e8653a776d
* add: mobilenetv2 Python network definition API * add: mobilenetv3 base code * add: mobilenetv2 Python network definition API * restructure: mobilenetv2 code * add: Alexnet Python Network Definition API * update: README according to new folder architecture * add: mobilenetv3 small and large python network definition API * add: DenseNet121 Python Network Definition API
294 lines
10 KiB
Python
294 lines
10 KiB
Python
import os
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import sys
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import struct
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import argparse
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import numpy as np
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import pycuda.autoinit
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import pycuda.driver as cuda
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import tensorrt as trt
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BATCH_SIZE = 1
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INPUT_H = 224
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INPUT_W = 224
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OUTPUT_SIZE = 1000
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INPUT_BLOB_NAME = "data"
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OUTPUT_BLOB_NAME = "prob"
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EPS = 1e-5
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WEIGHT_PATH = "./densenet121.wts"
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ENGINE_PATH = "./densenet121.engine"
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TRT_LOGGER = trt.Logger(trt.Logger.INFO)
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def load_weights(file):
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print(f"Loading weights: {file}")
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assert os.path.exists(file), 'Unable to load weight file.'
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weight_map = {}
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with open(file, "r") as f:
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lines = [line.strip() for line in f]
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count = int(lines[0])
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assert count == len(lines) - 1
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for i in range(1, count + 1):
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splits = lines[i].split(" ")
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name = splits[0]
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cur_count = int(splits[1])
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assert cur_count + 2 == len(splits)
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values = []
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for j in range(2, len(splits)):
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# hex string to bytes to float
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values.append(struct.unpack(">f", bytes.fromhex(splits[j])))
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weight_map[name] = np.array(values, dtype=np.float32)
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return weight_map
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def add_batch_norm_2d(network, weight_map, input, layer_name):
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gamma = weight_map[layer_name + ".weight"]
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beta = weight_map[layer_name + ".bias"]
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mean = weight_map[layer_name + ".running_mean"]
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var = weight_map[layer_name + ".running_var"]
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var = np.sqrt(var + EPS)
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scale = gamma / var
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shift = -mean / var * gamma + beta
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return network.add_scale(input=input,
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mode=trt.ScaleMode.CHANNEL,
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shift=shift,
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scale=scale)
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def add_dense_layer(network, input, weight_map, lname):
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bn1 = add_batch_norm_2d(network, weight_map, input, lname + ".norm1")
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relu1 = network.add_activation(bn1.get_output(0), type=trt.ActivationType.RELU)
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assert relu1
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conv1 = network.add_convolution(input=relu1.get_output(0),
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num_output_maps=128,
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kernel_shape=(1, 1),
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kernel=weight_map[lname + ".conv1.weight"],
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bias=trt.Weights())
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assert conv1
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conv1.stride = (1, 1)
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bn2 = add_batch_norm_2d(network, weight_map, conv1.get_output(0), lname + ".norm2")
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relu2 = network.add_activation(bn2.get_output(0), type=trt.ActivationType.RELU)
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assert relu2
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conv2 = network.add_convolution(input=relu2.get_output(0),
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num_output_maps=32,
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kernel_shape=(3, 3),
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kernel=weight_map[lname + ".conv2.weight"],
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bias=trt.Weights())
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assert conv2
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conv2.stride = (1, 1)
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conv2.padding = (1, 1)
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return conv2
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def add_transition(network, input, weight_map, outch, lname):
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bn1 = add_batch_norm_2d(network, weight_map, input, lname + ".norm")
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relu1 = network.add_activation(bn1.get_output(0), type=trt.ActivationType.RELU)
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assert relu1
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conv1 = network.add_convolution(input=relu1.get_output(0),
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num_output_maps=outch,
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kernel_shape=(1, 1),
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kernel=weight_map[lname + ".conv.weight"],
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bias=trt.Weights())
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assert conv1
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conv1.stride = (1, 1)
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pool1 = network.add_pooling(input=conv1.get_output(0),
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type=trt.PoolingType.AVERAGE,
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window_size=trt.DimsHW(2, 2))
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assert pool1
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pool1.stride_nd = (2, 2)
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pool1.padding_nd = (0, 0)
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return pool1
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def add_dense_block(network, input, weight_map, num_dense_layers, lname):
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input_tensors = [None for _ in range(num_dense_layers+1)]
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input_tensors[0] = input
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c = add_dense_layer(network, input, weight_map, lname + ".denselayer" + str(1))
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for i in range(1, num_dense_layers):
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input_tensors[i] = c.get_output(0)
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concat = network.add_concatenation(input_tensors[:i+1])
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assert concat
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c = add_dense_layer(network, concat.get_output(0), weight_map, lname + ".denselayer" + str(i+1))
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input_tensors[num_dense_layers] = c.get_output(0)
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concat = network.add_concatenation(input_tensors)
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assert concat
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return concat
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def create_engine(max_batch_size, builder, config, dt):
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weight_map = load_weights(WEIGHT_PATH)
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network = builder.create_network()
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data = network.add_input(INPUT_BLOB_NAME, dt, (3, INPUT_H, INPUT_W))
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assert data
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conv0 = network.add_convolution(input=data,
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num_output_maps=64,
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kernel_shape=(7, 7),
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kernel=weight_map["features.conv0.weight"],
