def run():
assert isfile(MODEL_PB)
g, gd = fropen()
print(g)
# totb(g)
sym, params = from_tensorflow(gd)
print(sym)
def block1_correctness2():
na = 0.8 * np.ones(shape=(1, 108, 21, 32))
with tf.Session(graph=tf.Graph()) as sess:
p = tf.placeholder(tf.float32, shape=(1, 108, 21, 32), name='a')
o = block1_block_tf(p)
gd = sess.graph.as_graph_def(add_shapes=True)
sym, params = from_tensorflow(gd)
r2 = with_nnvm(0, 1, [na], lambda a: sym, params)
print(r2)
def tvm_import(
opt_level: int = 2,
iname: str = MODEL_INPUT,
oname: str = MODEL_OUTPUT
) -> Tuple[TVM_Graph, str, Params, TF_Tensor, TF_Tensor]:
g, gd = fropen()
sym, params = from_tensorflow(gd)
i = g.get_tensor_by_name(iname + ':0')
o = g.get_tensor_by_name(oname + ':0')
i_shape_dict = {iname + ':0': i.shape.as_list()}
i_dtype_dict = {iname + ':0': i.dtype.as_numpy_dtype()}
with nnvm.compiler.build_config(opt_level=opt_level):
graph, lib, params = nnvm.compiler.build(graph=sym,
target='llvm',
shape=i_shape_dict,
dtype=i_dtype_dict,
params=params)
# print(graph.ir())
return graph, lib, params, i, o
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name="")
graphdef = sess.graph.as_graph_def(add_shapes=True)
return sess.graph, graphdef
def common_init(init_method, shape, dtype):
if init_method == 'zeros':
return np.zeros(shape=shape, dtype=dtype)
elif init_method == 'std':
return np.random.uniform(low=-50, high=51,
size=shape).astype(dtype=dtype)
else:
raise ValueError("invalid 'init' argument")
MODEL_PB = join(environ['CWD'], "data/freeze.pb")
MODEL_INPUT = 'Rcnn_ctcV3/Inputs'
MODEL_OUTPUTS = [
'Rcnn_ctcV3/expand_conv1/add_1/add', 'Rcnn_ctcV3/expand_conv2/add_7/add',
'Rcnn_ctcV3/expand_conv3/add_13/add', 'Rcnn_ctcV3/expand_conv4/add_17/add',
'Rcnn_ctcV3/conv2d_116/BiasAdd'
]
MODEL_OUTPUT = MODEL_OUTPUTS[-1]
DEF_LOG_DIR = './_logs'
MODEL_PARAMS = from_tensorflow(fropen()[1])[1]
def nnvmS_run(opt_level: int = 2,
nthreads: int = None,
iname: str = MODEL_INPUT,
oname: str = MODEL_OUTPUT,
init_method='std',
nwarmup: int = 0,
nloops: int = 1,
**kwargs) -> Result:
""" Run staged NNVM model """
r = Result()
print("Warning: unused args:", kwargs) if kwargs != {} else None
try:
g, gd = fropen()
_, params = from_tensorflow(
gd) # We still need from_tensorflow to get the parameters
mo, savepoints = staged_model()
nnvm_graph = nnvm.graph.create(savepoints[oname])
print('synthesized')
i = g.get_tensor_by_name(iname + ':0')
o = g.get_tensor_by_name(oname + ':0')
i_shape_dict = {iname + ':0': i.shape.as_list()}
i_dtype_dict = {iname + ':0': i.dtype.as_numpy_dtype()}
with nnvm.compiler.build_config(opt_level=opt_level):
graph, lib, params = nnvm.compiler.build(graph=nnvm_graph,
target='llvm',
shape=i_shape_dict,
dtype=i_dtype_dict,
params=params)
# print(graph.ir())
if nthreads is None:
nthreads = 20
get_global_func('runtime.config_threadpool')(1, nthreads)
m = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
print('compiled')
perfs: List[float] = []
for it in range(nwarmup + nloops):
i_data = common_init(init_method,
shape=i.shape.as_list(),
dtype=i.dtype.as_numpy_dtype())
m.set_input(iname, tvm.nd.array(i_data))
m.set_input(**params)
b = perf_counter()
m.run()
e = perf_counter()
o_data = m.get_output(
0, tvm.nd.empty(o.shape.as_list(), o.dtype.name)).asnumpy()
print('tvms', e - b)
if it >= nwarmup:
perfs.append(e - b)
r.set_perfs(perfs)
r.last_data = o_data
except KeyboardInterrupt:
raise
except Exception as e:
warn('exception: ' + str(e))
r.err = e
return r