fix eval fp16 bug and codex

example/mobilenetv2_imagenet2012/eval.py

@@ -42,6 +42,9 @@ if __name__ == '__main__':
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
     net = mobilenet_v2(num_classes=config.num_classes)
     net.to_float(mstype.float16)
+    for _, cell in net.cells_and_names():
+        if isinstance(cell, nn.Dense):
+            cell.add_flags_recursive(fp32=True)
 
     dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
     step_size = dataset.get_dataset_size()

example/mobilenetv2_imagenet2012/launch.py

@@ -17,6 +17,7 @@ import os
 import sys
 import json
 import subprocess
+import shutil
 from argparse import ArgumentParser
 
 def parse_args():
@@ -126,25 +127,33 @@ def main():
     # spawn the processes
     processes = []
     cmds = []
+    log_files = []
+    env = os.environ.copy()
+    env['RANK_SIZE'] = str(args.nproc_per_node)
     for rank_id in range(0, args.nproc_per_node):
         device_id = visible_devices[rank_id]
         device_dir = os.path.join(os.getcwd(), 'device{}'.format(rank_id))
-        rank_process = 'export RANK_SIZE={} && export RANK_ID={} && export DEVICE_ID={} && '.format(args.nproc_per_node,
+        env['RANK_ID'] = str(rank_id)
-                                                                                                    rank_id, device_id)
+        env['DEVICE_ID'] = str(device_id)
         if args.nproc_per_node > 1:
-            rank_process += 'export MINDSPORE_HCCL_CONFIG_PATH={} && '.format(table_fn)
+            env['MINDSPORE_HCCL_CONFIG_PATH'] = table_fn
-            rank_process += 'export RANK_TABLE_FILE={} && '.format(table_fn)
+            env['RANK_TABLE_FILE'] = table_fn
-        rank_process += 'rm -rf {dir} && mkdir {dir} && cd {dir} && python {script} '.format(dir=device_dir,
+        if os.path.exists(device_dir):
-                                                                                             script=args.training_script
+            shutil.rmtree(device_dir)
-                                                                                             )
+        os.mkdir(device_dir)
-        rank_process += ' '.join(args.training_script_args) + ' > log{}.log 2>&1 &'.format(rank_id)
+        cmd = [sys.executable, '-u']
-        process = subprocess.Popen(rank_process, shell=True)
+        cmd.append(args.training_script)
+        cmd.extend(args.training_script_args)
+        log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
+        process = subprocess.Popen(cmd, stdout=log_file, env=env)
         processes.append(process)
-        cmds.append(rank_process)
+        cmds.append(cmd)
-    for process, cmd in zip(processes, cmds):
+        log_files.append(log_file)
+    for process, cmd, log_file in zip(processes, cmds, log_files):
         process.wait()
         if process.returncode != 0:
             raise subprocess.CalledProcessError(returncode=process, cmd=cmd)
+        log_file.close()
 
 
 if __name__ == "__main__":

example/mobilenetv2_imagenet2012/train.py

@@ -119,7 +119,7 @@ class Monitor(Callback):
         print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
                                                                                       per_step_mseconds,
                                                                                       np.mean(self.losses)
-                                                                                      ), flush=True)
+                                                                                      ))
 
     def step_begin(self, run_context):
         self.step_time = time.time()
@@ -139,7 +139,7 @@ class Monitor(Callback):
 
         print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.3f}]".format(
             cb_params.cur_epoch_num - 1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
-            np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]), flush=True)
+            np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
 
 
 if __name__ == '__main__':