diff --git a/nnvm/python/nnvm/frontend/darknet.py b/nnvm/python/nnvm/frontend/darknet.py
index 1c5fdfa87406ca98b02baba486e10ec357d3e2c6..cb73b5a4345a7e4aeb71090788821a1d03d11b49 100644
--- a/nnvm/python/nnvm/frontend/darknet.py
+++ b/nnvm/python/nnvm/frontend/darknet.py
@@ -3,12 +3,11 @@ DarkNet symbol frontend.
"""
from __future__ import absolute_import as _abs
-from enum import IntEnum
import numpy as np
import tvm
from .. import symbol as _sym
-class LAYERTYPE(IntEnum):
+class LAYERTYPE(object):
"""Darknet LAYERTYPE Class constant."""
CONVOLUTIONAL = 0
DECONVOLUTIONAL = 1
@@ -36,7 +35,7 @@ class LAYERTYPE(IntEnum):
REORG = 23
BLANK = 24
-class ACTIVATION(IntEnum):
+class ACTIVATION(object):
"""Darknet ACTIVATION Class constant."""
LOGISTIC = 0
RELU = 1
@@ -323,33 +322,31 @@ def _darknet_op_not_support(inputs, attrs):
raise NotImplementedError(err)
_DARKNET_CONVERT_MAP = {
- 'CONVOLUTIONAL' : _darknet_conv2d,
- 'DECONVOLUTIONAL' : _darknet_conv2d_transpose,
- 'CONNECTED' : _darknet_dense,
- 'MAXPOOL' : _darknet_maxpooling,
- 'SOFTMAX' : _darknet_softmax_output,
- 'DROPOUT' : _darknet_dropout,
- 'AVGPOOL' : _darknet_avgpooling,
- 'BATCHNORM' : _darknet_batch_norm,
- 'RESHAPE' : _darknet_reshape,
- 'ROUTE' : _darknet_route,
- 'REORG' : _darknet_reorg,
- 'REGION' : _darknet_region,
- 'ACTIVATION' : _darknet_activations,
- 'SHORTCUT' : _darknet_shortcut,
- 'DETECTION' : _darknet_op_not_support,
- 'CROP' : _darknet_op_not_support,
- 'COST' : _darknet_op_not_support,
- 'NORMALIZATION' : _darknet_op_not_support,
- 'LOCAL' : _darknet_op_not_support,
- 'ACTIVE' : _darknet_op_not_support,
- 'RNN' : _darknet_op_not_support,
- 'GRU' : _darknet_op_not_support,
- 'LSTM' : _darknet_op_not_support,
- 'CRNN' : _darknet_op_not_support,
- 'NETWORK' : _darknet_op_not_support,
- 'XNOR' : _darknet_op_not_support,
- 'BLANK' : _darknet_op_not_support,
+ LAYERTYPE.CONVOLUTIONAL : _darknet_conv2d,
+ LAYERTYPE.DECONVOLUTIONAL : _darknet_conv2d_transpose,
+ LAYERTYPE.CONNECTED : _darknet_dense,
+ LAYERTYPE.MAXPOOL : _darknet_maxpooling,
+ LAYERTYPE.SOFTMAX : _darknet_softmax_output,
+ LAYERTYPE.DROPOUT : _darknet_dropout,
+ LAYERTYPE.AVGPOOL : _darknet_avgpooling,
+ LAYERTYPE.BATCHNORM : _darknet_batch_norm,
+ LAYERTYPE.ROUTE : _darknet_route,
+ LAYERTYPE.REORG : _darknet_reorg,
+ LAYERTYPE.REGION : _darknet_region,
+ LAYERTYPE.SHORTCUT : _darknet_shortcut,
+ LAYERTYPE.DETECTION : _darknet_op_not_support,
+ LAYERTYPE.CROP : _darknet_op_not_support,
+ LAYERTYPE.COST : _darknet_op_not_support,
+ LAYERTYPE.NORMALIZATION : _darknet_op_not_support,
+ LAYERTYPE.LOCAL : _darknet_op_not_support,
+ LAYERTYPE.ACTIVE : _darknet_op_not_support,
+ LAYERTYPE.RNN : _darknet_op_not_support,
+ LAYERTYPE.GRU : _darknet_op_not_support,
+ LAYERTYPE.LSTM : _darknet_op_not_support,
