_size
self.buffer = deque(maxlen=self.buffer_size)
self.batch_size = args.batch_size
# 自我对局1次后进行训练
self.n_games = args.n_games
# 自我对局后进行5次训练
self.epochs = args.epochs
# 打印保存模型间隔
self.check_freq = args.check_freq
# 总共游戏次数
self.game_num = args.game_num
self.net_path = net_path
self.net = MyNet().to(self.device)
self.MSELoss = nn.MSELoss()
self.optimizer = torch.optim.Adam(self.net.parameters(),
weight_decay=1e-4)
# 实例化蒙特卡洛玩家，参数：游戏策略，探索常数，模拟次数，是否自我对弈（测试时为False）
self.mcts_player = Player(policy=self.policy,
number_playout=self.number_playout,
is_self_play=True)
self.writer = SummaryWriter()
if os.path.exists(net_path):
self.net.load_state_dict(torch.load(net_path))
else:
self.net.apply(self.weight_init)
def __init__(self, net_path, board_size, n):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.net = MyNet().to(self.device)
self.net.load_state_dict(torch.load(net_path))
# self.board_size = args.board_size
# self.n = args.number
self.number_playout = args.n_playout
self.env = GameState(board_size, n)
self.net.eval()
self.mcts_player = Player(policy=self.policy,
number_playout=1000,
is_self_play=False,
print_detail=True)
def start_test():
'''
测试
'''
# 初始化网络并加载预训练模型
myNet = MyNet().to("cuda:0")
myNet.eval()
with torch.no_grad():
input = input.to("cuda:0")
# 若想推理加速，在精度接受范围内img\model手动half()为FP16，然后只能GPU推理
# input=input.half()
# myNet=myNet.half()
feature = myNet(input)
def start_train():
'''
训练
'''
use_amp = True
# 前向反传N次，再更新参数 目的：增大batch（理论batch= batch_size * N）
iter_size = 8
myNet = MyNet(use_amp).to("cuda:0")
myNet = torch.nn.DataParallel(myNet, device_ids=[0, 1]) # 数据并行
myNet.train()
# 训练开始前初始化 梯度缩放器
scaler = GradScaler() if use_amp else None
# 加载预训练权重
if resume_train:
scaler.load_state_dict(checkpoint['scaler']) # amp自动混合精度用到
optimizer.load_state_dict(checkpoint['optimizer'])
myNet.load_state_dict(checkpoint["model"])
for epoch in range(1, 100):
for batch_idx, (input, target) in enumerate(dataloader_train):
# 数据 转到每个并行模型的主卡上
input = input.to("cuda:0")
target = target.to("cuda:0")
# 自动混合精度训练
if use_amp:
# 自动广播 将支持半精度操作自动转为FP16
with autocast():
# 提取特征
feature = myNet(input)
losses = loss_function(target, feature)
loss = losses / iter_size
scaler.scale(loss).backward()
else:
feature = myNet(input, target)
losses = loss_function(target, feature)
loss = losses / iter_size
loss.backward()
# 梯度累积,再更新参数
if (batch_idx + 1) % iter_size == 0:
# 梯度更新
if use_amp:
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
# 梯度清零
optimizer.zero_grad()
# scaler 具有状态。恢复训练时需要加载
state = {
'net': myNet.state_dict(),
'optimizer': optimizer.state_dict(),
'scaler': scaler.state_dict()
}
torch.save(state, "filename.pth")
def do_eval(data_path, model_name='mymodel', use_gpu=False):
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
with fluid.dygraph.guard(place):
model = MyNet()
model_state_dict, _ = fluid.load_dygraph(
os.path.join(MODEL_PATH, model_name))
model.load_dict(model_state_dict)
model.eval()
eval_loader = load_data(data_path, mode='eval')
avg_acc_set = []
avg_loss_set = []
for _, data in enumerate(eval_loader()):
x_data, y_data = data
img = fluid.dygraph.to_variable(x_data)
label = fluid.dygraph.to_variable(y_data)
predict, avg_acc = model(img, label)
loss = fluid.layers.cross_entropy(input=predict, label=label)
avg_loss = fluid.layers.mean(loss)
avg_acc_set.append(float(avg_acc.numpy()))
avg_loss_set.append(float(avg_loss.numpy()))
#计算多个batch的平均损失和准确率
avg_acc_val_mean = np.array(avg_acc_set).mean()
avg_loss_val_mean = np.array(avg_loss_set).mean()
print('loss={}, acc={}'.format(avg_loss_val_mean, avg_acc_val_mean))
class Detector:
def __init__(self, net_path, board_size, n):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.net = MyNet().to(self.device)
self.net.load_state_dict(torch.load(net_path))
