add optimizor construct function

fastNLP/action/optimizor.py

@@ -0,0 +1,49 @@
+from torch import optim
+
+def get_torch_optimizor(params, alg_name='sgd', **args):
+    '''
+    construct pytorch optimizor by algorithm's name
+    optimizor's argurments can be splicified, for different optimizor's argurments, please see pytorch doc
+
+    usage:
+        optimizor = get_torch_optimizor(model.parameters(), 'SGD', lr=0.01)
+
+    '''
+    
+    name = alg_name.lower()
+    if name == 'adadelta':
+        return optim.Adadelta(params, **args)
+    elif name == 'adagrad':
+        return optim.Adagrad(params, **args)
+    elif name == 'adam':
+        return optim.Adam(params, **args)
+    elif name == 'adamax':
+        return optim.Adamax(params, **args)
+    elif name == 'asgd':
+        return optim.ASGD(params, **args)
+    elif name == 'lbfgs':
+        return optim.LBFGS(params, **args)
+    elif name == 'rmsprop':
+        return optim.RMSprop(params, **args)
+    elif name == 'rprop':
+        return optim.Rprop(params, **args)
+    elif name == 'sgd':
+        #SGD's parameter lr is required
+        if 'lr' not in args:
+            args['lr'] = 0.01
+        return optim.SGD(params, **args)
+    elif name == 'sparseadam':
+        return optim.SparseAdam(params, **args)
+    else:
+        raise TypeError('no such optimizor named {}'.format(alg_name))
+
+
+# example usage
+if __name__ == '__main__':
+    from torch.nn.modules import Linear
+    net = Linear(2, 5)
+
+    test1 = get_torch_optimizor(net.parameters(),'adam', lr=1e-2, weight_decay=1e-3)
+    print(test1)
+    test2 = get_torch_optimizor(net.parameters(), 'SGD')
+    print(test2)