def inference():
model_name = 'AtrousFCN_DeResnet50_asUnet'
weight_file = 'checkpoint_weights_3.hdf5'
model_input_size = (720, 720)
batch_shape = (1, ) + model_input_size + (3, )
model_weight_path = os.path.join('kerasModel', weight_file)
keras_model = globals()[model_name](batch_shape=batch_shape,
input_shape=(model_input_size[0],
model_input_size[1], 3))
keras_model.load_weights(model_weight_path, by_name=True)
input_data = np.random.random(keras_model.input.shape)
caffe_model_dir = 'caffeModel'
caffe_net_file = os.path.join(caffe_model_dir,
'caffeModel_deploy_3.prototxt')
caffe_params_file = os.path.join(caffe_model_dir,
'caffeModel_3.caffemodel')
keras2caffe.convert(keras_model, caffe_net_file, caffe_params_file)
caffe_model = caffe.Net(caffe_net_file, caffe_params_file, caffe.TEST)
caffe_model.blobs['data'].data[...] = np.transpose(
input_data, (0, 3, 1, 2)) #执行上面设置的图片预处理操作，并将图片载入到blob�?
caffe_out = caffe_model.forward()
print(caffe_out)
output = caffe_out['batch_normalization_6']
output = np.transpose(output, (0, 2, 3, 1))
print('caffe_out ' + str(output.shape))
print(output[0, :, :, 0])
keras_out = keras_model.predict(input_data, batch_size=1)
print(
'keras_out' + str(keras_out.shape) +
'========================================================================================================================'
)
print(keras_out[0, :, :, 0])
for layer_name, blob in caffe_model.blobs.iteritems():
print(layer_name + '\t' + str(blob.data.shape))
keras_model = load_model(MODEL_HDF5)
else:
keras_model = load_model(MODEL_HDF5)
keras_model.summary()
# ----------------------------------------------
# convert to caffe model
# ----------------------------------------------
if OPTIONAL_MODE == "caffemodel":
os.environ["GLOG_minloglevel"] = "2"
import caffe
import keras2caffe
prototxt = DATASET_ROOT_PATH + 'pretrain/agegender_' + ANNOTATIONS + '_' + MODELS + DATASET_NAME + '.prototxt'
caffemodel = DATASET_ROOT_PATH + 'pretrain/agegender_' + ANNOTATIONS + '_' + MODELS + DATASET_NAME + '.caffemodel'
keras2caffe.convert(keras_model, prototxt, caffemodel)
# ----------------------------------------------
# Benchmark
# ----------------------------------------------
if OPTIONAL_MODE == "benchmark":
BENCHMARK_DATASET_NAME = "imdb"
BENCHMARK_DATASET_TARGET = "validation"
BATCH_SIZE = 64
shape = keras_model.layers[0].get_output_at(0).get_shape().as_list()
disp_generator = ImageDataGenerator(rescale=1.0 / 255).flow_from_directory(
DATASET_ROOT_PATH + 'dataset/agegender_' + BENCHMARK_DATASET_NAME +
'/annotations/' + ANNOTATIONS + '/' + BENCHMARK_DATASET_TARGET,
#converting
keras_model = MobileNet(input_shape=(224, 224, 3),
alpha=1.0,
depth_multiplier=1,
dropout=1e-3,
include_top=True,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000)
caffe_proto = 'MobileNet.prototxt'
caffe_weights = 'MobileNet.caffemodel'
keras2caffe.convert(keras_model, caffe_proto, caffe_weights)
#testing the model
net = caffe.Net(caffe_proto, caffe_weights, caffe.TEST)
img = cv2.imread(DATA_DIR + 'bear.jpg')
img = cv2.resize(img, (224, 224))
img = img[..., ::-1] #RGB 2 BGR
data = np.array(img, dtype=np.float32)
data = data.transpose((2, 0, 1))
data.shape = (1, ) + data.shape
data /= 128
data -= 1.0
config.gpu_options.per_process_gpu_memory_fraction = 0.5
set_session(tf.Session(config=config))
import keras2caffe
import sys
sys.path.append('/home/data/keras-models/keras-inceptionV4')
import inception_v4
#import evalute_image
#converting
keras_model = inception_v4.create_model(weights='imagenet', include_top=True, dropout_prob=0.8)
