Comments (4)
traveller59
commented on September 26, 2024
2
example in readme generate a engine with two outputs. so you need to alloc memory for second output and add it to bindings.
I recommend to write a simple high-level API based on allocate_buffers in common.py in tensorrt examples to decrease these kind of bugs. you can get all shapes, dtypes and names of inputs and outputs from a engine instance, use them to write API.
from torch2trt.
oscarriddle
commented on September 26, 2024
Hi @traveller59
Thanks for your reply.
After update the 2 outputs to the test script, the error disappeared.
import torch
import torchvision
import tensorrt as trt
import torch2trt
import time
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit
if __name__ == '__main__':
img = np.random.rand(1, 3, 299, 299)
#img /= 255.0
#img -= 0.5
#img *= 2.0
bindings = []
img = np.ascontiguousarray(img)
#out = infer(get_engine('test.engine'), input, 1)
engine = get_engine('test.engine')
#runtime = trt.infer.create_infer_runtime(G_LOGGER)
stream = cuda.Stream()
context = engine.create_execution_context()
output = np.empty(1000, dtype = np.float32)
output2 = np.empty(1000, dtype = np.float32)
d_input = cuda.mem_alloc(1 * img.nbytes)
d_output = cuda.mem_alloc(1 * output.nbytes)
d_output2 = cuda.mem_alloc(1 * output2.nbytes)
bindings = [int(d_input), int(d_output), int(d_output2)]
for i in range(100):
a1 = time.time()
cuda.memcpy_htod_async(d_input, img, stream)
context.execute_async(1, bindings, stream.handle, None)
cuda.memcpy_dtoh_async(output, d_output, stream)
cuda.memcpy_dtoh_async(output2, d_output2, stream)
stream.synchronize()
a2 = time.time()
print('Batch {}-th, Time {}ms'.format(i, a2-a1))
print(output)
print(output2)
I snooped the data in array output and output2, the output2 is all zero and the output is as below:
[1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0.1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 1. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1. 1. 1. 1. 0. 1. 0. 0.]
Meanwhile, I randomized the same shape input by torch.rand().cuda() and input it into the pytorch model, and the output tensor is as below:
tensor([[-1.1718e+00, 4.6266e-01, 1.0212e+00, 2.2632e-01, 4.7583e-01,
-6.3883e-01, 1.0015e-01, 1.5616e+00, 1.0254e+00, 1.1679e+00,
1.8992e+00, 2.9743e+00, 3.4913e+00, 2.1630e+00, 2.3985e+00,
2.6715e+00, 3.0376e+00, 1.4133e+00, 3.0594e+00, 2.1019e+00,
1.1495e+00, 5.6692e+00, 3.9588e+00, 3.3769e+00, 2.3353e+00,
-1.0645e+00, -7.9425e-01, -8.1189e-01, -1.1870e+00, -5.1096e-01,
-8.8577e-02, 2.2844e-01, -1.5711e+00, -2.0256e-01, -3.3481e-01,
...
Seems my conversion configuration is incorrect, would you give some advice?
Thanks
Below is my conversion script:
import torch
import torchvision
import tensorrt as trt
import torch2trt
import time
import numpy
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
net = torchvision.models.inception_v3(pretrained=True).eval()
inputs = torch.rand(1, 3, 299, 299)
graph_pth = torch2trt.GraphModule(net, inputs, param_exclude=".*AuxLogits.*")
torch_mode_out = graph_pth(inputs)
def toy_example(x):
return torch.softmax(x, 1), torch.sigmoid(x)
graph_pth_toy = torch2trt.GraphModule(toy_example, torch_mode_out)
probs, sigmoid = graph_pth_toy(torch_mode_out, verbose=True)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as trt_net:
builder.max_workspace_size = 1 << 30
with torch2trt.trt_network(trt_net): # must use this to enter trt mode
img = trt_net.add_input(name="image", shape=[3, 299, 299], dtype=trt.float32)
trt_mode_out = graph_pth(img, verbose=True) # call graph_pth like torch module call
trt_mode_out, sigmoid = graph_pth_toy(trt_mode_out)
trt_mode_out.name = "output_softmax"
sigmoid.name = "output_sigmoid"
trt_net.mark_output(tensor=trt_mode_out)
trt_net.mark_output(tensor=sigmoid)
engine = builder.build_cuda_engine(trt_net)
with open("test.engine", "wb") as f:
f.write(engine.serialize())
from torch2trt.
traveller59
commented on September 26, 2024
you need to convert image to np.float32.
please don't use raw API... you can try my high-level API in newest code.
from torch2trt.
oscarriddle
commented on September 26, 2024
Hi, I tried your newest inference code and get the output results.
I noticed you compared different results by norm to check the coherency.
I also tried to import the exactly same input to original pytorch method, like below
import torch
import torchvision
import tensorrt as trt
import torch2trt
import time
import numpy as np
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
net = torchvision.models.inception_v3(pretrained=True).eval()
img = np.load('input_raw.bin.npy')
inputs = torch.from_numpy(img)
model = net.cuda()
inp = inputs.cuda()
for i in range(1):
a1 = time.time()
out = model(inp)
a2 = time.time()
print('{}, {}, {}'.format(i, out, a2-a1))
But got a different result compared to the tensorrt way. Would you leave some comments about how to address this issue?
from torch2trt.
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