解析Caffe框架的prototxt模型文件

前言

  Caffe全称为Convolutional Architecture for Fast Feature Embedding，是一个应用广泛的开源深度学习框架，在TensorFlow出现之前一直是深度学习领域GitHub star最多的项目，由伯克利视觉学中心(Berkeley Vision and Learning Center，BVLC)进行维护。核心编程语言为C++，同时提供了python和matlab接口。
  本篇博客主要介绍Caffe的模型定义文件.prototxt及如何将Caffe的模型权重文件.caffemodel转为PyTorch的.pth。

1. 解析示例

2. 层解析

# this is model name
# cifar10_full.prototxt
name: "CIFAR10_full_deploy"
# N.B. input image must be in CIFAR-10 format
# as described at http://www.cs.toronto.edu/~kriz/cifar.html

# 输入层
layer {
  name: "data"		# 表示该层的名字为 data
  type: "Input"		# 表示该层的类型为 Input
  top: "data"		# 表示输出结果保存到 data 层中, 也就是本层
  input_param { 	# 表示输入参数
  	shape: { 		# 表示输入 shape 为 (1,3,32,32)
    	dim: 1 
        dim: 3 
        dim: 32 
        dim: 32 
    } 
  }
}

# 卷积层
layer {
  name: "conv1"			# 表示该层的名字为 data
  type: "Convolution"	# 表示该层的类型为 Convolution
  bottom: "data"		# 表示输入来自 data 层
  top: "conv1"			# 表示输出保存到 conv1 层
  param {
    lr_mult: 1			# 表示本层参数的学习率需要乘上的一个系数
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32		# 表示本层输出 dim 为 32
    pad: 2				# 表示卷积核的 padding 为 2, kernel-size 为 5, stride 为 1
    kernel_size: 5
    stride: 1
  }
}

# 池化层
layer {
  name: "pool1"		# 表示该层的名字为 pool1
  type: "Pooling"	# 表示该层的类型为 Pooling
  bottom: "conv1"	# 表示输入来自 conv1 层
  top: "pool1"		# 表示输出保存到 pool1 层
  pooling_param {
    pool: MAX		# 表示最大池化
    kernel_size: 3	# 表示 kernel-size 为 3, stride 为 2
    stride: 2
  }
}

# 激活层
layer {
  name: "relu1"		# 表示该层的名字为 relu1
  type: "ReLU"		# 表示该层的类型为 ReLU
  bottom: "pool1"	# 表示输入来自 pool1 层
  top: "pool1"		# 表示输出保存到 pool1 层
}

# 归一层
layer {
  name: "norm1"		# 表示该层的名字为 norm1
  type: "LRN"		# 表示该层的类型为 LRN
  bottom: "pool1"	# 表示输入来自 pool1 层
  top: "norm1"		# 表示输出保存到 norm1 层
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}

...

layer {
  name: "ip1"			# 表示该层的名字为 ip1
  type: "InnerProduct"	# 表示该层的类型为 InnerProduct
  bottom: "pool3"		# 表示输入来自 pool3 层
  top: "ip1"			# 表示输出保存到 ip1 层
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
  }
}

layer {
  name: "prob"		# 表示该层的名字为 prob
  type: "Softmax"	# 表示该层的类型为 Softmax
  bottom: "ip1"		# 表示输入来自 ip1 层
  top: "prob"		# 表示输出保存到 prob 层
}

3. 完整文件

# cifar10_full.prototxt
name: "CIFAR10_full_deploy"
# N.B. input image must be in CIFAR-10 format
# as described at http://www.cs.toronto.edu/~kriz/cifar.html
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "ip1"
  top: "prob"
}

  该文件可以在ethereon网站上进行可视化。

4. caffe模型权重转pytorch模型权重

  具体支持的模型可参考链接：caffemodel2pytorch。

# 运行指令
python -m caffemodel2pytorch resnet50-float.caffemodel -o resnet50.pt

# caffemodel2pytorch.py
import os
import sys
import time
import argparse
import tempfile
import subprocess
import collections
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import reduce

try:
	from urllib.request import urlopen
except:
	from urllib2 import urlopen # Python 2 support.

import google.protobuf.descriptor
import google.protobuf.descriptor_pool
import google.protobuf.symbol_database
import google.protobuf.text_format
from google.protobuf.descriptor import FieldDescriptor as FD