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bias=trt.Weights())
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assert conv0
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conv0.stride = (2, 2)
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conv0.padding = (3, 3)
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bn0 = add_batch_norm_2d(network, weight_map, conv0.get_output(0), "features.norm0")
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relu0 = network.add_activation(bn0.get_output(0), type=trt.ActivationType.RELU)
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assert relu0
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pool0 = network.add_pooling(input=relu0.get_output(0),
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type=trt.PoolingType.MAX,
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window_size=trt.DimsHW(3, 3))
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assert pool0
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pool0.stride_nd = (2, 2)
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pool0.padding_nd = (1, 1)
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dense1 = add_dense_block(network, pool0.get_output(0), weight_map, 6, "features.denseblock1")
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transition1 = add_transition(network, dense1.get_output(0), weight_map, 128, "features.transition1")
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dense2 = add_dense_block(network, transition1.get_output(0), weight_map, 12, "features.denseblock2")
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transition2 = add_transition(network, dense2.get_output(0), weight_map, 256, "features.transition2")
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dense3 = add_dense_block(network, transition2.get_output(0), weight_map, 24, "features.denseblock3")
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transition3 = add_transition(network, dense3.get_output(0), weight_map, 512, "features.transition3")
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dense4 = add_dense_block(network, transition3.get_output(0), weight_map, 16, "features.denseblock4")
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bn5 = add_batch_norm_2d(network, weight_map, dense4.get_output(0), "features.norm5")
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relu5 = network.add_activation(bn5.get_output(0), type=trt.ActivationType.RELU)
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pool5 = network.add_pooling(relu5.get_output(0), type=trt.PoolingType.AVERAGE, window_size=trt.DimsHW(7, 7))
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fc1 = network.add_fully_connected(input=pool5.get_output(0),
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num_outputs=OUTPUT_SIZE,
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kernel=weight_map["classifier.weight"],
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bias=weight_map["classifier.bias"])
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assert fc1
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fc1.get_output(0).name = OUTPUT_BLOB_NAME
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network.mark_output(fc1.get_output(0))
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# Build Engine
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builder.max_batch_size = max_batch_size
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builder.max_workspace_size = 1 << 20
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engine = builder.build_engine(network, config)
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del network
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del weight_map
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return engine
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def API_to_model(max_batch_size):
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builder = trt.Builder(TRT_LOGGER)
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config = builder.create_builder_config()
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engine = create_engine(max_batch_size, builder, config, trt.float32)
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assert engine
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with open(ENGINE_PATH, "wb") as f:
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f.write(engine.serialize())
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del engine
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del builder
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del config
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class HostDeviceMem(object):
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def __init__(self, host_mem, device_mem):
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self.host = host_mem
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self.device = device_mem
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def __str__(self):
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return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
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def __repr__(self):
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return self.__str__()
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def allocate_buffers(engine):
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inputs = []
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outputs = []
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bindings = []
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stream = cuda.Stream()
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for binding in engine:
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size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
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dtype = trt.nptype(engine.get_binding_dtype(binding))
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# Allocate host and device buffers
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host_mem = cuda.pagelocked_empty(size, dtype)
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device_mem = cuda.mem_alloc(host_mem.nbytes)
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# Append the device buffer to device bindings.
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bindings.append(int(device_mem))
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# Append to the appropriate list.
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if engine.binding_is_input(binding):
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inputs.append(HostDeviceMem(host_mem, device_mem))
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else:
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outputs.append(HostDeviceMem(host_mem, device_mem))
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return inputs, outputs, bindings, stream
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def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
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# Transfer input data to the GPU.
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[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
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# Run inference.
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context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
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# Transfer predictions back from the GPU.
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[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
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# Synchronize the stream
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stream.synchronize()
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# Return only the host outputs.
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return [out.host for out in outputs]
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-s", action='store_true')
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parser.add_argument("-d", action='store_true')
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args = parser.parse_args()
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if not (args.s ^ args.d):
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print(
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"arguments not right!\n"
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"python densenet121.py -s # serialize model to plan file\n"
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"python densenet121.py -d # deserialize plan file and run inference"
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)
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sys.exit()
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if args.s:
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API_to_model(BATCH_SIZE)
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else:
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runtime = trt.Runtime(TRT_LOGGER)
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assert runtime
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with open(ENGINE_PATH, "rb") as f:
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engine = runtime.deserialize_cuda_engine(f.read())
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assert engine
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context = engine.create_execution_context()
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assert context
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data = np.ones((BATCH_SIZE * 3 * INPUT_H * INPUT_W), dtype=np.float32)
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inputs, outputs, bindings, stream = allocate_buffers(engine)
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inputs[0].host = data
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trt_outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
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print(f'Output: \n{trt_outputs[0][:10]}\n{trt_outputs[0][-10:]}')
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