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.model_downloader.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
workspace.SwitchWorkspace("gpu_test", True)
with core.DeviceScope(device_option):
workspace.FeedBlob(input_name, data)
workspace.RunNetOnce(init_net)
workspace.CreateNet(pred_net)
for _ in range(warmup):
workspace.RunNet(pred_net.name)
start = time.time()
for _ in range(repeat):
workspace.RunNet(pred_net.name)
end = time.time()
c2_time = end - start
output_values = [workspace.FetchBlob(name) for name in net_outputs]
Y_c2 = namedtupledict('Outputs', net_outputs)(*output_values)
workspace.ResetWorkspace()
# Fill the workspace with the weights
with core.DeviceScope(device_option):
workspace.RunNetOnce(init_net)
# Cut the graph
start = time.time()
pred_net_cut = transform_caffe2_net(pred_net,
{input_name: input_blob_dims},
build_serializable_op=False)
del init_net, pred_net
pred_net_cut.device_option.CopyFrom(device_option)
for op in pred_net_cut.op:
op.device_option.CopyFrom(device_option)
#_print_net(pred_net_cut)
Y_trt = None
input_name = pred_net_cut.external_input[0]
print("C2 runtime: {}s".format(c2_time))
with core.DeviceScope(device_option):
workspace.FeedBlob(input_name, data)
workspace.CreateNet(pred_net_cut)
end = time.time()
print("Conversion time: {:.2f}s".format(end -start))
for _ in range(warmup):
workspace.RunNet(pred_net_cut.name)
start = time.time()
for _ in range(repeat):
workspace.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 = [workspace.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)
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
workspace.SwitchWorkspace("gpu_test", True)
with core.DeviceScope(device_option):
workspace.FeedBlob(input_name, data)
workspace.RunNetOnce(init_net)
workspace.CreateNet(pred_net)
for _ in range(warmup):
workspace.RunNet(pred_net.name)
start = time.time()
for _ in range(repeat):
workspace.RunNet(pred_net.name)
end = time.time()
c2_time = end - start
output_values = [workspace.FetchBlob(name) for name in net_outputs]
Y_c2 = namedtupledict('Outputs', net_outputs)(*output_values)
workspace.ResetWorkspace()
# Fill the workspace with the weights
with core.DeviceScope(device_option):
workspace.RunNetOnce(init_net)
# Cut the graph
start = time.time()
pred_net_cut = transform_caffe2_net(pred_net,
{input_name: input_blob_dims},
build_serializable_op=True)
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))
with core.DeviceScope(device_option):
workspace.FeedBlob(input_name, data)
workspace.CreateNet(pred_net_cut)
end = time.time()
print("Conversion time: {:.2f}s".format(end -start))
for _ in range(warmup):
workspace.RunNet(pred_net_cut.name)
start = time.time()
for _ in range(repeat):
workspace.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 = [workspace.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)