Caffe2 - (七)Caffemodel 转换为 Caffe2 pb 模型
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Caffe2 - Caffemodel 转换为 Caffe2 pb 模型
1. 单输入单输出 - caffe_translator.py
Caffe2 提供了将 caffemodel 转换为 caffe2 模型的工具——caffe_translator.py.
其使用：
python -m caffe2.python.caffe_translator deploy.prototxt pretrained.caffemodel即得到 caffe2 的模型：init_net.pb，predict_net.pb.
现在支持转换的网络层有：
- Input
- VideoData
- Data
- Convolution3D
- Convolution
- Deconvolution
- Crop
- ReLU
- Pooling
- Pooling3D
- LRN
- InnerProduct
- Dropout
- Softmax
- SoftmaxWithLoss
- Accuracy
- Concat
- TanH
- InstanceNorm
- BatchNorm
- Eltwise
- Scale
- Reshape
- Sigmoid
- ROIPooling
- PReLU
- Reduction
2. 多输入多输出
Caffe2 提供的模型转换工具貌似只支持单输入单输出，由于自己的网络模型有多个输入多个输出，故，基于caffe_translator.py 进行修改，得到新的 caffe_translator_multi.py：
# ----------------------------------
# caffe_translator_multi.py
# multi-inputs, multi-outputs
# ----------------------------------
#!/usr/bin/env python
import argparse
import copy
import logging
import re
import numpy as np # noqa
import sys
sys.path.insert(0, '/path/to/caffe2/build')
from caffe2.proto import caffe2_pb2, caffe2_legacy_pb2
from caffe.proto import caffe_pb2
from caffe2.python import core, utils, workspace
from google.protobuf import text_format
logging.basicConfig()
log = logging.getLogger("caffe_translator")
log.setLevel(logging.INFO)
def _StateMeetsRule(state, rule):
"""A function that reproduces Caffe's StateMeetsRule functionality."""
if rule.HasField('phase') and rule.phase != state.phase:
return False
if rule.HasField('min_level') and state.level < rule.min_level:
return False
if rule.HasField('max_level') and state.level > rule.max_level:
return False
curr_stages = set(list(state.stage))
# all stages in rule.stages should be in, otherwise it's not a match.
if len(rule.stage) and any([s not in curr_stages for s in rule.stage]):
return False
# none of the stage in rule.stages should be in, otherwise it's not a match.
if len(rule.not_stage) and any([s in curr_stages for s in rule.not_stage]):
return False
# If none of the nonmatch happens, return True.
return True
def _ShouldInclude(net_state, layer):
"""A function that reproduces Caffe's inclusion and exclusion rule."""
ret = (len(layer.include) == 0)
# check exclude rules: if any exclusion is met, we shouldn't include.
ret &= not any([_StateMeetsRule(net_state, rule) for rule in layer.exclude])
if len(layer.include):
# check include rules: if any inclusion is met, we should include.
ret |= any([_StateMeetsRule(net_state, rule) for rule in layer.include])
return ret
def _GetLegacyDims(net, net_params, dummy_input, legacy_pad_ops):
dim_map = {}
ws = workspace.C.Workspace()
for param in net_params.protos:
ws.create_blob(param.name) \
.feed(utils.Caffe2TensorToNumpyArray(param))
external_input = net.op[0].input[0]
ws.create_blob(external_input).feed(dummy_input)
# Get dimensions with legacy pad
for i in range(len(net.op)):
op_def = net.op[i]
ws._run_operator(op_def.SerializeToString())
if i in legacy_pad_ops:
output = op_def.output[0]
blob_legacy = ws.fetch_blob(output)
dim_map[i] = blob_legacy.shape
return dim_map
def _GetLegacyPadArgs(op_def, arg_map):
pads = {}
keys = ['pad_l', 'pad_t', 'pad_r', 'pad_b']
is_pad = 'pad' in arg_map
if is_pad:
for k in keys:
pads[k] = arg_map['pad'].i
else:
pads = {x: arg_map[x].i for x in keys}
return pads
def _AdjustDims(op_def, arg_map, pads, dim1, dim2):
n1, c1, h1, w1 = dim1
n2, c2, h2, w2 = dim2
assert(n1 == n2)
assert(c1 == c2)
is_pad = 'pad' in arg_map
if h1 != h2 or w1 != w2:
if h1 == h2 + 1:
pads['pad_b'] += 1
elif h1 != h2:
raise Exception("Unexpected dimensions for height:", h1, h2)
if w1 == w2 + 1:
pads['pad_r'] += 1
elif w1 != w2:
raise Exception("Unexpected dimensions for width:", w1, w2)
