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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
import onnx
import onnx.defs
from onnx.helper import make_node, make_graph, make_tensor, make_tensor_value_info, make_model
from onnx.backend.base import namedtupledict
from caffe2.python.models.download import downloadFromURLToFile, getURLFromName, deleteDirectory
import caffe2.python.onnx.backend as c2
from caffe2.python.onnx.workspace import Workspace
from caffe2.python.trt.transform import convert_onnx_model_to_trt_op, transform_caffe2_net
from caffe2.python.onnx.tests.test_utils import TestCase
import numpy as np
import os.path
import json
import time
import unittest
import tarfile
import tempfile
import shutil
from six.moves.urllib.request import urlretrieve
def _print_net(net):
for i in net.external_input:
print("Input: {}".format(i))
for i in net.external_output:
print("Output: {}".format(i))
for op in net.op:
print("Op {}".format(op.type))
for x in op.input:
print(" input: {}".format(x))
for y in op.output:
print(" output: {}".format(y))
_BASE_URL = 'https://s3.amazonaws.com/download.onnx/models/opset_{}'.format(onnx.defs.onnx_opset_version())
# TODO: This is copied from https://github.com/onnx/onnx/blob/master/onnx/backend/test/runner/__init__.py. Maybe we should
# expose a model retrival API from ONNX
def _download_onnx_model(model_name):
onnx_home = os.path.expanduser(os.getenv('ONNX_HOME', os.path.join('~', '.onnx')))
models_dir = os.getenv('ONNX_MODELS',
os.path.join(onnx_home, 'models'))
model_dir = os.path.join(models_dir, model_name)
if not os.path.exists(os.path.join(model_dir, 'model.onnx')):
if os.path.exists(model_dir):
bi = 0
while True:
dest = '{}.old.{}'.format(model_dir, bi)
if os.path.exists(dest):
bi += 1
continue
shutil.move(model_dir, dest)
break
os.makedirs(model_dir)
# On Windows, NamedTemporaryFile can not be opened for a
# second time
url = '{}/{}.tar.gz'.format(_BASE_URL, model_name)
download_file = tempfile.NamedTemporaryFile(delete=False)
try:
download_file.close()
print('Start downloading model {} from {}'.format(
model_name, url))
urlretrieve(url, download_file.name)
print('Done')
with tarfile.open(download_file.name) as t:
t.extractall(models_dir)
except Exception as e:
print('Failed to prepare data for model {}: {}'.format(
model_name, e))
raise
finally:
os.remove(download_file.name)
return model_dir
class TensorRTOpTest(TestCase):
def _test_relu_graph(self, X, batch_size, trt_max_batch_size):
node_def = make_node("Relu", ["X"], ["Y"])
Y_c2 = c2.run_node(node_def, {"X": X})
graph_def = make_graph(
[node_def],
name="test",
inputs=[make_tensor_value_info("X", onnx.TensorProto.FLOAT, [batch_size, 1, 3, 2])],
outputs=[make_tensor_value_info("Y", onnx.TensorProto.FLOAT, [batch_size, 1, 3, 2])])
model_def = make_model(graph_def, producer_name='relu-test')
op_outputs = [x.name for x in model_def.graph.output]
op = convert_onnx_model_to_trt_op(model_def, max_batch_size=trt_max_batch_size)
device_option = core.DeviceOption(caffe2_pb2.CUDA, 0)
op.device_option.CopyFrom(device_option)
Y_trt = None
ws = Workspace()
with core.DeviceScope(device_option):
ws.FeedBlob("X", X)
ws.RunOperatorsOnce([op])
output_values = [ws.FetchBlob(name) for name in op_outputs]
Y_trt = namedtupledict('Outputs', op_outputs)(*output_values)
np.testing.assert_almost_equal(Y_c2, Y_trt)
@unittest.skipIf('TEST_C2_TRT' not in os.environ, "No TensortRT support")
def test_relu_graph_simple(self):
X = np.random.randn(1, 1, 3, 2).astype(np.float32)
self._test_relu_graph(X, 1, 50)
@unittest.skipIf('TEST_C2_TRT' not in os.environ, "No TensortRT support")
def test_relu_graph_big_batch(self):
X = np.random.randn(52, 1, 3, 2).astype(np.float32)
self._test_relu_graph(X, 52, 50)
@unittest.skipIf('TEST_C2_TRT' not in os.environ, "No TensortRT support")
def test_resnet50(self):
input_blob_dims = (1, 3, 224, 224)
model_dir = _download_onnx_model('resnet50')
model_def = onnx.load(os.path.join(model_dir, 'model.onnx'))
op_inputs = [x.name for x in model_def.graph.input]
op_outputs = [x.name for x in model_def.graph.output]
n, c, h, w = input_blob_dims
data = np.random.randn(n, c, h, w).astype(np.float32)
Y_c2 = c2.run_model(model_def, {op_inputs[0]: data})
op = convert_onnx_model_to_trt_op(model_def)
device_option = core.DeviceOption(caffe2_pb2.CUDA, 0)
op.device_option.CopyFrom(device_option)
Y_trt = None
ws = Workspace()
with core.DeviceScope(device_option):
ws.FeedBlob(op_inputs[0], data)
ws.RunOperatorsOnce([op])
output_values = [ws.FetchBlob(name) for name in op_outputs]
Y_trt = namedtupledict('Outputs', op_outputs)(*output_values)
np.testing.assert_allclose(Y_c2, Y_trt, rtol=1e-3)
class TensorRTTransformTest(TestCase):
def _model_dir(self, model):
caffe2_home = os.path.expanduser(os.getenv('ONNX_HOME', '~/.caffe2'))
models_dir = os.getenv('ONNX_MODELS', os.path.join(caffe2_home, 'models'))
return os.path.join(models_dir, model)
def _download(self, model):
model_dir = self._model_dir(model)
assert not os.path.exists(model_dir)
os.makedirs(model_dir)
for f in ['predict_net.pb', 'init_net.pb', 'value_info.json']:
url = getURLFromName(model, f)
dest = os.path.join(model_dir, f)
try:
try:
downloadFromURLToFile(url, dest,
show_progress=False)
except TypeError:
# show_progress not supported prior to
# Caffe2 78c014e752a374d905ecfb465d44fa16e02a28f1
# (Sep 17, 2017)
downloadFromURLToFile(url, dest)
except Exception as e:
print("Abort: {reason}".format(reason=e))
print("Cleaning up...")
