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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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()))