if is_pad:
op_def.arg.remove(arg_map['pad'])
args = []
for name in pads.keys():
arg = caffe2_pb2.Argument()
arg.name = name
arg.i = pads[name]
args.append(arg)
op_def.arg.extend(args)
else:
for name in pads.keys():
arg_map[name].i = pads[name]
def _RemoveLegacyPad(net, net_params, input_dims):
legacy_pad_ops = []
for i in range(len(net.op)):
op_def = net.op[i]
if re.match(r'^(Conv|ConvTranspose|MaxPool|AveragePool)(\dD)?$',
op_def.type):
for arg in op_def.arg:
if arg.name == 'legacy_pad':
legacy_pad_ops.append(i)
break
if legacy_pad_ops:
n, c, h, w = input_dims
dummy_input = np.random.randn(n, c, h, w).astype(np.float32)
dim_map = _GetLegacyDims(net, net_params, dummy_input, legacy_pad_ops)
# Running with the legacy pad argument removed
# compare the dimensions and adjust pad argument when necessary
ws = workspace.C.Workspace()
external_input = net.op[0].input[0]
ws.create_blob(external_input).feed_blob(dummy_input)
for param in net_params.protos:
ws.create_blob(param.name) \
.feed_blob(utils.Caffe2TensorToNumpyArray(param))
for i in range(len(net.op)):
op_def = net.op[i]
if i in legacy_pad_ops:
arg_map = {}
for arg in op_def.arg:
arg_map[arg.name] = arg
pads = _GetLegacyPadArgs(op_def, arg_map)
# remove legacy pad arg
for j in range(len(op_def.arg)):
arg = op_def.arg[j]
if arg.name == 'legacy_pad':
del op_def.arg[j]
break
output = op_def.output[0]
# use a new name to avoid the interference with inplace
nonlegacy_output = output + '_nonlegacy'
op_def.output[0] = nonlegacy_output
ws._run_operator(op_def.SerializeToString())
blob_nonlegacy = ws.fetch_blob(nonlegacy_output)
# reset output name
op_def.output[0] = output
dim1 = dim_map[i]
dim2 = blob_nonlegacy.shape
_AdjustDims(op_def, arg_map, pads, dim1, dim2)
ws._run_operator(op_def.SerializeToString())
return net
def _GetBlobDimMap(net, net_params, dummy_input):
dim_map = {}
ws = workspace.C.Workspace()
for param in net_params.protos:
ws.create_blob(param.name) \
.feed(utils.Caffe2TensorToNumpyArray(param))
external_input = net.op[0].input[0]
ws.create_blob(external_input).feed(dummy_input)
# Get dimensions with legacy pad
for i in range(len(net.op)):
op_def = net.op[i]
ws._run_operator(op_def.SerializeToString())
for output in op_def.output:
blob = ws.fetch_blob(output)
dim_map[output] = blob.shape
return dim_map
def _GetInputDims(caffe_net):
input_dims = []
if caffe_net.input_dim:
input_dims = caffe_net.input_dim
elif caffe_net.input_shape:
input_dims = caffe_net.input_shape[0].dim
elif caffe_net.layer[0].input_param.shape:
# getting input dimension from first layer
input_dims = caffe_net.layer[0].input_param.shape[0].dim
return input_dims
class TranslatorRegistry(object):
registry_ = {}
@classmethod
def Register(cls, op_name):
"""A decorator for registering gradient mappings."""
def Wrapper(func):
cls.registry_[op_name] = func
return func
return Wrapper
@classmethod
def TranslateLayer(cls, layer, pretrained_blobs, is_test, **kwargs):
try:
caffe_ops, params = cls.registry_[layer.type](
layer, pretrained_blobs, is_test, **kwargs)
except KeyError:
raise KeyError('No translator registered for layer: %s yet.' %
str(layer))
if caffe_ops is None:
caffe_ops = []
if type(caffe_ops) is not list:
caffe_ops = [caffe_ops]
return caffe_ops, params
@classmethod
def TranslateModel(
cls,
caffe_net,
pretrained_net,
is_test=False,
net_state=None,
remove_legacy_pad=False,
input_dims=None
):
net_state = caffe_pb2.NetState() if net_state is None else net_state
net = caffe2_pb2.NetDef()
net.name = caffe_net.name
net_params = caffe2_pb2.TensorProtos()
if len(caffe_net.layers) > 0:
raise ValueError(
'I think something is wrong. This translation script '
'only accepts new style layers that are stored in the '
'layer field.'
