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mst_trt_api.py
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# ====TensorRT Hackathon 2022 Medcare AI Lab====
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#
'''
使用TensorRT API重新搭建 MST++网络并进行加速！！！ 支持 FP32,FP16,INT8
'''
import os
import ctypes
from glob import glob
import numpy as np
import argparse
import configparser
import tensorrt as trt
# import pycuda.autoinit
# import pycuda.driver as cuda
from cuda import cudart
import warnings
warnings.filterwarnings("ignore")
logger = trt.Logger(trt.Logger.VERBOSE) # trt.Logger.VERBOSE
# plugin creater
trt.init_libnvinfer_plugins(logger, '')
layernorm = ctypes.CDLL("./LayerNormPlugin.so",mode=ctypes.RTLD_GLOBAL)
# gemm = ctypes.CDLL("./CuBLASGemmPlugin.so",mode=ctypes.RTLD_GLOBAL)
normalize = ctypes.CDLL("./NormalizePlugin.so",mode=ctypes.RTLD_GLOBAL)
plg_registry = trt.get_plugin_registry()
ln_plg_creator = plg_registry.get_plugin_creator("LayerNorm", "1", "")
# gemm_creator = plg_registry.get_plugin_creator("CuBLASGemm", "1", "")
norm_creator = plg_registry.get_plugin_creator("Normalize", "1", "")
PLUGIN_CREATORS = plg_registry.plugin_creator_list
EXPLICIT_BATCH = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
builder = trt.Builder(logger)
network = builder.create_network(EXPLICIT_BATCH)
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30
def torch_normalize(network,inputs,weights=None,name="L2Norm"):
'''inputs : input list, 1 element
weights: weights list, 0 elemnt
'''
# reduce_l2 axes=-1, keepdims=1 会被拆分为 sqrt(sum( pow(x,2) ))
# pow(2)
# pow2_0 = network.add_scale(input=inputs[0],mode=trt.ScaleMode.UNIFORM,shift=np.array([0.0],dtype=np.float32),scale=np.array([1.0],dtype=np.float32),power=np.array([2],dtype=np.float32))
# pow2_0.name="normalize_pow"
pow_shape = inputs[0].shape
pow_data = np.ones(pow_shape,dtype=np.float32)*2
pow_val = network.add_constant(pow_shape, pow_data)
pow2_0 = network.add_elementwise(input1 = inputs[0],input2=pow_val.get_output(0),op=trt.ElementWiseOperation.POW)
pow2_0.name = f"{name}_pow2"
# sum
reduce_l2_0 = network.add_reduce(input=pow2_0.get_output(0),op=trt.ReduceOperation.SUM,axes=1<<3, keep_dims=1) # 第4个维度规约？
reduce_l2_0.name = f"{name}_ReduceL2"
#sqrt
sqrt_shape = reduce_l2_0.get_output(0).shape
sqrt_data = np.ones(sqrt_shape,dtype=np.float32)*0.5
sqrt_val = network.add_constant(sqrt_shape, sqrt_data)
# sqrt_val = network.add_constant((1,1,1,1), np.array([[[[0.5]]]],dtype=np.float32))
sqrt_0 = network.add_elementwise(input1 = reduce_l2_0.get_output(0),input2=sqrt_val.get_output(0),op=trt.ElementWiseOperation.POW)
sqrt_0.name = f"{name}_Sqrt"
# pow2_1 = network.add_scale(input=reduce_l2_0.get_output(0),mode=trt.ScaleMode.UNIFORM,shift=np.array([0.0],dtype=np.float32),scale=np.array([1.0],dtype=np.float32),power=np.array([0.5],dtype=np.float32))
# # clip_0 = network.add_activation(input=pow2_1.get_output(0), type=trt.ActivationType.CLIP)
# # clip_0.alpha = -np.inf
# # clip_0.beta = 1e-12
clip_val = network.add_constant((1,1,1,1), np.array([[[[1e-12]]]],dtype=np.float32))
clip_0 = network.add_elementwise(input1 = sqrt_0.get_output(0),input2=clip_val.get_output(0),op=trt.ElementWiseOperation.MAX)
# div
div_0 = network.add_elementwise(input1 = inputs[0],input2=clip_0.get_output(0),op=trt.ElementWiseOperation.DIV)
div_0.name = f"{name}_Div"
return network,div_0
# [N, C, H, W]
def torch_normalize_plugin():
'''调用了TensorRT 官方的版本，发现有问题
https://github.com/NVIDIA/TensorRT/issues/2020
'''
plugin = None
for plugin_creator in PLUGIN_CREATORS:
if plugin_creator.name == "Normalize":
acrossSpatial = trt.PluginField("acrossSpatial", np.array([1],np.int32),trt.PluginFieldType.INT32)
channelShared = trt.PluginField("channelShared", np.array([1],np.int32),trt.PluginFieldType.INT32)
eps = trt.PluginField("eps", np.array([1e-14],dtype=np.float32) , trt.PluginFieldType.FLOAT32)
