def __init__(self, model=defaultModel, imgDim=96, cuda=False):
"""__init__(self, model=defaultModel, imgDim=96, cuda=False)
Instantiate a 'TorchNeuralNet' object.
:param model: The path to the Torch model to use.
:type model: str
:param imgDim: The edge length of the square input image.
:type imgDim: int
:param cuda: Flag to use CUDA in the subprocess.
:type cuda: bool
"""
assert model is not None
assert imgDim is not None
assert cuda is not None
torch.setdefaulttensortype('torch.FloatTensor')
self._net = torch.load(model)
self._net.evaluate(self._net)
self._tensor = torch.Tensor(1, 3, imgDim, imgDim)
self._cuda_tensor = None
if cuda:
lua.require('cutorch')
lua.require('cunn')
self._net = self._net._cuda()
self._cuda_tensor = torch.CudaTensor(1, 3, imgDim, imgDim)
self._cuda = cuda
self._imgDim = imgDim
def __init__(self, model=defaultModel, imgDim=96, cuda=False):
"""__init__(self, model=defaultModel, imgDim=96, cuda=False)
Instantiate a 'TorchNeuralNet' object.
:param model: The path to the Torch model to use.
:type model: str
:param imgDim: The edge length of the square input image.
:type imgDim: int
:param cuda: Flag to use CUDA in the subprocess.
:type cuda: bool
"""
assert model is not None
assert imgDim is not None
assert cuda is not None
torch.setdefaulttensortype('torch.FloatTensor')
self._net = torch.load(model)
self._net.evaluate(self._net)
self._tensor = torch.Tensor(1, 3, imgDim, imgDim)
self._cuda_tensor = None
if cuda:
lua.require('cutorch')
lua.require('cunn')
self._net = self._net._cuda()
self._cuda_tensor = torch.CudaTensor(1, 3, imgDim, imgDim)
self._cuda = cuda
self._imgDim = imgDim
#!/usr/bin/env python
# see 1) https://github.com/BVLC/caffe/issues/290 (to read binaryproto)
# see 2) https://github.com/hughperkins/pytorch/issues/7 (LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libgfortran.so.3.0.0) for me just conda install libgfortran
import cv2
import sys
import numpy as np
import lutorpy as lua
import os
torch = lua.require("torch")
loadcaffe = lua.require('loadcaffe')
image = lua.require('image')
# setup runtime and use zero-based index(optional, enabled by default)
# lua.LuaRuntime(zero_based_index=True)
imageHeight = 224
imageWidth = 224
def preprocess(img_bgr, img_mean):
img_bgr = image.scale(img_bgr,imageHeight,imageWidth,'bilinear')
img_mean_bgr = img_mean._squeeze()
if img_mean is not None:
for i in range(1, 4):
img_bgr[i]._add(-img_mean_bgr[i])
return img_bgr
net = loadcaffe.load('/home/gao/Hadoop/hduser/data/models/VGG_CNN_S_deploy.prototxt', '/home/gao/Hadoop/hduser/data/models/VGG_CNN_S.caffemodel', 'nn')
net._evaluate(); #This sets the mode of the Module (or sub-modules) to train=false. This is useful for modules like Dropout that have a different behaviour during training vs evaluation.
synset_words = []
fo = open('/home/gao/Hadoop/hduser/data/synset_words.txt', 'r')
for line in fo.readlines():
"""
A interface for Torch models
"""
import lutorpy as lua
import numpy as np
import os
import sys
import time
lua.require('nn')
lua.require('cunn')
lua.require('cudnn')
lua.eval("torch.setdefaulttensortype('torch.FloatTensor')")
import logging
from skimage import io, transform
def thumbnail(img, size=150):
"""
Resize given image by the shortest edge
"""
from math import floor
width, height = img.shape[1], img.shape[0]
if width == height:
img = transform.resize(img, [size, size])
elif height > width:
ratio = float(height)/float(width)
newheight = ratio * size
img = transform.resize(img, [int(floor(newheight)), size ])
#
# This file is Vitalius Parubochyi's modification of `torch_neural_net.py`
# to use lutorpy instead of calling a Lua subprocess.
# It's currently not used by default to avoid adding an
# additional dependency.
# More details are available on this mailing list thread:
# https://groups.google.com/forum/#!topic/cmu-openface/Jj68LJBdN-Y
"""Module for Torch-based neural network usage."""
import lutorpy as lua
import numpy as np
import binascii
import cv2
import os
torch = lua.require('torch')
nn = lua.require('nn')
dpnn = lua.require('dpnn')
image = lua.require('image')
myDir = os.path.dirname(os.path.realpath(__file__))
class TorchNeuralNet:
"""Use a `Torch <http://torch.ch>` and `Lutorpy <https://github.com/imodpasteur/lutorpy>`."""
#: The default Torch model to use.
defaultModel = os.path.join(myDir, '..', 'models', 'openface',
'nn4.v1.ascii.t7')
def __init__(self, model=defaultModel, imgDim=96, cuda=False):
# see 1) https://github.com/BVLC/caffe/issues/290
# see 2) https://github.com/hughperkins/pytorch/issues/7 (LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libgfortran.so.3.0.0)
import caffe
import numpy as np
import lutorpy as lua
# setup runtime and use zero-based index(optional, enabled by default)
lua.LuaRuntime(zero_based_index=True)
torch = lua.require("torch")
blob = caffe.proto.caffe_pb2.BlobProto()
in_binary_proto_path = "models/VGG_mean.binaryproto"
out_path = "out.npy"
data = open(in_binary_proto_path , 'rb' ).read()
blob.ParseFromString(data)
arr = np.array(caffe.io.blobproto_to_array(blob))
out = arr[0]
np.save(out_path, out)
t = torch.DoubleTensor(11, 11)._fill(1)
torch.save("test_save_from_python.t7", t)
image_mean_torch_tensor = torch.fromNumpyArray(arr)
torch.save("models/VGG_mean.t7", image_mean_torch_tensor)
np.save("models/VGG_mean.npy", arr)
# This file is Vitalius Parubochyi's modification of `torch_neural_net.py`
# to use lutorpy instead of calling a Lua subprocess.
# It's currently not used by default to avoid adding an
# additional dependency.
# More details are available on this mailing list thread:
# https://groups.google.com/forum/#!topic/cmu-openface/Jj68LJBdN-Y
"""Module for Torch-based neural network usage."""
import lutorpy as lua
import numpy as np
import binascii
import cv2
import os
torch = lua.require('torch')
nn = lua.require('nn')
dpnn = lua.require('dpnn')
image = lua.require('image')
myDir = os.path.dirname(os.path.realpath(__file__))
class TorchNeuralNet:
"""Use a `Torch <http://torch.ch>` and `Lutorpy <https://github.com/imodpasteur/lutorpy>`."""
#: The default Torch model to use.
defaultModel = os.path.join(
myDir, '..', 'models', 'openface', 'nn4.small2.v1.t7')