)
if not input_dims:
input_dims = _GetInputDims(caffe_net)
for layer in caffe_net.layer:
if not _ShouldInclude(net_state, layer):
log.info('Current net state does not need layer {}'
.format(layer.name))
continue
log.info('Translate layer {}'.format(layer.name))
# Get pretrained one
pretrained_layers = (
[l for l in pretrained_net.layer
if l.name == layer.name] + [l
for l in pretrained_net.layers
if l.name == layer.name]
)
if len(pretrained_layers) > 1:
raise ValueError(
'huh? more than one pretrained layer of one name?')
elif len(pretrained_layers) == 1:
pretrained_blobs = [
utils.CaffeBlobToNumpyArray(blob)
for blob in pretrained_layers[0].blobs
]
else:
# No pretrained layer for the given layer name. We'll just pass
# no parameter blobs.
# print 'No pretrained layer for layer', layer.name
pretrained_blobs = []
operators, params = cls.TranslateLayer(layer, pretrained_blobs, is_test, net=net, net_params=net_params, input_dims=input_dims)
net.op.extend(operators)
net_params.protos.extend(params)
if remove_legacy_pad:
assert input_dims, \
'Please specify input_dims to remove legacy_pad'
net = _RemoveLegacyPad(net, net_params, input_dims)
return net, net_params
def TranslateModel(*args, **kwargs):
return TranslatorRegistry.TranslateModel(*args, **kwargs)
# ---------------------------------------------------------------------------------
def ConvertTensorProtosToInitNet(net_params, input_name1, input_name2):
"""Takes the net_params returned from TranslateModel, and wrap it as an
init net that contain GivenTensorFill.
This is a very simple feature that only works with float tensors, and is
only intended to be used in an environment where you want a single
initialization file - for more complex cases, use a db to store the
parameters.
"""
init_net = caffe2_pb2.NetDef()
for tensor in net_params.protos:
if len(tensor.float_data) == 0:
raise RuntimeError(
"Only float tensors are supported in this util.")
op = core.CreateOperator(
"GivenTensorFill", [], [tensor.name],
arg=[
utils.MakeArgument("shape", list(tensor.dims)),
utils.MakeArgument("values", tensor.float_data)])
init_net.op.extend([op])
init_net.op.extend([core.CreateOperator("ConstantFill", [], [input_name1], shape=[1])])
init_net.op.extend([core.CreateOperator("ConstantFill", [], [input_name2], shape=[1])])
return init_net
# ---------------------------------------------------------------------------------
def BaseTranslate(layer, caffe2_type):
"""A simple translate interface that maps the layer input and output."""
caffe2_op = caffe2_pb2.OperatorDef()
caffe2_op.type = caffe2_type
caffe2_op.input.extend(layer.bottom)
caffe2_op.output.extend(layer.top)
return caffe2_op
def AddArgument(op, key, value):
"""Makes an argument based on the value type."""
op.arg.extend([utils.MakeArgument(key, value)])
################################################################################
# Common translators for layers.
################################################################################
@TranslatorRegistry.Register("Input")
def TranslateInput(layer, pretrained_blobs, is_test, **kwargs):
return [], []
@TranslatorRegistry.Register("VideoData")
def TranslateVideoData(layer, pretrained_blobs, is_test, **kwargs):
return [], []
@TranslatorRegistry.Register("Data")
def TranslateData(layer, pretrained_blobs, is_test, **kwargs):
return [], []
# A function used in convolution, pooling and deconvolution to deal with
# conv pool specific parameters.
def _TranslateStridePadKernelHelper(param, caffe_op):
try:
if (len(param.stride) > 1 or len(param.kernel_size) > 1 or
len(param.pad) > 1):
raise NotImplementedError(
"Translator currently does not support non-conventional "
"pad/kernel/stride settings."