weights = trt.PluginField("weights", np.array([1.0],dtype=np.float32) , trt.PluginFieldType.FLOAT32)
nbweights = trt.PluginField("nbWeights", np.array([1],dtype=np.int32) , trt.PluginFieldType.INT32)
plugin_version = trt.PluginField("plugin_version", np.array(["1"],dtype=np.string_), trt.PluginFieldType.CHAR)
field_collection = trt.PluginFieldCollection([acrossSpatial,channelShared,eps,weights,nbweights,plugin_version])
plugin = plugin_creator.create_plugin(name="Normalize", field_collection=field_collection)
return plugin
# def gemm_plugin(weight):
# parameterList = []
# parameterList.append(trt.PluginField("weight", np.float32(weight), trt.PluginFieldType.FLOAT32))
# parameterList.append(trt.PluginField("k", np.int32(weight.shape[2]), trt.PluginFieldType.INT32))
# parameterList.append(trt.PluginField("n", np.int32(weight.shape[3]), trt.PluginFieldType.INT32))
# return gemm_creator.create_plugin("CuBLASGemm", trt.PluginFieldCollection(parameterList))
def self_attention(network,K,Q,V,weights,name="SelfAttention"):
'''softmax(K'Q/sqrt(HeadSize))
K : 1x1x31x249856, K matrix
Q : 1x1x31x249856, Q matrix
V : 1x1x249856x31, V matrix
weights: weight list
'''
# #transpose
# transpose_0 = network.add_shuffle(K)
# transpose_0.first_transpose = [0, 1, 3, 2]
#matmul
matmul_0 = network.add_matrix_multiply(Q,trt.MatrixOperation.NONE,K,trt.MatrixOperation.NONE)
matmul_0.op0 = trt.MatrixOperation.NONE
matmul_0.op1 = trt.MatrixOperation.TRANSPOSE
matmul_0.name = f"{name}_MatMul"
# /sqrt(headsize)
weights_1_val = np.array([weights[0]])
weights_1_const = network.add_constant(weights_1_val.shape, weights_1_val)
mul_0 = network.add_elementwise(matmul_0.get_output(0),weights_1_const.get_output(0),trt.ElementWiseOperation.PROD)
mul_0.name = f"{name}_Mul"
#softmax
softmax_0 = network.add_softmax(mul_0.get_output(0))
softmax_0.axes = 1<<3 #第4维上softmax
softmax_0.name = f"{name}_SoftMax"
# V
transpose_3 = network.add_shuffle(V)
transpose_3.reshape_dims = weights[1] # 1x249856x1x31
transpose_3.second_transpose = [0,2,3,1] # 1x1x31x249856
transpose_4 = network.add_shuffle(transpose_3.get_output(0))
transpose_4.reshape_dims = weights[2]
transpose_4.second_transpose = [0,1,3,2]
matmul_1 = network.add_matrix_multiply(softmax_0.get_output(0),trt.MatrixOperation.NONE,transpose_3.get_output(0),trt.MatrixOperation.NONE)
matmul_1.name = f"{name}_V_MatMul"
#transpose and reshape
transpose_1 = network.add_shuffle(matmul_1.get_output(0))
transpose_1.first_transpose = [0, 3, 1, 2]
transpose_1.reshape_dims = weights[3]
#matmul
weights_4_val = np.array([weights[4]])
weights_4_const = network.add_constant(weights_4_val.shape,weights_4_val)
matmul_2 = network.add_matrix_multiply(transpose_1.get_output(0),trt.MatrixOperation.NONE,weights_4_const.get_output(0),trt.MatrixOperation.NONE)
# fc_0 = network.add_fully_connected(transpose_1.get_output(0), 1, params["onnx::MatMul_4337"], params["body.0.bottleneck.blocks.0.0.proj.bias"])
matmul_2.name = f"{name}_Score_MatMul"
weights_5_val = np.array([[weights[5]]])
weights_5_const = network.add_constant(weights_5_val.shape,weights_5_val)
add_0 = network.add_elementwise(matmul_2.get_output(0),weights_5_const.get_output(0),trt.ElementWiseOperation.SUM)
add_0.name = f"{name}_Add"
#reshape
transpose_2 = network.add_shuffle(add_0.get_output(0))
transpose_2.reshape_dims = weights[6]
return network, transpose_2
def self_attention_plugin():
pass
def gelu_plugin(type_id=np.array([0],dtype=np.int32)):
plugin = None
for plugin_creator in PLUGIN_CREATORS:
if plugin_creator.name == "CustomGeluPluginDynamic":
type_id = trt.PluginField("type_id", type_id, trt.PluginFieldType.INT32)
plugin_version = trt.PluginField("plugin_version", np.array(["1"],dtype=np.string_), trt.PluginFieldType.CHAR)
field_collection = trt.PluginFieldCollection([type_id,plugin_version])
