return
print('File {} exists, skip.'.format(path))
return
print('Downloading from url {} to {}'.format(url, path))
try:
urllib.request.urlretrieve(url, path)
print('')
except:
urllib.urlretrieve(url, path)
DARKNET_LIB = 'libdarknet2.0.so'
DARKNETLIB_URL = 'https://github.com/siju-samuel/darknet/blob/master/lib/' \
+ DARKNET_LIB + '?raw=true'
_download(DARKNETLIB_URL, DARKNET_LIB)
LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)
def _read_memory_buffer(shape, data, dtype='float32'):
length = 1
for x in shape:
length *= x
data_np = np.zeros(length, dtype=dtype)
for i in range(length):
data_np[i] = data[i]
return data_np.reshape(shape)
def _get_tvm_output(net, data, build_dtype='float32'):
'''Compute TVM output'''
dtype = 'float32'
by the script.
"""
import numpy as np
import tvm
from tvm.contrib import graph_runtime
from tvm.contrib.download import download_testdata
download_testdata.__test__ = False
from nnvm import frontend
from nnvm.testing.darknet import LAYERTYPE
from nnvm.testing.darknet import __darknetffi__
import nnvm.compiler
DARKNET_LIB = 'libdarknet2.0.so'
DARKNETLIB_URL = 'https://github.com/siju-samuel/darknet/blob/master/lib/' \
+ DARKNET_LIB + '?raw=true'
LIB = __darknetffi__.dlopen(download_testdata(DARKNETLIB_URL, DARKNET_LIB, module='darknet'))
DARKNET_TEST_IMAGE_NAME = 'dog.jpg'
DARKNET_TEST_IMAGE_URL = 'https://github.com/siju-samuel/darknet/blob/master/data/' + DARKNET_TEST_IMAGE_NAME +'?raw=true'
DARKNET_TEST_IMAGE_PATH = download_testdata(DARKNET_TEST_IMAGE_URL, DARKNET_TEST_IMAGE_NAME, module='data')
def _read_memory_buffer(shape, data, dtype='float32'):
length = 1
for x in shape:
length *= x
data_np = np.zeros(length, dtype=dtype)
for i in range(length):
data_np[i] = data[i]
return data_np.reshape(shape)
def _get_tvm_output(net, data, build_dtype='float32'):
weights_path = download_testdata(WEIGHTS_URL, WEIGHTS_NAME, module="darknet")
# Download and Load darknet library
if sys.platform in ['linux', 'linux2']:
DARKNET_LIB = 'libdarknet2.0.so'
DARKNET_URL = REPO_URL + 'lib/' + DARKNET_LIB + '?raw=true'
elif sys.platform == 'darwin':
DARKNET_LIB = 'libdarknet_mac2.0.so'
DARKNET_URL = REPO_URL + 'lib_osx/' + DARKNET_LIB + '?raw=true'
else:
err = "Darknet lib is not supported on {} platform".format(sys.platform)
raise NotImplementedError(err)
lib_path = download_testdata(DARKNET_URL, DARKNET_LIB, module="darknet")
DARKNET_LIB = __darknetffi__.dlopen(lib_path)
net = DARKNET_LIB.load_network(cfg_path.encode('utf-8'),
weights_path.encode('utf-8'), 0)
dtype = 'float32'
batch_size = 1
print("Converting darknet to nnvm symbols...")