TRAIN = 0

TEST = 1

caffe_pb2 = None

def initialize(caffe_proto = 'https://raw.githubusercontent.com/BVLC/caffe/master/src/caffe/proto/caffe.proto', codegen_dir = tempfile.mkdtemp(), shadow_caffe = True):
	global caffe_pb2
	if caffe_pb2 is None:
		local_caffe_proto = os.path.join(codegen_dir, os.path.basename(caffe_proto))
		with open(local_caffe_proto, 'w') as f:
			mybytes = urlopen(caffe_proto).read()
			mystr = mybytes.decode('ascii', 'ignore')
			f.write(mystr)
			#f.write((urlopen if 'http' in caffe_proto else open)(caffe_proto).read())
		subprocess.check_call(['protoc', '--proto_path', os.path.dirname(local_caffe_proto), '--python_out', codegen_dir, local_caffe_proto])
		sys.path.insert(0, codegen_dir)
		old_pool = google.protobuf.descriptor._message.default_pool
		old_symdb = google.protobuf.symbol_database._DEFAULT
		google.protobuf.descriptor._message.default_pool = google.protobuf.descriptor_pool.DescriptorPool()
		google.protobuf.symbol_database._DEFAULT = google.protobuf.symbol_database.SymbolDatabase(pool = google.protobuf.descriptor._message.default_pool)
		import caffe_pb2 as caffe_pb2
		google.protobuf.descriptor._message.default_pool = old_pool
		google.protobuf.symbol_database._DEFAULT = old_symdb
		sys.modules[__name__ + '.proto'] = sys.modules[__name__]
		if shadow_caffe:
			sys.modules['caffe'] = sys.modules[__name__]
			sys.modules['caffe.proto'] = sys.modules[__name__]
	return caffe_pb2

def set_mode_gpu():
	global convert_to_gpu_if_enabled
	convert_to_gpu_if_enabled = lambda obj: obj.cuda()

def set_device(gpu_id):
	torch.cuda.set_device(gpu_id)

class Net(nn.Module):
	def __init__(self, prototxt, *args, **kwargs):
		super(Net, self).__init__()
		# to account for both constructors, see https://github.com/BVLC/caffe/blob/master/python/caffe/test/test_net.py#L145-L147
		caffe_proto = kwargs.pop('caffe_proto', None) 
		weights = kwargs.pop('weights', None)
		phase = kwargs.pop('phase', None)
		weights = weights or (args + (None, None))[0]
		phase = phase or (args + (None, None))[1]

		self.net_param = initialize(caffe_proto).NetParameter()
		google.protobuf.text_format.Parse(open(prototxt).read(), self.net_param)

		for layer in list(self.net_param.layer) + list(self.net_param.layers):
			layer_type = layer.type if layer.type != 'Python' else layer.python_param.layer
			if isinstance(layer_type, int):
				layer_type = layer.LayerType.Name(layer_type)
			module_constructor = ([v for k, v in modules.items() if k.replace('_', '').upper() in [layer_type.replace('_', '').upper(), layer.name.replace('_', '').upper()]] + [None])[0]
			if module_constructor is not None:
				param = to_dict(([v for f, v in layer.ListFields() if f.name.endswith('_param')] + [None])[0])
				caffe_input_variable_names = list(layer.bottom)
				caffe_output_variable_names = list(layer.top)
				caffe_loss_weight = (list(layer.loss_weight) or [1.0 if layer_type.upper().endswith('LOSS') else 0.0]) * len(layer.top)
				caffe_propagate_down = list(getattr(layer, 'propagate_down', [])) or [True] * len(caffe_input_variable_names)
				caffe_optimization_params = to_dict(layer.param)
				param['inplace'] = len(caffe_input_variable_names) == 1 and caffe_input_variable_names == caffe_output_variable_names
				module = module_constructor(param)
				self.add_module(layer.name, module if isinstance(module, nn.Module) else CaffePythonLayerModule(module, caffe_input_variable_names, caffe_output_variable_names, param.get('param_str', '')) if type(module).__name__.endswith('Layer') else FunctionModule(module))
				module = getattr(self, layer.name)
				module.caffe_layer_name = layer.name
				module.caffe_layer_type = layer_type
				module.caffe_input_variable_names = caffe_input_variable_names
				module.caffe_output_variable_names = caffe_output_variable_names
				module.caffe_loss_weight = caffe_loss_weight
				module.caffe_propagate_down = caffe_propagate_down
				module.caffe_optimization_params = caffe_optimization_params
				for optim_param, p in zip(caffe_optimization_params, module.parameters()):
					p.requires_grad = optim_param.get('lr_mult', 1) != 0
			else:
				print('Skipping layer [{}, {}, {}]: not found in caffemodel2pytorch.modules dict'.format(layer.name, layer_type, layer.type))

