def compare_tf_tvm(gdef, input_, output_, runtime="vm", output_tensors=None):
"""compare tf and tvm execution for the same input.
Parameters
----------
gdef: TF2 graph def extracted to be fed into from_tensorflow parser.
(https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
input_: a single numpy array object
output_: the expected output from TF to match TVM output with
runtime: choose TVM runtime; either "vm" for VirtualMachine or "graph" for GraphExecutor
output_tensors : List of output tensor names (Optional)
if not specified then the last node is assumed as graph output.
"""
mod, params = from_tensorflow(gdef, outputs=output_tensors)
if runtime == "vm":
exec_ = compile_vm(mod, params)
tvm_out = run_vm(exec_, input_)
elif runtime == "graph":
lib = compile_graph_executor(mod, params)
tvm_out = run_graph_executor(lib, input_)
else:
raise RuntimeError("Runtime input not supported: %s" % runtime)
tvm.testing.assert_allclose(output_, tvm_out, atol=1e-5)
def check_equal(graph, tf_out):
mod, params = from_tensorflow(graph.as_graph_def(add_shapes=True))
ex = relay.create_executor('debug', mod=mod)
relay_out = ex.evaluate()(**params)
if isinstance(relay_out, relay.backend.interpreter.TensorValue):
np.testing.assert_allclose(tf_out, relay_out.asnumpy())
else:
if not isinstance(tf_out, list):
tf_out = [tf_out]
for x, y in zip(tf_out, [r.asnumpy() for r in relay_out]):
np.testing.assert_allclose(x, y)
def check_equal(graph, tf_out):
expr, params = from_tensorflow(graph.as_graph_def(add_shapes=True))
ex = relay.create_executor('debug')
relay_out = ex.evaluate(expr)(**params)
if isinstance(relay_out, relay.backend.interpreter.TensorValue):
np.testing.assert_allclose(tf_out, relay_out.asnumpy())
else:
if not isinstance(tf_out, list):
tf_out = [tf_out]
for x, y in zip(tf_out, [r.asnumpy() for r in relay_out]):
np.testing.assert_allclose(x, y)
def check_equal(graph, tf_out, input_map=None):
mod, params = from_tensorflow(graph.as_graph_def(add_shapes=True))
if input_map is not None:
params.update(input_map)
relay_out = relay.create_executor("vm", mod=mod).evaluate()(**params)
if isinstance(relay_out, nd.NDArray):
np.testing.assert_allclose(tf_out, relay_out.numpy())
else:
if not isinstance(tf_out, (list, tuple)):
tf_out = [tf_out]
for x, y in zip(tf_out, [r.numpy() for r in relay_out]):
np.testing.assert_allclose(x, y)
def run_relay(graph, shape_dict=None, *vars):
mod, params = from_tensorflow(graph.as_graph_def(add_shapes=True), shape=shape_dict)
ex = relay.create_executor("debug", mod=mod)
return ex.evaluate()(*vars)
def run_relay(graph):
mod, params = from_tensorflow(graph.as_graph_def(add_shapes=True))
ex = relay.create_executor('debug', mod=mod)
return ex.evaluate()(**params)
