batchsize=batchsize)
pq_test = PictureQueue(dsl=test_UCF0101_Temporal(),
Gen=GenVariables_Temporal,
batchsize=batchsize)
elif TEST == 'S':
pq_train = PictureQueue(dsl=train_UCF0101_Spatial(),
Gen=GenVariables_Spatial,
batchsize=batchsize)
pq_test = PictureQueue(dsl=test_UCF0101_Spatial(),
Gen=GenVariables_Spatial,
batchsize=batchsize)
elif TEST == 'C3D':
pq_train = PictureQueue(dsl=train_UCF101_C3D(),
Gen=GenVariables_C3D,
batchsize=batchsize)
pq_test = PictureQueue(dsl=test_UCF101_C3D(),
Gen=GenVariables_C3D,
batchsize=batchsize)
print('before:', time.asctime())
a, b = GenVariables_Temporal(train_UCF0101_Temporal(), batchsize=batchsize)
print('after:', time.asctime())
cnt = 0
for epoch in range(epochs):
for l in range(loops):
cnt += 1
print('{}: {}'.format(time.asctime(), cnt))
imgs, labels = pq_train.Get()
if cnt % 25 == 0:
# for i in range(worker):
# self.ts.append(threading.Thread(target=self.pr,name='Producter_{}'.format(i)))
# for i in range(worker):
# self.ts[i].start()
#
# def pr(self):
# while True:
# self.q.put(self.Gen(self.dsl,self.batchsize))
#
# def Get(self):
# imgs,labels = self.q.get()
# return imgs.cuda(),labels.cuda()
#
# def Close(self):
# '''
# May Be Need To Stop Threads
# :return:
# '''
# pass
if __name__ == '__main__':
dsl = test_UCF101_C3D()
dsl = test_UCF0101_Spatial()
dsl = test_UCF0101_Temporal()
itemss = random.choices(dsl, k=3)
pq = PictureQueue(dsl, GenVariables_C3D, 3)
pa, pb = pq.Get()
def C3D_Net_Run():
epochs = 81
loops = 2001
learningrate = 0.0001
attenuation = 0.1
model = C3D(drop=0.9).cuda()
if Config.LOAD_SAVED_MODE_PATH is not None:
import types
model.try_to_load_state_dict = types.MethodType(
try_to_load_state_dict, model)
model.try_to_load_state_dict(torch.load(Config.LOAD_SAVED_MODE_PATH))
print('LOAD {} done!'.format(Config.LOAD_SAVED_MODE_PATH))
lossfunc = nn.CrossEntropyLoss()
optim = torch.optim.Adam(model.parameters(), lr=learningrate)
pq_train = PictureQueue(dsl=train_UCF101_C3D(),
Gen=GenVariables_C3D,
batchsize=batchsize,
worker=5)
pq_test = PictureQueue(dsl=test_UCF101_C3D(),
Gen=GenVariables_C3D,
batchsize=batchsize,
worker=2)
cnt = 0
for epoch in range(epochs):
for l in range(loops):
cnt += 1
imgs, labels = pq_train.Get()
model.zero_grad()
pred = model(imgs)
loss = lossfunc(pred, labels)
logger.scalar_summary('C3D/train_loss', loss.data[0], cnt)
loss.backward()
optim.step()
print('C3D epoch: {} cnt: {} loss: {}'.format(
epoch, cnt, loss.data[0]))
if cnt % 25 == 0:
imgs, labels = pq_test.Get()
pred = model.inference(imgs)
loss = lossfunc(pred, labels)
logger.scalar_summary('C3D/test_loss', loss.data[0], cnt)
# acc
acc = accuracy(pred, labels, topk=(1, 5, 10))
logger.scalar_summary('C3D/test_acc@1', acc[0], cnt)
logger.scalar_summary('C3D/test_acc@5', acc[1], cnt)
logger.scalar_summary('C3D/test_acc@10', acc[2], cnt)
imgs, labels = pq_train.Get()
pred = model.inference(imgs)
acc = accuracy(pred, labels, topk=(1, 5, 10))
logger.scalar_summary('C3D/train_acc@1', acc[0], cnt)
logger.scalar_summary('C3D/train_acc@5', acc[1], cnt)
logger.scalar_summary('C3D/train_acc@10', acc[2], cnt)
if cnt % 2000 == 0:
savefile = savepath + 'C3D_EX1_{:02d}.pt'.format(epoch % 20)
print('C3D save model to {}'.format(savefile))
torch.save(model.state_dict(), savefile)
if epoch in [20, 40, 60]:
learningrate = learningrate * attenuation
optim = torch.optim.Adam(model.parameters(), lr=learningrate)