b671024a27
* add ghostnet * add ghostnet * add ghostnetV1&ghostnetV2 * Fix object destruction order in APIToModel function to avoid undefined behavior * Fix pre-commit errors in ghostnet/README.md * Fix pre-commit errors * Fix pre-commit errors in mobilenetV3 * Add a noqa marker in mobilenet py files
439 lines
17 KiB
Python
439 lines
17 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.driver as cuda
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import pycuda.autoinit # noqa: F401
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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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BS = 1
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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_SMALL = "./mobilenetv3.wts"
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ENGINE_PATH = "./mobilenetv3.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, eps):
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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_h_swish(network, input):
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h_sig = network.add_activation(input, type=trt.ActivationType.HARD_SIGMOID)
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assert h_sig
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h_sig.alpha = 1.0 / 6.0
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h_sig.beta = 0.5
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hsw = network.add_elementwise(input, h_sig.get_output(0), trt.ElementWiseOperation.PROD)
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assert hsw
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return hsw
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def conv_bn_h_swish(network, weight_map, input, outch, ksize, s, g, lname):
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p = (ksize - 1) // 2
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conv1 = network.add_convolution(input=input,
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num_output_maps=outch,
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kernel_shape=(ksize, ksize),
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kernel=weight_map[lname + "0.weight"],
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bias=trt.Weights()
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)
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assert conv1
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conv1.stride = (s, s)
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conv1.padding = (p, p)
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conv1.num_groups = g
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bn1 = add_batch_norm_2d(network, weight_map, conv1.get_output(0), lname + "1", EPS)
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hsw = add_h_swish(network, bn1.get_output(0))
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assert hsw
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return hsw
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def add_se_layer(network, weight_map, input, c, w, lname):
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h = w
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l1 = network.add_pooling(input=input,
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type=trt.PoolingType.AVERAGE,
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window_size=trt.DimsHW(w, h))
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assert l1
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l1.stride_nd = (w, h)
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l2 = network.add_fully_connected(input=l1.get_output(0),
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num_outputs=BS * c // 4,
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kernel=weight_map[lname + "fc.0.weight"],
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bias=weight_map[lname + "fc.0.bias"])
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relu1 = network.add_activation(l2.get_output(0), type=trt.ActivationType.RELU)
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l4 = network.add_fully_connected(input=relu1.get_output(0),
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num_outputs=BS * c,
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kernel=weight_map[lname + "fc.2.weight"],
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bias=weight_map[lname + "fc.2.bias"])
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se = add_h_swish(network, l4.get_output(0))
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return se
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def conv_seq_1(network, weight_map, input, output, hdim, k, s, use_se, use_hs, w, lname):
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p = (k - 1) // 2
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conv1 = network.add_convolution(input=input,
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num_output_maps=hdim,
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kernel_shape=(k, k),
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kernel=weight_map[lname + "0.weight"],
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bias=trt.Weights())
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assert conv1
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conv1.stride = (s, s)
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conv1.padding = (p, p)
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conv1.num_groups = hdim
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bn1 = add_batch_norm_2d(network, weight_map, conv1.get_output(0), lname + "1", EPS)
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if use_hs:
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hsw = add_h_swish(network, bn1.get_output(0))
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tensor3 = hsw.get_output(0)
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else:
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relu1 = network.add_activation(bn1.get_output(0), type=trt.ActivationType.RELU)
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tensor3 = relu1.get_output(0)
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if use_se:
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se1 = add_se_layer(network, weight_map, tensor3, hdim, w, lname + "3.")
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tensor4 = se1.get_output(0)
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else:
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tensor4 = tensor3
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conv2 = network.add_convolution(input=tensor4,
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num_output_maps=output,
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kernel_shape=(1, 1),
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kernel=weight_map[lname + "4.weight"],
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bias=trt.Weights())
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bn2 = add_batch_norm_2d(network, weight_map, conv2.get_output(0), lname + "5", EPS)
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assert bn2
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return bn2
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def conv_seq_2(network, weight_map, input, output, hdim, k, s, use_se, use_hs, w, lname):
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p = (k - 1) // 2
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conv1 = network.add_convolution(input=input,
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num_output_maps=hdim,
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kernel_shape=(1, 1),
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kernel=weight_map[lname + "0.weight"],
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bias=trt.Weights())
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bn1 = add_batch_norm_2d(network, weight_map, conv1.get_output(0), lname + "1", EPS)
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if use_hs:
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hsw1 = add_h_swish(network, bn1.get_output(0))
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tensor3 = hsw1.get_output(0)
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else:
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relu1 = network.add_activation(bn1.get_output(0), type=trt.ActivationType.RELU)
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tensor3 = relu1.get_output(0)
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conv2 = network.add_convolution(input=tensor3,
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num_output_maps=hdim,
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kernel_shape=(k, k),
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kernel=weight_map[lname + "3.weight"],
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bias=trt.Weights())
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conv2.stride = (s, s)
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conv2.padding = (p, p)
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conv2.num_groups = hdim
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bn2 = add_batch_norm_2d(network, weight_map, conv2.get_output(0), lname + "4", EPS)
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if use_se:
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se1 = add_se_layer(network, weight_map, bn2.get_output(0), hdim, w, lname + "5.")