+ LAYERTYPE.CRNN : _darknet_op_not_support,
+ LAYERTYPE.NETWORK : _darknet_op_not_support,
+ LAYERTYPE.XNOR : _darknet_op_not_support,
+ LAYERTYPE.BLANK : _darknet_op_not_support,
}
def _darknet_convert_symbol(op_name, inputs, attrs):
@@ -376,7 +373,7 @@ def _darknet_convert_symbol(op_name, inputs, attrs):
if op_name in _DARKNET_CONVERT_MAP:
sym, out_name = _DARKNET_CONVERT_MAP[op_name](inputs, attrs)
else:
- _darknet_raise_not_supported('Operator: ' + op_name)
+ _darknet_raise_not_supported('Operator type ' + str(op_name))
if out_name is None:
out_name = sym.list_output_names()[0].replace('_output', '')
return out_name, sym
@@ -397,10 +394,6 @@ def _read_memory_buffer(shape, data, dtype):
data_np[i] = data[i]
return data_np.reshape(shape)
-def _get_darknet_layername(layer_type):
- """Get the layer name from the darknet enums."""
- return str((LAYERTYPE(layer_type))).replace('LAYERTYPE.', '')
-
def _get_convolution_weights(layer, opname, params, dtype):
"""Get the convolution layer weights and biases."""
if layer.nweights == 0:
@@ -460,8 +453,6 @@ def _get_darknet_attrs(net, layer_num):
attr = {}
use_flatten = True
layer = net.layers[layer_num]
- op_name = _get_darknet_layername(layer.type)
-
if LAYERTYPE.CONVOLUTIONAL == layer.type:
attr.update({'layout' : 'NCHW'})
attr.update({'pad' : str(layer.pad)})
@@ -551,10 +542,10 @@ def _get_darknet_attrs(net, layer_num):
attr.update({'background' : layer.background})
attr.update({'softmax' : layer.softmax})
else:
- err = "Darknet layer {} is not supported in nnvm.".format(op_name)
+ err = "Darknet layer type {} is not supported in nnvm.".format(layer.type)
raise NotImplementedError(err)
- return op_name, attr
+ return layer.type, attr
def _get_tvm_params_name(opname, arg_name):
"""Makes the params name for the k,v pair."""
diff --git a/nnvm/python/nnvm/testing/darknet.py b/nnvm/python/nnvm/testing/darknet.py
index 9e2d7a6e7258ef61328486e76187fd6f21637863..ed8bf021c46282175f440a31cbc241cd207c9509 100644
--- a/nnvm/python/nnvm/testing/darknet.py
+++ b/nnvm/python/nnvm/testing/darknet.py
@@ -8,7 +8,6 @@ These are utility functions used for testing and tutorial file.
"""
from __future__ import division
import math
-from enum import IntEnum
import numpy as np
import cv2
from cffi import FFI
@@ -91,7 +90,7 @@ def load_image(image, resize_width, resize_height):
img = load_image_color(image)
return _letterbox_image(img, resize_width, resize_height)
-class LAYERTYPE(IntEnum):
+class LAYERTYPE(object):
"""Darknet LAYERTYPE Class constant."""
CONVOLUTIONAL = 0
DECONVOLUTIONAL = 1
@@ -119,7 +118,7 @@ class LAYERTYPE(IntEnum):
REORG = 23
BLANK = 24
-class ACTIVATION(IntEnum):
+class ACTIVATION(object):
"""Darknet ACTIVATION Class constant."""
LOGISTIC = 0
RELU = 1