!11929 some words are misssplelled in centernet script

From: @caojian05
Reviewed-by: @wuxuejian,@oacjiewen,@liangchenghui
Signed-off-by: @liangchenghui

model_zoo/research/cv/centernet/README.md

@@ -300,7 +300,6 @@ Parameters for dataset (Training/Evaluation):
     aug_rot                         properbility of image rotation during data augmenation: N, default is 0.0
     rotate                          maximum value of rotation angle during data augmentation: N, default is 0.0
     flip_prop                       properbility of image flip during data augmenation: N, default is 0.5
-    color_aug                       whether use color augmentation: True | False, default is False
     mean                            mean value of RGB image
     std                             variance of RGB image
     flip_idx                        the corresponding point index of keypoints when flip the image

model_zoo/research/cv/centernet/eval.py

@@ -45,7 +45,7 @@ parser.add_argument("--data_dir", type=str, default="", help="Dataset directory,
                                                              "and the relative path in anno_path")
 parser.add_argument("--run_mode", type=str, default="test", help="test or validation, default is test.")
 parser.add_argument("--visual_image", type=str, default="false", help="Visulize the ground truth and predicted image")
-parser.add_argument("--enable_eval", type=str, default="true", help="Wether evaluate accuracy after prediction")
+parser.add_argument("--enable_eval", type=str, default="true", help="Whether evaluate accuracy after prediction")
 parser.add_argument("--save_result_dir", type=str, default="", help="The path to save the predict results")
 
 args_opt = parser.parse_args()

model_zoo/research/cv/centernet/scripts/convert_dataset_to_mindrecord.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash convert_dataset_to_mindrecord.sh /path/coco_dataset_dir /path/mindrecord_dataset_dir"
 echo "=============================================================================================================="
 
@@ -28,4 +28,4 @@ PROJECT_DIR=$(cd "$(dirname "$0")" || exit; pwd)
 python ${PROJECT_DIR}/../src/dataset.py  \
     --coco_data_dir=$COCO_DIR \
     --mindrecord_dir=$MINDRECORD_DIR \
-    --mindrecord_prefix="coco_hp.train.mind" > create_dataset.log 2>&1 &
+    --mindrecord_prefix="coco_hp.train.mind" > create_dataset.log 2>&1 &

model_zoo/research/cv/centernet/scripts/run_standalone_eval_ascend.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_standalone_eval_ascend.sh DEVICE_ID RUN_MODE DATA_DIR LOAD_CHECKPOINT_PATH"
 echo "for example of validation: bash run_standalone_eval_ascend.sh 0 val /path/coco_dataset /path/load_ckpt"
 echo "for example of test: bash run_standalone_eval_ascend.sh 0 test /path/coco_dataset /path/load_ckpt"

model_zoo/research/cv/centernet/scripts/run_standalone_eval_cpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_standalone_eval_cpu.sh RUN_MODE DATA_DIR LOAD_CHECKPOINT_PATH"
 echo "for example of validation: bash run_standalone_eval_cpu.sh val /path/coco_dataset /path/load_ckpt"
 echo "for example of test: bash run_standalone_eval_cpu.sh test /path/coco_dataset /path/load_ckpt"

model_zoo/research/cv/centernet/scripts/run_standalone_train_ascend.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_standalone_train_ascend.sh DEVICE_ID MINDRECORD_DIR LOAD_CHECKPOINT_PATH"
 echo "for example: bash run_standalone_train_ascend.sh 0 /path/mindrecord_dataset /path/load_ckpt"
 echo "if no ckpt, just run: bash run_standalone_train_ascend.sh 0 /path/mindrecord_dataset"
@@ -52,4 +52,4 @@ python ${PROJECT_DIR}/../train.py  \
     --mindrecord_dir=$MINDRECORD_DIR \
     --mindrecord_prefix="coco_hp.train.mind" \
     --visual_image=false \
-    --save_result_dir="" > training_log.txt 2>&1 &
+    --save_result_dir="" > training_log.txt 2>&1 &

model_zoo/research/cv/centernet/scripts/run_standalone_train_cpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_standalone_train_cpu.sh MINDRECORD_DIR LOAD_CHECKPOINT_PATH"
 echo "for example: bash run_standalone_train_cpu.sh /path/mindrecord_dataset /path/load_ckpt"
 echo "if no ckpt, just run: bash run_standalone_train_cpu.sh /path/mindrecord_dataset"
@@ -47,4 +47,4 @@ python ${PROJECT_DIR}/../train.py  \
     --mindrecord_dir=$MINDRECORD_DIR \
     --mindrecord_prefix="coco_hp.train.mind" \
     --visual_image=false \
-    --save_result_dir="" > training_log.txt 2>&1 &
+    --save_result_dir="" > training_log.txt 2>&1 &

model_zoo/research/cv/centernet/src/backbone_dla.py

@@ -276,7 +276,7 @@ class DLAUp(nn.Cell):
     Upsampling of DLA network.
 
