mmr/tkDNN
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Shortcat ok

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This commit is contained in:
Davide Sapienza
2019-10-29 15:08:44 +01:00
parent 058f9b795f
commit e9ec582223
5 changed files with 87 additions and 29 deletions
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CMakeLists.txt
-2
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@@ -92,8 +92,6 @@ target_link_libraries(test_yolo3_berkeley tkDNN)
add_executable(test_yolo3_flir tests/yolo3_flir/yolo3_flir.cpp)
target_link_libraries(test_yolo3_flir tkDNN)
add_executable(test_yolo3_BCDS6 tests/yolo3_BCDS6/yolo3_BCDS6.cpp)
target_link_libraries(test_yolo3_BCDS6 tkDNN)
add_executable(test_resnet101 tests/resnet101/resnet101.cpp)
target_link_libraries(test_resnet101 tkDNN)
src/LayerWgs.cpp
+19 -1
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@@ -19,7 +19,14 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
readBinaryFile(weights_path.c_str(), inputs*outputs*kh*kw*kl, &data_h, &data_d, seek);
seek += inputs*outputs*kh*kw*kl;
readBinaryFile(weights_path.c_str(), outputs, &bias_h, &bias_d, seek);
std::cout<<"w:\n";
printDeviceVector(64, data_d, true);
std::cout<<"b:\n";
printDeviceVector(64, bias_d, true);
std::cout<<"step----------------------------------------\n";
this->batchnorm = batchnorm;
if(batchnorm) {
seek += outputs;
@@ -29,6 +36,17 @@ LayerWgs::LayerWgs(Network *net, int inputs, int outputs,
seek += outputs;
readBinaryFile(weights_path.c_str(), outputs, &variance_h, &variance_d, seek);
std::cout<<"s:\n";
printDeviceVector(64, scales_d, true);
std::cout<<"m:\n";
printDeviceVector(64, mean_d, true);
std::cout<<"v:\n";
printDeviceVector(64, variance_d, true);
std::cout<<"END----------------------------------------\n";
float eps = CUDNN_BN_MIN_EPSILON;
power_h = new dnnType[outputs];
src/utils.cpp
+2 -1
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@@ -54,6 +54,7 @@ void printDeviceVector(int size, dnnType* vec_d, bool device)
vec = new dnnType[size];
cudaDeviceSynchronize();
cudaMemcpy(vec, vec_d, size*sizeof(dnnType), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
} else {
vec = vec_d;
}
@@ -78,7 +79,7 @@ int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device) {
cudaDeviceSynchronize();
cudaMemcpy(data_h, data_d, size*sizeof(dnnType), cudaMemcpyDeviceToHost);
cudaMemcpy(correct_h, correct_d, size*sizeof(dnnType), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
} else {
data_h = data_d;
correct_h = correct_d;
tests/resnet101/resnet101.cpp
+16 -8
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@@ -148,7 +148,7 @@ const char *layer4_2_conv3_bin = "../tests/resnet101/layers/layer4-2-conv3.bin";
//final
const char *fc_bin = "../tests/resnet101/layers/fc.bin";
const char *output_bin = "../tests/resnet101/debug/layer1-0-conv3.bin";
const char *output_bin = "../tests/resnet101/debug/layer1-0-relu.bin";
int main()
{
@@ -161,16 +161,21 @@ int main()
tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Pooling maxpool4(&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
//layer 1
tk::dnn::Conv2d layer1_0_conv1(&net, 64, 1, 1, 1, 1, 0, 0, layer1_0_conv1_bin, true);
tk::dnn::Activation relu1_0_1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d layer1_0_conv2(&net, 64, 3, 3, 1, 1, 1, 1, layer1_0_conv2_bin, true);
tk::dnn::Activation relu1_0_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d layer1_0_conv3(&net, 256, 1, 1, 1, 1, 0, 0, layer1_0_conv3_bin, true);
/*
tk::dnn::Layer *m83_layers[1] = { &maxpool4 };
tk::dnn::Route m83 (&net, m83_layers, 1);
tk::dnn::Conv2d layer1_0_downsample_0(&net, 256, 1, 1, 1, 1, 0, 0, layer1_0_downsample_0_bin, true);
tk::dnn::Shortcut s1_0 (&net, &layer1_0_conv3);
tk::dnn::Activation layer1_0_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d layer1_0_downsample_0(&net, 256, 1, 1, 1, 1, 1, 1, layer1_0_downsample_0, true);
/*