# self.board_size = args.board_size
# self.n = args.number
self.number_playout = args.n_playout
self.env = GameState(board_size, n)
self.net.eval()
self.mcts_player = Player(policy=self.policy,
number_playout=1000,
is_self_play=False,
print_detail=True)
def policy(self, env):
# 获取可用动作 15*15=225
action_avail = env.action_avail
# 获得当前状态
state = torch.from_numpy(env.get_state).unsqueeze(0).to(self.device)
# 放入神经网络得到预测的log动作概率以及当前状态的胜率
log_action_probs, value = self.net(state)
# 把 log 动作概率转换为动作概率并过滤不可用动作
act_probs = torch.exp(
log_action_probs).detach().cpu().numpy().flatten()
act_probs = zip(action_avail, act_probs[action_avail])
value = value.item()
# 返回动作概率，当前局面价值
return act_probs, value
def detect(self):
while True:
action = None
# 当玩家切换到人机以及游戏未结束时，人机使用MCTS算法得到最优动作
if self.env.current_player == 1 and not self.env.pause:
action = self.mcts_player.get_action(self.env.game)
self.env.step(action)
def test_net():
data_set = TestDataset()
data_loader = DataLoader(data_set,
batch_size=1,
shuffle=True,
drop_last=False)
classes = data_set.classes
net = MyNet(classes)
_, _, last_time_model = get_check_point()
# assign directly
# last_time_model='./weights/weights_21_110242'
if os.path.exists(last_time_model):
model = torch.load(last_time_model)
if cfg.test_use_offline_feat:
net.load_state_dict(model)
else:
net.load_state_dict(model)
print("Using the model from the last check point:`%s`" %
(last_time_model))
else:
raise ValueError("no model existed...")
net.eval()
is_cuda = cfg.use_cuda
did = cfg.device_id
# img_src=cv2.imread("/root/workspace/data/VOC2007_2012/VOCdevkit/VOC2007/JPEGImages/000012.jpg")
# img_src=cv2.imread('./example.jpg')
img_src = cv2.imread('./dog.jpg') # BGR
img = img_src[:, :, ::-1] # RGB
h, w, _ = img.shape
img = img.transpose(2, 0, 1) # [c,h,w]
img = preprocess(img)
img = img[None]
img = torch.tensor(img)
if is_cuda:
net.cuda(did)
img = img.cuda(did)
boxes, labels, probs = net(img, torch.tensor([[w, h]]).type_as(img))[0]
prob_mask = probs > cfg.out_thruth_thresh
boxes = boxes[prob_mask]
labels = labels[prob_mask].long()
probs = probs[prob_mask]
draw_box(img_src,
boxes,
color='pred',
text_list=[
classes[_] + '[%.3f]' % (__) for _, __ in zip(labels, probs)
])
show_img(img_src, -1)
if args.subset_len:
data_loader = get_subnet_dataloader(args.data_dir,
batch_size,
args.subset_len,
num_workers=args.num_workers,
shuffle=False,
train=False,
download=download)
else:
data_loader = get_dataloader(args.data_dir,
args.batch_size,
num_workers=args.num_workers,
shuffle=False,
train=False,
download=download)
model = MyNet()
model = load_weights(model, args.pretrained)
input_signature = torch.randn([1, 3, 32, 32], dtype=torch.float32)
input_signature = input_signature.to(device)
model = model.to(device)
pruning_runner = get_pruning_runner(model, input_signature, 'one_step')
pruning_runner.search(gpus=gpus,
calibration_fn=calibration_fn,
calib_args=(data_loader, ),
num_subnet=args.num_subnet,
removal_ratio=args.sparsity,
eval_fn=eval_fn,
eval_args=(data_loader, ))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--batchsize',
'-b',
type=int,
default=32,
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')
args = parser.parse_args()
# 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 = MyNet()
model.to_gpu() # Copy the model to the GPU
# Setup an optimizer
optimizer = chainer.optimizers.SGD(lr=0.0001)
optimizer.setup(model)
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist()
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
# Learning loop
while train_iter.epoch < args.epoch:
sum_loss = 0
itr = 0
train_batch = train_iter.next()
x_data = []
t_data = []
for data in train_batch:
x_data.append(data[0].reshape((1, 28, 28)))
if data[1] % 2 == 0:
t_data.append([1])
else:
t_data.append([0])
x = Variable(cuda.to_gpu(np.array(x_data, dtype=np.float32)))
t = Variable(cuda.to_gpu(np.array(t_data, dtype=np.int32)))
y = model(x)
model.cleargrads()
loss = F.sigmoid_cross_entropy(y, t)
loss.backward()
optimizer.update()
sum_loss += loss.data
itr += 1
if train_iter.is_new_epoch:
print('epoch:{} train_loss:{} '.format(train_iter.epoch,
sum_loss / itr),
end='')
sum_test_loss = 0
sum_test_accuracy = 0
test_itr = 0
while True:
test_batch = test_iter.next()
x_test_data = []
t_test_data = []
for test_data in test_batch:
x_test_data.append(test_data[0].reshape((1, 28, 28)))
if test_data[1] % 2 == 0:
t_test_data.append([1])
else:
t_test_data.append([0])
x_test = Variable(
cuda.to_gpu(np.array(x_test_data, dtype=np.float32)))
t_test = Variable(
cuda.to_gpu(np.array(t_test_data, dtype=np.int32)))
y_test = model(x_test)
sum_test_loss += F.sigmoid_cross_entropy(y_test, t_test).data
sum_test_accuracy += F.binary_accuracy(y_test, t_test).data
test_itr += 1
if test_iter.is_new_epoch:
test_iter.epoch = 0
test_iter.current_position = 0
test_iter.is_new_epoch = False
test_iter._pushed_position = None
break
print('val_loss:{} val_accuracy:{}'.format(
sum_test_loss / test_itr, sum_test_accuracy / test_itr))
parser.add_argument('--weight_decay',
type=float,
default=1e-4,
help='Weight decay')
parser.add_argument('--momentum', type=float, default=0.9, help='Momentum')
args, _ = parser.parse_known_args()
device = 'cuda'
gpus = get_gpus(args.gpus)
if __name__ == '__main__':
torch.distributed.init_process_group(backend="nccl")
torch.cuda.set_device(args.local_rank)
batch_size = args.batch_size * len(gpus)
model = MyNet()
model.cuda()
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
if os.path.exists(args.data_dir):
download = False
else:
download = True
train_loader = get_dataloader_ddp(args.data_dir,
batch_size,
num_workers=args.num_workers,
mask_inds = np.delete(mask_inds, np.argwhere(mask_inds == 255))
inds_sim = torch.from_numpy(np.where(mask == 255)[0])
inds_scr = torch.from_numpy(np.where(mask != 255)[0])
target_scr = torch.from_numpy(mask.astype(np.int))
if use_cuda:
inds_sim = inds_sim.cuda()
inds_scr = inds_scr.cuda()
target_scr = target_scr.cuda()
target_scr = Variable(target_scr)
# set minLabels
args.minLabels = len(mask_inds)
# train
model = MyNet(profile["count"], args.nChannel, args.nConv)
if use_cuda:
model.cuda()
model.train()
# similarity loss definition
loss_fn = torch.nn.CrossEntropyLoss()
# scribble loss definition
loss_fn_scr = torch.nn.CrossEntropyLoss()
# continuity loss definition
loss_hpy = torch.nn.L1Loss(size_average=True)
class Trainer:
def __init__(self, net_path, board_size=15, n=5):
# 游戏棋盘大小
self.board_size = board_size
# 连五子胜利
self.n = n
# 环境实例化
self.env = Game(board_size, n)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.number_playout = args.n_playout
# 记忆库大小
self.buffer_size = args.buffer_size
self.buffer = deque(maxlen=self.buffer_size)
self.batch_size = args.batch_size
# 自我对局1次后进行训练
self.n_games = args.n_games
# 自我对局后进行5次训练
self.epochs = args.epochs
# 打印保存模型间隔
self.check_freq = args.check_freq
# 总共游戏次数
self.game_num = args.game_num
self.net_path = net_path
self.net = MyNet().to(self.device)
self.MSELoss = nn.MSELoss()
self.optimizer = torch.optim.Adam(self.net.parameters(),
weight_decay=1e-4)
# 实例化蒙特卡洛玩家，参数：游戏策略，探索常数，模拟次数，是否自我对弈（测试时为False）
self.mcts_player = Player(policy=self.policy,
number_playout=self.number_playout,
is_self_play=True)
self.writer = SummaryWriter()
if os.path.exists(net_path):
self.net.load_state_dict(torch.load(net_path))
else:
self.net.apply(self.weight_init)
def weight_init(self, net):
if isinstance(net, nn.Linear) or isinstance(net, nn.Conv2d):
nn.init.normal_(net.weight, mean=0., std=0.1)
nn.init.constant_(net.bias, 0.)