plugin = plugin_creator.create_plugin(name="CustomGeluPluginDynamic", field_collection=field_collection)
return plugin
def layer_norm(network,inputs,weights):
'''inputs : input list, 1 element
weights: weight list, 2 element
'''
# add_0 = network.add_elementwise(inputs[0],inputs[1],trt.ElementWiseOperation.SUM)
reducemean_0 = network.add_reduce(inputs[0],op=trt.ReduceOperation.AVG,axes=1<<0,keep_dims=True)
sub_0 = network.add_elementwise(inputs[0],reducemean_0.get_output(0),trt.ElementWiseOperation.SUB)
pow_const = network.add_constant((1,1,1,1),np.array([[[[2]]]],dtype=np.float32))
pow_0 = network.add_elementwise(sub_0.get_output(0),pow_const.get_output(0),trt.ElementWiseOperation.POW)
reducemean_1 = network.add_reduce(pow_0.get_output(0),op=trt.ReduceOperation.AVG,axes=1<<3,keep_dims=True) # 按照第4个维度求均值
add_const = network.add_constant((1,1,1,1),np.array([[[[1e-6]]]],dtype=np.float32))
add_1 = network.add_elementwise(reducemean_1.get_output(0),add_const.get_output(0),trt.ElementWiseOperation.SUM)
sqrt_const = network.add_constant((1,1,1,1),np.array([[[[0.5]]]],dtype=np.float32))
sqrt_0 = network.add_elementwise(add_1.get_output(0),sqrt_const.get_output(0),trt.ElementWiseOperation.POW)
div_0 = network.add_elementwise(sub_0.get_output(0),sqrt_0.get_output(0),trt.ElementWiseOperation.DIV)
# beta,gamma
weights_0_val = np.array([[[weights[0]]]],dtype=np.float32).reshape(1,1,-1,1) # 和batch_size有关？
weights_0_const = network.add_constant(weights_0_val.shape,weights_0_val)
matmul_0 = network.add_matrix_multiply(div_0.get_output(0),trt.MatrixOperation.NONE,weights_0_const.get_output(0),trt.MatrixOperation.NONE)
weights_1_val = np.array([[[weights[1]]]])
weights_1_const = network.add_constant(weights_1_val.shape,weights_1_val)
add_2 = network.add_elementwise(matmul_0.get_output(0),weights_1_const.get_output(0),trt.ElementWiseOperation.SUM)
return network, add_2
def layernorm_plugin():
# one-flow
epsilon = trt.PluginField("epsilon", np.array([1e-6]), trt.PluginFieldType.FLOAT32)
field_collection = trt.PluginFieldCollection([epsilon])
plugin = ln_plg_creator.create_plugin(name="LayerNorm", field_collection=field_collection)
return plugin
def s_msa(network,inputs,weights,conv_param,batch_size=1,type_id=np.array([0],dtype=np.int32),name="s_msa"):
'''inputs : input list, 1 element
weights: weight
'''
# conv_1
#
conv_0 = network.add_convolution(input=inputs[0],num_output_maps=weights[0].shape[0],kernel_shape=(int(conv_param["conv1_kernel"]),int(conv_param["conv1_kernel"])),kernel=weights[0])
conv_0.stride = (int(conv_param["conv1_strid"]),int(conv_param["conv1_strid"]))
conv_0.padding = (int(conv_param["conv1_pad"]),int(conv_param["conv1_pad"]))
conv_0.dilation = (int(conv_param["conv1_dilation"]),int(conv_param["conv1_dilation"]))
conv_0.num_groups= int(conv_param["conv1_group"])
# conv_0.precision =trt.DataType.FLOAT
# # conv_0.reset_precision()
# print(conv_0.precision)
#transpose and reshape
transpose_0 = network.add_shuffle(conv_0.get_output(0))
transpose_0.first_transpose = [0, 2, 3, 1]
transpose_0.reshape_dims = weights[1]
# #matmul
weights_2_new = np.array([weights[2]])
weights_2_const = network.add_constant(weights_2_new.shape, weights_2_new)
matmul_0 = network.add_matrix_multiply(transpose_0.get_output(0),trt.MatrixOperation.NONE,weights_2_const.get_output(0),trt.MatrixOperation.NONE)
#transpose and reshape
transpose_1 = network.add_shuffle(matmul_0.get_output(0))
transpose_1.reshape_dims = weights[3] #1x249856x1x31
# transpose_1.second_transpose = [1,0,2,3] #[N,c,h,w] 249856x1x1x31
transpose_1.second_transpose = [0,2,3,1] #1x1x31x249856 #换回来
# K
# norm_0 = network.add_plugin_v2(inputs=[transpose_1.get_output(0)], plugin=torch_normalize_plugin()) #<-------------
# #1x1x249856x31
# transpose_2 = network.add_shuffle(norm_0.get_output(0)) # 249856x1x1x31
# transpose_2.first_transpose = [1,2,3,0] #[N,c,h,w] 1x1x31x249856
network,norm_0 = torch_normalize(network,[transpose_1.get_output(0)],name=f"{name}_K_L2Norm") #换回来