sym, params = nnvm.frontend.darknet.from_darknet(net, dtype)
######################################################################
# Compile the model on NNVM
# -------------------------
# compile the model
target = 'llvm'
ctx = tvm.cpu(0)
data = np.empty([batch_size, net.c, net.h, net.w], dtype)
# -----------------------
# Download cfg and weights file if first time.
CFG_NAME = MODEL_NAME + '.cfg'
WEIGHTS_NAME = MODEL_NAME + '.weights'
REPO_URL = 'https://github.com/dmlc/web-data/blob/master/darknet/'
CFG_URL = REPO_URL + 'cfg/' + CFG_NAME + '?raw=true'
WEIGHTS_URL = REPO_URL + 'weights/' + WEIGHTS_NAME + '?raw=true'
cfg_path = download_testdata(CFG_URL, CFG_NAME, module='darknet')
weights_path = download_testdata(WEIGHTS_URL, WEIGHTS_NAME, module='darknet')
# Download and Load darknet library
DARKNET_LIB = 'libdarknet.so'
DARKNET_URL = REPO_URL + 'lib/' + DARKNET_LIB + '?raw=true'
lib_path = download_testdata(DARKNET_URL, DARKNET_LIB, module='darknet')
DARKNET_LIB = __darknetffi__.dlopen(lib_path)
net = DARKNET_LIB.load_network(cfg_path.encode('utf-8'), weights_path.encode('utf-8'), 0)
dtype = 'float32'
batch_size = 1
# Import the graph to NNVM
# ------------------------
# Import darknet graph definition to nnvm.
#
# Results:
# sym: nnvm graph for rnn model
# params: params converted from darknet weights
print("Converting darknet rnn model to nnvm symbols...")
sym, params = nnvm.frontend.darknet.from_darknet(net, dtype)
# Compile the model on NNVM
download(weights_url, weights_name)
######################################################################
# Download and Load darknet library
# ---------------------------------
dtype = 'float32'
darknet_lib = 'libdarknet.so'
darknetlib_url = 'https://github.com/siju-samuel/darknet/blob/master/lib/' + \
darknet_lib + '?raw=true'
download(darknetlib_url, darknet_lib)
#if the file doesnt exist, then exit normally.
if os.path.isfile('./' + darknet_lib) is False:
exit(0)
darknet_lib = __darknetffi__.dlopen('./' + darknet_lib)
cfg = "./" + str(cfg_name)
weights = "./" + str(weights_name)
net = darknet_lib.load_network(cfg.encode('utf-8'), weights.encode('utf-8'), 0)
batch_size = 1
print("Converting darknet to nnvm symbols...")
sym, params = nnvm.frontend.darknet.from_darknet(net, dtype)
######################################################################
# Target Creation [CPU/GPU(OPENCL/VULKAN)]
# -------------------------
if exec_flavor == "cpu":
# Mobile CPU
target = "llvm -target=%s-linux-android" % arch
target_host = None
# -----------------------
# Download cfg and weights file if first time.
CFG_NAME = MODEL_NAME + '.cfg'
WEIGHTS_NAME = MODEL_NAME + '.weights'
REPO_URL = 'https://github.com/dmlc/web-data/blob/master/darknet/'
CFG_URL = REPO_URL + 'cfg/' + CFG_NAME + '?raw=true'
WEIGHTS_URL = REPO_URL + 'weights/' + WEIGHTS_NAME + '?raw=true'
download(CFG_URL, CFG_NAME)
download(WEIGHTS_URL, WEIGHTS_NAME)
# Download and Load darknet library
DARKNET_LIB = 'libdarknet.so'
DARKNET_URL = REPO_URL + 'lib/' + DARKNET_LIB + '?raw=true'
download(DARKNET_URL, DARKNET_LIB)
DARKNET_LIB = __darknetffi__.dlopen('./' + DARKNET_LIB)
cfg = "./" + str(CFG_NAME)
weights = "./" + str(WEIGHTS_NAME)
net = DARKNET_LIB.load_network(cfg.encode('utf-8'), weights.encode('utf-8'), 0)
dtype = 'float32'
batch_size = 1
# Import the graph to NNVM
# ------------------------
# Import darknet graph definition to nnvm.
#
# Results:
# sym: nnvm graph for rnn model
# params: params converted from darknet weights
print("Converting darknet rnn model to nnvm symbols...")
sym, params = nnvm.frontend.darknet.from_darknet(net, dtype)
