add ST and CI workflow

mindocr/utils/train_step_wrapper.py

@@ -5,6 +5,8 @@
 from mindspore.ops import composite as C
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
+import mindspore.context as context
+
 
 _ema_op = C.MultitypeFuncGraph("grad_ema_op")
 _grad_scale = C.MultitypeFuncGraph("grad_scale")
@@ -62,6 +64,9 @@ def __init__(
             self.weights_all = ms.ParameterTuple(list(network.get_parameters()))
             self.ema_weight = self.weights_all.clone("ema", init="same")
 
+        self.is_cpu_device = context.get_context("device_target") == 'CPU' # to support CPU run
+        print('\n====-> device: ', context.get_context("device_target") )
+
     def ema_update(self):
         """Update EMA parameters."""
         self.updates += 1
@@ -75,8 +80,9 @@ def construct(self, *inputs):
         weights = self.weights
         loss = self.network(*inputs)
         scaling_sens = self.scale_sense
-
-        status, scaling_sens = self.start_overflow_check(loss, scaling_sens)
+
+        if not self.is_cpu_device:
+            status, scaling_sens = self.start_overflow_check(loss, scaling_sens)
 
         scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss))
         grads = self.grad(self.network, weights)(*inputs, scaling_sens_filled)
@@ -85,8 +91,13 @@ def construct(self, *inputs):
         grads = self.grad_reducer(grads)
 
         # get the overflow buffer
-        cond = self.get_overflow_status(status, grads)
-        overflow = self.process_loss_scale(cond)
+        if not self.is_cpu_device:
+            cond = self.get_overflow_status(status, grads)
+            overflow = self.process_loss_scale(cond)
+        else:
+            overflow = False
+            cond = False 
+
         if self.drop_overflow_update:
             # if there is no overflow, do optimize
             if not overflow :

requirements.txt

@@ -3,6 +3,10 @@ lmdb
 mindcv
 pyclipper
 shapely
+tqdm
+addict
+scikit-learn
+matplotlib
 rapidfuzz==2.13.7
 numpy==1.21.6
 opencv-python-headless==3.4.18.65

tests/st/rec_crnn_test.yaml

@@ -98,7 +98,7 @@ train:
       batch_size: *batch_size
       drop_remainder: True
       max_rowsize: 16
-      num_workers: 10
+      num_workers: 2
 
 eval:
   ckpt_load_path: './tmp_rec/best.ckpt'

tests/st/test_train_eval_dummy.py

@@ -35,13 +35,13 @@ def test_train_eval(task):
         image_dir = f'{data_dir}/{split}/dogs' 
         new_label_path = f'data/Canidae/{split}/{task}_gt.txt'
         img_paths = glob.glob(os.path.join(image_dir, '*.JPEG'))
-        print(len(img_paths))
+        #print(len(img_paths))
         with open(new_label_path, 'w') as f_w:
             with open(label_path, 'r') as f_r: 
                 i = 0
                 for line in f_r:
                     _, label = line.strip().split('\t')
-                    print(i)
+                    #print(i)
                     img_name = os.path.basename(img_paths[i])
                     new_img_label = img_name + '\t' + label
                     f_w.write(new_img_label + '\n')