PaddlePaddle · rainyfly · Oct 14, 2022 · Sep 28, 2022
diff --git a/modules/image/classification/efficientnetb6_imagenet/README.md b/modules/image/classification/efficientnetb6_imagenet/README.md
@@ -131,6 +131,11 @@
 * 1.1.0
 
   提升预测性能以及易用性
+
+* 1.2.0
+
+  移除 Fluid API
+
   - ```shell
-    $ hub install efficientnetb6_imagenet==1.1.0
+    $ hub install efficientnetb6_imagenet==1.2.0
     ```
diff --git a/modules/image/classification/efficientnetb6_imagenet/README_en.md b/modules/image/classification/efficientnetb6_imagenet/README_en.md
@@ -130,6 +130,11 @@
 * 1.1.0
 
   Improve the prediction performance and users' experience
+
+* 1.2.0
+
+  Remove Fluid API
+
   - ```shell
-    $ hub install efficientnetb6_imagenet==1.1.0
+    $ hub install efficientnetb6_imagenet==1.2.0
     ```
diff --git a/modules/image/classification/efficientnetb6_imagenet/__init__.py b/modules/image/classification/efficientnetb6_imagenet/__init__.py
diff --git a/modules/image/classification/efficientnetb6_imagenet/data_feed.py b/modules/image/classification/efficientnetb6_imagenet/data_feed.py
@@ -0,0 +1,93 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import time
+from collections import OrderedDict
+
+import numpy as np
+from PIL import Image
+
+__all__ = ['reader']
+
+DATA_DIM = 224
+img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+
+
+def resize_short(img, target_size):
+    percent = float(target_size) / min(img.size[0], img.size[1])
+    resized_width = int(round(img.size[0] * percent))
+    resized_height = int(round(img.size[1] * percent))
+    img = img.resize((resized_width, resized_height), Image.LANCZOS)
+    return img
+
+
+def crop_image(img, target_size, center):
+    width, height = img.size
+    size = target_size
+    if center == True:
+        w_start = (width - size) / 2
+        h_start = (height - size) / 2
+    else:
+        w_start = np.random.randint(0, width - size + 1)
+        h_start = np.random.randint(0, height - size + 1)
+    w_end = w_start + size
+    h_end = h_start + size
+    img = img.crop((w_start, h_start, w_end, h_end))
+    return img
+
+
+def process_image(img):
+    img = resize_short(img, target_size=256)
+    img = crop_image(img, target_size=DATA_DIM, center=True)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
+    img -= img_mean
+    img /= img_std
+    return img
+
+
+def reader(images=None, paths=None):
+    """
+    Preprocess to yield image.
+
+    Args:
+        images (list[numpy.ndarray]): images data, shape of each is [H, W, C].
+        paths (list[str]): paths to images.
+
+    Yield:
+        each (collections.OrderedDict): info of original image, preprocessed image.
+    """
+    component = list()
+    if paths:
+        for im_path in paths:
+            each = OrderedDict()
+            assert os.path.isfile(im_path), "The {} isn't a valid file path.".format(im_path)
+            each['org_im_path'] = im_path
+            each['org_im'] = Image.open(im_path)
+            each['org_im_width'], each['org_im_height'] = each['org_im'].size
+            component.append(each)
+    if images is not None:
+        assert type(images), "images is a list."
+        for im in images:
+            each = OrderedDict()
+            each['org_im'] = Image.fromarray(im[:, :, ::-1])
+            each['org_im_path'] = 'ndarray_time={}'.format(round(time.time(), 6) * 1e6)
+            each['org_im_width'], each['org_im_height'] = each['org_im'].size
+            component.append(each)
+
+    for element in component:
+        element['image'] = process_image(element['org_im'])
+        yield element