     Args:
-        startp(int): The begining stage startup upsampling
+        startp(int): The beginning stage startup upsampling
         channels(list int): The channels of each stage after upsampling
         last_level(int): The ending stage of the final upsampling
 

model_zoo/research/cv/centernet/src/config.py

@@ -32,7 +32,6 @@ dataset_config = edict({
     'aug_rot': 0.0,
     'rotate': 0,
     'flip_prop': 0.5,
-    'color_aug': False,
     'mean': np.array([0.40789654, 0.44719302, 0.47026115], dtype=np.float32),
     'std': np.array([0.28863828, 0.27408164, 0.27809835], dtype=np.float32),
     'flip_idx': [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]],

model_zoo/research/cv/centernet/src/dataset.py

@@ -26,7 +26,7 @@ import pycocotools.coco as coco
 import mindspore.dataset as ds
 from mindspore import log as logger
 from mindspore.mindrecord import FileWriter
-from src.image import color_aug, get_affine_transform, affine_transform
+from src.image import get_affine_transform, affine_transform
 from src.image import gaussian_radius, draw_umich_gaussian, draw_msra_gaussian, draw_dense_reg
 from src.visual import visual_image
 
@@ -37,7 +37,7 @@ cv2.setNumThreads(0)
 class COCOHP(ds.Dataset):
     """
     Encapsulation class of COCO person keypoints datast.
-    Initilize and preprocess of image for training and testing.
+    Initialize and preprocess of image for training and testing.
 
     Args:
         data_dir(str): Path of coco dataset.
@@ -67,7 +67,7 @@ class COCOHP(ds.Dataset):
                 os.makedirs(self.save_path)
 
     def init(self, data_dir, keep_res=False):
-        """initailize additional info"""
+        """initialize additional info"""
         logger.info('Initializing coco 2017 {} data.'.format(self.run_mode))
         if not os.path.isdir(data_dir):
             raise RuntimeError("Invalid dataset path")
@@ -236,9 +236,8 @@ class COCOHP(ds.Dataset):
 
         return eval_image, meta
 
-    def preprocess_fn(self, img, num_objects, keypoints, bboxes, category_id):
-        """image pre-process and augmentation"""
-        num_objs = min(num_objects, self.data_opt.max_objs)
+    def get_aug_param(self, img):
+        """get data augmentation parameters"""
         img = cv2.imdecode(img, cv2.IMREAD_COLOR)
         width = img.shape[1]
         c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
@@ -266,21 +265,22 @@ class COCOHP(ds.Dataset):
             flipped = True
             img = img[:, ::-1, :]
             c[0] = width - c[0] - 1
+        return img, width, c, s, rot, flipped
+
+    def preprocess_fn(self, img, num_objects, keypoints, bboxes, category_id):
+        """image pre-process and augmentation"""
+        num_objs = min(num_objects, self.data_opt.max_objs)
+        img, width, c, s, rot, flipped = self.get_aug_param(img)
 
         trans_input = get_affine_transform(c, s, rot, self.data_opt.input_res)
         inp = cv2.warpAffine(img, trans_input, (self.data_opt.input_res[0], self.data_opt.input_res[1]),
                              flags=cv2.INTER_LINEAR)
-        if self.run_mode == "train" and self.data_opt.color_aug:
-            color_aug(self._data_rng, inp / 255., self.data_opt.eig_val, self.data_opt.eig_vec)
-            inp *= 255.
 
         # caution: image normalization and transpose to nchw will both be done on device
         # inp = (inp.astype(np.float32) / 255. - self.data_opt.mean) / self.data_opt.std
         # inp = inp.transpose(2, 0, 1)
 
-        if self.data_opt.output_res[0] != self.data_opt.output_res[1]:
-            raise ValueError("Only square image was supported to used as output for convinient")
-
+        assert self.data_opt.output_res[0] == self.data_opt.output_res[1]
         output_res = self.data_opt.output_res[0]
         num_joints = self.data_opt.num_joints
         max_objs = self.data_opt.max_objs
@@ -314,22 +314,20 @@ class COCOHP(ds.Dataset):
                 for e in self.data_opt.flip_idx:
                     pts[e[0]], pts[e[1]] = pts[e[1]].copy(), pts[e[0]].copy()
 
