多输入时,通道顺序为何会乱? Python接口怎么用多输入? - Toybrick

import platform
import os
import torch
import numpy as np
from rknn.api import RKNN
import onnx
from onnx_tf.backend import prepare
class LSTM(torch.nn.Module):
def __init__(self, input_size, hidden_size):
super(LSTM, self).__init__()
self._input_size = input_size
self._hidden_size = hidden_size
self._fc = torch.nn.Linear(input_size, input_size)
self._tanh = torch.nn.Tanh()
self._sigmoid = torch.nn.Sigmoid()
def forward(self, x, h0, c0):
return self._fc(x), self._sigmoid(h0), self._sigmoid(c0)
#ox, (h1, c1) = self._lstm(x, (h0, c0))
return ox, h1, c1
if __name__ == '__main__':
system = platform.system()
isize = 128
hsize = 64
#m = torch.nn.Linear(isize, hsize)
m = LSTM(isize, hsize)
m.eval()
m.float()
h0 = torch.full((1, 1, hsize), fill_value=0.15, dtype=torch.float)
c0 = torch.full((1, 1, hsize//2), fill_value=0.07, dtype=torch.float)
x = torch.full((1, isize), fill_value=0.1, dtype=torch.float)
#x1 = torch.full((1, hsize), fill_value=0.2, dtype=torch.float)
pt_file = "lstm{%dx%d}.pt" % (isize, hsize)
rknn = RKNN(verbose=True)
rknn.config(batch_size=1,
epochs=1) # asymmetric_quantized-u8, quantized_dtype='dynamic_fixed_point-16' , channel_mean_value='0 0 0 1', reorder_channel='0 1 2',
#pt = torch.jit.trace(m, x, h0, c0)
#pt.save(pt_file)
#rknn.load_pytorch(model=pt_file, input_size_list=[[1, isize]])
onnx_file = pt_file + '.onnx'
torch.onnx.export(m, (x, h0, c0), onnx_file,
export_params=True, # store the trained parameter weights inside the model file
# do_constant_folding=True, # whether to execute constant folding for optimization
opset_version=9, # the ONNX version to export the model to
input_names=['input', 'h0', 'c0'], # the model's input names
output_names=['output', 'h1', 'c1'] # the model's output names
# dynamic_axes={'input' : {0 : 'batch_size'}, # variable lenght axes
# 'output' : {0 : 'batch_size'}}
)
rknn.load_onnx(model=onnx_file)
rknn_file = pt_file + '.rknn'
ret = rknn.build(do_quantization=False, dataset='./dataset.txt')
rknn.export_rknn(rknn_file)
print('--> Init runtime environment')
if system == 'Windows':
print("rknn run target rk1808")
ret = rknn.init_runtime(target='rk1808', target_sub_class='AICS')
else:
print("rknn run target self")
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
ix = x.numpy()
tf_model = onnx.load(onnx_file)
tf_rep = prepare(tf_model)
tf_out, tf_h1, tf_c1 = tf_rep.run((ix, h0, c0))
tf_out = np.array(tf_out)
print("onnx out:", ix.shape, ', ', tf_out.shape)
print(tf_out)
torch_out, th_h1, th_c1 = m.forward(x, h0, c0)
print("torch [out]:", x.shape, ', ', torch_out.shape)
print(torch_out.detach().unsqueeze(0).numpy())
ih0 = h0.numpy()
ic0 = c0.numpy()
rknn_out = rknn.inference(inputs=[(ix, ih0, ic0)], data_type='float32', data_format='nchw') # , inputs_pass_through=[1]
rknn_out = np.array(rknn_out)
print("rknn out:", ix.shape, ', ', rknn_out.shape)
print(rknn_out)
rknn.release()

ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &in_out_num, sizeof(in_out_num));
if (ret < 0) {
printf(_LINE_MARK_ " rknn_query fail: query in out num, ret=%d\n", ret);
goto Error;
}
assert(_NInChs == in_out_num.n_input && _NOutChs == in_out_num.n_output);
for (int i = 0; i < _NInChs; i++) {
inputs_attr[i].index = i;
ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(inputs_attr[i]), sizeof(inputs_attr[i]));
if (ret < 0) {
printf(_LINE_MARK_ " rknn_query fail: query inputs attrs, ret=%d\n", ret);
goto Error;
}
}
for (int i = 0; i < _NOutChs; i++) {
outputs_attr[i].index = i;
ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(outputs_attr[i]), sizeof(outputs_attr[i]));
if (ret < 0) {
printf(_LINE_MARK_ " rknn_query fail: query outputs attrs ret=%d\n", ret);
goto Error;
}
}

import torch
from rknn.api import RKNN
import numpy as np
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1,6,3)
self.conv2 = torch.nn.Conv2d(2,12,3)
self.conv3 = torch.nn.Conv2d(3,24,3)
def forward(self, x, y, z):
x = self.conv1(x)
y = self.conv2(y)
z = self.conv3(z)
return x,y,z
def E_D(vector1, vector2):
print(np.linalg.norm(vector1 - vector2))
def cos_d(vector1, vector2):
d = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * (np.linalg.norm(vector2)))
print(d)
if __name__ == '__main__':
net = Net()
i1 = torch.rand(1,1,5,5)
i2 = torch.rand(1,2,7,7)
i3 = torch.rand(1,3,9,9)
trace_model = torch.jit.trace(net, (i1,i2,i3))
trace_model.save('test.pt')
rknn = RKNN(verbose=True)
rknn.config(batch_size=1,
channel_mean_value='0 1#0 0 1#0 0 0 1',
reorder_channel='0 1 2#0 1 2#0 1 2',
epochs=1)
ret = rknn.load_pytorch(model='test.pt', input_size_list=[[1,5,5],[2,7,7],[3,9,9]])
# ret = rknn.load_onnx(model='lstm{128x64}.pt.onnx')
if ret != 0:
print('Load pytorch model failed!')
exit(ret)
ret = rknn.build(do_quantization=False, dataset='./dataset.txt')
if ret != 0:
print('Build pytorch failed!')
exit(ret)
ret = rknn.init_runtime(target='rk1808',device_id='7e9f3eb02ede60e8')
if ret != 0:
print('Init runtime environment failed')
exit(ret)
rknn_r = rknn.inference(inputs=[i1.numpy(), i2.numpy(), i3.numpy()],
data_type='float32',
data_format='nchw')
pytorch_r = net(i1,i2,i3)
for d1, d2 in zip(rknn_r, pytorch_r):
d1 = d1.ravel()
d2 = d2.detach().numpy().ravel()
E_D(d1, d2)
cos_d(d1, d2)
print()