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tensor6 = se1.get_output(0)
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else:
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tensor6 = bn2.get_output(0)
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if use_hs:
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hsw2 = add_h_swish(network, tensor6)
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tensor7 = hsw2.get_output(0)
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else:
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relu2 = network.add_activation(tensor6, type=trt.ActivationType.RELU)
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tensor7 = relu2.get_output(0)
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conv3 = network.add_convolution(input=tensor7,
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num_output_maps=output,
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kernel_shape=(1, 1),
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kernel=weight_map[lname + "7.weight"],
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bias=trt.Weights())
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bn3 = add_batch_norm_2d(network, weight_map, conv3.get_output(0), lname + "8", EPS)
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assert bn3
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return bn3
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def inverted_res(network, weight_map, input, lname, inch, outch, s, hidden, k, use_se, use_hs, w):
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use_res_connect = (s == 1 and inch == outch)
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if inch == hidden:
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conv = conv_seq_1(network, weight_map, input, outch, hidden, k, s, use_se, use_hs, w, lname + "conv.")
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else:
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conv = conv_seq_2(network, weight_map, input, outch, hidden, k, s, use_se, use_hs, w, lname + "conv.")
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if not use_res_connect:
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return conv
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ew3 = network.add_elementwise(input, conv.get_output(0), trt.ElementWiseOperation.SUM)
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assert ew3
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return ew3
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def create_engine_small(max_batch_size, builder, config, dt):
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weight_map = load_weights(WEIGHT_PATH_SMALL)
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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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ew1 = conv_bn_h_swish(network, weight_map, data, 16, 3, 2, 1, "features.0.")
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ir1 = inverted_res(network, weight_map, ew1.get_output(0), "features.1.", 16, 16, 2, 16, 3, 1, 0, 56)
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ir2 = inverted_res(network, weight_map, ir1.get_output(0), "features.2.", 16, 24, 2, 72, 3, 0, 0, 28)
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ir3 = inverted_res(network, weight_map, ir2.get_output(0), "features.3.", 24, 24, 1, 88, 3, 0, 0, 28)
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ir4 = inverted_res(network, weight_map, ir3.get_output(0), "features.4.", 24, 40, 2, 96, 5, 1, 1, 14)
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ir5 = inverted_res(network, weight_map, ir4.get_output(0), "features.5.", 40, 40, 1, 240, 5, 1, 1, 14)
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ir6 = inverted_res(network, weight_map, ir5.get_output(0), "features.6.", 40, 40, 1, 240, 5, 1, 1, 14)
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ir7 = inverted_res(network, weight_map, ir6.get_output(0), "features.7.", 40, 48, 1, 120, 5, 1, 1, 14)
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ir8 = inverted_res(network, weight_map, ir7.get_output(0), "features.8.", 48, 48, 1, 144, 5, 1, 1, 14)
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ir9 = inverted_res(network, weight_map, ir8.get_output(0), "features.9.", 48, 96, 2, 288, 5, 1, 1, 7)
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ir10 = inverted_res(network, weight_map, ir9.get_output(0), "features.10.", 96, 96, 1, 576, 5, 1, 1, 7)
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ir11 = inverted_res(network, weight_map, ir10.get_output(0), "features.11.", 96, 96, 1, 576, 5, 1, 1, 7)
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ew2 = conv_bn_h_swish(network, weight_map, ir11.get_output(0), 576, 1, 1, 1, "conv.0.")
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se1 = add_se_layer(network, weight_map, ew2.get_output(0), 576, 7, "conv.1.")
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pool1 = network.add_pooling(input=se1.get_output(0),
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type=trt.PoolingType.AVERAGE,
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window_size=trt.DimsHW(7, 7))
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assert pool1
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pool1.stride_nd = (7, 7)
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sw1 = add_h_swish(network, pool1.get_output(0))
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fc1 = network.add_fully_connected(input=sw1.get_output(0),
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num_outputs=1280,
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kernel=weight_map["classifier.0.weight"],
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bias=weight_map["classifier.0.bias"])
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assert fc1
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bn1 = add_batch_norm_2d(network, weight_map, fc1.get_output(0), "classifier.1", EPS)
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sw2 = add_h_swish(network, bn1.get_output(0))
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fc2 = network.add_fully_connected(input=sw2.get_output(0),
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num_outputs=OUTPUT_SIZE,
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kernel=weight_map["classifier.3.weight"],
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bias=weight_map["classifier.3.bias"])
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bn2 = add_batch_norm_2d(network, weight_map, fc2.get_output(0), "classifier.4", EPS)
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sw3 = add_h_swish(network, bn2.get_output(0))
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sw3.get_output(0).name = OUTPUT_BLOB_NAME
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network.mark_output(sw3.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 create_engine_large(max_batch_size, builder, config, dt):
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weight_map = load_weights(WEIGHT_PATH_SMALL)
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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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ew1 = conv_bn_h_swish(network, weight_map, data, 16, 3, 2, 1, "features.0.")