)
stride = param.stride[0] if len(param.stride) else 1
pad = param.pad[0] if len(param.pad) else 0
kernel = param.kernel_size[0] if len(param.kernel_size) else 0
except TypeError:
# This catches the case of a PoolingParameter, in which case we are
# having non-repeating pad, stride and kernel.
stride = param.stride
pad = param.pad
kernel = param.kernel_size
# Get stride
if param.HasField("stride_h") or param.HasField("stride_w"):
AddArgument(caffe_op, "stride_h", param.stride_h)
AddArgument(caffe_op, "stride_w", param.stride_w)
else:
AddArgument(caffe_op, "stride", stride)
# Get pad
if param.HasField("pad_h") or param.HasField("pad_w"):
if param.pad_h == param.pad_w:
AddArgument(caffe_op, "pad", param.pad_h)
else:
AddArgument(caffe_op, "pad_t", param.pad_h)
AddArgument(caffe_op, "pad_b", param.pad_h)
AddArgument(caffe_op, "pad_l", param.pad_w)
AddArgument(caffe_op, "pad_r", param.pad_w)
else:
AddArgument(caffe_op, "pad", pad)
# Get kernel
if param.HasField("kernel_h") or param.HasField("kernel_w"):
AddArgument(caffe_op, "kernel_h", param.kernel_h)
AddArgument(caffe_op, "kernel_w", param.kernel_w)
else:
AddArgument(caffe_op, "kernel", kernel)
@TranslatorRegistry.Register("Convolution3D")
def TranslateConvNd(layer, pretrained_blobs, is_test, **kwargs):
param = layer.convolution3d_param
caffe_op = BaseTranslate(layer, "Conv")
output = caffe_op.output[0]
caffe_op.input.append(output + '_w')
AddArgument(
caffe_op,
"kernels",
[param.kernel_depth, param.kernel_size, param.kernel_size])
AddArgument(
caffe_op,
"strides",
[param.temporal_stride, param.stride, param.stride])
temporal_pad = 0
spatial_pad = 0
if hasattr(param, 'temporal_pad'):
temporal_pad = param.temporal_pad
if hasattr(param, 'pad'):
spatial_pad = param.pad
AddArgument(caffe_op, "pads", [temporal_pad, spatial_pad, spatial_pad] * 2)
# weight
params = [
utils.NumpyArrayToCaffe2Tensor(pretrained_blobs[0], output + '_w')]
# bias
if len(pretrained_blobs) == 2:
caffe_op.input.append(output + '_b')
params.append(
utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[1].flatten(), output + '_b'))
return caffe_op, params
@TranslatorRegistry.Register("Convolution")
def TranslateConv(layer, pretrained_blobs, is_test, **kwargs):
param = layer.convolution_param
caffe_op = BaseTranslate(layer, "Conv")
output = caffe_op.output[0]
caffe_op.input.append(output + '_w')
_TranslateStridePadKernelHelper(param, caffe_op)
# weight
params = [
utils.NumpyArrayToCaffe2Tensor(pretrained_blobs[0], output + '_w')]
# bias
if len(pretrained_blobs) == 2:
caffe_op.input.append(output + '_b')
params.append(
utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[1].flatten(), output + '_b'))
# Group convolution option
if param.group != 1:
AddArgument(caffe_op, "group", param.group)
# Get dilation - not tested. If you have a model and this checks out,
# please provide a test and uncomment this.
if len(param.dilation) > 0:
if len(param.dilation) == 1:
AddArgument(caffe_op, "dilation", param.dilation[0])
elif len(param.dilation) == 2:
AddArgument(caffe_op, "dilation_h", param.dilation[0])
AddArgument(caffe_op, "dilation_w", param.dilation[1])
return caffe_op, params
@TranslatorRegistry.Register("Deconvolution")
def TranslateDeconv(layer, pretrained_blobs, is_test, **kwargs):
param = layer.convolution_param
if param.group > 1:
raise NotImplementedError(
"Translator currently does not support group deconvolution."