		if weights is not None:
			self.copy_from(weights)

		self.blobs = collections.defaultdict(Blob)
		self.blob_loss_weights = {name : loss_weight for module in self.children() for name, loss_weight in zip(module.caffe_output_variable_names, module.caffe_loss_weight)}

		self.train(phase != TEST)
		convert_to_gpu_if_enabled(self)

	def forward(self, data = None, **variables):
		if data is not None:
			variables['data'] = data
		numpy = not all(map(torch.is_tensor, variables.values()))
		variables = {k : convert_to_gpu_if_enabled(torch.from_numpy(v.copy()) if numpy else v) for k, v in variables.items()}

		for module in [module for module in self.children() if not all(name in variables for name in module.caffe_output_variable_names)]:
			for name in module.caffe_input_variable_names:
				assert name in variables, 'Variable [{}] does not exist. Pass it as a keyword argument or provide a layer which produces it.'.format(name)
			inputs = [variables[name] if propagate_down else variables[name].detach() for name, propagate_down in zip(module.caffe_input_variable_names, module.caffe_propagate_down)]
			outputs = module(*inputs)
			if not isinstance(outputs, tuple):
				outputs = (outputs, )
			variables.update(dict(zip(module.caffe_output_variable_names, outputs)))

		self.blobs.update({k : Blob(data = v, numpy = numpy) for k, v in variables.items()})
		caffe_output_variable_names = set([name for module in self.children() for name in module.caffe_output_variable_names]) - set([name for module in self.children() for name in module.caffe_input_variable_names if name not in module.caffe_output_variable_names])
		return {k : v.detach().cpu().numpy() if numpy else v for k, v in variables.items() if k in caffe_output_variable_names}

	def copy_from(self, weights):
		try:
			import h5py, numpy
			state_dict = self.state_dict()
			for k, v in h5py.File(weights, 'r').items():
				if k in state_dict:
					state_dict[k].resize_(v.shape).copy_(torch.from_numpy(numpy.array(v)))
			print('caffemodel2pytorch: loaded model from [{}] in HDF5 format'.format(weights))
		except Exception as e:
			print('caffemodel2pytorch: loading model from [{}] in HDF5 format failed [{}], falling back to caffemodel format'.format(weights, e))
			bytes_weights = open(weights, 'rb').read()
			bytes_parsed = self.net_param.ParseFromString(bytes_weights)
			if bytes_parsed != len(bytes_weights):
				print('caffemodel2pytorch: loading model from [{}] in caffemodel format, WARNING: file length [{}] is not equal to number of parsed bytes [{}]'.format(weights, len(bytes_weights), bytes_parsed))
			for layer in list(self.net_param.layer) + list(self.net_param.layers):
				module = getattr(self, layer.name, None)
				if module is None:
					continue
				parameters = {name : convert_to_gpu_if_enabled(torch.FloatTensor(blob.data)).view(list(blob.shape.dim) if len(blob.shape.dim) > 0 else [blob.num, blob.channels, blob.height, blob.width]) for name, blob in zip(['weight', 'bias'], layer.blobs)}
				if len(parameters) > 0:
					module.set_parameters(**parameters)
			print('caffemodel2pytorch: loaded model from [{}] in caffemodel format'.format(weights))

	def save(self, weights):
		import h5py
		with h5py.File(weights, 'w') as h:
			for k, v in self.state_dict().items():
				h[k] = v.cpu().numpy()
		print('caffemodel2pytorch: saved model to [{}] in HDF5 format'.format(weights))

	@property
	def layers(self):
		return list(self.children())

class Blob(object):
	AssignmentAdapter = type('', (object, ), dict(shape = property(lambda self: self.contents.shape), __setitem__ = lambda self, indices, values: setattr(self, 'contents', values)))

	def __init__(self, data = None, diff = None, numpy = False):
		self.data_ = data if data is not None else Blob.AssignmentAdapter()
		self.diff_ = diff if diff is not None else Blob.AssignmentAdapter()
		self.shape_ = None
		self.numpy = numpy

	def reshape(self, *args):
		self.shape_ = args

	def count(self, *axis):
		return reduce(lambda x, y: x * y, self.shape_[slice(*(axis + [-1])[:2])])

	@property
	def data(self):
		if self.numpy and torch.is_tensor(self.data_):
			self.data_ = self.data_.detach().cpu().numpy()
		return self.data_

