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)
train_UCF0101_Spatial, test_UCF0101_Spatial, train_UCF0101_Temporal, test_UCF0101_Temporal, train_UCF101_C3D, \
test_UCF101_C3D
from VideoClassification.utils.Others.toolkits import accuracy, try_to_load_state_dict
from VideoClassification.utils.DataSetLoader.UCF101_DataSetLoader_FromFileName.PictureQueue import PictureQueue, \
GenVariables_Spatial, GenVariables_Temporal, GenVariables_C3D
batchsize = 86
epochs = 80
loops = 2000
TEST = 'S'
if TEST == 'T':
pq_train = PictureQueue(dsl=train_UCF0101_Temporal(),
Gen=GenVariables_Temporal,
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)
def VGG_Spatial_Net_Run():
epochs = 80
loops = 2000
learningrate = 0.001
attenuation = 0.1
model = VGG_Spatial_Net(pretrained=False, dropout1=0.8, dropout2=0.7).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.SGD(model.parameters(), lr=learningrate, momentum=0.9)
cnt = 0
pq_train = PictureQueue(dsl=train_UCF0101_Spatial(), Gen=GenVariables_Spatial, batchsize=batchsize)
pq_test = PictureQueue(dsl=test_UCF0101_Spatial(), Gen=GenVariables_Spatial, batchsize=batchsize)
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('Spatial/train_loss', loss.data[0], cnt)
loss.backward()
optim.step()
print('Spatial epoch: {} cnt: {} loss: {}'.format(epoch, cnt, loss.data[0]))
if cnt % 20 == 0:
imgs, labels = pq_test.Get()
pred = model.inference(imgs)
loss = lossfunc(pred, labels)
logger.scalar_summary('Spatial/test_loss', loss.data[0], cnt)
acc = accuracy(pred, labels, topk=(1, 5, 10))
logger.scalar_summary('Spatial/test_acc@1', acc[0], cnt)
logger.scalar_summary('Spatial/test_acc@5', acc[1], cnt)
logger.scalar_summary('Spatial/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('Spatial/train_acc@1', acc[0], cnt)
logger.scalar_summary('Spatial/train_acc@5', acc[1], cnt)
logger.scalar_summary('Spatial/train_acc@10', acc[2], cnt)
if cnt % 2000 == 0:
savefile = savepath + 'VGG_Spatial_EX1_{:02d}.pt'.format(epoch % 50)
print('Spatial save model to {}'.format(savefile))
torch.save(model.state_dict(), savefile)
if epoch in [10, 20, 50, 60]:
learningrate = learningrate * attenuation
optim = torch.optim.SGD(model.parameters(), lr=learningrate, momentum=0.9, )