def test_flatten_forward(self):
x = np.asarray(rng.uniform(low=-1, high=1, size=(500, 10 ,28, 28)))
x = theano.shared(x,borrow = True)
flatten = layer.Flatten()
y = flatten.forward((x,))
y_shape = y[0].eval().shape
self.assertEqual(y_shape, (500, 7840))
def test_flatten_forwaredSize(self):
x = [(100, 10, 28, 28)]
flatten = layer.Flatten()
y = flatten.forwardSize(x)
self.assertEqual(y, [(100, 7840)])
def convert(def_path, caffemodel_path, output_path, phase):
# read from .prototxt file to collect layers.
params = caffe.proto.caffe_pb2.NetParameter()
with open(def_path, 'r') as def_file:
text_format.Merge(def_file.read(), params)
layerName2InstanceMap = {} # dict of str:layer
layerName2Bottom = {} # dict of str:list
inplaceOpMap = {} # dict of str:str
inputLayer = None
for layer in params.layer:
lname = layer.name
ltype = layer.type
ltop = layer.top
lbottom = layer.bottom
if len(ltop) > 0 and len(lbottom) > 0 and ltop[0] == lbottom[0]:
inplaceOpMap[lbottom[0]] = lname
if ltype == 'Input':
nl = L.RawInput((layer.input_param.shape[0].dim[1],
layer.input_param.shape[0].dim[2],
layer.input_param.shape[0].dim[3]))
nl.name = lname
layerName2InstanceMap[layer.name] = nl
inputLayer = nl
elif ltype == 'Convolution':
name = layer.name
bottom = layer.bottom
output_feature_map = layer.convolution_param.num_output
kernel_size = layer.convolution_param.kernel_size[0]
stride = (1, 1)
if len(layer.convolution_param.stride) > 0:
stride = layer.convolution_param.stride[0]
stride = (stride, stride)
nl = L.Conv2d(filter_size=(kernel_size, kernel_size),
output_feature=output_feature_map,
subsample=stride)
nl.name = lname
layerName2InstanceMap[name] = nl
layerName2Bottom[name] = layer.bottom
elif ltype == 'ReLU':
nl = L.Relu()
nl.name = lname
name = layer.name
bottom = layer.bottom
top = layer.top
layerName2InstanceMap[name] = nl
layerName2Bottom[name] = layer.bottom
elif layer.type == 'Pooling':
name = layer.name
# 0: max, 1: average, 2: stochastic
poolingMethod = layer.pooling_param.pool
if poolingMethod == 0:
poolingMethod = 'max'
elif poolingMethod == 1:
poolingMethod = 'avg'
kernel_size = layer.pooling_param.kernel_size
stride = layer.pooling_param.stride
pad = layer.pooling_param.pad
nl = L.Pooling(dsize=(kernel_size, kernel_size),
stride=(stride, stride),
pad=(pad, pad),
mode=poolingMethod)
nl.name = lname
layerName2InstanceMap[name] = nl
layerName2Bottom[name] = layer.bottom
elif layer.type == 'LRN':
name = layer.name
local_size = layer.lrn_param.local_size
alpha = layer.lrn_param.alpha
beta = layer.lrn_param.beta
nl = L.LRN(local_size=local_size, alpha=alpha, beta=beta)
nl.name = lname
layerName2InstanceMap[name] = nl
layerName2Bottom[name] = layer.bottom
elif layer.type == 'Concat':
name = layer.name
nl = L.Concat()
nl.name = lname
layerName2InstanceMap[name] = nl
layerName2Bottom[name] = layer.bottom
elif layer.type == 'Dropout':
name = layer.name
ratio = layer.dropout_param.dropout_ratio
nl = L.Dropout(p=ratio)
nl.name = lname
layerName2InstanceMap[name] = nl
layerName2Bottom[name] = layer.bottom
elif layer.type == 'InnerProduct':
name = layer.name
output_feature = layer.inner_product_param.num_output
nl = L.FullConn(output_feature=output_feature)
nl.name = lname
layerName2InstanceMap[name] = nl
if not isinstance(layerName2InstanceMap[layer.bottom[0]],
L.Flatten):
print('Insert flatten layer before full connection')
fl = L.Flatten()
fl.name = name + 'pre_flatten'
layerName2InstanceMap[fl.name] = fl
layerName2Bottom[fl.name] = layer.bottom
layerName2Bottom[name] = (fl.name, )
else:
layerName2Bottom[name] = layer.bottom
elif layer.type == 'Softmax':
name = layer.name
nl = L.SoftMax()
nl.name = lname
layerName2InstanceMap[name] = nl
layerName2Bottom[name] = layer.bottom
else:
print(layer.type)
raise NotImplementedError('unknown caffe layer.')