keras2caffe.convert(keras_model, 'InceptionV4.prototxt', 'InceptionV4.caffemodel')
#testing the model
caffe.set_mode_gpu()
net = caffe.Net('InceptionV4.prototxt', 'InceptionV4.caffemodel', caffe.TEST)
img = cv2.imread('bear.jpg')
#img = evaluate_image.central_crop(im, 0.875)
img = cv2.resize(img, (299, 299))
img = img[...,::-1] #RGB 2 BGR
data = np.array(img, dtype=np.float32)
data = data.transpose((2, 0, 1))
data.shape = (1,) + data.shape
MODEL_HDF5 = 'pretrain/gender_mini_XCEPTION.21-0.95.hdf5'
ANNOTATION_WORDS = 'words/agegender_gender_words.txt'
IMAGE_SIZE = 32
if (MODELS == 'simple_cnn'):
IMAGE_SIZE = 48
if (MODELS == 'miniXception'):
IMAGE_SIZE = 64
if (MODELS == 'vgg16'):
IMAGE_SIZE = 224
keras_model = load_model(MODEL_HDF5)
keras_model.summary()
keras2caffe.convert(
keras_model,
'pretrain/agegender_' + ANNOTATIONS + '_' + MODELS + '.prototxt',
'pretrain/agegender_' + ANNOTATIONS + '_' + MODELS + '.caffemodel')
net = caffe.Net(
'pretrain/agegender_' + ANNOTATIONS + '_' + MODELS + '.prototxt',
'pretrain/agegender_' + ANNOTATIONS + '_' + MODELS + '.caffemodel',
caffe.TEST)
# ----------------------------------------------
# data
# ----------------------------------------------
#img = cv2.imread('dataset/agegender/annotations/agegender/validation/0_0-2_m/landmark_aligned_face.84.8277643357_43f107482d_o.jpg')
img = cv2.imread(
'dataset/agegender/annotations/agegender/validation/11_15-20_f/landmark_aligned_face.290.11594063605_713764ddeb_o.jpg'
)
from keras.applications.inception_v3 import InceptionV3
#TensorFlow backend uses all GPU memory by default, so we need limit
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5
set_session(tf.Session(config=config))
#converting
keras_model = InceptionV3(input_shape=(299, 299, 3),
weights='imagenet',
include_top=True)
keras2caffe.convert(keras_model, 'deploy.prototxt', 'InceptionV3.caffemodel')
#testing the model
caffe.set_mode_gpu()
net = caffe.Net('deploy.prototxt', 'InceptionV3.caffemodel', caffe.TEST)
img = cv2.imread(DATA_DIR + 'bear.jpg')
img = cv2.resize(img, (299, 299))
img = img[..., ::-1] #RGB 2 BGR
data = np.array(img, dtype=np.float32)
data = data.transpose((2, 0, 1))
data.shape = (1, ) + data.shape
data /= 128
sys.path.insert(0, parentdir)
import Models
from Models import build_model
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="unet")
parser.add_argument("--n_classes", type=int, default=2)
parser.add_argument("--input_height", type=int, default=224)
parser.add_argument("--input_width", type=int, default=224)
parser.add_argument("--input_model", type=str, default="../weights/xxx.hd55")
parser.add_argument("--output_model", type=str, default="./unet.prototxt")
parser.add_argument("--output_weight", type=str, default="./unet.caffemodel")
args = parser.parse_args()
n_classes = args.n_classes
input_height = args.input_height
input_width = args.input_width
model_name = args.model_name
input_model_path = args.input_model
output_model = args.output_model
output_weight = args.output_weight
model = build_model(model_name,
n_classes,
input_height=input_height,
input_width=input_width)
model.load_weights(input_model_path)
keras2caffe.convert(model, output_model, output_weight)
import sys
sys.path.append('../../')
import keras2caffe
from keras.models import load_model
model = load_model('simple_CNN.81-0.96.hdf5')
keras2caffe.convert(model, 'deploy.prototxt', 'weights.caffemodel')
for i in range(48):
mean_and_std = mean_and_std + str(data_mean_3d[i]) + ","
mean_and_std = mean_and_std + "};\n"
mean_and_std = mean_and_std + "float data_std_3d[32]={"
for i in range(48):
