From 9f6e0c596c524a7363e7e6006a439d4bdf63fc98 Mon Sep 17 00:00:00 2001 From: Pariksheet Pinjari Date: Wed, 16 May 2018 21:14:42 +0530 Subject: [PATCH] Fixed issue #483, removing enum dependancy (#485) --- nnvm/python/nnvm/frontend/darknet.py | 69 ++++++++++++---------------- nnvm/python/nnvm/testing/darknet.py | 5 +- 2 files changed, 32 insertions(+), 42 deletions(-) diff --git a/nnvm/python/nnvm/frontend/darknet.py b/nnvm/python/nnvm/frontend/darknet.py index 1c5fdfa8..cb73b5a4 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 9e2d7a6e..ed8bf021 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