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ir1 = inverted_res(network, weight_map, ew1.get_output(0), "features.1.", 16, 16, 1, 16, 3, 0, 0, 112)
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ir2 = inverted_res(network, weight_map, ir1.get_output(0), "features.2.", 16, 24, 2, 64, 3, 0, 0, 56)
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ir3 = inverted_res(network, weight_map, ir2.get_output(0), "features.3.", 24, 24, 1, 72, 3, 0, 0, 56)
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ir4 = inverted_res(network, weight_map, ir3.get_output(0), "features.4.", 24, 40, 2, 72, 5, 1, 0, 28)
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ir5 = inverted_res(network, weight_map, ir4.get_output(0), "features.5.", 40, 40, 1, 120, 5, 1, 0, 28)
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ir6 = inverted_res(network, weight_map, ir5.get_output(0), "features.6.", 40, 40, 1, 120, 5, 1, 0, 28)
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ir7 = inverted_res(network, weight_map, ir6.get_output(0), "features.7.", 40, 80, 2, 240, 3, 0, 1, 14)
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ir8 = inverted_res(network, weight_map, ir7.get_output(0), "features.8.", 80, 80, 1, 200, 3, 0, 1, 14)
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ir9 = inverted_res(network, weight_map, ir8.get_output(0), "features.9.", 80, 80, 1, 184, 3, 0, 1, 14)
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ir10 = inverted_res(network, weight_map, ir9.get_output(0), "features.10.", 80, 80, 1, 184, 3, 0, 1, 14)
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ir11 = inverted_res(network, weight_map, ir10.get_output(0), "features.11.", 80, 112, 1, 480, 3, 1, 1, 14)
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ir12 = inverted_res(network, weight_map, ir11.get_output(0), "features.12.", 112, 112, 1, 672, 3, 1, 1, 14)
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ir13 = inverted_res(network, weight_map, ir12.get_output(0), "features.13.", 112, 160, 1, 672, 5, 1, 1, 14)
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ir14 = inverted_res(network, weight_map, ir13.get_output(0), "features.14.", 160, 160, 2, 672, 5, 1, 1, 7)
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ir15 = inverted_res(network, weight_map, ir14.get_output(0), "features.15.", 160, 160, 1, 960, 5, 1, 1, 7)
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ew2 = conv_bn_h_swish(network, weight_map, ir15.get_output(0), 960, 1, 1, 1, "conv.0.")
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pool1 = network.add_pooling(input=ew2.get_output(0),
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type=trt.PoolingType.AVERAGE,
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window_size=trt.DimsHW(7, 7))
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assert pool1
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pool1.stride_nd = (7, 7)
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sw1 = add_h_swish(network, pool1.get_output(0))
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fc1 = network.add_fully_connected(input=sw1.get_output(0),
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num_outputs=1280,
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kernel=weight_map["classifier.0.weight"],
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bias=weight_map["classifier.0.bias"])
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assert fc1
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sw2 = add_h_swish(network, fc1.get_output(0))
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fc2 = network.add_fully_connected(input=sw2.get_output(0),
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num_outputs=OUTPUT_SIZE,
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kernel=weight_map["classifier.3.weight"],
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bias=weight_map["classifier.3.bias"])
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fc2.get_output(0).name = OUTPUT_BLOB_NAME
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network.mark_output(fc2.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, model_type):
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builder = trt.Builder(TRT_LOGGER)
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config = builder.create_builder_config()
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if model_type == "small":
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engine = create_engine_small(max_batch_size, builder, config, trt.float32)
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assert engine
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else:
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engine = create_engine_large(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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parser.add_argument("-t", help='indicate small or large model')
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|
args = parser.parse_args()
|
|
|
|
if not (args.s ^ args.d):
|
|
print(
|
|
"arguments not right!\n"
|
|
"python mobilenet_v2.py -s # serialize model to plan file\n"
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|
"python mobilenet_v2.py -d # deserialize plan file and run inference"
|
|
)
|
|
sys.exit()
|
|
|
|
if args.s:
|
|
API_to_model(BATCH_SIZE, args.t)
|
|
else:
|
|
runtime = trt.Runtime(TRT_LOGGER)
|
|
assert runtime
|
|
|
|
with open(ENGINE_PATH, "rb") as f:
|
|
engine = runtime.deserialize_cuda_engine(f.read())
|
|
assert engine
|
|
|
|
context = engine.create_execution_context()
|
|
assert context
|
|
|
|
data = np.ones((BATCH_SIZE * 3 * INPUT_H * INPUT_W), dtype=np.float32)
|
|
inputs, outputs, bindings, stream = allocate_buffers(engine)
|
|
inputs[0].host = data
|
|
|
|
trt_outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
|
|
|
|
print(f'Output: \n{trt_outputs[0][:10]}\n{trt_outputs[0][-10:]}')
|