tk::dnn::Conv2d layer1_1_conv1(&net, 64, 1, 1, 1, 1, 1, 1, layer1_1_conv1_bin, true);
tk::dnn::Conv2d layer1_1_conv2(&net, 64, 3, 3, 1, 1, 1, 1, layer1_1_conv2_bin, true);
tk::dnn::Conv2d layer1_1_conv3(&net, 256, 1, 1, 1, 1, 1, 1, layer1_1_conv3_bin, true);
@@ -347,6 +352,7 @@ int main()
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
printDeviceVector(64, data, true);
//print network model
net.print();
@@ -356,7 +362,7 @@ int main()
*/
tk::dnn::dataDim_t out_dim;
out_dim = layer1_0_conv3.output_dim;
out_dim = net.layers[net.num_layers-1]->output_dim;
dnnType *cudnn_out, *rt_out;
tk::dnn::dataDim_t dim1 = dim; //input dim
@@ -368,7 +374,9 @@ int main()
TIMER_STOP
dim1.print();
}
cudnn_out = layer1_0_conv3.dstData;
cudnn_out = net.layers[net.num_layers-1]->dstData;
printDeviceVector(64, cudnn_out, true);
/*
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);
tests/resnet101/resnet101_weightsexporter.py
+50 -17
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@@ -4,6 +4,14 @@ from PIL import Image
from torchvision import transforms
from torchsummary import summary
import numpy as np
import struct
def bin_write(f, data):
data =data.flatten()
# print(data)
fmt = 'f'*len(data)
bin = struct.pack(fmt, *data)
f.write(bin)
def hook(module, input, output):
setattr(module, "_value_hook", output)
@@ -12,7 +20,7 @@ def hook(module, input, output):
def print_wb(model, folder):
for name, param in model.named_parameters():
print ("Layer", name)
# print ("Layer", name)
t = name.split('.')[0:-1]
arg = name.split('.')[-1]
t = '-'.join(t)
@@ -43,20 +51,22 @@ def print_wb_output(model, input_batch):
f = None
for n, m in model.named_modules():
in_output = m._value_hook
print(n, ' ----------------------------------------------------------------')
o = in_output.data.numpy()
o = np.array(o, dtype=np.float32)
t = '-'.join(n.split('.'))
o.tofile("debug/" + t + ".bin", format="f")
if 'Conv2d' in str(m.type) or 'Linear' in str(m.type):
f = open("layers/" + t + ".bin", mode='wb')
if f is None:
if not(' of Conv2d' in str(m.type) or ' of Linear' in str(m.type) or ' of BatchNorm2d' in str(m.type)):
continue
if ' of Conv2d' in str(m.type) or ' of Linear' in str(m.type):
file_name = "layers/" + t + ".bin"
print("open file: ", file_name)
f = open(file_name, mode='wb')
print(n, ' ----------------------------------------------------------------')
# print(m._parameters)
print(m.type)
#print(m.type)
w = np.array([])
b = np.array([])
@@ -71,20 +81,43 @@ def print_wb_output(model, input_batch):
b = m._parameters['bias'].data.numpy()
b = np.array(b, dtype=np.float32)
print (" bias shape:", np.shape(b))
else:
b = np.zeros(w.shape[0], dtype=np.float32)
print (" bias shape:", np.shape(b))
# else:
# b = np.zeros(w.shape[0], dtype=np.float32)
# print (" bias shape:", np.shape(b))
if 'BatchNorm2d' in str(m.type):
s = np.zeros(w.shape[0], dtype=np.float32)+1
s.tofile(f, format="f")
b = m._parameters['bias'].data.numpy()
b = np.array(b, dtype=np.float32)
s = m._parameters['weight'].data.numpy()
s = np.array(s, dtype=np.float32)
rm = m.running_mean.data.numpy()
rm = np.array(rm, dtype=np.float32)
rv = m.running_var.data.numpy()
rv = np.array(rv, dtype=np.float32)
#s.tofile(f, format="f")
bin_write(f,b)
bin_write(f,s)
bin_write(f,rm)
bin_write(f,rv)
print (" s shape:", np.shape(s))
print (" rm shape:", np.shape(rm))
print (" rv shape:", np.shape(rv))
w.tofile(f, format="f")
b.tofile(f, format="f")
else:
# w.tofile(f, format="f")
bin_write(f,w)
# print("w- ",w)
if b.size > 0:
# b.tofile(f, format="f")
bin_write(f,b)
# print("b - ",b)
if 'BatchNorm2d' in str(m.type) or 'Linear' in str(m.type):
if ' of BatchNorm2d' in str(m.type) or ' of Linear' in str(m.type):
f.close()
print("close file")
f = None
# return
@@ -120,9 +153,9 @@ if __name__ == '__main__':
output = model(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
print(output)
# print(output)
print_wb_output(model, input_batch)
print(list(model.children()))
# print(list(model.children()))