Add performance recording and plot during training

README.md

@@ -3,7 +3,7 @@ A toolbox of OCR models, algorithms, and pipelines based on MindSpore
 
 **Features:**
 
-- Unified framework to support training text detection and recogintion models
+- Support training and evaluation for text detection and recogintion models
 
 
 ## Quick Start (for dev)

configs/det/db_r50_icdar15.yaml

@@ -47,9 +47,9 @@ scheduler:
   scheduler: "cosine_decay"
   min_lr: 0.00001
   lr: 0.007
-  num_epochs: 100
-  warmup_epochs: 3
-  decay_epochs: 97
+  num_epochs: 5
+  warmup_epochs: 2
+  decay_epochs: 3
 
 optimizer:
   opt: "momentum"

configs/rec/crnn_icdar15.yaml

@@ -135,7 +135,7 @@ eval:
     num_keys_to_net: 1 # num inputs for network forward func
 
   loader:
-      shuffle: True # TODO: tbc
+      shuffle: False
       batch_size: 64
       drop_remainder: False
       max_rowsize: 12

mindocr/data/transforms/modelzoo_transforms.py

@@ -20,7 +20,6 @@
 __all__ = ['MZRandomColorAdjust', 'MZScalePad', 'MZResizeByGrid', 'MZRandomCropData',
             'MZRandomScaleByShortSide', 'MZMakeSegDetectionData', 'MZMakeBorderMap', 'MZIncorrectNormToCHW']
 
-# TODO: the input pixel value is in range [0, 255] or [0, 1]?
 # TODO: Does it support BGR mode?
 class MZRandomColorAdjust():
     def __init__(self, brightness=32.0 / 255, saturation=0.5, to_numpy=False):
@@ -134,18 +133,7 @@ def __call__(self, data):
         if polys is None:
             return data
 
-        # TODO: compute in np array, not list 
-        '''
         #if self.is_train:
-        #    new_polys = []
-            for poly in polys:
-                poly[:, 0] = poly[:, 0] * w_scale
-                poly[:, 1] = poly[:, 1] * h_scale
-                new_polys.append(poly)
-            polys = new_polys
-            polys[:, :, 0] = polys[:, :, 0] * w_scale
-            polys[:, :, 1] = polys[:, :, 1] * h_scale
-        '''
         polys[:, :, 0] = polys[:, :, 0] * w_scale
         polys[:, :, 1] = polys[:, :, 1] * h_scale
 

mindocr/metrics/det_metrics.py

@@ -3,17 +3,16 @@
 TODO: overwrite
 
 '''
-
 from typing import List
 
 import numpy as np
 from mindspore import nn
 from shapely.geometry import Polygon
+from .meters import AverageMeter
 
 __all__ = ['DetMetric']
 
 
-
 def _get_intersect(pD, pG):
     return pD.intersection(pG).area
 
@@ -22,24 +21,6 @@ def _get_iou(pD, pG):
     return pD.intersection(pG).area / pD.union(pG).area
 
 
-class AverageMeter:
-    """Computes and stores the average and current value"""
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
 class DetectionIoUEvaluator:
     def __init__(self, min_iou=0.5, min_intersect=0.5):
         self._min_iou = min_iou

mindocr/metrics/meters.py

@@ -0,0 +1,23 @@
+'''
+Contain meter classes for recording and aggregating measured values 
+'''
+
+class AverageMeter:
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def get(self):
+        return self.avg

mindocr/metrics/rec_metrics.py

@@ -27,7 +27,7 @@ def __init__(self,
             ignore_space=True, 
             filter_ood=True,  
             lower=True, 
-            print_flag=True, 
+            print_flag=False, 
             **kwargs):
         super().__init__()
         self.clear()