Merge pull request #41 from CortexFoundation/wlt

update train mnist

Author: wlt-cortex

.github/workflows/ccpp.yml

@@ -6,10 +6,22 @@
 from mrt import conf, utils
 
 import numpy as np
+import argparse
+
+parser = argparse.ArgumentParser(description='Mnist Traning')
+parser.add_argument('--cpu', default=False, action='store_true',
+                    help='whether enable cpu (default use gpu)')
+parser.add_argument('--gpu-id', type=int, default=0,
+                    help='gpu device id')
+parser.add_argument('--net', type=str, default='',
+                    help='choose available networks, optional: lenet, mlp')
+
+args = parser.parse_args()
 
 def load_fname(version, suffix=None, with_ext=False):
     suffix = "."+suffix if suffix is not None else ""
-    prefix = "{}/mnist_{}{}".format(conf.MRT_MODEL_ROOT, version, suffix)
+    version = "_"+version if version is not None else ""
+    prefix = "{}/mnist{}{}".format(conf.MRT_MODEL_ROOT, version, suffix)
     return utils.extend_fname(prefix, with_ext)
 
 def data_xform(data):
@@ -25,10 +37,12 @@ def data_xform(data):
 train_loader = mx.gluon.data.DataLoader(train_data, shuffle=True, batch_size=batch_size)
 val_loader = mx.gluon.data.DataLoader(val_data, shuffle=False, batch_size=batch_size)
 
-version = ''
+version = args.net
+print ("Training {} Mnist".format(version))
 
 # Set the gpu device id
-ctx = mx.gpu(0)
+ctx = mx.cpu() if args.cpu else mx.gpu(args.gpu_id)
+print ("Using device: {}".format(ctx))
 
 def train_mnist():
     # Select a fixed random seed for reproducibility
@@ -70,6 +84,8 @@ def train_mnist():
                 nn.Dense(64, activation='relu'),
                 nn.Dense(10, activation=None)  # loss function includes softmax already, see below
             )
+    else:
+        assert False
 
     # Random initialize all the mnist model parameters
     net.initialize(mx.init.Xavier(), ctx=ctx)
@@ -118,5 +134,4 @@ def train_mnist():
         fout.write(sym.tojson())
     net.collect_params().save(param_file)
 
-print ("Test mnist", version)
 train_mnist()