mean_and_std = mean_and_std + str(data_std_3d[i]) + ","
mean_and_std = mean_and_std + "};\n"
print(mean_and_std)
#converting
keras_model = load_model('3d-pose-baseline.hdf5')
keras_model.summary()
keras2caffe.convert(keras_model, '3d-pose-baseline.prototxt',
'3d-pose-baseline.caffemodel')
net = caffe.Net('3d-pose-baseline.prototxt', '3d-pose-baseline.caffemodel',
caffe.TEST)
data = np.random.rand(32)
data = np.reshape(np.array(data), (1, 32))
#verify
pred = keras_model.predict(data)[0]
print(pred)
out = net.forward_all(data=data)
pred = out['dense_6']
print(pred)
from keras.preprocessing import image
import keras2caffe
#TensorFlow backend uses all GPU memory by default, so we need limit
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5
set_session(tf.Session(config=config))
#converting
keras_model = VGG16(input_shape=(224, 224, 3), weights='imagenet', include_top=True)
keras2caffe.convert(keras_model, 'VGG16.prototxt', 'VGG16.caffemodel')
#testing the model
#caffe.set_mode_gpu()
net = caffe.Net('VGG16.prototxt', 'VGG16.caffemodel', caffe.TEST)
img = cv2.imread('bear.jpg')
img = cv2.resize(img, (224, 224))
img = img[...,::-1] #RGB 2 BGR
data = np.array(img, dtype=np.float32)
data = data.transpose((2, 0, 1))
data.shape = (1,) + data.shape
data -= 128
import sys
sys.path.append('/media/toshiba_ml/models/keras-models/keras-squeezenet')
from keras_squeezenet import SqueezeNet
#TensorFlow backend uses all GPU memory by default, so we need limit
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5
set_session(tf.Session(config=config))
#converting
keras_model = SqueezeNet()
keras2caffe.convert(keras_model, 'deploy.prototxt', 'SqueezeNet.caffemodel')
#testing the model
caffe.set_mode_gpu()
net = caffe.Net('deploy.prototxt', 'SqueezeNet.caffemodel', caffe.TEST)
img = cv2.imread(DATA_DIR+'bear.jpg')
img = cv2.resize(img, (227, 227))
img = img[...,::-1] #RGB 2 BGR
data = np.array(img, dtype=np.float32)
data = data.transpose((2, 0, 1))
data.shape = (1,) + data.shape
MODEL_HDF5 = DATASET_ROOT_PATH + 'pretrain/gender_mini_XCEPTION.21-0.95.hdf5'
ANNOTATION_WORDS = 'words/agegender_gender_words.txt'
IMAGE_SIZE = 32
if (MODELS == 'simple_cnn'):
IMAGE_SIZE = 48
if (MODELS == 'miniXception'):
IMAGE_SIZE = 64
if (MODELS == 'vgg16'):
IMAGE_SIZE = 224
keras_model = load_model(MODEL_HDF5)
keras_model.summary()
keras2caffe.convert(
keras_model, DATASET_ROOT_PATH + 'pretrain/agegender_' + ANNOTATIONS +
'_' + MODELS + '.prototxt', DATASET_ROOT_PATH + 'pretrain/agegender_' +
ANNOTATIONS + '_' + MODELS + '.caffemodel')
net = caffe.Net(
DATASET_ROOT_PATH + 'pretrain/agegender_' + ANNOTATIONS + '_' + MODELS +
'.prototxt', DATASET_ROOT_PATH + 'pretrain/agegender_' + ANNOTATIONS +
'_' + MODELS + '.caffemodel', caffe.TEST)
# ----------------------------------------------
# data
# ----------------------------------------------
#img = cv2.imread('dataset/agegender/annotations/agegender/validation/0_0-2_m/landmark_aligned_face.84.8277643357_43f107482d_o.jpg')
img = cv2.imread(
'dataset/agegender/annotations/agegender/validation/11_15-20_f/landmark_aligned_face.290.11594063605_713764ddeb_o.jpg'
)
from datetime import datetime
import keras2caffe
from keras.models import load_model, model_from_json
if __name__ == '__main__':
if len(sys.argv) < 2:
raise Exception('ERROR: No input model.')
model_file = sys.argv[1]
if not os.path.isfile(model_file):
raise Exception('ERROR: model file is not exist!')
if len(sys.argv) < 3:
raise Exception('ERROR: No input weights.')
w_file = sys.argv[2]
if not os.path.isfile(model_file):
raise Exception('ERROR: w file is not exist!')
