def convert_tf2tf(dst_params_file_path, dst_params, dst_param_keys, src_params,
src_param_keys):
import re
src_param_keys = [key.replace('/W:', '/kernel:') for key in src_param_keys]
src_param_keys = [key.replace('/b:', '/bias:') for key in src_param_keys]
src_param_keys = [
key.replace('linear/', 'output/') for key in src_param_keys
]
src_param_keys = [
key.replace('stage', 'features/stage') for key in src_param_keys
]
src_param_keys = [
re.sub('^conv1/', 'features/init_block/conv/', key)
for key in src_param_keys
]
src_param_keys = [
re.sub('^conv5/', 'features/final_block/conv/', key)
for key in src_param_keys
]
src_param_keys = [
key.replace('/dconv_bn/', '/dconv/bn/') for key in src_param_keys
]
src_param_keys = [
key.replace('/shortcut_dconv_bn/', '/shortcut_dconv/bn/')
for key in src_param_keys
]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: [
'{:10}'.format(int(x)) if x.isdigit() else x
for x in re.findall(r'[^0-9]|[0-9]+', var)
])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: [
'{:10}'.format(int(x)) if x.isdigit() else x
for x in re.findall(r'[^0-9]|[0-9]+', var)
])
src_param_keys = [key.replace('/kernel:', '/W:') for key in src_param_keys]
src_param_keys = [key.replace('/bias:', '/b:') for key in src_param_keys]
src_param_keys = [
key.replace('output/', 'linear/') for key in src_param_keys
]
src_param_keys = [
key.replace('features/stage', 'stage') for key in src_param_keys
]
src_param_keys = [
key.replace('features/init_block/conv/', 'conv1/')
for key in src_param_keys
]
src_param_keys = [
key.replace('features/final_block/conv/', 'conv5/')
for key in src_param_keys
]
src_param_keys = [
key.replace('/dconv/bn/', '/dconv_bn/') for key in src_param_keys
]
src_param_keys = [
key.replace('/shortcut_dconv/bn/', '/shortcut_dconv_bn/')
for key in src_param_keys
]
assert (len(src_param_keys) == len(dst_param_keys))
import tensorflow as tf
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i, (src_key,
dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
assert (src_params[src_key].shape == tuple(
dst_params[dst_key].get_shape().as_list()))
sess.run(dst_params[dst_key].assign(src_params[src_key]))
from tensorflow_.utils import save_model_params
save_model_params(sess=sess, file_path=dst_params_file_path)
def convert_gl2tf(dst_params_file_path, dst_params, dst_param_keys, src_params,
src_param_keys):
dst_param_keys = [
key.replace('/kernel:', '/weight:') for key in dst_param_keys
]
dst_param_keys = [
key.replace('/dw_kernel:', '/weight_dw:') for key in dst_param_keys
]
dst_param_keys = [
key.replace('/post_activ/', '/stageN/post_activ/')
for key in dst_param_keys
]
dst_param_keys = [
key.replace('/final_block/', '/stageN/final_block/')
for key in dst_param_keys
]
dst_param_keys = [
key.replace('/stem1_unit/', '/stage0/stem1_unit/')
for key in dst_param_keys
]
dst_param_keys = [
key.replace('/stem2_unit/', '/stage0/stem2_unit/')
for key in dst_param_keys
]
src_param_keys.sort()
src_param_keys.sort(key=lambda var: [
'{:10}'.format(int(x)) if x.isdigit() else x
for x in re.findall(r'[^0-9]|[0-9]+', var)
])
dst_param_keys.sort()
dst_param_keys.sort(key=lambda var: [
'{:10}'.format(int(x)) if x.isdigit() else x
for x in re.findall(r'[^0-9]|[0-9]+', var)
])
dst_param_keys = [
key.replace('/weight:', '/kernel:') for key in dst_param_keys
]
dst_param_keys = [
key.replace('/weight_dw:', '/dw_kernel:') for key in dst_param_keys
]
dst_param_keys = [
key.replace('/stageN/post_activ/', '/post_activ/')
for key in dst_param_keys
]
dst_param_keys = [
key.replace('/stageN/final_block/', '/final_block/')
for key in dst_param_keys
]
dst_param_keys = [
key.replace('/stage0/stem1_unit/', '/stem1_unit/')
for key in dst_param_keys
]
dst_param_keys = [
key.replace('/stage0/stem2_unit/', '/stem2_unit/')
for key in dst_param_keys
]
dst_param_keys_orig = dst_param_keys.copy()
dst_param_keys = [
s[:(s.find("convgroup") + 9)] + "/" +
s.split('/')[-1] if s.find("convgroup") >= 0 else s
for s in dst_param_keys
]
dst_param_keys_uniq, dst_param_keys_index = np.unique(dst_param_keys,
return_index=True)
dst_param_keys = list(dst_param_keys_uniq[dst_param_keys_index.argsort()])
assert (len(src_param_keys) == len(dst_param_keys))
import tensorflow as tf
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
def process_width(src_key, dst_key, src_weight):
if len(src_weight.shape) == 4:
if dst_key.split("/")[-1][:-2] == "dw_kernel":
src_weight = np.transpose(src_weight, axes=(2, 3, 0, 1))
else:
src_weight = np.transpose(src_weight, axes=(2, 3, 1, 0))
elif len(src_weight.shape) == 2:
src_weight = np.transpose(src_weight, axes=(1, 0))
assert (tuple(
dst_params[dst_key].get_shape().as_list()) == src_weight.shape)
sess.run(dst_params[dst_key].assign(src_weight))
# print(dst_params[dst_key].eval(sess))
for i, (src_key,
dst_key) in enumerate(zip(src_param_keys, dst_param_keys)):
if dst_key.find("convgroup") >= 0:
dst_key_stem = dst_key[:(dst_key.find("convgroup") + 9)]
dst_keys = [
s for s in dst_param_keys_orig
if s.startswith(dst_key_stem)
]
if src_key.endswith("weight"):
dst_keys = [s for s in dst_keys if s.endswith("kernel:0")]
elif src_key.endswith("bias"):
dst_keys = [s for s in dst_keys if s.endswith("bias:0")]
groups = len(dst_keys)
src_weight0 = src_params[src_key]._data[0]
src_weight0_list = mx.nd.split(src_weight0,
axis=0,
num_outputs=groups)
for gi in range(groups):
src_weight_gi = src_weight0_list[gi].asnumpy()
dst_key_gi = dst_keys[gi]
process_width(src_key, dst_key_gi, src_weight_gi)
else:
src_weight = src_params[src_key]._data[0].asnumpy()
process_width(src_key, dst_key, src_weight)
# saver = tf.train.Saver()
# saver.save(
# sess=sess,
# save_path=dst_params_file_path)
from tensorflow_.utils import save_model_params
save_model_params(sess=sess, file_path=dst_params_file_path)