def get_predict_file(args):
assert (os.path.exists(args.lfw_align))
pairs = load_pairs(args.pairs)
_, model_args, model_auxs = mx.model.load_checkpoint(
args.model_prefix, args.epoch)
symbol = lightened_cnn_b_feature()
with open(args.predict_file, 'w') as f:
for pair in pairs:
name1, name2, same = pairs_info(pair, args.suffix)
logging.info("processing name1:{} <---> name2:{}".format(
name1, name2))
model_args['data'] = mx.nd.array(
read2img(args.lfw_align, name1, name2, args.size, ctx), ctx)
exector = symbol.bind(ctx,
model_args,
args_grad=None,
grad_req="null",
aux_states=model_auxs)
exector.forward(is_train=False)
exector.outputs[0].wait_to_read()
output = exector.outputs[0].asnumpy()
dis = np.dot(output[0], output[1]) / np.linalg.norm(
output[0]) / np.linalg.norm(output[1])
f.write(name1 + '\t' + name2 + '\t' + str(dis) + '\t' + str(same) +
'\n')
def get_predict_file(args):
# assert(os.path.exists(args.lfw_align))
# pairs = load_pairs(args.pairs)
_, model_args, model_auxs = mx.model.load_checkpoint(args.model_prefix, args.epoch)
symbol = lightened_cnn_b_feature()
print("processing id image")
with open("id.txt", "w") as f:
for fname in os.listdir(args.idimage_align):
print(fname)
model_args['data'] = mx.nd.array(readimg(args.idimage_align, fname, args.size, ctx), ctx)
exector = symbol.bind(ctx, model_args ,args_grad=None, grad_req="null", aux_states=model_auxs)
exector.forward(is_train=False)
exector.outputs[0].wait_to_read()
output = exector.outputs[0].asnumpy()
f.write(fname + ' ')
output.tofile(f, ' ')
f.write('\n')
print("processing normal image")
with open("normal.txt", "w") as f:
for fname in os.listdir(args.normalimage_align):
print(fname)
model_args['data'] = mx.nd.array(readimg(args.normalimage_align, fname, args.size, ctx), ctx)
exector = symbol.bind(ctx, model_args ,args_grad=None, grad_req="null", aux_states=model_auxs)
exector.forward(is_train=False)
exector.outputs[0].wait_to_read()
output = exector.outputs[0].asnumpy()
f.write(fname + ' ')
output.tofile(f, ' ')
f.write('\n')
def get_predict_file(args):
assert(os.path.exists(args.lfw_align))
pairs = load_pairs(args.pairs)
_, model_args, model_auxs = mx.model.load_checkpoint(args.model_prefix, args.epoch)
symbol = lightened_cnn_b_feature()
with open(args.predict_file, 'w') as f:
for pair in pairs:
name1, name2, same = pairs_info(pair, args.suffix)
logging.info("processing name1:{} <---> name2:{}".format(name1, name2))
model_args['data'] = mx.nd.array(read2img(args.lfw_align, name1, name2, args.size, ctx), ctx)
exector = symbol.bind(ctx, model_args ,args_grad=None, grad_req="null", aux_states=model_auxs)
exector.forward(is_train=False)
exector.outputs[0].wait_to_read()
output = exector.outputs[0].asnumpy()
dis = np.dot(output[0], output[1])/np.linalg.norm(output[0])/np.linalg.norm(output[1])
f.write(name1 + '\t' + name2 + '\t' + str(dis) + '\t' + str(same) + '\n')
def load_exector(model_prefix, epoch, size):
_, model_args, model_auxs = mx.model.load_checkpoint(model_prefix, epoch)
symbol = lightened_cnn_b_feature()
return symbol, model_args, model_auxs
def load_exector(model_prefix, epoch, size):
_, model_args, model_auxs = mx.model.load_checkpoint(model_prefix, epoch)
symbol = lightened_cnn_b_feature()
return symbol, model_args, model_auxs
def __load_exector(self):
_, model_args, model_auxs = mx.model.load_checkpoint(
config.model_prefix, config.epoch)
symbol = lightened_cnn_b_feature()
return symbol, model_args, model_auxs
import logging
import os
import sys
import cv2
import mxnet as mx
import numpy as np
from sklearn.cross_validation import KFold
from sklearn.metrics import accuracy_score
from lightened_cnn import lightened_cnn_b_feature
# load model
ctx = mx.gpu(0)
_, model_args, model_auxs = mx.model.load_checkpoint("./lightened_cnn/lightened_cnn", 166)
symbol = lightened_cnn_b_feature()
# load data0
size = 128
img_arr = np.zeros((1, 1, size, size), dtype=float)
img = np.expand_dims(cv2.imread("test.png", 0), axis=0)
img_arr[0][:] = img / 255.0
# caculate feature0
model_args["data"] = mx.nd.array(img_arr, ctx)
exector = symbol.bind(ctx, model_args, args_grad=None, grad_req="null", aux_states=model_auxs)
exector.forward(is_train=False)
output0 = exector.outputs[0].asnumpy()
# load data1
img = np.expand_dims(cv2.imread("test1.png", 0), axis=0)
def load_model(self, model_prefix="model/lightened_cnn/lightened_cnn"):
_, self.model_args, self.model_auxs = mx.model.load_checkpoint(
model_prefix, self.epoch)
self.symbol = lightened_cnn_b_feature()
print "MXNet-Face loaded."