# Q
weights_4_new = np.array([weights[4]])
weights_4_const = network.add_constant(weights_4_new.shape, weights_4_new)
matmul_1 = network.add_matrix_multiply(transpose_0.get_output(0),trt.MatrixOperation.NONE,weights_4_const.get_output(0),trt.MatrixOperation.NONE)
matmul_1.name=f"{name}_Q_MatMul"
# #transpose and reshape
transpose_3 = network.add_shuffle(matmul_1.get_output(0))
transpose_3.reshape_dims = weights[5]
# transpose_3.second_transpose = [1,0,2,3] #[N,c,h,w] 249856x1x1x31
transpose_3.second_transpose = [0,2,3,1] #1x1x31x249856
# norm_1 = network.add_plugin_v2(inputs=transpose_3.get_output(0), plugin=torch_normalize_plugin())
# #1x1x249856x31
# transpose_4 = network.add_shuffle(norm_1.get_output(0)) # 249856x1x1x31
# transpose_4.first_transpose = [1,2,3,0] #[N,c,h,w] 1x1x31x249856
network,norm_1 = torch_normalize(network,[transpose_3.get_output(0)],name=f"{name}_Q_L2Norm")
# # V
weights_6_new = np.array([weights[6]])
weights_6_const = network.add_constant(weights_6_new.shape, weights_6_new)
matmul_3 = network.add_matrix_multiply(transpose_0.get_output(0),trt.MatrixOperation.NONE,weights_6_const.get_output(0),trt.MatrixOperation.NONE)
matmul_3.name = f"{name}_V_MatMul"
# self attention
weights_3 = weights[7] #list
# print(len(weights_3))
network,transpose_4_0 = self_attention(network=network,K=norm_0.get_output(0),Q=norm_1.get_output(0),V=matmul_3.get_output(0),weights=weights_3,name=f"{name}_SelfAttention") #换回来
# network,transpose_4_0 = self_attention(network=network,K=transpose_2.get_output(0),Q=norm_1.get_output(0),V=matmul_3.get_output(0),weights=weights_3)
transpose_5 = network.add_shuffle(matmul_3.get_output(0))
transpose_5.reshape_dims = weights[8]
transpose_5.second_transpose = [0,3,1,2]
#conv_2
conv_1 = network.add_convolution(input=transpose_5.get_output(0),num_output_maps=weights[9].shape[0],kernel_shape=(int(conv_param["conv2_kernel"]),int(conv_param["conv2_kernel"])),kernel=weights[9])
conv_1.stride = (int(conv_param["conv2_strid"]),int(conv_param["conv2_strid"]))
conv_1.padding = (int(conv_param["conv2_pad"]),int(conv_param["conv2_pad"]))
conv_1.dilation = (int(conv_param["conv2_dilation"]),int(conv_param["conv2_dilation"]))
conv_1.num_groups= int(conv_param["conv2_group"])
#gelu #可能需要unsqueeze一下
gelu_0 = network.add_plugin_v2(inputs=[conv_1.get_output(0)], plugin=gelu_plugin(type_id))
#conv_3
conv_2 = network.add_convolution(input=gelu_0.get_output(0),num_output_maps=weights[10].shape[0],kernel_shape=(int(conv_param["conv3_kernel"]),int(conv_param["conv3_kernel"])),kernel=weights[10])
conv_2.stride = (int(conv_param["conv3_strid"]),int(conv_param["conv3_strid"]))
conv_2.padding = (int(conv_param["conv3_pad"]),int(conv_param["conv3_pad"]))
conv_2.dilation = (int(conv_param["conv3_dilation"]),int(conv_param["conv3_dilation"]))
conv_2.num_groups= int(conv_param["conv3_group"])
transpose_6 = network.add_shuffle(conv_2.get_output(0))
transpose_6.first_transpose = [0,2,3,1]
# # add
add_0 = network.add_elementwise(transpose_6.get_output(0),transpose_4_0.get_output(0),trt.ElementWiseOperation.SUM)
transpose_7 = network.add_shuffle(conv_0.get_output(0))
transpose_7.first_transpose = [0,2,3,1]
# add
add_0_0 = network.add_elementwise(add_0.get_output(0),transpose_7.get_output(0),trt.ElementWiseOperation.SUM)
# # layer norm
# inputs_3 = [add_0_0.get_output(0)]
# weights_5 = weights[11]
# network,add_1 = layer_norm(network,inputs_3,weights_5)
# 替换为layernorm plugin one-flow版
transpose_layernorm = network.add_shuffle(add_0_0.get_output(0))
transpose_layernorm.reshape_dims = [-1,add_0_0.get_output(0).shape[2],add_0_0.get_output(0).shape[3]]
layernorm = network.add_plugin_v2(inputs=[transpose_layernorm.get_output(0)],plugin=layernorm_plugin())
