remove dtype in model symbol (#310)

nnvm/python/nnvm/testing/resnet.py

@@ -24,7 +24,6 @@ Implemented the following paper:
 Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks"
 '''
 # pylint: disable=unused-argument
-import numpy as np
 from .. import symbol as sym
 from . utils import create_workload
 
@@ -91,7 +90,7 @@ def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck=True):
         return sym.elemwise_add(conv2, shortcut)
 
 def resnet(units, num_stages, filter_list, num_classes, image_shape,
-           bottle_neck=True, dtype='float32'):
+           bottle_neck=True):
     """Return ResNet symbol of
     Parameters
     ----------
@@ -105,17 +104,10 @@ def resnet(units, num_stages, filter_list, num_classes, image_shape,
         Ouput size of symbol
     dataset : str
         Dataset type, only cifar10 and imagenet supports
-    dtype : str
-        Precision (float32 or float16)
     """
     num_unit = len(units)
     assert num_unit == num_stages
     data = sym.Variable(name='data')
-    if dtype == 'float32':
-        data = data
-    else:
-        if dtype == 'float16':
-            data = sym.cast(data=data, dtype=np.float16)
     data = sym.batch_norm(data=data, epsilon=2e-5, name='bn_data')
     (_, height, _) = image_shape
     if height <= 32:            # such as cifar10
@@ -144,11 +136,9 @@ def resnet(units, num_stages, filter_list, num_classes, image_shape,
     pool1 = sym.global_avg_pool2d(data=relu1, name='pool1')
     flat = sym.flatten(data=pool1)
     fc1 = sym.dense(data=flat, units=num_classes, name='fc1')
-    if dtype == 'float16':
-        fc1 = sym.cast(data=fc1, dtype=np.float32)
     return sym.softmax(data=fc1, name='softmax')
 
-def get_symbol(num_classes, num_layers=50, image_shape=(3, 224, 224), dtype='float32', **kwargs):
+def get_symbol(num_classes, num_layers=50, image_shape=(3, 224, 224), **kwargs):
     """
     Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py
     Original author Wei Wu
@@ -197,8 +187,7 @@ def get_symbol(num_classes, num_layers=50, image_shape=(3, 224, 224), dtype='flo
                   filter_list=filter_list,
                   num_classes=num_classes,
                   image_shape=image_shape,
-                  bottle_neck=bottle_neck,
-                  dtype=dtype)
+                  bottle_neck=bottle_neck)
 
 def get_workload(batch_size=1, num_classes=1000, num_layers=18,
                  image_shape=(3, 224, 224), dtype="float32", **kwargs):
@@ -233,5 +222,5 @@ def get_workload(batch_size=1, num_classes=1000, num_layers=18,
         The parameters.
     """
     net = get_symbol(num_classes=num_classes, num_layers=num_layers,
-                     image_shape=image_shape, dtype=dtype, **kwargs)
+                     image_shape=image_shape, **kwargs)
     return create_workload(net, batch_size, image_shape, dtype)

nnvm/python/nnvm/testing/vgg.py

@@ -20,7 +20,6 @@
 Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for
 large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
 """
-import numpy as np
 from .. import symbol as sym
 from . utils import create_workload
 
@@ -51,7 +50,7 @@ def get_classifier(input_data, num_classes):
     fc8 = sym.dense(data=drop7, units=num_classes, name="fc8")
     return fc8
 
-def get_symbol(num_classes, num_layers=11, batch_norm=False, dtype='float32'):
+def get_symbol(num_classes, num_layers=11, batch_norm=False):
     """
     Parameters
     ----------
@@ -61,8 +60,6 @@ def get_symbol(num_classes, num_layers=11, batch_norm=False, dtype='float32'):
         Number of layers for the variant of densenet. Options are 11, 13, 16, 19.
     batch_norm : bool, default False
         Use batch normalization.
-    dtype: str, float32 or float16
-        Data precision.
     """
     vgg_spec = {11: ([1, 1, 2, 2, 2], [64, 128, 256, 512, 512]),
                 13: ([2, 2, 2, 2, 2], [64, 128, 256, 512, 512]),
@@ -72,12 +69,8 @@ def get_symbol(num_classes, num_layers=11, batch_norm=False, dtype='float32'):
         raise ValueError("Invalide num_layers {}. Choices are 11,13,16,19.".format(num_layers))
     layers, filters = vgg_spec[num_layers]
     data = sym.Variable(name="data")
-    if dtype == 'float16':
-        data = sym.cast(data=data, dtype=np.float16)
     feature = get_feature(data, layers, filters, batch_norm)
     classifier = get_classifier(feature, num_classes)
-    if dtype == 'float16':
-        classifier = sym.cast(data=classifier, dtype=np.float32)
     symbol = sym.softmax(data=classifier, name='softmax')
     return symbol
 
@@ -110,5 +103,5 @@ def get_workload(batch_size, num_classes=1000, image_shape=(3, 224, 224),
     params : dict of str to NDArray
         The parameters.
     """
-    net = get_symbol(num_classes=num_classes, dtype=dtype, **kwargs)
+    net = get_symbol(num_classes=num_classes, **kwargs)
     return create_workload(net, batch_size, image_shape, dtype)