-            lt = [bbox[0], bbox[3]]
-            rb = [bbox[2], bbox[1]]
+            lt, rb = [bbox[0], bbox[3]], [bbox[2], bbox[1]]
             bbox[:2] = affine_transform(bbox[:2], trans_output_rot)
             bbox[2:] = affine_transform(bbox[2:], trans_output_rot)
             if rot != 0:
                 lt = affine_transform(lt, trans_output_rot)
                 rb = affine_transform(rb, trans_output_rot)
-                bbox[0] = min(lt[0], rb[0], bbox[0], bbox[2])
-                bbox[2] = max(lt[0], rb[0], bbox[0], bbox[2])
-                bbox[1] = min(lt[1], rb[1], bbox[1], bbox[3])
-                bbox[3] = max(lt[1], rb[1], bbox[1], bbox[3])
+                for i in range(2):
+                    bbox[i] = min(lt[i], rb[i], bbox[i], bbox[i+2])
+                    bbox[i+2] = max(lt[i], rb[i], bbox[i], bbox[i+2])
             bbox = np.clip(bbox, 0, output_res - 1)
             h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
             if h <= 0 or w <= 0:
                 continue
-            radius = gaussian_radius((math.ceil(h), math.ceil(w)))
+            hp_radius = radius = gaussian_radius((math.ceil(h), math.ceil(w)))
             ct = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32)
             ct_int = ct.astype(np.int32)
             wh[k] = 1. * w, 1. * h
@@ -341,7 +339,6 @@ class COCOHP(ds.Dataset):
                 hm[cls_id, ct_int[1], ct_int[0]] = 0.9999
                 reg_mask[k] = 0
 
-            hp_radius = radius
             for j in range(num_joints):
                 if pts[j, 2] > 0:
                     pts[j, :2] = affine_transform(pts[j, :2], trans_output_rot)

model_zoo/research/cv/centernet/src/dcn_v2.py

@@ -163,7 +163,7 @@ class DeformConv2d(nn.Cell):
         stride (int): The distance of kernel moving. Default: 1.
         padding (int): Implicit paddings size on both sides of the input. Default: 1.
         has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
-        modulation (bool): If True, modulated defomable convolution (Deformable ConvNets v2). Defaut: True.
+        modulation (bool): If True, modulated defomable convolution (Deformable ConvNets v2). Default: True.
     Returns:
         Tensor, detection of images(bboxes, score, keypoints and category id of each objects)
     """

model_zoo/research/cv/centernet/src/decode.py

@@ -62,7 +62,7 @@ class NMS(nn.Cell):
 
 class GatherTopK(nn.Cell):
     """
-    Gather topk features through all channeles
+    Gather topk features through all channels
 
     Args: None
 
@@ -107,7 +107,7 @@ class GatherTopKChannel(nn.Cell):
     Args: None
 
     Returns:
-        Tuple of Tensors, top_k scores, indexes, and the indexes in height and width direcction repectively.
+        Tuple of Tensors, top_k scores, indexes, and the indexes in height and width direcction respectively.
     """
     def __init__(self):
         super(GatherTopKChannel, self).__init__()
@@ -281,7 +281,7 @@ class FlipLROff(nn.Cell):
         self.concat = ops.Concat(axis=1)
 
     def construct(self, kps):
-        """flip and gather kps at specfied position"""
+        """flip and gather kps at specified position"""
         # kps: 2b, 2J, h, w
         kps_o, kps_f = self.half(kps)
         # b, 2J, h, w

model_zoo/research/cv/centernet/src/utils.py

@@ -501,7 +501,7 @@ class LossCallBack(Callback):
 
     def step_begin(self, run_context):
         """
-        Get begining time of each step
+        Get beginning time of each step
         """
         self._begin_time = time.time()
 
@@ -575,7 +575,7 @@ class CenterNetMultiEpochsDecayLR(LearningRateSchedule):
     Args:
         learning_rate(float): Initial learning rate.
         warmup_steps(int): Warmup steps.
-        multi_steps(list int): The steps coresponding to decay learning rate.
+        multi_steps(list int): The steps corresponding to decay learning rate.
         steps_per_epoch(int): How many steps for each epoch.
         factor(int): Learning rate decay factor. Default: 10.
 
@@ -612,7 +612,7 @@ class MultiEpochsDecayLR(LearningRateSchedule):
 
     Args:
         learning_rate(float): Initial learning rate.
-        multi_steps(list int): The steps coresponding to decay learning rate.
+        multi_steps(list int): The steps corresponding to decay learning rate.
         steps_per_epoch(int): How many steps for each epoch.
         factor(int): Learning rate decay factor. Default: 10.