	@property
	def diff(self):
		if self.numpy and torch.is_tensor(self.diff_):
			self.diff_ = self.diff_.detach().cpu().numpy()
		return self.diff_

	@property
	def shape(self):
		return self.shape_ if self.shape_ is not None else self.data_.shape

	@property
	def num(self):
		return self.shape[0]

	@property
	def channels(self):
		return self.shape[1]

	@property
	def height(self):
		return self.shape[2]

	@property
	def width(self):
		return self.shape[3]

class Layer(torch.autograd.Function):
	def __init__(self, caffe_python_layer = None, caffe_input_variable_names = None, caffe_output_variable_names = None, caffe_propagate_down = None):
		self.caffe_python_layer = caffe_python_layer
		self.caffe_input_variable_names = caffe_input_variable_names
		self.caffe_output_variable_names = caffe_output_variable_names
		self.caffe_propagate_down = caffe_propagate_down

	def forward(self, *inputs):
		bottom = [Blob(data = v.cpu().numpy()) for v in inputs]
		top = [Blob() for name in self.caffe_output_variable_names]

		#self.caffe_python_layer.reshape()
		self.caffe_python_layer.setup(bottom, top)
		self.caffe_python_layer.setup = lambda *args: None

		self.caffe_python_layer.forward(bottom, top)
		outputs = tuple(convert_to_gpu_if_enabled(torch.from_numpy(v.data.contents.reshape(*v.shape))) for v in top)
		self.save_for_backward(*(inputs + outputs))
		return outputs

	def backward(self, grad_outputs):
		inputs, outputs = self.saved_tensors[:len(self.caffe_input_variable_names)], self.saved_tensors[len(self.caffe_input_variable_names):]
		bottom = [Blob(data = v.cpu().numpy()) for v in inputs]
		top = [Blob(data = output.cpu().numpy(), diff = grad_output.cpu().numpy()) for grad_output, output in zip(grad_outputs, outputs)]
		self.caffe_python_layer.backward(top, self.caffe_propagate_down, bottom)
		return tuple(convert_to_gpu_if_enabled(torch.from_numpy(blob.diff.contents.reshape(*v.reshape))) if propagate_down else None for v, propagate_down in zip(bottom, self.caffe_propagate_down))
			
class SGDSolver(object):
	def __init__(self, solver_prototxt):
		solver_param = initialize().SolverParameter()
		google.protobuf.text_format.Parse(open(solver_prototxt).read(), solver_param)
		solver_param = to_dict(solver_param)
		self.net = Net(solver_param.get('train_net') or solver_param.get('net'), phase = TRAIN)
		self.iter = 1
		self.iter_size = solver_param.get('iter_size', 1)
		self.optimizer_params = dict(lr = solver_param.get('base_lr') / self.iter_size, momentum = solver_param.get('momentum', 0), weight_decay = solver_param.get('weight_decay', 0))
		self.lr_scheduler_params = dict(policy = solver_param.get('lr_policy'), step_size = solver_param.get('stepsize'), gamma = solver_param.get('gamma'))
		self.optimizer, self.scheduler = None, None

	def init_optimizer_scheduler(self):
		self.optimizer = torch.optim.SGD([dict(params = [param], lr = self.optimizer_params['lr'] * mult.get('lr_mult', 1), weight_decay = self.optimizer_params['weight_decay'] * mult.get('decay_mult', 1), momentum = self.optimizer_params['momentum']) for module in self.net.children() for param, mult in zip(module.parameters(), module.caffe_optimization_params + [{}, {}]) if param.requires_grad])
		self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size = self.lr_scheduler_params['step_size'], gamma = self.lr_scheduler_params['gamma']) if self.lr_scheduler_params.get('policy') == 'step' else type('', (object, ), dict(step = lambda self: None))()

	def step(self, iterations = 1, **inputs):
		loss_total = 0.0
		for i in range(iterations):
			tic = time.time()
			if self.optimizer is not None:
				self.optimizer.zero_grad()
			
			loss_batch = 0
			losses_batch = collections.defaultdict(float)
			for j in range(self.iter_size):
				outputs = [kv for kv in self.net(**inputs).items() if self.net.blob_loss_weights[kv[0]] != 0]
				loss = sum([self.net.blob_loss_weights[k] * v.sum() for k, v in outputs])
				loss_batch += float(loss) / self.iter_size
				for k, v in outputs:
					losses_batch[k] += float(v.sum()) / self.iter_size
				if self.optimizer is None:
					self.init_optimizer_scheduler()
					self.optimizer.zero_grad()
				loss.backward()

