imagefile.append(imagedir)
elif nimages == 0:
#imagefile = glob.glob(imagedir + '/*.png')[:3]
#imagefile = glob.glob(imagedir + '/*.png') # 全画像ではメモリを食い尽くす...
imagefile = glob.glob(imagedir + '/*.png')[:50]
else:
for i in range(nimages):
imagefile.append('testdata/img_' + ('0000' + str(i))[-4:] + '.jpg')
print 'found', len(imagefile), 'imagefiles :', imagefile[:3], '...'
# scale辞書の読み込み
scale_dict = json.load(
file(scale_dict_file)) # activation scaleを入れる前のスケール辞書
scale_dict_a = copy.deepcopy(
scale_dict) # activation scaleを入れたスケール辞書(初期値は変更前と同じ)
ptxt = csslib.load_pb2(model) # 変換前prototxt
net = csslib.load_network(model, param) # 変換前ネットワーク
net_as = csslib.load_network(model, param) # 変換後ネットワーク（変換前を初期値とする）
net.blobs['data'].reshape(1, nchannels, imgheight,
imgwidth) # reshape to image size
transformer = csslib.gen_transformer(net) # 入力データのtransformerを作成
#
conv_layers = []
for layer in ptxt.layer:
if layer.type == 'Convolution':
#conv_layers.append(layer.name) # nameではなく
conv_layers.append(layer.top[0]) # topを登録する
print "conv_layers:", conv_layers
#
layer_names = []
gen_layer_namelist(net, layer_names)
# int16 -> fp32へ戻すためのスケールを抽出
def extract_i2f_scale():
layers = ('cls_score_i2f', 'bbox_pred_i2f', 'joints_pred_i2f')
i = 0
print 'const float i2f_scale' + layer_name + '[] = {'
for layer in layers:
print ' %14.12f' % sdict[layer], ', //', i, layer
i = i + 1
print '};'
print ''
##### main #####
if __name__ == '__main__':
print '\n// ----- 05-extract_prm.py -----', sys.argv[1], sys.argv[2]
ptxt = csslib.load_pb2(sys.argv[1])
sdict = csslib.load_dict(sys.argv[2])
out_layer = sys.argv[3]
if len(sys.argv) > 4:
layer_name = sys.argv[4]
else:
layer_name = ''
#for layer in ptxt.layer:
# print layer.name
print '// ----- for css_pose.cpp -----'
extract_conv_scale()
extract_eltwise_scale()
extract_i2f_scale()
#print '// ----- for Makefile -----'
#print '// SCALE_INTxx =', sdict[out_layer]
'param_str':'{"scale":1.0, "shift":0}'})
i2f.bottom.append(layer_top)
i2f.top.append(i2f_top)
elif layer.type == 'Eltwise':
# eltwise layerにeltwise_paramを追加
weight = []
for i in range(len(layer.bottom)):
weight.append(1.0) # 各層のweight : とりあえず全部１
ew_ws = net_dst.layer.add(name=layer.top[0],
type='Eltwise',
eltwise_param={'operation':'SUM',
'coeff':weight}) # weightはタプルで指定
for i in range(len(layer.bottom)):
ew_ws.bottom.append(layer.bottom[i])
ew_ws.top.append(layer.top[0])
else:
net_dst.layer.extend([layer])
return net_dst
##### main #####
if __name__ == '__main__':
print '\n----- 01-add_python_layer.py -----', sys.argv[1], '->',sys.argv[2]
model_in = sys.argv[1]
model_out = sys.argv[2]
#
net = csslib.load_pb2(model_in)
net_out = add_python_layer(net)
print 'saving modified prototxt to', model_out
csslib.save_pb2(net_out, model_out)