def predict(self,address,dp):
img = scipy.ndimage.imread(address)
print img.shape
size_1 = int(img.shape[1]*1.0/img.shape[0]*224.0)
offset = int((size_1-224.0)/2)
# print size_1,offset
img = scipy.misc.imresize(img, (224,size_1), interp='bilinear', mode=None)[:,offset:offset+224,:]
print img.shape
img = img.reshape(224**2,3).T.reshape(1,3,224,224)
img = img.astype("double")
nn.show_images(img,(1,1),unit = True)
assert nn.backend == nn.GnumpyBackend
img = nn.garray(img - dp.data_mean.reshape(1,3,256,256)[:,:,:224,:224])
if dp.mode == "vgg": img = img[:,::-1,:,:]
self.feedforward(img)
H_cpu = self.H[-1].as_numpy_array()
mask = nn.NumpyBackend.k_sparsity_mask(H_cpu,5,1)
# H_cpu *= mask
index = np.nonzero(mask)
# print index[1]
# print H_cpu[:100]
for i in xrange(len(index[1])):
print index[1][i],dp.words(index[1][i]),H_cpu[index][i]
def load_npz(self, name):
f = np.load(name)
w = f['w']
# print w.shape
# print self.weights.mem.shape
if nn.backend == nn.GnumpyBackend: self.weights.mem[:] = nn.garray(w)
else: self.weights.mem[:] = w
def predict(self, address, dp):
img = scipy.ndimage.imread(address)
print img.shape
size_1 = int(img.shape[1] * 1.0 / img.shape[0] * 224.0)
offset = int((size_1 - 224.0) / 2)
# print size_1,offset
img = scipy.misc.imresize(img, (224, size_1),
interp='bilinear',
mode=None)[:, offset:offset + 224, :]
print img.shape
img = img.reshape(224**2, 3).T.reshape(1, 3, 224, 224)
img = img.astype("double")
nn.show_images(img, (1, 1), unit=True)
assert nn.backend == nn.GnumpyBackend
img = nn.garray(img -
dp.data_mean.reshape(1, 3, 256, 256)[:, :, :224, :224])
if dp.mode == "vgg": img = img[:, ::-1, :, :]
self.feedforward(img)
H_cpu = self.H[-1].as_numpy_array()
mask = nn.NumpyBackend.k_sparsity_mask(H_cpu, 5, 1)
# H_cpu *= mask
index = np.nonzero(mask)
# print index[1]
# print H_cpu[:100]
for i in xrange(len(index[1])):
print index[1][i], dp.words(index[1][i]), H_cpu[index][i]
def load_text(self, name):
if nn.backend == nn.GnumpyBackend:
print "gnumpy"
self.weights.mem = nn.garray(
np.loadtxt("./out/" + name, delimiter=','))
else:
print "numpy"
self.weights.mem = np.loadtxt("./out/" + name, delimiter=',')
def make_tied_copy(self, i, j):
wk0, bk0 = self[i]
wk1, bk1 = self[j]
if wk0.ndim == 2:
wk0 = wk1.T
wk1[:] = wk0.T
else:
w = wk1.as_numpy_array()
for ch in xrange(wk1.shape[0]):
for f in range(wk1.shape[1]):
x = w[ch, f, :, :]
x_flip = np.flipud(np.fliplr(x))
wk0[f, ch, :, :] = nn.garray(x_flip)
def make_tied_copy(self,i,j):
wk0,bk0 = self[i]
wk1,bk1 = self[j]
if wk0.ndim==2:
wk0 = wk1.T
wk1[:] = wk0.T
else:
w=wk1.as_numpy_array()
for ch in xrange(wk1.shape[0]):
for f in range(wk1.shape[1]):
x = w[ch,f,:,:]
x_flip = np.flipud(np.fliplr(x))
wk0[f,ch,:,:] = nn.garray(x_flip)
def load_npz(self,name):
f=np.load(name); w = f['w']
# print w.shape
# print self.weights.mem.shape
if nn.backend==nn.GnumpyBackend: self.weights.mem[:]=nn.garray(w)
else: self.weights.mem[:]=w
def load_text(self,name):
if nn.backend==nn.GnumpyBackend: print "gnumpy"; self.weights.mem=nn.garray(np.loadtxt("./out/"+name, delimiter=','))
else: print "numpy" ; self.weights.mem=np.loadtxt("./out/"+name, delimiter=',')