def train(self):
for i in range(self.game_num):
# 环境先自我对弈获得棋局状态，动作概率以及玩家可以赢的概率值
for _ in range(self.n_games):
winner, data = self.env.self_play(self.mcts_player, temp=1.0)
# 打印每局对局信息
print(self.env, "\n", "------------------xx--------")
# 将获得的数据多样化存入样本池
self.extend_sample(data)
# 取样训练
batch = random.sample(self.buffer,
min(len(self.buffer), self.batch_size))
# 解包
state_batch, mcts_probs_batch, winner_value_batch = zip(*batch)
loss = 0.
for _ in range(self.epochs):
# 数据处理
state_batch = torch.tensor(state_batch).to(self.device)
mcts_probs_batch = torch.tensor(mcts_probs_batch).to(
self.device)
winner_value_batch = torch.tensor(winner_value_batch).to(
self.device)
# 通过神经网络输出动作概率，价值用于训练
log_act_probs, value = self.net(state_batch)
# 计算损失
# 价值损失：输出价值与该状态所在对局最终胜负的值（-1/0/1）（均方差）
# 策略损失：蒙特卡洛树模拟的概率值与神经网络模拟的概率值的相似度 (-log(pi) * p)(交叉熵)
value_loss = self.MSELoss(value,
winner_value_batch.view_as(value))
policy_loss = -torch.mean(
torch.sum(mcts_probs_batch * log_act_probs, dim=-1))
loss = value_loss + policy_loss
# 反向传播
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
print(f"epoch:{i},loss:{loss}")
self.writer.add_scalar("loss", loss, i)
self.net.add_histogram(self.writer, i)
if (i + 1) % self.check_freq == 0:
torch.save(self.net.state_dict(), self.net_path)
# 多样化数据样本
def extend_sample(self, data):
extend_data = []
for state, mcts_prob, winner_value in data:
extend_data.append((state, mcts_prob, winner_value))
# 分别旋转 90度/180度/270度，增加数据多样性
for i in range(1, 4):
# 同时旋转棋盘状态和概率值
state_ = np.rot90(state, i, (1, 2))
mcts_prob_ = np.rot90(
mcts_prob.reshape(self.env.height, self.env.width), i)
extend_data.append(
(state_, mcts_prob_.flatten(), winner_value))
# 翻转棋盘,将矩阵中的每一位玩家的状态进行翻转
state_ = np.array([np.fliplr(s) for s in state_])
mcts_prob_ = np.fliplr(mcts_prob_)
extend_data.append(
(state_, mcts_prob_.flatten(), winner_value))
# 将样本存入样本池
self.buffer.extend(extend_data)
# 用于player调用神经网络获得动作概率，当前局面价值
def policy(self, env):
# 获取可用动作 15*15=225
action_avail = env.action_avail
# 获得当前状态
state = torch.from_numpy(env.get_state).unsqueeze(0).to(self.device)
# 放入神经网络得到预测的log动作概率以及当前状态的胜率
log_action_probs, value = self.net(state)
# 把 log 动作概率转换为动作概率并过滤不可用动作
act_probs = torch.exp(
log_action_probs).detach().cpu().numpy().flatten()
act_probs = zip(action_avail, act_probs[action_avail])
value = value.item()
# 返回动作概率，当前局面价值
return act_probs, value
def train():
print("my name is van")
# let the random counld be the same
if cfg.train_use_offline_feat:
data_set = TrainSetExt()
else:
data_set = TrainDataset()
data_loader = DataLoader(data_set,
batch_size=1,
shuffle=True,
drop_last=False)
net = MyNet(data_set.classes)
net.print_net_info()
epoch, iteration, w_path = get_check_point()
if w_path:
model = torch.load(w_path)
if cfg.train_use_offline_feat:
net.load_state_dict(model)
else:
net.load_state_dict(model)
print("Using the model from the last check point:%s" % (w_path),
end=" ")
epoch += 1
net.train()
is_cuda = cfg.use_cuda
did = cfg.device_id
if is_cuda:
net.cuda(did)
while epoch < cfg.epochs:
# train the rpn
print('******epoch %d*********' % (epoch))
for i, (imgs, boxes, labels, scale,
img_sizes) in tqdm(enumerate(data_loader)):
if is_cuda:
imgs = imgs.cuda(did)
labels = labels.cuda(did)
boxes = boxes.cuda(did)