deleteDirectory(model_dir)
exit(1)
def _get_c2_model(self, model_name):
model_dir = self._model_dir(model_name)
if not os.path.exists(model_dir):
self._download(model_name)
c2_predict_pb = os.path.join(model_dir, 'predict_net.pb')
c2_predict_net = caffe2_pb2.NetDef()
with open(c2_predict_pb, 'rb') as f:
c2_predict_net.ParseFromString(f.read())
c2_predict_net.name = model_name
c2_init_pb = os.path.join(model_dir, 'init_net.pb')
c2_init_net = caffe2_pb2.NetDef()
with open(c2_init_pb, 'rb') as f:
c2_init_net.ParseFromString(f.read())
c2_init_net.name = model_name + '_init'
value_info = json.load(open(os.path.join(model_dir, 'value_info.json')))
return c2_init_net, c2_predict_net, value_info
def _add_head_tail(self, pred_net, new_head, new_tail):
orig_head = pred_net.external_input[0]
orig_tail = pred_net.external_output[0]
# Add head
head = caffe2_pb2.OperatorDef()
head.type = "Copy"
head.input.append(new_head)
head.output.append(orig_head)
dummy = caffe2_pb2.NetDef()
dummy.op.extend(pred_net.op)
del pred_net.op[:]
pred_net.op.extend([head])
pred_net.op.extend(dummy.op)
pred_net.external_input[0] = new_head
# Add tail
tail = caffe2_pb2.OperatorDef()
tail.type = "Copy"
tail.input.append(orig_tail)
tail.output.append(new_tail)
pred_net.op.extend([tail])
pred_net.external_output[0] = new_tail
@unittest.skipIf('TEST_C2_TRT' not in os.environ, "No TensortRT support")
def test_resnet50_core(self):
N = 2
warmup = 20
repeat = 100
print("Batch size: {}, repeat inference {} times, warmup {} times".format(N, repeat, warmup))
init_net, pred_net, _ = self._get_c2_model('resnet50')
self._add_head_tail(pred_net, 'real_data', 'real_softmax')
input_blob_dims = (N, 3, 224, 224)
input_name = "real_data"
device_option = core.DeviceOption(caffe2_pb2.CUDA, 0)
init_net.device_option.CopyFrom(device_option)
pred_net.device_option.CopyFrom(device_option)
for op in pred_net.op:
op.device_option.CopyFrom(device_option)
op.engine = 'CUDNN'
net_outputs = pred_net.external_output
Y_c2 = None
data = np.random.randn(*input_blob_dims).astype(np.float32)
c2_time = 1
ws = Workspace()
with core.DeviceScope(device_option):
ws.FeedBlob(input_name, data)
ws.RunNetOnce(init_net)
ws.CreateNet(pred_net)
for _ in range(warmup):
ws.RunNet(pred_net.name)
start = time.time()
for _ in range(repeat):
ws.RunNet(pred_net.name)
end = time.time()
c2_time = end - start
output_values = [ws.FetchBlob(name) for name in net_outputs]
Y_c2 = namedtupledict('Outputs', net_outputs)(*output_values)
ws.ResetWorkspace()
# Cut the graph
init_net_cut, pred_net_cut = transform_caffe2_net(init_net, pred_net, {input_name: input_blob_dims})
del init_net, pred_net
#print_net(pred_net_cut)
Y_trt = None
input_name = pred_net_cut.external_input[0]
print("C2 runtime: {}s".format(c2_time))
ws = Workspace()
with core.DeviceScope(device_option):
ws.FeedBlob(input_name, data)
ws.RunNetOnce(init_net_cut)
ws.CreateNet(pred_net_cut)
for _ in range(warmup):
ws.RunNet(pred_net_cut.name)
start = time.time()
for _ in range(repeat):
ws.RunNet(pred_net_cut.name)
end = time.time()
trt_time = end - start
print("TRT runtime: {}s, improvement: {}%".format(trt_time, (c2_time-trt_time)/c2_time*100))
output_values = [ws.FetchBlob(name) for name in net_outputs]
Y_trt = namedtupledict('Outputs', net_outputs)(*output_values)
np.testing.assert_allclose(Y_c2, Y_trt, rtol=1e-3)
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