OUTPUT_DIR = datetime.now().strftime("output_%y%m%d_%H%M%S/")
if not os.path.isdir(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
model = model_from_json(
open(sys.argv[1],
'r').read()) if sys.argv[1][-5:] == '.json' else load_model(
sys.argv[1])
model.load_weights(sys.argv[2])
keras2caffe.convert(model, OUTPUT_DIR + 'deploy.prototxt',
OUTPUT_DIR + 'model.caffemodel')
sys.path.append('/home/lc/keras2caffe/ssrnet/')
import keras_ssrnet
#import evalute_image
#converting
keras_model = keras_ssrnet.create_model(3,
3,
3,
0.5,
0.5,
weights='imdb',
include_top=True)
keras2caffe.convert(keras_model, 'ssrnet.prototxt', 'ssrnet.caffemodel')
#testing the model
#caffe.set_mode_cpu()
#net = caffe.Net('ssrnet.prototxt', 'ssrnet.caffemodel', caffe.TEST)
#img = cv2.imread('man.JPG')
#img = evaluate_image.central_crop(im, 0.875)
#img = cv2.resize(img, (64, 64))
#img = img[...,::-1] #RGB 2 BGR
#data = np.array(img, dtype=np.float32)
#data = data.transpose((2, 0, 1))
#data.shape = (1,) + data.shape
import Models
from Models import build_model
parser = argparse.ArgumentParser()
parser.add_argument("--model_name", type=str, default="unet")
parser.add_argument("--n_classes", type=int, default=2)
parser.add_argument("--input_height", type=int, default=224)
parser.add_argument("--input_width", type=int, default=224)
parser.add_argument("--input_model", type=str, default="../weights/xxx.hd55")
parser.add_argument("--output_model", type=str, default="./unet.prototxt")
parser.add_argument("--output_weight", type=str, default="./unet.caffemodel")
args = parser.parse_args()
n_classes = args.n_classes
input_height = args.input_height
input_width = args.input_width
model_name = args.model_name
input_model_path = args.input_model
output_model = args.output_weight
output_weight = args.output_weight
model = build_model(model_name,
n_classes,
input_height=input_height,
input_width=input_width)
model.load_weights(input_model_path)
keras2caffe.convert(model, output_model, output_model)
if(ANNOTATIONS=="gender_octavio"):
MODEL_HDF5=DATASET_ROOT_PATH+'pretrain/gender_mini_XCEPTION.21-0.95.hdf5'
ANNOTATION_WORDS='words/agegender_gender_words.txt'
IMAGE_SIZE = 32
if(MODELS=='simple_cnn'):
IMAGE_SIZE = 48
if(MODELS=='miniXception'):
IMAGE_SIZE = 64
if(MODELS=='vgg16'):
IMAGE_SIZE = 224
keras_model = load_model(MODEL_HDF5)
keras_model.summary()
keras2caffe.convert(keras_model, DATASET_ROOT_PATH+'pretrain/agegender_'+ANNOTATIONS+'_'+MODELS+'.prototxt', DATASET_ROOT_PATH+'pretrain/agegender_'+ANNOTATIONS+'_'+MODELS+'.caffemodel')
net = caffe.Net(DATASET_ROOT_PATH+'pretrain/agegender_'+ANNOTATIONS+'_'+MODELS+'.prototxt', DATASET_ROOT_PATH+'pretrain/agegender_'+ANNOTATIONS+'_'+MODELS+'.caffemodel', caffe.TEST)
# ----------------------------------------------
# data
# ----------------------------------------------
#img = cv2.imread('dataset/agegender/annotations/agegender/validation/0_0-2_m/landmark_aligned_face.84.8277643357_43f107482d_o.jpg')
img = cv2.imread('dataset/agegender/annotations/agegender/validation/11_15-20_f/landmark_aligned_face.290.11594063605_713764ddeb_o.jpg')
#img = cv2.imread('dataset/agegender/annotations/agegender/validation/3_15-20_m/landmark_aligned_face.291.11593859573_1419d380b4_o.jpg')
img = cv2.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
if(ANNOTATIONS=='emotion' or ANNOTATIONS=='gender_octavio'):
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = np.expand_dims(img, axis=2)