scale = scale.cuda(did).float()
img_sizes = img_sizes.cuda(did).float()
loss = net.train_once(imgs, boxes, labels, scale, img_sizes,
cfg.train_use_offline_feat)
tqdm.write('Epoch:%d, iter:%d, loss:%.5f' %
(epoch, iteration, loss))
iteration += 1
if cfg.train_use_offline_feat:
torch.save(net.state_dict(),
'%sweights_%d_%d' % (cfg.weights_dir, epoch, iteration))
else:
torch.save(net.state_dict(),
'%sweights_%d_%d' % (cfg.weights_dir, epoch, iteration))
_map = eval_net(net=net, num=100, shuffle=True)['map']
print("map:", _map)
epoch += 1
print(eval_net(net=net))
def eval_net(net=None, num=cfg.eval_number, shuffle=False):
if cfg.test_use_offline_feat:
data_set = TestSetExt()
else:
data_set = TestDataset()
data_loader = DataLoader(data_set,
batch_size=1,
shuffle=shuffle,
drop_last=False)
is_cuda = cfg.use_cuda
did = cfg.device_id
if net is None:
classes = data_set.classes
net = MyNet(classes)
_, _, last_time_model = get_check_point()
# assign directly
# last_time_model='./weights/weights_21_110242'
if os.path.exists(last_time_model):
model = torch.load(last_time_model)
if cfg.test_use_offline_feat:
net.load_state_dict(model)
else:
net.load_state_dict(model)
print("Using the model from the last check point:`%s`" %
(last_time_model))
if is_cuda:
net.cuda(did)
else:
raise ValueError("no model existed...")
net.eval()
upper_bound = num
gt_bboxes = []
gt_labels = []
gt_difficults = []
pred_bboxes = []
pred_classes = []
pred_scores = []
for i, (img, sr_im_size, cur_im_size, gt_box, label,
diff) in tqdm(enumerate(data_loader)):
if i > upper_bound:
break
sr_im_size = sr_im_size.float()
cur_im_size = cur_im_size.float()
if is_cuda:
img = img.cuda(did)
im_size = sr_im_size.cuda(did)
cur_im_size = cur_im_size.cuda(did)
pred_box, pred_class, pred_prob = net(
img,
im_size,
offline_feat=cfg.test_use_offline_feat,
cur_image_size=cur_im_size)[0]
prob_mask = pred_prob > cfg.out_thruth_thresh
pbox = pred_box[prob_mask]
plabel = pred_class[prob_mask].long()
pprob = pred_prob[prob_mask]
gt_bboxes += list(gt_box.numpy())
gt_labels += list(label.numpy())
gt_difficults += list(diff.numpy().astype('bool'))
pred_bboxes += [pbox.cpu().detach().numpy()]
pred_classes += [plabel.cpu().numpy()]
pred_scores += [pprob.cpu().detach().numpy()]
# pred_bboxes+=[np.empty(0) ]
# pred_classes+=[np.empty(0) ]
# pred_scores+=[np.empty(0) ]
res = voc_eval(pred_bboxes,
pred_classes,
pred_scores,
gt_bboxes,
gt_labels,
gt_difficults,
use_07_metric=True)
# print(res)
# avoid potential error
net.train()
return res
type=str,
default='./dataset/cifar10',
help='Dataset directory')
parser.add_argument('--num_workers',
type=int,
default=64,
help='Number of workers used in dataloading')
parser.add_argument('--batch_size', type=int, default=128, help='Batch size')
args, _ = parser.parse_known_args()
device = 'cuda'
gpus = get_gpus(args.gpus)
if __name__ == '__main__':
model = MyNet()
model = torch.nn.DataParallel(model, device_ids=gpus)
batch_size = args.batch_size * len(gpus)
if os.path.exists(args.data_dir):
download = False
else:
download = True
val_loader = get_dataloader(args.data_dir,
batch_size,
num_workers=args.num_workers,
shuffle=False,
train=False,
download=download)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=batch_size,
shuffle=True,
num_workers=2)