)
caffe_op = BaseTranslate(layer, "ConvTranspose")
output = caffe_op.output[0]
_TranslateStridePadKernelHelper(param, caffe_op)
caffe_op.input.extend([output + '_w'])
AddArgument(caffe_op, "order", "NCHW")
weight = utils.NumpyArrayToCaffe2Tensor(pretrained_blobs[0], output + '_w')
if param.bias_term:
bias = utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[1].flatten(), output + '_b'
)
caffe_op.input.extend([output + '_b'])
return caffe_op, [weight, bias]
else:
return caffe_op, [weight]
@TranslatorRegistry.Register("Crop")
def TranslateCrop(layer, pretrained_blobs, is_test, **kwargs):
net, net_params, input_dims = kwargs['net'], kwargs['net_params'],
kwargs['input_dims']
n, c, h, w = input_dims
dummy_input = np.random.randn(n, c, h, w).astype(np.float32)
dim_map = _GetBlobDimMap(net, net_params, dummy_input)
param = layer.crop_param
axis, offsets = param.axis, param.offset
caffe_op = BaseTranslate(layer, "Slice")
input_1 = caffe_op.input[1]
input_1_dim = dim_map[input_1]
starts, ends = [], []
dims = len(dim_map[input_1])
assert len(offsets) == 1, 'Caffe Translator for Crop only works for offset \
of 1 for now'
for _ in range(axis):
starts.append(0)
ends.append(-1)
end_offset = [int(offsets[0] + input_1_dim[i]) for i in range(axis, dims)]
ends.extend(end_offset)
starts.extend([offsets[0]] * len(end_offset))
op = caffe2_pb2.OperatorDef()
op.input.extend([caffe_op.input[0]])
op.output.extend(caffe_op.output)
op.arg.extend(caffe_op.arg)
op.type = caffe_op.type
AddArgument(op, "starts", starts)
AddArgument(op, "ends", ends)
return op, []
@TranslatorRegistry.Register("ReLU")
def TranslateRelu(layer, pretrained_blobs, is_test, **kwargs):
return BaseTranslate(layer, "Relu"), []
@TranslatorRegistry.Register("Pooling")
def TranslatePool(layer, pretrained_blobs, is_test, **kwargs):
param = layer.pooling_param
if param.pool == caffe_pb2.PoolingParameter.MAX:
caffe_op = BaseTranslate(layer, "MaxPool")
elif param.pool == caffe_pb2.PoolingParameter.AVE:
caffe_op = BaseTranslate(layer, "AveragePool")
_TranslateStridePadKernelHelper(param, caffe_op)
AddArgument(caffe_op, "order", "NCHW")
try:
# In the Facebook port of Caffe, a torch_pooling field was added to
# map the pooling computation of Torch. Essentially, it uses
# floor((height + 2 * padding - kernel) / stride) + 1
# instead of
# ceil((height + 2 * padding - kernel) / stride) + 1
# which is Caffe's version.
# Torch pooling is actually the same as Caffe2 pooling, so we don't
# need to do anything.
is_torch_pooling = param.torch_pooling
except AttributeError:
is_torch_pooling = False
if not is_torch_pooling:
AddArgument(caffe_op, "legacy_pad",
caffe2_legacy_pb2.CAFFE_LEGACY_POOLING)
if param.global_pooling:
AddArgument(caffe_op, "global_pooling", 1)
return caffe_op, []
@TranslatorRegistry.Register("Pooling3D")
def TranslatePool3D(layer, pretrained_blobs, is_test, **kwargs):
param = layer.pooling3d_param
if param.pool == caffe_pb2.Pooling3DParameter.MAX:
caffe_op = BaseTranslate(layer, "MaxPool")
elif param.pool == caffe_pb2.Pooling3DParameter.AVE:
caffe_op = BaseTranslate(layer, "AveragePool")
AddArgument(caffe_op, "order", "NCHW")
AddArgument(
caffe_op,
"kernels",
[param.kernel_depth, param.kernel_size, param.kernel_size])
AddArgument(
caffe_op,
"strides",
[param.temporal_stride, param.stride, param.stride])
temporal_pad = 0
spatial_pad = 0
if hasattr(param, 'temporal_pad'):
temporal_pad = param.temporal_pad
if hasattr(param, 'pad'):
spatial_pad = param.pad
AddArgument(caffe_op, "pads", [temporal_pad, spatial_pad, spatial_pad] * 2)
return caffe_op, []
@TranslatorRegistry.Register("LRN")
def TranslateLRN(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "LRN")
caffe_op.output.extend(['_' + caffe_op.output[0] + '_scale'])
param = layer.lrn_param
if param.norm_region != caffe_pb2.LRNParameter.ACROSS_CHANNELS:
raise ValueError(
"Does not support norm region other than across channels.")