gamma_val = weights[11][0].reshape((1,1,-1))
gamma = network.add_constant(gamma_val.shape,gamma_val)
mul_layernorm = network.add_elementwise(layernorm.get_output(0),gamma.get_output(0),trt.ElementWiseOperation.PROD)
beta_val = weights[11][1].reshape((1,1,-1))
beta = network.add_constant(beta_val.shape,beta_val)
add_1 = network.add_elementwise(mul_layernorm.get_output(0),beta.get_output(0),trt.ElementWiseOperation.SUM)
#transpose
transpose_8 = network.add_shuffle(add_1.get_output(0))
transpose_8.reshape_dims = [batch_size,-1,add_1.get_output(0).shape[1],add_1.get_output(0).shape[2]]
transpose_8.second_transpose = [0,3,1,2]
#conv_4
conv_3 = network.add_convolution(input=transpose_8.get_output(0),num_output_maps=weights[12].shape[0],kernel_shape=(int(conv_param["conv4_kernel"]),int(conv_param["conv4_kernel"])),kernel=weights[12])
conv_3.stride = (int(conv_param["conv4_strid"]),int(conv_param["conv4_strid"]))
conv_3.padding = (int(conv_param["conv4_pad"]),int(conv_param["conv4_pad"]))
conv_3.dilation = (int(conv_param["conv4_dilation"]),int(conv_param["conv4_dilation"]))
conv_3.num_groups= int(conv_param["conv4_group"])
#gelu #可能需要unsqueeze一下
gelu_1 = network.add_plugin_v2(inputs=[conv_3.get_output(0)], plugin=gelu_plugin(type_id))
#conv_5
conv_4 = network.add_convolution(input=gelu_1.get_output(0),num_output_maps=weights[13].shape[0],kernel_shape=(int(conv_param["conv5_kernel"]),int(conv_param["conv5_kernel"])),kernel=weights[13])
conv_4.stride = (int(conv_param["conv5_strid"]),int(conv_param["conv5_strid"]))
conv_4.padding = (int(conv_param["conv5_pad"]),int(conv_param["conv5_pad"]))
conv_4.dilation = (int(conv_param["conv5_dilation"]),int(conv_param["conv5_dilation"]))
conv_4.num_groups= int(conv_param["conv5_group"])
gelu_2 = network.add_plugin_v2(inputs=[conv_4.get_output(0)], plugin=gelu_plugin(type_id))
#conv_6
conv_5 = network.add_convolution(input=gelu_2.get_output(0),num_output_maps=weights[14].shape[0],kernel_shape=(int(conv_param["conv6_kernel"]),int(conv_param["conv6_kernel"])),kernel=weights[14])
conv_5.stride = (int(conv_param["conv6_strid"]),int(conv_param["conv6_strid"]))
conv_5.padding = (int(conv_param["conv6_pad"]),int(conv_param["conv6_pad"]))
conv_5.dilation = (int(conv_param["conv6_dilation"]),int(conv_param["conv6_dilation"]))
conv_5.num_groups= int(conv_param["conv6_group"])
transpose_9 = network.add_shuffle(conv_5.get_output(0))
transpose_9.first_transpose = [0,2,3,1]
# # add
add_2 = network.add_elementwise(transpose_9.get_output(0),add_0_0.get_output(0),trt.ElementWiseOperation.SUM)
return network, add_2
def get_block_weights(block_w_cfg,params):
weights_1 = [params[block_w_cfg["w0"]],params[block_w_cfg["w1"]],params[block_w_cfg["w2"]],
params[block_w_cfg["w3"]],params[block_w_cfg["w4"]],params[block_w_cfg["w5"]],params[block_w_cfg["w6"]],
[params[block_w_cfg["w7_1"]],params[block_w_cfg["w7_2"]],params[block_w_cfg["w7_3"]],
params[block_w_cfg["w7_4"]],params[block_w_cfg["w7_5"]],params[block_w_cfg["w7_6"]],
params[block_w_cfg["w7_7"]]],
params[block_w_cfg["w8"]],params[block_w_cfg["w9"]],params[block_w_cfg["w10"]],
[params[block_w_cfg["w11_1"]],params[block_w_cfg["w11_2"]]],params[block_w_cfg["w12"]],
params[block_w_cfg["w13"]],params[block_w_cfg["w14"]] ]
return weights_1
def mst_plus_plus_trt_api(network,config,params,params_cfg="./mst_config.ini",batch_size=1,type_id=np.array([0],dtype=np.int64)):
'''mst++由s_msa bloack堆叠而成，其类似于UNet的U型结构，其中共15个s_msa block!!!
'''
mst_input = network.add_input("input",trt.DataType.FLOAT,(batch_size,3,512,482))
padd_0 = network.add_padding_nd(mst_input,(0,0),(0,0))
padd_0.post_padding = (0,6)
# 该conv有3个分支
conv_44 = network.add_convolution_nd(input=padd_0.get_output(0),num_output_maps=31,kernel_shape=(3, 3),kernel=params["conv_in.weight"])
conv_44.stride = (1,1)
conv_44.padding = (1,1) #?
