def train(args):
# train image path
image_data = glob(SEGMENTATION_PATH_DATA)
image_data.sort()
# test image path
image_test = glob(SEGMENTATION_PATH_TEST)
image_test.sort()
# data load
data_train = Train_DataLoader(args.sequence)
data_test = Test_DataLoader(args.sequence)
# Define model
model = Model(i_size=args.i_size, h_size=args.h_size, o_size=args.o_size, train=True)
cuda.get_device_from_id(args.GPU_ID).use()
model.to_gpu()
# optimizer
optimizer = optimizers.RMSprop(lr=args.learning_rate)
optimizer.setup(model)
# initial loss
train_loss = []
test_loss = []
# iteration
n_iter = 0
n_iter_test = 0
for epoch in range(1, args.epoch + 1):
print('epoch', epoch)
start = time.time()
'''TRAIN'''
print("TRAIN")
# -5 to delete extra data
for data_iter in range(0, len(data_train.all_data) - 5, args.batch):
# initial loss
loss_train = 0
x = [] # input
t = [] # target
image_append = [] # list image pixels
# list scenes
tel_train = []
n_iter += 1
model.cleargrads()
model.reset_state()
for batch_ in range(args.batch):
list_random = np.random.randint(0, len(data_train.list))
# image load
data_image_train = cv2.imread(image_data[list_random], 0)
data_image_train //= 10
image_append.append(data_image_train)
# coordinate match the scene
trajectory_random = np.random.randint(0, len(data_train.list_coordinate[list_random]))
trajectory = data_train.list_coordinate[list_random][trajectory_random]
# Since -1 is necessary for the target data
index = np.random.randint(0, len(trajectory) - args.sequence - 1)
x.append(trajectory[index:index + args.sequence, :])
t.append(trajectory[index + 1:index + 1 + args.sequence, :])
tel_train.append(list_random)
x = np.asarray(x, dtype=np.float32)
t = np.asarray(t, dtype=np.float32)
x_copy = np.copy(x[:, :10, :])
# Calculation of movement amount
x = np.diff(x, axis=1)
t = np.diff(t, axis=1)
for sequence in range(args.relate_coordinate):
image_coordinate_image = []
xy = x[:, sequence, :] # input
training = t[:, sequence, :] # test
# Absolute coordinates of target
xy_center = np.copy(x_copy[:, sequence, :])
# calculation of target environment
for k in range(args.batch):
image_coordinate = Image_Read(image_append[k], xy_center[k], tel_train[k], train=True)
image_coordinate_image.append(image_coordinate.img)
# target centered (100 x 100 x CHANNEL x BATCH_SIZE)
image_coordinate_image = np.array(image_coordinate_image, dtype=np.float32)
image_coordinate_image_ = chainer.Variable(cuda.to_gpu(image_coordinate_image))
# learning GPU
xy = chainer.Variable(cuda.to_gpu(xy))
training = chainer.Variable(cuda.to_gpu(training))
loss_train += model(image_coordinate_image_, xy, training, train=True)
print("iteration:", n_iter, "loss:", loss_train)
loss_train.backward()
optimizer.update()
end = time.time()
f = open('loss.txt', 'a')
print(("epoch:{}, loss:{}, time:{}".format(epoch, loss_train.data, end - start)), file=f)
f.close()
# save epoch
if epoch % 20 == 0:
# Move to CPU once to prevent GPU dependence problems during testing
model.to_cpu()
serializers.save_npz('./Train/' + str(epoch) + '.model', model)
serializers.save_npz('./Train/' + str(epoch) + '.state', optimizer)
model.to_gpu()
'''TEST'''
print("TEST")
# -7 to delete extra data
for test_iter in range(0, len(data_test.all_data) - 7, args.batch):
# initial loss
loss_test = 0
x = [] # input
t = [] # target
image_append_test = []
# list scenes
tel_test = []
n_iter_test += 1
model.cleargrads()
model.reset_state()
for batch_test in range(args.batch):
list_random_test = np.random.randint(0, len(data_test.list))
# image load
data_image_test = cv2.imread(image_test[list_random_test], 0)
data_image_test //= 10
image_append_test.append(data_image_test)
# coordinate match the scene
trajectory_random = np.random.randint(0, len(data_test.list_coordinate[list_random_test]))
trajectory_test = data_test.list_coordinate[list_random_test][trajectory_random]
# Since -1 is necessary for the target data
index_test = np.random.randint(0, len(trajectory_test) - args.sequence - 1)
x.append(trajectory_test[index_test:index_test + args.sequence, :])
t.append(trajectory_test[index_test + 1:index_test + 1 + args.sequence, :])
tel_test.append(list_random_test)
x = np.asarray(x, dtype=np.float32)
t = np.asarray(t, dtype=np.float32)
x_copy = np.copy(x[:, :10, :])
# Calculation of movement amount
x = np.diff(x, axis=1)
t = np.diff(t, axis=1)
for sequence in range(args.relate_coordinate):
image_coordinate_image = []
xy = x[:, sequence, :]
training = t[:, sequence, :]
# Absolute coordinates of target
xy_center = np.copy(x_copy[:, sequence, :])
# calculation of target environment
for k in range(args.batch):
