# pylint: skip-file | |
import mxnet as mx | |
import numpy as np | |
import sys | |
import logging | |
logger = logging.getLogger() | |
logger.setLevel(logging.INFO) | |
# make a bilinear interpolation kernel, return a numpy.ndarray | |
def upsample_filt(size): | |
factor = (size + 1) // 2 | |
if size % 2 == 1: | |
center = factor - 1.0 | |
else: | |
center = factor - 0.5 | |
og = np.ogrid[:size, :size] | |
return (1 - abs(og[0] - center) / factor) * \ | |
(1 - abs(og[1] - center) / factor) | |
def init_from_vgg16(ctx, fcnxs_symbol, vgg16fc_args, vgg16fc_auxs): | |
fcnxs_args = vgg16fc_args.copy() | |
fcnxs_auxs = vgg16fc_auxs.copy() | |
for k,v in fcnxs_args.items(): | |
if(v.context != ctx): | |
fcnxs_args[k] = mx.nd.zeros(v.shape, ctx) | |
v.copyto(fcnxs_args[k]) | |
for k,v in fcnxs_auxs.items(): | |
if(v.context != ctx): | |
fcnxs_auxs[k] = mx.nd.zeros(v.shape, ctx) | |
v.copyto(fcnxs_auxs[k]) | |
data_shape=(1,3,500,500) | |
arg_names = fcnxs_symbol.list_arguments() | |
arg_shapes, _, _ = fcnxs_symbol.infer_shape(data=data_shape) | |
rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes) | |
if x[0] in ['score_weight', 'score_bias', 'score_pool4_weight', 'score_pool4_bias', \ | |
'score_pool3_weight', 'score_pool3_bias']]) | |
fcnxs_args.update(rest_params) | |
deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) | |
if x[0] in ["bigscore_weight", 'score2_weight', 'score4_weight']]) | |
for k, v in deconv_params.items(): | |
filt = upsample_filt(v[3]) | |
initw = np.zeros(v) | |
initw[range(v[0]), range(v[1]), :, :] = filt # becareful here is the slice assing | |
fcnxs_args[k] = mx.nd.array(initw, ctx) | |
return fcnxs_args, fcnxs_auxs | |
def init_from_fcnxs(ctx, fcnxs_symbol, fcnxs_args_from, fcnxs_auxs_from): | |
""" use zero initialization for better convergence, because it tends to oputut 0, | |
and the label 0 stands for background, which may occupy most size of one image. | |
""" | |
fcnxs_args = fcnxs_args_from.copy() | |
fcnxs_auxs = fcnxs_auxs_from.copy() | |
for k,v in fcnxs_args.items(): | |
if(v.context != ctx): | |
fcnxs_args[k] = mx.nd.zeros(v.shape, ctx) | |
v.copyto(fcnxs_args[k]) | |
for k,v in fcnxs_auxs.items(): | |
if(v.context != ctx): | |
fcnxs_auxs[k] = mx.nd.zeros(v.shape, ctx) | |
v.copyto(fcnxs_auxs[k]) | |
data_shape=(1,3,500,500) | |
arg_names = fcnxs_symbol.list_arguments() | |
arg_shapes, _, _ = fcnxs_symbol.infer_shape(data=data_shape) | |
rest_params = {} | |
deconv_params = {} | |
# this is fcn8s init from fcn16s | |
if 'score_pool3_weight' in arg_names: | |
rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes) | |
if x[0] in ['score_pool3_bias', 'score_pool3_weight']]) | |
deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \ | |
in ["bigscore_weight", 'score4_weight']]) | |
# this is fcn16s init from fcn32s | |
elif 'score_pool4_weight' in arg_names: | |
rest_params = dict([(x[0], mx.nd.zeros(x[1], ctx)) for x in zip(arg_names, arg_shapes) | |
if x[0] in ['score_pool4_weight', 'score_pool4_bias']]) | |
deconv_params = dict([(x[0], x[1]) for x in zip(arg_names, arg_shapes) if x[0] \ | |
in ["bigscore_weight", 'score2_weight']]) | |
# this is fcn32s init | |
else: | |
logging.error("you are init the fcn32s model, so you should use init_from_vgg16()") | |
sys.exit() | |
fcnxs_args.update(rest_params) | |
for k, v in deconv_params.items(): | |
filt = upsample_filt(v[3]) | |
initw = np.zeros(v) | |
initw[range(v[0]), range(v[1]), :, :] = filt # becareful here is the slice assing | |
fcnxs_args[k] = mx.nd.array(initw, ctx) | |
return fcnxs_args, fcnxs_auxs |