conv_44.dilation = (1,1)
conv_44.num_groups= 1
configer_m = configparser.ConfigParser()
configer_m.read(params_cfg)
# s_msa_1
block_1_w_cfg = configer_m['block1_weight']
block_1_c_cfg = configer_m['block1_conv']
inputs_1 = [conv_44.get_output(0)]
weights_1 = get_block_weights(block_1_w_cfg,params)
network, s_msa_1 = s_msa(network,inputs_1,weights_1,block_1_c_cfg,batch_size,type_id=type_id,name="s_mas1")
# s_msa_2
transpose_280 = network.add_shuffle(s_msa_1.get_output(0))
transpose_280.first_transpose = [0,3,1,2]
block_2_w_cfg = configer_m['block2_weight']
block_2_c_cfg = configer_m['block2_conv']
inputs_2 = [transpose_280.get_output(0)]
weights_2 = get_block_weights(block_2_w_cfg,params)
network, s_msa_2 = s_msa(network,inputs_2,weights_2,block_2_c_cfg,batch_size,type_id=type_id,name="s_mas2")
# s_msa_3
transpose_516 = network.add_shuffle(s_msa_2.get_output(0))
transpose_516.first_transpose = [0,3,1,2]
block_3_w_cfg = configer_m['block3_weight']
block_3_c_cfg = configer_m['block3_conv']
inputs_3 = [transpose_516.get_output(0)]
weights_3 = get_block_weights(block_3_w_cfg,params)
network, s_msa_3 = s_msa(network,inputs_3,weights_3,block_3_c_cfg,batch_size,type_id=type_id,name="s_mas3")
# s_msa_4
transpose_752 = network.add_shuffle(s_msa_3.get_output(0))
transpose_752.first_transpose = [0,3,1,2]
convtranspose_753 = network.add_deconvolution(input=transpose_752.get_output(0),num_output_maps=62,kernel_shape=(2,2),kernel=params["body.0.decoder_layers.0.0.weight"],
bias=params["body.0.decoder_layers.0.0.bias"])
convtranspose_753.stride=(2,2)
convtranspose_753.padding=(0,0)
convtranspose_753.num_groups=1
concat_754 = network.add_concatenation([convtranspose_753.get_output(0),transpose_516.get_output(0)])
concat_754.axis = 1
block_4_w_cfg = configer_m['block4_weight']
block_4_c_cfg = configer_m['block4_conv']
inputs_4 = [concat_754.get_output(0)]
weights_4 = get_block_weights(block_4_w_cfg,params)
network, s_msa_4 = s_msa(network,inputs_4,weights_4,block_4_c_cfg,batch_size,type_id=type_id,name="s_mas4")
# s_msa_5
transpose_990 = network.add_shuffle(s_msa_4.get_output(0))
transpose_990.first_transpose = [0,3,1,2]
convtranspose_991 = network.add_deconvolution(input=transpose_990.get_output(0),num_output_maps=31,kernel_shape=(2,2),kernel=params["body.0.decoder_layers.1.0.weight"],
bias=params["body.0.decoder_layers.1.0.bias"])
convtranspose_991.stride=(2,2)
convtranspose_991.padding=(0,0)
convtranspose_991.num_groups=1
concat_992 = network.add_concatenation([convtranspose_991.get_output(0),transpose_280.get_output(0)])
concat_992.axis = 1
block_5_w_cfg = configer_m['block5_weight']
block_5_c_cfg = configer_m['block5_conv']
inputs_5 = [concat_992.get_output(0)]
weights_5 = get_block_weights(block_5_w_cfg,params)
network, s_msa_5 = s_msa(network,inputs_5,weights_5,block_5_c_cfg,batch_size,type_id=type_id,name="s_mas5")
# s_msa_6
transpose_1228 = network.add_shuffle(s_msa_5.get_output(0))
transpose_1228.first_transpose = [0,3,1,2]
conv_1229 = network.add_convolution(input=transpose_1228.get_output(0),num_output_maps=31,kernel_shape=(3, 3),kernel=params["body.0.mapping.weight"])
conv_1229.stride = (1,1)
conv_1229.padding = (1,1)
conv_1229.dilation = (1,1)
conv_1229.num_groups= 1
add_1230 = network.add_elementwise(conv_1229.get_output(0),conv_44.get_output(0),trt.ElementWiseOperation.SUM)
block_6_w_cfg = configer_m['block6_weight']
block_6_c_cfg = configer_m['block6_conv']
inputs_6 = [add_1230.get_output(0)]
weights_6 = get_block_weights(block_6_w_cfg,params)
network, s_msa_6 = s_msa(network,inputs_6,weights_6,block_6_c_cfg,batch_size,type_id=type_id,name="s_mas6")
# s_msa_7
transpose_1466 = network.add_shuffle(s_msa_6.get_output(0))
transpose_1466.first_transpose = [0,3,1,2]
block_7_w_cfg = configer_m['block7_weight']
block_7_c_cfg = configer_m['block7_conv']
inputs_7 = [transpose_1466.get_output(0)]
weights_7 = get_block_weights(block_7_w_cfg,params)
network, s_msa_7 = s_msa(network,inputs_7,weights_7,block_7_c_cfg,batch_size,type_id=type_id,name="s_mas7")
# s_msa_8
transpose_1702 = network.add_shuffle(s_msa_7.get_output(0))
transpose_1702.first_transpose = [0,3,1,2]
block_8_w_cfg = configer_m['block8_weight']