image_coordinate = Image_Read(image_append_test[k], xy_center[k], tel_test[k], train=False)
image_coordinate_image.append(image_coordinate.img)
# target centered (100 x 100 x CHANNEL x BATCH_SIZE)
image_coordinate_image = np.array(image_coordinate_image, dtype=np.float32)
image_coordinate_image_ = chainer.Variable(cuda.to_gpu(image_coordinate_image))
# learning GPU
xy = chainer.Variable(cuda.to_gpu(xy))
training = chainer.Variable(cuda.to_gpu(training))
loss_test += model(image_coordinate_image_, xy, training, train=True)
print("iteration:", n_iter_test, "loss:", loss_test)
# figure
train_loss.append(loss_train.data)
test_loss.append(loss_test.data)
plt.plot(train_loss, label="train_loss")
plt.plot(test_loss, label="test_loss")
plt.yscale('log')
plt.legend()
plt.grid(True)
plt.title("loss")
plt.xlabel("epoch")
plt.ylabel("loss")
plt.savefig("Fig/fig_loss.png")
plt.clf()
def train(args):
# data_load
data_train = Train_DataLoader(args.sequence)
data_test = Test_DataLoader(args.sequence)
# define model
model = Model(i_size=args.i_size,
h_size=args.h_size,
o_size=args.o_size,
train=True)
cuda.get_device_from_id(args.GPU_ID).use()
model.to_gpu()
# optimizer
optimizer = optimizers.RMSprop(lr=args.learning_rate)
optimizer.setup(model)
# initial value
train_loss = []
test_loss = []
# iteration
n_iter_train = 0
n_iter_test = 0
for epoch in range(1, args.epoch + 1):
print 'epoch', epoch
start = time.time()
'''TRAIN'''
print "TRAIN"
# -5 to delete extra data
for data_iter in xrange(0, len(data_train.all_data) - 5, args.batch):
# initial loss
loss_train = 0
x = [] # input
t = [] # target
n_iter_train += 1
model.cleargrads()
model.reset_state()
for _ in xrange(args.batch):
list_random = np.random.randint(0, len(data_train.list))
# coordinate match the scene
trajectory_random = np.random.randint(
0, len(data_train.list_coordinate[list_random]))
trajectory = data_train.list_coordinate[list_random][
trajectory_random]
# Since -1 is necessary for the target data
index = np.random.randint(0,
len(trajectory) - args.sequence - 1)
x.append(trajectory[index:index + args.sequence, :])
t.append(trajectory[index + 1:index + 1 + args.sequence, :])
x = np.asarray(x, dtype=np.float32)
t = np.asarray(t, dtype=np.float32)
# Calculation of movement amount
x = np.diff(x, axis=1)
t = np.diff(t, axis=1)
for sequence in xrange(args.relate_coordinate):
xy = x[:, sequence, :] # input
training = t[:, sequence, :] # target
# learning by GPU
xy = chainer.Variable(cuda.to_gpu(xy))
training = chainer.Variable(cuda.to_gpu(training))
loss_train += model(xy, training, train=True)
print "iteration:", n_iter_train, "loss:", loss_train
loss_train.backward()
optimizer.update()
end = time.time()
f = open('loss.txt', 'a')
print >> f, ("epoch:{}, loss:{}, time:{}".format(
epoch, loss_train.data, end - start))
f.close()
# save epoch
if epoch % 20 == 0:
# Move to CPU once to prevent GPU dependence problems during testing
model.to_cpu()
serializers.save_npz('./Train/' + str(epoch) + '.model', model)
serializers.save_npz('./Train/' + str(epoch) + '.state', optimizer)
model.to_gpu()
'''TEST'''
print "TEST"
# -7 to delete extra data
for test_iter in xrange(0, len(data_test.all_data) - 7, args.batch):
# initial loss
loss_test = 0
x = []
t = []
n_iter_test += 1
model.cleargrads()
model.reset_state()
for _ in xrange(args.batch):
list_random_test = np.random.randint(0, len(data_test.list))
# coordinate match the scene
trajectory_random = np.random.randint(
0, len(data_test.list_coordinate[list_random_test]))
trajectory = data_test.list_coordinate[list_random_test][
trajectory_random]
index_test = np.random.randint(
0,
len(trajectory) - args.sequence - 1)
x.append(trajectory[index_test:index_test + args.sequence, :])
t.append(trajectory[index_test + 1:index_test + 1 +
args.sequence, :])
x = np.asarray(x, dtype=np.float32)
t = np.asarray(t, dtype=np.float32)
# Calculation of movement amount
x = np.diff(x, axis=1)
t = np.diff(t, axis=1)
for sequence in xrange(args.relate_coordinate):
xy = x[:, sequence, :] # input
training = t[:, sequence, :] # target
# learning by GPU
xy = chainer.Variable(cuda.to_gpu(xy))
training = chainer.Variable(cuda.to_gpu(training))
loss_test += model(xy, training, train=True)
print "iteration:", n_iter_test, "loss:", loss_test
# figure
train_loss.append(loss_train.data)
test_loss.append(loss_test.data)
plt.plot(train_loss, label="train_loss")
plt.plot(test_loss, label="test_loss")
plt.yscale('log')
plt.legend()
plt.grid(True)
plt.title("loss")
plt.xlabel("epoch")
plt.ylabel("loss")
plt.savefig("Fig/fig_loss.png")
plt.clf()