AddArgument(caffe_op, "size", int(param.local_size))
AddArgument(caffe_op, "alpha", float(param.alpha))
AddArgument(caffe_op, "beta", float(param.beta))
AddArgument(caffe_op, "bias", float(param.k))
AddArgument(caffe_op, "order", "NCHW")
return caffe_op, []
@TranslatorRegistry.Register("InnerProduct")
def TranslateInnerProduct(layer, pretrained_blobs, is_test, **kwargs):
param = layer.inner_product_param
try:
if param.axis != 1 or param.transpose:
raise ValueError(
"We don't have testing case for non-default axis and transpose "
"cases yet so we are disabling it for now. If you have a model "
"with this, please do send us your model for us to update this "
"support, and you are more than welcome to send a PR for this.")
except AttributeError:
# We might be using an historic Caffe protobuf that does not have axis
# and transpose arguments, so we will silently pass.
pass
caffe_op = BaseTranslate(layer, "FC")
output = caffe_op.output[0]
caffe_op.input.extend([output + '_w', output + '_b'])
# To provide the old-style 4-dimensional blob (1, 1, dim_output, dim_input)
# case, we always explicitly reshape the pretrained blob.
if pretrained_blobs[0].ndim not in [2, 4]:
raise ValueError("Unexpected weight ndim.")
if (pretrained_blobs[0].ndim == 4 and
list(pretrained_blobs[0].shape[:2]) != [1, 1]):
raise ValueError(
"If pretrained blob has 4 dims (old-style Caffe), the first two "
"should be of value 1, but I got " + str(pretrained_blobs[0].shape))
weight = utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[0].reshape(-1, pretrained_blobs[0].shape[-1]),
output + '_w'
)
bias = utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[1].flatten(), output + '_b'
)
return caffe_op, [weight, bias]
@TranslatorRegistry.Register("Dropout")
def TranslateDropout(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "Dropout")
caffe_op.output.extend(['_' + caffe_op.output[0] + '_mask'])
param = layer.dropout_param
AddArgument(caffe_op, "ratio", param.dropout_ratio)
if (is_test):
AddArgument(caffe_op, "is_test", 1)
return caffe_op, []
@TranslatorRegistry.Register("Softmax")
def TranslateSoftmax(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "Softmax")
return caffe_op, []
@TranslatorRegistry.Register("SoftmaxWithLoss")
def TranslateSoftmaxWithLoss(layer, pretrained_blobs, is_test, **kwargs):
softmax_op = core.CreateOperator(
"Softmax", [layer.bottom[0]],
layer.bottom[0] + "_translator_autogen_softmax")
xent_op = core.CreateOperator(
"LabelCrossEntropy",
[softmax_op.output[0], layer.bottom[1]],
layer.bottom[0] + "_translator_autogen_xent")
loss_op = core.CreateOperator(
"AveragedLoss",
xent_op.output[0],
layer.top[0])
return [softmax_op, xent_op, loss_op], []
@TranslatorRegistry.Register("Accuracy")
def TranslateAccuracy(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "Accuracy")
if layer.accuracy_param.top_k != 1:
AddArgument(caffe_op, "top_k", layer.accuracy_param.top_k)
return caffe_op, []
@TranslatorRegistry.Register("Concat")
def TranslateConcat(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "Concat")
caffe_op.output.extend(['_' + caffe_op.output[0] + '_dims'])
AddArgument(caffe_op, "order", "NCHW")
return caffe_op, []
@TranslatorRegistry.Register("TanH")
def TranslateTanH(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "Tanh")
return caffe_op, []
@TranslatorRegistry.Register("InstanceNorm")
def TranslateInstanceNorm(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "InstanceNorm")
output = caffe_op.output[0]
weight = utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[0].flatten(), output + '_w')
bias = utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[1].flatten(), output + '_b')
caffe_op.input.extend([output + '_w', output + '_b'])
AddArgument(caffe_op, "order", "NCHW")
return caffe_op, [weight, bias]
@TranslatorRegistry.Register("BatchNorm")
def TranslateBatchNorm(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "SpatialBN")
output = caffe_op.output[0]
param = layer.batch_norm_param
AddArgument(caffe_op, "is_test", is_test)
AddArgument(caffe_op, "epsilon", param.eps)
AddArgument(caffe_op, "order", "NCHW")
caffe_op.input.extend(
[output + "_scale",
output + "_bias",
output + "_mean",
output + "_var"])
if not is_test:
caffe_op.output.extend(
[output + "_mean",
output + "_var",
output + "_saved_mean",
output + "_saved_var"])
n_channels = pretrained_blobs[0].shape[0]
if pretrained_blobs[2][0] != 0:
mean = utils.NumpyArrayToCaffe2Tensor(
(1. / pretrained_blobs[2][0]) * pretrained_blobs[0],
output + '_mean')
var = utils.NumpyArrayToCaffe2Tensor(
(1. / pretrained_blobs[2][0]) * pretrained_blobs[1],
output + '_var')
else:
raise RuntimeError("scalar is zero.")
