inference.py

@@ -98,7 +98,7 @@ if __name__ == "__main__":
     if not os.path.exists(data_dir):
         os.makedirs(data_dir)
     if not os.path.exists(result_dir):
-            os.makedirs(result_dir)
+        os.makedirs(result_dir)
     
     ###Copy dataset from obs to inference image
     ObsToEnv(args.data_url, data_dir)

inference_for_multidataset.py

@@ -0,0 +1,158 @@
+"""
+######################## multi-dataset inference lenet example ########################
+This example is a single-dataset inference tutorial. 
+
+######################## Instructions for using the inference environment ########################
+1、Inference task requires predefined functions
+(1)Copy multi dataset from obs to inference image.
+function MultiObsToEnv(obs_data_url, data_dir)
+
+(2)Copy ckpt file from obs to inference image.
+function ObsUrlToEnv(obs_ckpt_url, ckpt_url)
+
+(3)Copy the output result to obs.
+function EnvToObs(train_dir, obs_train_url)
+
+3、5 parameters need to be defined.
+--data_url is the first dataset you selected on the Qizhi platform
+--multi_data_url is the multi dataset you selected on the Qizhi platform
+--ckpt_url is the weight file you choose on the Qizhi platform
+--result_url is the output 
+
+--data_url,--multi_data_url,--ckpt_url,--result_url,--device_target,These 5 parameters must be defined first in a single dataset, 
+otherwise an error will be reported. 
+There is no need to add these parameters to the running parameters of the Qizhi platform, 
+because they are predefined in the background, you only need to define them in your code.                    
+
+4、How the dataset is used
+Multi-datasets use multi_data_url as input, data_dir + dataset name + file or folder name in the dataset as the 
+calling path of the dataset in the inference image.
+For example, the calling path of the test folder in the MNIST_Data dataset in this example is 
+data_dir + "/MNIST_Data" +"/test"
+
+For details, please refer to the following sample code.
+"""
+
+import os
+import argparse
+import moxing as mox
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from mindspore import Tensor
+import numpy as np
+from glob import glob
+from dataset import create_dataset
+from config import mnist_cfg as cfg
+from lenet import LeNet5
+import json
+
+### Copy multiple datasets from obs to inference image ###
+def MultiObsToEnv(multi_data_url, data_dir):
+    #--multi_data_url is json data, need to do json parsing for multi_data_url
+    multi_data_json = json.loads(multi_data_url)  
+    for i in range(len(multi_data_json)):
+        path = data_dir + "/" + multi_data_json[i]["dataset_name"]
+        if not os.path.exists(path):
+            os.makedirs(path)
+        try:
+            mox.file.copy_parallel(multi_data_json[i]["dataset_url"], path) 
+            print("Successfully Download {} to {}".format(multi_data_json[i]["dataset_url"],path))
+        except Exception as e:
+            print('moxing download {} to {} failed: '.format(
+                multi_data_json[i]["dataset_url"], path) + str(e))
+    return 
+### Copy ckpt file from obs to inference image###
+### To operate on folders, use mox.file.copy_parallel. If copying a file. 
+### Please use mox.file.copy to operate the file, this operation is to operate the file
+def ObsUrlToEnv(obs_ckpt_url, ckpt_url):
+    try:
+        mox.file.copy(obs_ckpt_url, ckpt_url)
+        print("Successfully Download {} to {}".format(obs_ckpt_url,ckpt_url))
+    except Exception as e:
+        print('moxing download {} to {} failed: '.format(obs_ckpt_url, ckpt_url) + str(e)) 
+    return  
+### Copy the output result to obs###
+def EnvToObs(train_dir, obs_train_url):
+    try:
+        mox.file.copy_parallel(train_dir, obs_train_url)
+        print("Successfully Upload {} to {}".format(train_dir,obs_train_url))
+    except Exception as e:
+        print('moxing upload {} to {} failed: '.format(train_dir,obs_train_url) + str(e))
+    return      
+    
+
+
+### --data_url,--multi_data_url,--ckpt_url,--result_url,--device_target,These 5 parameters must be defined first in a multi dataset inference task, 
+### otherwise an error will be reported.
+### There is no need to add these parameters to the running parameters of the Qizhi platform, 
+### because they are predefined in the background, you only need to define them in your code.
+parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
+parser.add_argument('--data_url',
+                type=str,
+                default= '/cache/data1/',
+                help='path where the dataset is saved')      
+parser.add_argument('--multi_data_url',
+                type=str,
+                default= '/cache/data/',
+                help='path where the dataset is saved')                
+parser.add_argument('--ckpt_url',
+                help='model to save/load',
+                default=  '/cache/checkpoint.ckpt')  
+parser.add_argument('--result_url',
+                help='result folder to save/load',
+                default= '/cache/result/')   
+parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                    help='device where the code will be implemented (default: Ascend)')                
+
+if __name__ == "__main__":            
+    args = parser.parse_args()
+
+    ###Initialize the data and result directories in the inference image###
+    data_dir = '/cache/data'  
+    result_dir = '/cache/result'
+    ckpt_url = '/cache/checkpoint.ckpt'
+    if not os.path.exists(data_dir):
+        os.makedirs(data_dir)
+    if not os.path.exists(result_dir):
+        os.makedirs(result_dir)
+    
+    ###Copy multiple dataset from obs to inference image
+    MultiObsToEnv(args.multi_data_url, data_dir)
+
+    ###Copy ckpt file from obs to inference image
+    ObsUrlToEnv(args.ckpt_url, ckpt_url)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
+    repeat_size = cfg.epoch_size
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    print("============== Starting Testing ==============")
+
+    param_dict = load_checkpoint(os.path.join(ckpt_url))
+    load_param_into_net(network, param_dict)
+    ds_test = create_dataset(os.path.join(data_dir + "/MNISTData", "test"), batch_size=1).create_dict_iterator()
+    data = next(ds_test)
+    images = data["image"].asnumpy()
+    labels = data["label"].asnumpy()
+    print('Tensor:', Tensor(data['image']))
+    output = model.predict(Tensor(data['image']))
+    predicted = np.argmax(output.asnumpy(), axis=1)
+    pred = np.argmax(output.asnumpy(), axis=1)
+    print('predicted:', predicted)
+    print('pred:', pred)
+
+    print(f'Predicted: "{predicted[0]}", Actual: "{labels[0]}"')
+    filename = 'result.txt'
+    file_path = os.path.join(result_dir, filename)
+    with open(file_path, 'a+') as file:
+            file.write("    {}: {:.2f} \n".format("Predicted", predicted[0]))
+
+    ###Copy result data from the local running environment back to obs,
+    ###and download it in the inference task corresponding to the Qizhi platform
+    EnvToObs(result_dir, args.result_url)