Remove preprocessing and postprocessing of model exporting (#260)

* update *_batch_size_like ops

* remove the preprocess and postprocess of model exporting

pdseg/models/model_builder.py

@@ -111,53 +111,6 @@ def sigmoid_to_softmax(logit):
     return logit
 
 
-def export_preprocess(image):
-    """导出模型的预处理流程"""
-
-    image = fluid.layers.transpose(image, [0, 3, 1, 2])
-    origin_shape = fluid.layers.shape(image)[-2:]
-
-    # 不同AUG_METHOD方法的resize
-    if cfg.AUG.AUG_METHOD == 'unpadding':
-        h_fix = cfg.AUG.FIX_RESIZE_SIZE[1]
-        w_fix = cfg.AUG.FIX_RESIZE_SIZE[0]
-        image = fluid.layers.resize_bilinear(
-            image, out_shape=[h_fix, w_fix], align_corners=False, align_mode=0)
-    elif cfg.AUG.AUG_METHOD == 'rangescaling':
-        size = cfg.AUG.INF_RESIZE_VALUE
-        value = fluid.layers.reduce_max(origin_shape)
-        scale = float(size) / value.astype('float32')
-        image = fluid.layers.resize_bilinear(
-            image, scale=scale, align_corners=False, align_mode=0)
-
-    # 存储resize后图像shape
-    valid_shape = fluid.layers.shape(image)[-2:]
-
-    # padding到eval_crop_size大小
-    width = cfg.EVAL_CROP_SIZE[0]
-    height = cfg.EVAL_CROP_SIZE[1]
-    pad_target = fluid.layers.assign(
-        np.array([height, width]).astype('float32'))
-    up = fluid.layers.assign(np.array([0]).astype('float32'))
-    down = pad_target[0] - valid_shape[0]
-    left = up
-    right = pad_target[1] - valid_shape[1]
-    paddings = fluid.layers.concat([up, down, left, right])
-    paddings = fluid.layers.cast(paddings, 'int32')
-    image = fluid.layers.pad2d(image, paddings=paddings, pad_value=127.5)
-
-    # normalize
-    mean = np.array(cfg.MEAN).reshape(1, len(cfg.MEAN), 1, 1)
-    mean = fluid.layers.assign(mean.astype('float32'))
-    std = np.array(cfg.STD).reshape(1, len(cfg.STD), 1, 1)
-    std = fluid.layers.assign(std.astype('float32'))
-    image = (image / 255 - mean) / std
-    # 使后面的网络能通过类似image.shape获取特征图的shape
-    image = fluid.layers.reshape(
-        image, shape=[-1, cfg.DATASET.DATA_DIM, height, width])
-    return image, valid_shape, origin_shape
-
-
 def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
     if not ModelPhase.is_valid_phase(phase):
         raise ValueError("ModelPhase {} is not valid!".format(phase))
@@ -176,21 +129,7 @@ def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
         with fluid.unique_name.guard():
             # 在导出模型的时候，增加图像标准化预处理,减小预测部署时图像的处理流程
             # 预测部署时只须对输入图像增加batch_size维度即可
-            if ModelPhase.is_predict(phase):
-                if cfg.SLIM.PREPROCESS:
-                    image = fluid.data(
-                        name='image', shape=image_shape, dtype='float32')
-                else:
-                    origin_image = fluid.data(
-                        name='image',
-                        shape=[-1, -1, -1, cfg.DATASET.DATA_DIM],
-                        dtype='float32')
-                    image, valid_shape, origin_shape = export_preprocess(
-                        origin_image)
-
-            else:
-                image = fluid.data(
-                    name='image', shape=image_shape, dtype='float32')
+            image = fluid.data(name='image', shape=image_shape, dtype='float32')
             label = fluid.data(name='label', shape=grt_shape, dtype='int32')
             mask = fluid.data(name='mask', shape=grt_shape, dtype='int32')
 
@@ -293,21 +232,7 @@ def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
                 else:
                     logit = softmax(logit)
 
-                # 获取有效部分
-                if cfg.SLIM.PREPROCESS:
-                    return image, logit
-
-                else:
-                    logit = fluid.layers.slice(
-                        logit, axes=[2, 3], starts=[0, 0], ends=valid_shape)
-
-                    logit = fluid.layers.resize_bilinear(
-                        logit,
-                        out_shape=origin_shape,
-                        align_corners=False,
-                        align_mode=0)
-                    logit = fluid.layers.argmax(logit, axis=1)
-                return origin_image, logit
+                return image, logit
 
             if class_num == 1:
                 out = sigmoid_to_softmax(logit)