fix dataset conflict issue in single machine simulation

paddle_fl/core/trainer/fl_trainer.py

@@ -123,13 +123,13 @@ class FedAvgTrainer(FLTrainer):
         self.exe.run(self._recv_program)
         epoch = 0
         for i in range(num_epoch):
-	        print(epoch)
-	        for data in reader():
-			    self.exe.run(self._main_program,
-                          feed=feeder.feed(data),
+                print(epoch)
+                for data in reader():
+                    self.exe.run(self._main_program,
+                           feed=feeder.feed(data),
                            fetch_list=fetch)
-	        self.cur_step += 1
-	        epoch += 1
+                self.cur_step += 1
+                epoch += 1
         self._logger.debug("begin to run send program")
         self.exe.run(self._send_program)
     def run(self, feed, fetch):

paddle_fl/dataset/femnist.py

@@ -3,32 +3,34 @@ import os
 import json
 import tarfile
 import random
-url = "https://paddlefl.bj.bcebos.com/leaf/"
-target_path = "femnist_data"
-tar_path = target_path+".tar.gz"
-print(tar_path)
 
-def download(url):
+
+def download(url,tar_path):
 	r = requests.get(url)
 	with open(tar_path,'wb') as f:
 		f.write(r.content)
 
-def extract(tar_path):
+def extract(tar_path,target_path):
 	tar = tarfile.open(tar_path, "r:gz")
 	file_names = tar.getnames()
 	for file_name in file_names:
-		tar.extract(file_name)
+		tar.extract(file_name,target_path)
 
 	tar.close()
 
 def train(trainer_id,inner_step,batch_size,count_by_step):
-	if not os.path.exists(target_path):
+        target_path = "trainer%d_data" % trainer_id
+        data_path = target_path + "/femnist_data"
+        tar_path = data_path + ".tar.gz"
+        if not os.path.exists(target_path):
+            os.system("mkdir trainer%d_data" % trainer_id)
+	if not os.path.exists(data_path):
 		print("Preparing data...")
 		if not os.path.exists(tar_path):
-			download(url+tar_path)
-		extract(tar_path)
+			download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",tar_path)
+		extract(tar_path,target_path)
 	def train_data():
-		train_file = open("./femnist_data/train/all_data_%d_niid_0_keep_0_train_9.json" % trainer_id,'r')
+		train_file = open("./trainer%d_data/femnist_data/train/all_data_%d_niid_0_keep_0_train_9.json" % (trainer_id,trainer_id),'r')
 		json_train = json.load(train_file)
 		users = json_train["users"]
 		rand = random.randrange(0,len(users)) # random choose a user from each trainer
@@ -48,13 +50,18 @@ def train(trainer_id,inner_step,batch_size,count_by_step):
 	return train_data
 
 def test(trainer_id,inner_step,batch_size,count_by_step):
-	if not os.path.exists(target_path):
+        target_path = "trainer%d_data" % trainer_id
+        data_path = target_path + "/femnist_data"
+        tar_path = data_path + ".tar.gz"
+        if not os.path.exists(target_path):
+            os.system("mkdir trainer%d_data" % trainer_id)
+        if not os.path.exists(data_path):
                 print("Preparing data...")
                 if not os.path.exists(tar_path):
-                        download(url+tar_path)
-                extract(tar_path)
+                        download("https://paddlefl.bj.bcebos.com/leaf/femnist_data.tar.gz",tar_path)
+                extract(tar_path,target_path)
 	def test_data():
-		test_file = open("./femnist_data/test/all_data_%d_niid_0_keep_0_test_9.json" % trainer_id, 'r')
+		test_file = open("./trainer%d_data/femnist_data/test/all_data_%d_niid_0_keep_0_test_9.json" % (trainer_id,trainer_id), 'r')
 		json_test = json.load(test_file)
 		users = json_test["users"]
 		for user in users:

paddle_fl/examples/femnist_demo/fl_trainer.py

@@ -40,7 +40,7 @@ def train_test(train_test_program, train_test_feed, train_test_reader):
 epoch_id = 0
 step = 0
 epoch = 3000
-count_by_step = True
+count_by_step = False
 if count_by_step:
 	output_folder = "model_node%d" % trainer_id
 else: 
@@ -82,4 +82,4 @@ while not trainer.stop():
     print("Test with epoch %d, accuracy: %s" % (epoch_id, acc_val))
     if trainer_id == 0:  
     	save_dir = (output_folder + "/epoch_%d") % epoch_id
-    	trainer.save_inference_program(output_folder)
+        trainer.save_inference_program(output_folder)