			loss_total += loss_batch
			self.optimizer.step()
			self.scheduler.step()
			self.iter += 1

			log_prefix = self.__module__ + '.' + type(self).__name__ 
			print('{}] Iteration {}, loss: {}'.format(log_prefix, self.iter, loss_batch))
			for i, (name, loss) in enumerate(sorted(losses_batch.items())):
				print('{}]     Train net output #{}: {} = {} (* {} = {} loss)'.format(log_prefix, i, name, loss, self.net.blob_loss_weights[name], self.net.blob_loss_weights[name] * loss))
			print('{}] Iteration {}, lr = {}, time = {}'.format(log_prefix, self.iter, self.optimizer_params['lr'], time.time() - tic))
				
		return loss_total

modules = dict(
	Convolution = lambda param: Convolution(param),
	InnerProduct = lambda param: InnerProduct(param),
	Pooling = lambda param: [nn.MaxPool2d, nn.AvgPool2d][param['pool']](kernel_size = first_or(param, 'kernel_size', 1), stride = first_or(param, 'stride', 1), padding = first_or(param, 'pad', 0)),
	Softmax = lambda param: nn.Softmax(dim = param.get('axis', -1)),
	ReLU = lambda param: nn.ReLU(),
	Dropout = lambda param: nn.Dropout(p = param['dropout_ratio']),
	Eltwise = lambda param: [torch.mul, torch.add, torch.max][param.get('operation', 1)],
	LRN = lambda param: nn.LocalResponseNorm(size = param['local_size'], alpha = param['alpha'], beta = param['beta'])
)

class FunctionModule(nn.Module):
	def __init__(self, forward):
		super(FunctionModule, self).__init__()
		self.forward_func = forward

	def forward(self, *inputs):
		return self.forward_func(*inputs)

class CaffePythonLayerModule(nn.Module):
	def __init__(self, caffe_python_layer, caffe_input_variable_names, caffe_output_variable_names, param_str):
		super(CaffePythonLayerModule, self).__init__()
		caffe_python_layer.param_str = param_str
		self.caffe_python_layer = caffe_python_layer
		self.caffe_input_variable_names = caffe_input_variable_names
		self.caffe_output_variable_names = caffe_output_variable_names

	def forward(self, *inputs):
		return Layer(self.caffe_python_layer, self.caffe_input_variable_names, self.caffe_output_variable_names)(*inputs)

	def __getattr__(self, name):
		return nn.Module.__getattr__(self, name) if name in dir(self) else getattr(self.caffe_python_layer, name)

class Convolution(nn.Conv2d):
	def __init__(self, param):
		super(Convolution, self).__init__(first_or(param,'group',1), param['num_output'], kernel_size = first_or(param, 'kernel_size', 1), stride = first_or(param, 'stride', 1), padding = first_or(param, 'pad', 0), dilation = first_or(param, 'dilation', 1), groups = first_or(param, 'group', 1))
		self.weight, self.bias = nn.Parameter(), nn.Parameter()
		self.weight_init, self.bias_init = param.get('weight_filler', {}), param.get('bias_filler', {})

	def forward(self, x):
		if self.weight.numel() == 0 and self.bias.numel() == 0:
			requires_grad = [self.weight.requires_grad, self.bias.requires_grad]
			super(Convolution, self).__init__(x.size(1), self.out_channels, kernel_size = self.kernel_size, stride = self.stride, padding = self.padding, dilation = self.dilation)
			convert_to_gpu_if_enabled(self)
			init_weight_bias(self, requires_grad = requires_grad)
		return super(Convolution, self).forward(x)

	def set_parameters(self, weight = None, bias = None):
		init_weight_bias(self, weight = weight, bias = bias.view(-1) if bias is not None else bias)
		self.in_channels = self.weight.size(1)

class InnerProduct(nn.Linear):
	def __init__(self, param):
		super(InnerProduct, self).__init__(1, param['num_output'])
		self.weight, self.bias = nn.Parameter(), nn.Parameter()
		self.weight_init, self.bias_init = param.get('weight_filler', {}), param.get('bias_filler', {})

	def forward(self, x):
		if self.weight.numel() == 0 and self.bias.numel() == 0:
			requires_grad = [self.weight.requires_grad, self.bias.requires_grad]
			super(InnerProduct, self).__init__(x.size(1), self.out_features)
			convert_to_gpu_if_enabled(self)
			init_weight_bias(self, requires_grad = requires_grad)
		return super(InnerProduct, self).forward(x if x.size(-1) == self.in_features else x.view(len(x), -1))

