add clarity_assessment code

ppcls/configs/PULC/clarity_assessment/PPLCNet_x1_0.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 10
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 20
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+# model architecture
+Arch:
+  name: PPLCNet_x1_0
+  pretrained: True
+  use_ssld: True
+  class_num: 2
+  use_last_conv: False
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.14
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: CustomLabelDataset
+      image_root: ./dataset/
+      sample_list_path: ./dataset/ImageNet_OCR_det.txt
+      label_key: blur_image
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - BlurImage:
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 256
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 12
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/blur/
+      cls_label_path: ./dataset/blur/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: ./test_img/
+  batch_size: 1
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 1
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1]
+  Eval:
+    - TopkAcc:
+        topk: [1]

ppcls/data/preprocess/__init__.py

@@ -41,6 +41,7 @@ from ppcls.data.preprocess.ops.operators import RandomCropImage
 from ppcls.data.preprocess.ops.operators import RandomRotation
 from ppcls.data.preprocess.ops.operators import Padv2
 from ppcls.data.preprocess.ops.operators import RandomRot90
+from ppcls.data.preprocess.ops.operators import BlurImage
 from .ops.operators import format_data
 
 from ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, CutmixOperator, OpSampler, FmixOperator

ppcls/data/preprocess/ops/operators.py

@@ -718,8 +718,8 @@ class Pad(object):
         # Process fill color for affine transforms
         major_found, minor_found = (int(v)
                                     for v in PILLOW_VERSION.split('.')[:2])
-        major_required, minor_required = (int(v) for v in
-                                          min_pil_version.split('.')[:2])
+        major_required, minor_required = (
+            int(v) for v in min_pil_version.split('.')[:2])
         if major_found < major_required or (major_found == major_required and
                                             minor_found < minor_required):
             if fill is None:
@@ -781,3 +781,54 @@ class RandomRot90(object):
         if orientation:
             img = np.rot90(img, orientation)
         return {"img": img, "random_rot90_orientation": orientation}
+
+
+class BlurImage(object):
+    """BlurImage
+    """
+
+    def __init__(self,
+                 ratio=0.5,
+                 motion_max_ksize=12,
+                 motion_max_angle=45,
+                 gaussian_max_ksize=12):
+        self.ratio = ratio
+        self.motion_max_ksize = motion_max_ksize
+        self.motion_max_angle = motion_max_angle
+        self.gaussian_max_ksize = gaussian_max_ksize
+
+    def _gaussian_blur(self, img, max_ksize=12):
+        ksize = (np.random.choice(np.arange(5, max_ksize, 2)),
+                 np.random.choice(np.arange(5, max_ksize, 2)))
+        img = cv2.GaussianBlur(img, ksize, 0)
+        return img
+
+    def _motion_blur(self, img, max_ksize=12, max_angle=45):
+        degree = np.random.choice(np.arange(5, max_ksize, 2))
+        angle = np.random.choice(np.arange(-1 * max_angle, max_angle))
+
+        M = cv2.getRotationMatrix2D((degree / 2, degree / 2), angle, 1)
+        motion_blur_kernel = np.diag(np.ones(degree))
+        motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M,
+                                            (degree, degree))
+
+        motion_blur_kernel = motion_blur_kernel / degree
+        blurred = cv2.filter2D(img, -1, motion_blur_kernel)
+
+        cv2.normalize(blurred, blurred, 0, 255, cv2.NORM_MINMAX)
+        img = np.array(blurred, dtype=np.uint8)
+        return img
+
+    @format_data
+    def __call__(self, img):
+        if random.random() > self.ratio:
+            label = 0
+        else:
+            method = random.choice(["gaussian", "motion"])
+            if method == "gaussian":
+                img = self._gaussian_blur(img, self.gaussian_max_ksize)
+            else:
+                img = self._motion_blur(img, self.motion_max_ksize,
+                                        self.motion_max_angle)
+            label = 1
+        return {"img": img, "blur_image": label}