print(inplaceOpMap)
# create the network
n = N.Network()
for name in layerName2InstanceMap.keys():
if name in layerName2Bottom:
for bottomLayer in layerName2Bottom[name]:
if bottomLayer in inplaceOpMap.keys(
) and inplaceOpMap[bottomLayer] != name:
n.connect(layerName2InstanceMap[inplaceOpMap[bottomLayer]],
layerName2InstanceMap[name],
reload=True)
else:
n.connect(layerName2InstanceMap[bottomLayer],
layerName2InstanceMap[name],
reload=True)
n.setInput(inputLayer, reload=True)
n.buildForwardSize()
# read .caffemodel file to load real parameters.
net_param = caffe_pb2.NetParameter()
with open(caffemodel_path, 'rb') as f:
net_param.ParseFromString(f.read())
for layer in net_param.layers:
lname = layer.name # str
ltype = layer.type # int, the mapping is defined in caffe/src/caffe/proto/caffe.proto
bottom = layer.bottom # RepeatedScalarContainer
top = layer.top # RepeatedScalarContainer
print("{}, {}".format(lname, ltype))
if lname in layerName2InstanceMap.keys():
if ltype == 4: # convolution
w = np.array(layer.blobs[0].data).reshape(
(layer.blobs[0].num, layer.blobs[0].channels,
layer.blobs[0].height, layer.blobs[0].width), )
bias = np.array(layer.blobs[1].data).reshape(
(layer.blobs[1].width), )
print(w.shape)
if w.shape != layerName2InstanceMap[lname].w.get_value().shape:
print(w.shape)
print(layerName2InstanceMap[lname].w.get_value().shape)
raise Exception('Error, w shape do not match.')
if bias.shape != layerName2InstanceMap[lname].b.get_value(
).shape:
print(bias.shape)
print(layerName2InstanceMap[lname].b.get_value().shape)
raise Exception('Error, b shape do not match.')
layerName2InstanceMap[lname].w = theano.shared(floatX(w),
borrow=True)
layerName2InstanceMap[lname].b = theano.shared(floatX(bias),
borrow=True)
elif ltype == 5: # data
print('seen name: {}, {}'.format(lname, ltype))
elif ltype == 18: # relu
print('seen name: {}, {}'.format(lname, ltype))
elif ltype == 17: # pooling
print('seen name: {}, {}'.format(lname, ltype))
elif ltype == 15: # lrn
print('seen name: {}, {}'.format(lname, ltype))
elif ltype == 3: # concat
print('seen name: {}, {}'.format(lname, ltype))
elif ltype == 6: # dropout
print('seen name: {}, {}'.format(lname, ltype))
elif ltype == 14: # fullconn
print('seen name: {}, {}'.format(lname, ltype))
w = np.array(layer.blobs[0].data).reshape(
(layer.blobs[0].width, layer.blobs[0].height), )
bias = np.array(layer.blobs[1].data).reshape(
(layer.blobs[1].width), )
print("heh:{}".format(lname))
print(layerName2InstanceMap[lname])
print(layerName2InstanceMap[lname].w)
if w.shape != layerName2InstanceMap[lname].w.get_value().shape:
print(w.shape)
print(layerName2InstanceMap[lname].w.get_value().shape)
raise Exception('Error, w shape do not match.')
if bias.shape != layerName2InstanceMap[lname].b.get_value(
).shape:
print(bias.shape)
print(layerName2InstanceMap[lname].b.get_value().shape)
raise Exception('Error, b shape do not match.')
layerName2InstanceMap[lname].w = theano.shared(floatX(w),
borrow=True)
layerName2InstanceMap[lname].b = theano.shared(floatX(bias),
borrow=True)
elif ltype == 21: # 21 for Softmax output, 20 for softmax
print('seen name: {}, {}'.format(lname, ltype))
else:
print('seen name: {}, unknown type: {}'.format(lname, ltype))
else:
if len(layer.blobs) > 0:
#raise NotImplementedError('unseen layer with blobs: {}'.format(lname))
print('error, unseen layer with blobs: {}, {}'.format(
lname, ltype))
else:
#print('warning, unseen name: {}, {}'.format(lname, ltype))
pass
# finally build the network.
n.build(reload=True)
return n