class VisualHook(Hook):
def __init__(self, priority=1):
self.priority = priority
def run_begin(self, trainer):
rank = dist.get_rank()
if rank != 0:
return
logdir = os.path.join(trainer.output_dir, 'visual_dl')
if not os.path.exists(logdir):
os.makedirs(logdir)
self.writer = LogWriter(logdir=logdir)
# app.run(logdir=logdir, port=8040, host="0.0.0.0")
def train_epoch_end(self, trainer):
rank = dist.get_rank()
if rank != 0:
return
outputs = trainer.outputs
for k in outputs.keys():
v = trainer.logs[k].avg
self.writer.add_scalar(tag='train/{}'.format(k),
step=trainer.current_epoch,
value=v)
with paddle.no_grad():
if dist.get_world_size() > 1:
for name, param in trainer.model._layers.named_parameters():
if 'bn' not in name:
self.writer.add_histogram(name, param.numpy(),
trainer.current_epoch)
else:
for name, param in trainer.model.named_parameters():
if 'bn' not in name:
self.writer.add_histogram(name, param.numpy(),
trainer.current_epoch)
def run_end(self, trainer):
rank = dist.get_rank()
if rank != 0:
return
self.writer.close()
# Training
epoch_num = 10
report_freq = 10
base_acc = 0.0
model_save_path = r"E:\Projects\Engine_Inspection\VGG16CAM\ResNet50\model"
var_save_path = r"E:\Projects\Engine_Inspection\VGG16CAM\ResNet50\variable"
train_step, test_step = 0, 0
for epoch_id in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
train_cost, train_acc, params = exe.run(program=fluid.default_main_program(),feed=feeder.feed(data),fetch_list=[avg_cost,batch_acc,params_name])
# Write the training data into the LogWriter
log_writer.add_scalar("train_cost", train_cost[0], train_step)
log_writer.add_scalar("train_acc", train_acc[0], train_step)
log_writer.add_histogram("histogram", params.flatten(), train_step, buckets=50)
train_step += 1
# Printing the result every 100 batch
if batch_id % report_freq == 0:
print("Pass:%d,Batch:%d,Cost:%0.5f,Accuracy:%0.5f"%(epoch_id,batch_id,train_cost[0],train_acc[0]))
# Testing
test_accs=[]
test_costs=[]
for batch_id,data in enumerate(test_reader()):
test_cost,test_acc=exe.run(program=test_program,feed=feeder.feed(data),fetch_list=[avg_cost,batch_acc])
test_accs.append(test_acc[0])
test_costs.append(test_cost[0])
# Write the training data into the LogWriter
)[0].name
# 训练10次
for pass_id in range(10):
# 进行训练
for batch_id, data in enumerate(train_reader()):
train_cost, train_acc, params = exe.run(
program=fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost, acc, params_name])
# 保存训练的日志数据
train_step += 1
writer.add_scalar(tag="训练/损失值", step=train_step, value=train_cost[0])
writer.add_scalar(tag="训练/准确率", step=train_step, value=train_acc[0])
writer.add_histogram(tag="训练/参数分布",
step=train_step,
values=params.flatten(),
buckets=50)
# 每100个batch打印一次信息
if batch_id % 100 == 0:
print('Pass:%d, Batch:%d, Cost:%0.5f, Accuracy:%0.5f' %
(pass_id, batch_id, train_cost[0], train_acc[0]))
# 进行测试
test_accs = []
test_costs = []
for batch_id, data in enumerate(test_reader()):
test_cost, test_acc = exe.run(program=test_program,
feed=feeder.feed(data),
fetch_list=[avg_cost, acc])
# 保存测试的日志数据
class Linear_Model():
def __init__(self):
"""
Initialize the Linear Model
"""
self.learning_rate = 0.001
self.epoches = 10000
self.loss_function = torch.nn.MSELoss()
self.create_model()
logdir = os.path.join('./runs', datetime.now().strftime('%b%d_%H-%M-%S')+'_line_reg')
print("save to ", logdir)
self.writer = LogWriter(logdir=logdir)
print("initialize done")
def create_model(self):
self.model = LinearRegression()
self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.learning_rate)
def train(self, data, model_save_path="model.pth"):
"""
Train the model and save the parameters
Args:
model_save_path: saved name of model
data: (x, y) = data, and y = kx + b
Returns:
None
"""
x = data["x"]
y = data["y"]
for epoch in range(self.epoches):
prediction = self.model(x)
loss = self.loss_function(prediction, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if epoch % 500 == 0:
print("epoch: {}, loss is: {}".format(epoch, loss.item()))
# show param
for name, parameters in self.model.named_parameters():
# print(name, ":", parameters.view(torch.numel(parameters)))
self.writer.add_histogram(tag='net_param/'+name,
values=parameters.view(torch.numel(parameters)).detach().cpu().numpy(),
step=epoch,
buckets=200)
torch.save(self.model.state_dict(), "linear.pth")
def test(self, x, model_path="linear.pth"):
"""
Reload and test the model, plot the prediction
Args:
model_path: the model's path and name
data: (x, y) = data, and y = kx + b
Returns:
None
"""
x = data["x"]
y = data["y"]
self.model.load_state_dict(torch.load(model_path))
prediction = self.model(x)
plt.scatter(x.numpy(), y.numpy(), c=x.numpy())
plt.plot(x.numpy(), prediction.detach().numpy(), color="r")
plt.show()
def compare_epoches(self, data):
x = data["x"]
y = data["y"]
num_pictures = 16
fig = plt.figure(figsize=(10,10))
current_fig = 0
for epoch in range(self.epoches):
prediction = self.model(x)
loss = self.loss_function(prediction, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if epoch % (self.epoches/num_pictures) == 0:
current_fig += 1
plt.subplot(4, 4, current_fig)
plt.scatter(x.numpy(), y.numpy(), c=x.numpy())
plt.plot(x.numpy(), prediction.detach().numpy(), color="r")
plt.show()