pretrained_blobs[2][0] = 1
pretrained_blobs[2] = np.tile(pretrained_blobs[2], (n_channels, ))
scale = utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[2],
output + '_scale')
bias = utils.NumpyArrayToCaffe2Tensor(
np.zeros_like(pretrained_blobs[2]),
output + '_bias')
return caffe_op, [scale, bias, mean, var]
@TranslatorRegistry.Register("Eltwise")
def TranslateElementWise(layer, pretrained_blobs, is_test, **kwargs):
param = layer.eltwise_param
# TODO(jiayq): if we have a protobuf that uses this, lift this constraint
# and verify that we can correctly translate.
if len(param.coeff) or param.operation != 1:
raise RuntimeError("This eltwise layer is not yet supported.")
caffe_op = BaseTranslate(layer, "Sum")
return caffe_op, []
@TranslatorRegistry.Register("Scale")
def TranslateScale(layer, pretrained_blobs, is_test, **kwargs):
mul_op = BaseTranslate(layer, "Mul")
scale_param = layer.scale_param
AddArgument(mul_op, "axis", scale_param.axis)
AddArgument(mul_op, "broadcast", True)
if len(mul_op.input) == 1:
# the scale parameter is in pretrained blobs
if scale_param.num_axes != 1:
raise RuntimeError("This path has not been verified yet.")
output = mul_op.output[0]
mul_op_param = output + '_w'
mul_op.input.append(mul_op_param)
weights = []
weights.append(utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[0].flatten(), mul_op_param))
add_op = None
if len(pretrained_blobs) == 1:
# No bias-term in Scale layer
pass
elif len(pretrained_blobs) == 2:
# Caffe Scale layer supports a bias term such that it computes
# (scale_param * X + bias), whereas Caffe2 Mul op doesn't.
# Include a separate Add op for the bias followed by Mul.
add_op = copy.deepcopy(mul_op)
add_op.type = "Add"
add_op_param = output + '_b'
internal_blob = output + "_internal"
del mul_op.output[:]
mul_op.output.append(internal_blob)
del add_op.input[:]
add_op.input.append(internal_blob)
add_op.input.append(add_op_param)
weights.append(utils.NumpyArrayToCaffe2Tensor(
pretrained_blobs[1].flatten(), add_op_param))
else:
raise RuntimeError("Unexpected number of pretrained blobs in Scale")
caffe_ops = [mul_op]
if add_op:
caffe_ops.append(add_op)
assert len(caffe_ops) == len(weights)
return caffe_ops, weights
elif len(mul_op.input) == 2:
# TODO(jiayq): find a protobuf that uses this and verify.
raise RuntimeError("This path has not been verified yet.")
else:
raise RuntimeError("Unexpected number of inputs.")
@TranslatorRegistry.Register("Reshape")
def TranslateReshape(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "Reshape")
caffe_op.output.append("_" + caffe_op.input[0] + "_dims")
reshape_param = layer.reshape_param
AddArgument(caffe_op, 'shape', reshape_param.shape.dim)
return caffe_op, []
@TranslatorRegistry.Register("Flatten")
def TranslateFlatten(layer, pretrained_blobs, is_test, **kwargs):
param = layer.flatten_param
if param.end_axis != -1:
raise NotImplementedError("flatten_param.end_axis not supported yet.")