block_8_c_cfg = configer_m['block8_conv']
inputs_8 = [transpose_1702.get_output(0)]
weights_8 = get_block_weights(block_8_w_cfg,params)
network, s_msa_8 = s_msa(network,inputs_8,weights_8,block_8_c_cfg,batch_size,type_id=type_id,name="s_mas8")
# s_msa_9
transpose_1938 = network.add_shuffle(s_msa_8.get_output(0))
transpose_1938.first_transpose = [0,3,1,2]
convtranspose_1939 = network.add_deconvolution(input=transpose_1938.get_output(0),num_output_maps=62,kernel_shape=(2,2),kernel=params["body.1.decoder_layers.0.0.weight"],
bias=params["body.1.decoder_layers.0.0.bias"])
convtranspose_1939.stride=(2,2)
convtranspose_1939.padding=(0,0)
convtranspose_1939.num_groups=1
concat_1940 = network.add_concatenation([convtranspose_1939.get_output(0),transpose_1702.get_output(0)])
concat_1940.axis = 1
block_9_w_cfg = configer_m['block9_weight']
block_9_c_cfg = configer_m['block9_conv']
inputs_9 = [concat_1940.get_output(0)]
weights_9 = get_block_weights(block_9_w_cfg,params)
network, s_msa_9 = s_msa(network,inputs_9,weights_9,block_9_c_cfg,batch_size,type_id=type_id,name="s_mas9")
# s_msa_10
transpose_2176 = network.add_shuffle(s_msa_9.get_output(0))
transpose_2176.first_transpose = [0,3,1,2]
convtranspose_2177 = network.add_deconvolution(input=transpose_2176.get_output(0),num_output_maps=31,kernel_shape=(2,2),kernel=params["body.1.decoder_layers.1.0.weight"],
bias=params["body.1.decoder_layers.1.0.bias"])
convtranspose_2177.stride=(2,2)
convtranspose_2177.padding=(0,0)
convtranspose_2177.num_groups=1
concat_2178 = network.add_concatenation([convtranspose_2177.get_output(0),transpose_1466.get_output(0)])
concat_2178.axis=1
block_10_w_cfg = configer_m['block10_weight']
block_10_c_cfg = configer_m['block10_conv']
inputs_10 = [concat_2178.get_output(0)]
weights_10 = get_block_weights(block_10_w_cfg,params)
network, s_msa_10 = s_msa(network,inputs_10,weights_10,block_10_c_cfg,batch_size,type_id=type_id,name="s_mas10")
#s_msa_11
transpose_2414 = network.add_shuffle(s_msa_10.get_output(0))
transpose_2414.first_transpose = [0,3,1,2]
conv_2415 = network.add_convolution(input=transpose_2414.get_output(0),num_output_maps=31,kernel_shape=(3, 3),kernel=params["body.1.mapping.weight"])
conv_2415.stride = (1,1)
conv_2415.padding = (1,1)
conv_2415.dilation = (1,1)
conv_2415.num_groups= 1
add_2416 = network.add_elementwise(conv_2415.get_output(0),add_1230.get_output(0),trt.ElementWiseOperation.SUM)
block_11_w_cfg = configer_m['block11_weight']
block_11_c_cfg = configer_m['block11_conv']
inputs_11 = [add_2416.get_output(0)]
weights_11 = get_block_weights(block_11_w_cfg,params)
network, s_msa_11 = s_msa(network,inputs_11,weights_11,block_11_c_cfg,batch_size,type_id=type_id,name="s_mas11")
# s_msa_12
transpose_2652 = network.add_shuffle(s_msa_11.get_output(0))
transpose_2652.first_transpose = [0,3,1,2]
block_12_w_cfg = configer_m['block12_weight']
block_12_c_cfg = configer_m['block12_conv']
inputs_12 = [transpose_2652.get_output(0)]
weights_12 = get_block_weights(block_12_w_cfg,params)
network, s_msa_12 = s_msa(network,inputs_12,weights_12,block_12_c_cfg,batch_size,type_id=type_id,name="s_mas12")
# s_msa_13
transpose_2888 = network.add_shuffle(s_msa_12.get_output(0))
transpose_2888.first_transpose = [0,3,1,2]
block_13_w_cfg = configer_m['block13_weight']
block_13_c_cfg = configer_m['block13_conv']
inputs_13 = [transpose_2888.get_output(0)]
weights_13 = get_block_weights(block_13_w_cfg,params)
network, s_msa_13 = s_msa(network,inputs_13,weights_13,block_13_c_cfg,batch_size,type_id=type_id,name="s_mas13")
# s_msa_14
transpose_3124 = network.add_shuffle(s_msa_13.get_output(0))
transpose_3124.first_transpose = [0,3,1,2]
convtranspose_3125 = network.add_deconvolution(input=transpose_3124.get_output(0),num_output_maps=62,kernel_shape=(2,2),kernel=params["body.2.decoder_layers.0.0.weight"],
bias=params["body.2.decoder_layers.0.0.bias"])
convtranspose_3125.stride=(2,2)
convtranspose_3125.padding=(0,0)
convtranspose_3125.num_groups=1
concat_3126 = network.add_concatenation([convtranspose_3125.get_output(0),transpose_2888.get_output(0)])
concat_3126.axis=1
block_14_w_cfg = configer_m['block14_weight']
block_14_c_cfg = configer_m['block14_conv']
inputs_14 = [concat_3126.get_output(0)]