	def set_parameters(self, weight = None, bias = None):
		init_weight_bias(self, weight = weight.view(weight.size(-2), weight.size(-1)) if weight is not None else None, bias = bias.view(-1) if bias is not None else None)
		self.in_features = self.weight.size(1)

def init_weight_bias(self, weight = None, bias = None, requires_grad = []):
	if weight is not None:
		self.weight = nn.Parameter(weight.type_as(self.weight), requires_grad = self.weight.requires_grad)
	if bias is not None:
		self.bias = nn.Parameter(bias.type_as(self.bias), requires_grad = self.bias.requires_grad)
	for name, requires_grad in zip(['weight', 'bias'], requires_grad):
		param, init = getattr(self, name), getattr(self, name + '_init')
		if init.get('type') == 'gaussian':
			nn.init.normal_(param, std = init['std'])
		elif init.get('type') == 'constant':
			nn.init.constant_(param, val = init['value'])
		param.requires_grad = requires_grad

def convert_to_gpu_if_enabled(obj):
	return obj

def first_or(param, key, default):
	return param[key] if isinstance(param.get(key), int) else (param.get(key, []) + [default])[0]
		
def to_dict(obj):
	return list(map(to_dict, obj)) if isinstance(obj, collections.Iterable) else {} if obj is None else {f.name : converter(v) if f.label != FD.LABEL_REPEATED else list(map(converter, v)) for f, v in obj.ListFields() for converter in [{FD.TYPE_DOUBLE: float, FD.TYPE_SFIXED32: float, FD.TYPE_SFIXED64: float, FD.TYPE_SINT32: int, FD.TYPE_SINT64: int, FD.TYPE_FLOAT: float, FD.TYPE_ENUM: int, FD.TYPE_UINT32: int, FD.TYPE_INT64: int, FD.TYPE_UINT64: int, FD.TYPE_INT32: int, FD.TYPE_FIXED64: float, FD.TYPE_FIXED32: float, FD.TYPE_BOOL: bool, FD.TYPE_STRING: str, FD.TYPE_BYTES: lambda x: x.encode('string_escape'), FD.TYPE_MESSAGE: to_dict}[f.type]]}

if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument(metavar = 'model.caffemodel', dest = 'model_caffemodel', help = 'Path to model.caffemodel')
	parser.add_argument('-o', dest = 'output_path', help = 'Path to converted model, supported file extensions are: h5, npy, npz, json, pt')
	parser.add_argument('--caffe.proto', metavar = '--caffe.proto', dest = 'caffe_proto', help = 'Path to caffe.proto (typically located at CAFFE_ROOT/src/caffe/proto/caffe.proto)', default = 'https://raw.githubusercontent.com/BVLC/caffe/master/src/caffe/proto/caffe.proto')
	args = parser.parse_args()
	args.output_path = args.output_path or args.model_caffemodel + '.pt'

	net_param = initialize(args.caffe_proto).NetParameter()
	net_param.ParseFromString(open(args.model_caffemodel, 'rb').read())
	blobs = {layer.name + '.' + name : dict(data = blob.data, shape = list(blob.shape.dim) if len(blob.shape.dim) > 0 else [blob.num, blob.channels, blob.height, blob.width]) for layer in list(net_param.layer) + list(net_param.layers) for name, blob in zip(['weight', 'bias'], layer.blobs)}

	if args.output_path.endswith('.json'):
		import json
		with open(args.output_path, 'w') as f:
			json.dump(blobs, f)
	elif args.output_path.endswith('.h5'):
		import h5py, numpy
		with h5py.File(args.output_path, 'w') as h:
			h.update(**{k : numpy.array(blob['data'], dtype = numpy.float32).reshape(*blob['shape']) for k, blob in blobs.items()})
	elif args.output_path.endswith('.npy') or args.output_path.endswith('.npz'):
		import numpy
		(numpy.savez if args.output_path[-1] == 'z' else numpy.save)(args.output_path, **{k : numpy.array(blob['data'], dtype = numpy.float32).reshape(*blob['shape']) for k, blob in blobs.items()})
	elif args.output_path.endswith('.pt'):
		torch.save({k : torch.FloatTensor(blob['data']).view(*blob['shape']) for k, blob in blobs.items()}, args.output_path)