if param.axis == 0:
caffe_op = BaseTranslate(layer, "FlattenToVec")
elif param.axis == 1:
caffe_op = BaseTranslate(layer, "Flatten")
else:
# This could be a Reshape op, but dim size is not known here.
raise NotImplementedError(
"Not supported yet for flatten_param.axis {}.".format(param.axis))
return caffe_op, []
@TranslatorRegistry.Register("Sigmoid")
def TranslateSigmoid(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "Sigmoid")
return caffe_op, []
@TranslatorRegistry.Register("ROIPooling")
def TranslateROIPooling(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "RoIPool")
AddArgument(caffe_op, "order", "NCHW")
if is_test:
AddArgument(caffe_op, "is_test", is_test)
else:
# Only used for gradient computation
caffe_op.output.append(caffe_op.output[0] + '_argmaxes')
param = layer.roi_pooling_param
if param.HasField('pooled_h'):
AddArgument(caffe_op, 'pooled_h', param.pooled_h)
if param.HasField('pooled_w'):
AddArgument(caffe_op, 'pooled_w', param.pooled_w)
if param.HasField('spatial_scale'):
AddArgument(caffe_op, 'spatial_scale', param.spatial_scale)
return caffe_op, []
@TranslatorRegistry.Register("PReLU")
def TranslatePRelu(layer, pretrained_blobs, is_test, **kwargs):
caffe_op = BaseTranslate(layer, "PRelu")
output = caffe_op.output[0]
caffe_op.input.extend([output + '_Slope'])
slope = utils.NumpyArrayToCaffe2Tensor(pretrained_blobs[0], output + '_Slope')
return caffe_op, [slope]
@TranslatorRegistry.Register("Reduction")
def TranslateReduction(layer, pretrained_blobs, is_test, **kwargs):
param = layer.reduction_param
if param.operation == caffe_pb2.ReductionParameter.SUM:
caffe_op = BaseTranslate(layer, "ReduceBackSum")
elif param.operation == caffe_pb2.ReductionParameter.MEAN:
caffe_op = BaseTranslate(layer, "ReduceBackMean")
else:
raise NotImplementedError("Not yet supported")
if param.axis > 0:
# We can't figure out the number of dims to reduce from positive axis
# for back reduction since the shape info is not known here.
raise NotImplementedError("Not yet supported")
num_reduce_dim = -param.axis
AddArgument(caffe_op, "num_reduce_dim", num_reduce_dim)
return caffe_op, []
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="Utilitity to convert pretrained caffe models to Caffe2 models.")
parser.add_argument("prototext", help="Caffe prototext.")
parser.add_argument("caffemodel", help="Caffe trained model.")
parser.add_argument("--init_net", help="Caffe2 initialization net.",
default="init_net.pb")
parser.add_argument("--predict_net", help="Caffe2 prediction net.",
default="predict_net.pb")
parser.add_argument("--remove_legacy_pad", help="Remove legacy pad \
(Only works for nets with one input blob)",
action="store_true",
default=False)
parser.add_argument("--input_dims", help="Dimension of input blob", nargs='+',
type=int, default=[])
args = parser.parse_args()
caffenet = caffe_pb2.NetParameter()
caffenet_pretrained = caffe_pb2.NetParameter()
input_proto = args.prototext
input_caffemodel = args.caffemodel
output_init_net = args.init_net
output_predict_net = args.predict_net
text_format.Merge(
open(input_proto, 'r').read(), caffenet
)
caffenet_pretrained.ParseFromString(
open(input_caffemodel, 'rb').read()
)
net, pretrained_params = TranslateModel(caffenet, caffenet_pretrained, is_test=True,
remove_legacy_pad=args.remove_legacy_pad,
input_dims=args.input_dims
)
# -------------------------------------
# Modified
# 2 inputs, 2 outputs
# -------------------------------------
# Assume there is two inputs and two outputs
external_input1 = net.op[0].input[0]
external_input2 = net.op[1].input[0]
# print(external_input1, external_input2)
external_output1 = net.op[-1].output[0]
external_output2 = net.op[-2].output[0]
# print(external_output1, external_output2)
net.external_input.extend([external_input1])
net.external_input.extend([external_input2])
net.external_input.extend([param.name for param in pretrained_params.protos])
net.external_output.extend([external_output1])
net.external_output.extend([external_output2])
# Modify
init_net = ConvertTensorProtosToInitNet(pretrained_params, external_input1, external_input2)
with open(output_predict_net, 'wb') as f:
f.write(net.SerializeToString())
with open(output_predict_net + 'txt', 'w') as f:
f.write(str(net))
with open(output_init_net, 'wb') as f:
f.write(init_net.SerializeToString())
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