### Initialize the neural net and begin the training loop.
net = MyNet()
# NOTE: Cross-entropy loss is the default for classification, but try looking
# into other loss functions that could be used.
criterion = nn.CrossEntropyLoss()
# NOTE: Check out the torch.optim library for other optimization algorithms that
# could be used instead.
optimizer = optim.SGD(net.parameters(), lr=learning_rate)
# Begin the training loop.
for epoch in range(num_epoch):
# Helps keep track of where you are in training. Don't like this method?
# Check out the tqdm module, which will print a loading bar for for loops.
print("Epoch:", epoch + 1)
# Load the testing dataset
testset = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
testloader = torch.utils.data.DataLoader(testset,
batch_size=10000,
shuffle=False,
num_workers=2)
# Define the classes in the dataset
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
# Reload the neural net
net = MyNet()
net.load_state_dict(torch.load("./" + net_name + ".pth"))
# Get accuracy of the trained net as a whole
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' %
(100 * correct / total))
parser = argparse.ArgumentParser()
parser.add_argument('--sparsity',
type=float,
default=0.5,
help='Sparsity ratio')
parser.add_argument('--save_dir',
type=str,
default='./',
help='Where to save retrained model')
args, _ = parser.parse_known_args()
device = 'cuda'
if __name__ == '__main__':
sparse_model_path = os.path.join(args.save_dir, 'mynet_sparse.pth')
assert os.path.exists(sparse_model_path), "No sparse model!"
slim_model_path = os.path.join(args.save_dir, 'mynet_slim.pth')
sparse_model = MyNet()
sparse_model = load_weights(sparse_model, sparse_model_path)
sparse_model.to(device)
input_signature = torch.randn([1, 3, 32, 32], dtype=torch.float32)
input_signature = input_signature.to(device)
pruning_runner = get_pruning_runner(sparse_model, input_signature,
'iterative')
slim_model = pruning_runner.prune(removal_ratio=args.sparsity, mode='slim')
torch.save(slim_model.state_dict(), slim_model_path)
print('Convert sparse model to slim model done!')
