Rename core.LoDTensor to fluid.LoDTensor in ocr, gan and icnet (#2141)

PaddleCV/gan/cycle_gan/infer.py

@@ -2,7 +2,6 @@ import argparse
 import functools
 import os
 from PIL import Image
-from paddle.fluid import core
 import paddle.fluid as fluid
 import paddle
 import numpy as np
@@ -51,7 +50,7 @@ def infer(args):
         if len(image.shape) != 3:
             continue
         data = image.transpose([2, 0, 1])[np.newaxis, :].astype("float32")
-        tensor = core.LoDTensor()
+        tensor = fluid.LoDTensor()
         tensor.set(data, place)
 
         fake_temp = exe.run(fetch_list=[fake.name], feed={"input": tensor})

PaddleCV/gan/cycle_gan/train.py

@@ -12,7 +12,6 @@ import numpy as np
 from scipy.misc import imsave
 import paddle.fluid as fluid
 import paddle.fluid.profiler as profiler
-from paddle.fluid import core
 import data_reader
 from utility import add_arguments, print_arguments, ImagePool
 from trainer import *
@@ -82,8 +81,8 @@ def train(args):
         for data_A, data_B in zip(A_test_reader(), B_test_reader()):
             A_name = data_A[1]
             B_name = data_B[1]
-            tensor_A = core.LoDTensor()
-            tensor_B = core.LoDTensor()
+            tensor_A = fluid.LoDTensor()
+            tensor_B = fluid.LoDTensor()
             tensor_A.set(data_A[0], place)
             tensor_B.set(data_B[0], place)
             fake_A_temp, fake_B_temp, cyc_A_temp, cyc_B_temp = exe.run(
@@ -168,8 +167,8 @@ def train(args):
         for i in range(max_images_num):
             data_A = next(A_reader)
             data_B = next(B_reader)
-            tensor_A = core.LoDTensor()
-            tensor_B = core.LoDTensor()
+            tensor_A = fluid.LoDTensor()
+            tensor_B = fluid.LoDTensor()
             tensor_A.set(data_A, place)
             tensor_B.set(data_B, place)
             s_time = time.time()

PaddleCV/icnet/infer.py

@@ -115,7 +115,7 @@ def infer(args):
             image_file, is_color=True).astype("float32")
         image -= IMG_MEAN
         img = paddle.dataset.image.to_chw(image)[np.newaxis, :]
-        image_t = fluid.core.LoDTensor()
+        image_t = fluid.LoDTensor()
         image_t.set(img, place)
         result = exe.run(inference_program,
                          feed={"image": image_t},

PaddleCV/icnet/utils.py

@@ -18,7 +18,6 @@ from __future__ import division
 from __future__ import print_function
 import distutils.util
 import numpy as np
-from paddle.fluid import core
 import six
 
 
@@ -72,7 +71,7 @@ def to_lodtensor(data, place):
         lod.append(cur_len)
     flattened_data = np.concatenate(data, axis=0).astype("int32")
     flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = core.LoDTensor()
+    res = fluid.LoDTensor()
     res.set(flattened_data, place)
     res.set_lod([lod])
     return res
@@ -80,17 +79,17 @@ def to_lodtensor(data, place):
 
 def get_feeder_data(data, place, for_test=False):
     feed_dict = {}
-    image_t = core.LoDTensor()
+    image_t = fluid.LoDTensor()
     image_t.set(data[0], place)
     feed_dict["image"] = image_t
 
     if not for_test:
-        labels_sub1_t = core.LoDTensor()
-        labels_sub2_t = core.LoDTensor()
-        labels_sub4_t = core.LoDTensor()
-        mask_sub1_t = core.LoDTensor()
-        mask_sub2_t = core.LoDTensor()
-        mask_sub4_t = core.LoDTensor()
+        labels_sub1_t = fluid.LoDTensor()
+        labels_sub2_t = fluid.LoDTensor()
+        labels_sub4_t = fluid.LoDTensor()
+        mask_sub1_t = fluid.LoDTensor()
+        mask_sub2_t = fluid.LoDTensor()
+        mask_sub4_t = fluid.LoDTensor()
 
         labels_sub1_t.set(data[1], place)
         labels_sub2_t.set(data[3], place)
@@ -105,8 +104,8 @@ def get_feeder_data(data, place, for_test=False):
         feed_dict["label_sub4"] = labels_sub4_t
         feed_dict["mask_sub4"] = mask_sub4_t
     else:
-        label_t = core.LoDTensor()
-        mask_t = core.LoDTensor()
+        label_t = fluid.LoDTensor()
+        mask_t = fluid.LoDTensor()
         label_t.set(data[1], place)
         mask_t.set(data[2], place)
         feed_dict["label"] = label_t

PaddleCV/ocr_recognition/utility.py

@@ -18,7 +18,6 @@ from __future__ import division
 from __future__ import print_function
 import distutils.util
 import numpy as np
-from paddle.fluid import core
 import paddle.fluid as fluid
 import six
 
@@ -73,14 +72,14 @@ def to_lodtensor(data, place):
         lod.append(cur_len)
     flattened_data = np.concatenate(data, axis=0).astype("int32")
     flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = core.LoDTensor()
+    res = fluid.LoDTensor()
     res.set(flattened_data, place)
     res.set_lod([lod])
     return res
 
 
 def get_ctc_feeder_data(data, place, need_label=True):
-    pixel_tensor = core.LoDTensor()
+    pixel_tensor = fluid.LoDTensor()
     pixel_data = None
     pixel_data = np.concatenate(
         list(map(lambda x: x[0][np.newaxis, :], data)),
@@ -98,7 +97,7 @@ def get_ctc_feeder_for_infer(data, place):
 
 
 def get_attention_feeder_data(data, place, need_label=True):
-    pixel_tensor = core.LoDTensor()
+    pixel_tensor = fluid.LoDTensor()
     pixel_data = None
     pixel_data = np.concatenate(
         list(map(lambda x: x[0][np.newaxis, :], data)),
@@ -130,7 +129,7 @@ def get_attention_feeder_for_infer(data, place):
     init_scores = fluid.create_lod_tensor(init_scores_data,
                                           init_recursive_seq_lens, place)
 
-    pixel_tensor = core.LoDTensor()
+    pixel_tensor = fluid.LoDTensor()
     pixel_data = None
     pixel_data = np.concatenate(
         list(map(lambda x: x[0][np.newaxis, :], data)),