weights_14 = get_block_weights(block_14_w_cfg,params)
network, s_msa_14 = s_msa(network,inputs_14,weights_14,block_14_c_cfg,batch_size,type_id=type_id,name="s_mas14")
# s_msa_15
transpose_3362 = network.add_shuffle(s_msa_14.get_output(0))
transpose_3362.first_transpose = [0,3,1,2]
convtranspose_3363 = network.add_deconvolution(input=transpose_3362.get_output(0),num_output_maps=31,kernel_shape=(2,2),kernel=params["body.2.decoder_layers.1.0.weight"],
bias=params["body.2.decoder_layers.1.0.bias"])
convtranspose_3363.stride=(2,2)
convtranspose_3363.padding=(0,0)
convtranspose_3363.num_groups=1
concat_3364 = network.add_concatenation([convtranspose_3363.get_output(0),transpose_2652.get_output(0)])
concat_3364.axis=1
block_15_w_cfg = configer_m['block15_weight']
block_15_c_cfg = configer_m['block15_conv']
inputs_15 = [concat_3364.get_output(0)]
weights_15 = get_block_weights(block_15_w_cfg,params)
network, s_msa_15 = s_msa(network,inputs_15,weights_15,block_15_c_cfg,batch_size,type_id=type_id,name="s_mas15")
# last
transpose_3600 = network.add_shuffle(s_msa_15.get_output(0))
transpose_3600.first_transpose = [0,3,1,2]
conv_3601 = network.add_convolution(input=transpose_3600.get_output(0),num_output_maps=31,kernel_shape=(3, 3),kernel=params["body.2.mapping.weight"])
conv_3601.stride = (1,1)
conv_3601.padding = (1,1)
conv_3601.dilation = (1,1)
conv_3601.num_groups= 1
add_3602 = network.add_elementwise(conv_3601.get_output(0),add_2416.get_output(0),trt.ElementWiseOperation.SUM)
conv_3603 = network.add_convolution(input=add_3602.get_output(0),num_output_maps=31,kernel_shape=(3, 3),kernel=params["conv_out.weight"])
conv_3603.stride = (1,1)
conv_3603.padding = (1,1)
conv_3603.dilation = (1,1)
conv_3603.num_groups= 1
add_3604 = network.add_elementwise(conv_3603.get_output(0),conv_44.get_output(0),trt.ElementWiseOperation.SUM)
slice_3609 = network.add_slice(input=add_3604.get_output(0),start=trt.Dims4((0,0,0,0)),shape=trt.Dims4((batch_size,31,512,488)),stride=(1,1,1,1))
slice_3614 = network.add_slice(input=slice_3609.get_output(0),start=trt.Dims4((0,0,0,0)),shape=trt.Dims4((batch_size,31,512,482)),stride=(1,1,1,1))
#output
slice_3614.get_output(0).name = "output"
network.mark_output(slice_3614.get_output(0))
return network, config
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='TensorRT API and Plugin for MST++ Model, and get MST++ TRT Engine, support FP32, FP16 and INT8 mode !!!')
parser.add_argument('--mode', type= str , default='FP32', help='FP32, FP16 or INT8')
parser.add_argument('--calibration_table_path', type=str,default='./model/mst_calibration.cache', help="INT8 calibration cache, invalid for FP32 and FP16")
parser.add_argument('--batch_size',type=int,default=1, help="Batch Size")
parser.add_argument('--plan_path', type=str,default='./model/mst_plus_plu_b1.plan', help="TRT Engine save path")
parser.add_argument('--weight_path', type=str,default='./model/mst_plus_plus_weights_b1.npz', help="weight path")
args = parser.parse_args()
mode = args.mode
batch_size = args.batch_size
calibration_table_path = args.calibration_table_path
plan_path = args.plan_path
weight_path = args.weight_path
type_id = np.array([0],dtype=np.int32)
# load weights
params = np.load(weight_path)
if mode == "FP16":
config.set_flag(trt.BuilderFlag.FP16)
type_id = np.array([1],dtype=np.int32)
print('FP16 mode enabled')
if mode == "INT8":
from calibrator import *
# config.set_tactic_sources(1 << int(trt.TacticSource.CUBLAS))
# config.set_tactic_sources(1 << int(trt.TacticSource.CUDNN))
config.set_flag(trt.BuilderFlag.INT8)
# config.set_flag(trt.BuilderFlag.STRICT_TYPES)
calibration_stream = DataLoader(batch_size=batch_size)
assert calibration_stream, 'Error: a calibration_stream should be provided for int8 mode'
config.int8_calibrator = Calibrator(calibration_stream, calibration_table_path)
print('INT8 mode enabled')
network,config = mst_plus_plus_trt_api(network,config,params,batch_size=batch_size,type_id=type_id)
plan = builder.build_serialized_network(network, config)
# engine = runtime.deserialize_cuda_engine(plan)
#save engine
with open(plan_path, "wb") as f:
f.write(plan)
print("TensorRT engine build successful!!!")