def do_train(data_path,
model_name='mymodel',
use_gpu=False,
epoch_num=5,
batch_size=100,
learning_rate=0.01):
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
with fluid.dygraph.guard(place):
model = MyNet()
model.train()
train_loader = load_data(data_path, mode='train')
optimizer = fluid.optimizer.SGDOptimizer(
learning_rate=learning_rate, parameter_list=model.parameters())
iter = 0
for epoch_id in range(epoch_num):
for batch_id, data in enumerate(train_loader()):
#准备数据，格式需要转换成符合框架要求的
image_data, label_data = data
# 将数据转为飞桨动态图格式
image = fluid.dygraph.to_variable(image_data)
label = fluid.dygraph.to_variable(label_data)
# #前向计算的过程
# predict = model(image)
#前向计算的过程，同时拿到模型输出值和分类准确率
predict, avg_acc = model(image, label)
#计算损失，取一个批次样本损失的平均值
# loss = fluid.layers.square_error_cost(predict, label)
loss = fluid.layers.cross_entropy(predict, label)
avg_loss = fluid.layers.mean(loss)
#每训练了1000批次的数据，打印下当前Loss的情况
if batch_id != 0 and batch_id % 100 == 0:
print(
"epoch: {}, batch: {}, loss is: {}, acc is: {}".format(
epoch_id, batch_id, avg_loss.numpy(),
avg_acc.numpy()))
log_writer.add_scalar(tag='acc',
step=iter,
value=avg_acc.numpy())
log_writer.add_scalar(tag='loss',
step=iter,
value=avg_loss.numpy())
iter = iter + 100
#后向传播，更新参数的过程
avg_loss.backward()
optimizer.minimize(avg_loss)
model.clear_gradients()
fluid.save_dygraph(
model.state_dict(),
os.path.join(CHECKPOINT_PATH,
f'{model_name}_epoch_{epoch_id}'))
fluid.save_dygraph(
optimizer.state_dict(),
os.path.join(CHECKPOINT_PATH,
f'{model_name}_epoch_{epoch_id}'))
# 保存模型
fluid.save_dygraph(model.state_dict(),
os.path.join(MODEL_PATH, model_name))
torch.cuda.set_device(args.local_rank)
batch_size = args.batch_size * len(gpus)
train_loader = get_dataloader_ddp(args.data_dir,
batch_size,
num_workers=args.num_workers,
shuffle=True,
train=True,
download=download)
val_loader = get_dataloader_ddp(args.data_dir,
batch_size,
num_workers=args.num_workers,
shuffle=False,
train=False,
download=download)
model = MyNet()
model = load_weights(model, args.pretrained)
input_signature = torch.randn([1, 3, 32, 32], dtype=torch.float32)
input_signature = input_signature.to(device)
model = model.to(device)
pruning_runner = get_pruning_runner(model, input_signature, 'iterative')
model = pruning_runner.prune(removal_ratio=args.sparsity, mode='sparse')
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
criterion = torch.nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
def eval_net(net=None,num=cfg.eval_number,shuffle=False):
data_set=TestDataset()
data_loader=DataLoader(data_set,batch_size=1,shuffle=shuffle,drop_last=False)
is_cuda=cfg.use_cuda
did=cfg.device_id
if net is None:
classes=data_set.classes
net=MyNet(len(classes)+1)
_,_,last_time_model=get_check_point()
if os.path.exists(last_time_model):
model=torch.load(last_time_model)
net.load_state_dict(model)
print("Using the model from the last check point:`%s`"%(last_time_model))
if is_cuda:
net.cuda(did)
else:
raise ValueError("no model existed...")
net.eval()
upper_bound=num
gt_bboxes=[]
gt_labels=[]
gt_difficults=[]
pred_bboxes=[]
pred_classes=[]
pred_scores=[]
for i,(img,sr_im_size,gt_box,label,diff) in tqdm(enumerate(data_loader)):
assert img.shape[0]==1
if i> upper_bound:
break
sr_im_size=sr_im_size.float()
if is_cuda:
img=img.cuda(did)
im_size=sr_im_size.cuda(did)
pred_box,pred_class,pred_prob=net.predict(img,im_size)[0]
prob_mask=pred_prob>cfg.out_thruth_thresh
pbox=pred_box[prob_mask ]
plabel=pred_class[prob_mask ].long()
pprob=pred_prob[prob_mask]
gt_box=gt_box.numpy()
if len(gt_box)!=0:
# print(gt_box.shape)
gt_box=gt_box[:,:,[1,0,3,2]] # change `xyxy` to `yxyx`
gt_bboxes += list(gt_box )
gt_labels += list(label.numpy())
gt_difficults += list(diff.numpy().astype('bool'))
pbox=pbox.cpu().detach().numpy()
if len(pbox)!=0:
pbox=pbox[:,[1,0,3,2]] # change `xyxy` to `yxyx`
pred_bboxes+=[pbox]
pred_classes+=[plabel.cpu().numpy()]
pred_scores+=[pprob.cpu().detach().numpy()]
# pred_bboxes+=[np.empty(0) ]
# pred_classes+=[np.empty(0) ]
# pred_scores+=[np.empty(0) ]
res=voc_eval(pred_bboxes,pred_classes,pred_scores,
gt_bboxes,gt_labels,gt_difficults,use_07_metric=True)
# print(res)
# avoid potential error
net.train()
return res