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profiler.lua
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profiler.lua
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local op_count
local op_used
local multiply_adds = opt.MACs
function count_ops(network, input)
op_count = 0
op_used = {}
network:apply(intercept_updateOutput)
inputImg = input[1]
network:forward(input)
network:apply(restore_updateOutput)
return op_count, op_used
end
-- Intercept updateOutput. At each call increment op_count appropriately.
function intercept_updateOutput(module)
module.updateOutput_original = module.updateOutput
module.updateOutput = function(self, input)
compute_ops(module, input)
return module:updateOutput_original(input)
end
end
-- Restore original network behaviour
function restore_updateOutput(module)
assert(module.updateOutput_original,
"restore_updateOutput should be called after intercept_updateOutput!")
module.updateOutput = module.updateOutput_original
module.updateOutput_original = nil
end
-- Compute #flops that specified module needs to process an input.
-- module_handlers table is at the bottom of this file
function compute_ops(module, input)
module_name = torch.type(module)
handler = module_handlers[module_name]
assert(handler, string.format("No handler for module %s!", module_name))
local ops = handler(module, input)
op_count = op_count + ops
local maps = 0
local neurons = 0
if torch.type(module) ~= 'nn.JoinTable' and torch.type(module) ~= 'nn.CAddTable' then
for i = 1, input:dim() do
if i == 1 then
maps = input:size(1)
neurons = input:size(1)
else
maps = maps .. ' x ' .. input:size(i)
neurons = neurons * input:size(i)
end
end
else
end
table.insert(op_used, {name = torch.type(module),
ops = ops,
maps = maps,
neurons = neurons})
end
--------------------------------------------------------------------------------
------------------------------- Module handlers --------------------------------
--------------------------------------------------------------------------------
local function ops_nothing(module, input)
return 0
end
local function ops_linear(module, input)
local batch_size = input:dim() == 2 and input:size(1) or 1
local weight_ops = module.weight:nElement() * (multiply_adds and 1 or 2)
local bias_ops = module.bias:nElement()
local ops_per_sample = weight_ops + bias_ops
return batch_size * ops_per_sample
end
local function ops_logsoftmax(module, input)
local batch_size = input:dim() == 2 and input:size(1) or 1
local input_dim = input:dim() == 2 and input:size(2) or input:size(1)
local expminusapprox_ops = 1 -- around 8 in Torch
-- +2 for accumulation and substraction in two loops
local ops_per_elem = expminusapprox_ops + 1 + 1
local ops_per_sample = input_dim * ops_per_elem
return batch_size * ops_per_sample
end
-- WARNING: an oversimplified version
local function ops_nonlinearity(module, input)
return input:nElement()
end
local function ops_convolution(module, input)
assert(input:dim() == 4, "ops_convolution supports only batched inputs!")
assert(input:size(2) == module.nInputPlane, "number of input planes doesn't match!")
local batch_size = input:size(1)
local input_planes = input:size(2)
local input_height = input:size(3)
local input_width = input:size(4)
-- ops per output element
local kernel_ops = module.kH * module.kW * input_planes * (multiply_adds and 1 or 2)
local bias_ops = 1
local ops_per_element = kernel_ops + bias_ops
local output_width = math.floor((input_width + 2 * module.padW - module.kW) / module.dW + 1)
local output_height = math.floor((input_height + 2 * module.padH - module.kH) / module.dH + 1)
return batch_size * module.nOutputPlane * output_width * output_height * ops_per_element
end
local function ops_fullconvolution(module, input)
assert(input:dim() == 4, "ops_fullconvolution supports only batched inputs!")
assert(input:size(2) == module.nInputPlane, "number of input planes doesn't match!")
local batch_size = input:size(1)
local input_planes = input:size(2)
local input_height = input:size(3)
local input_width = input:size(4)
-- ops per input element
local single_kernel_ops = module.kH * module.kW * input_planes * (multiply_adds and 1 or 2)
local sample_kernel_ops = input_planes * input_width * input_height * single_kernel_ops
local output_width = (input_width - 1) * module.dW - 2 * module.padW + module.kW + module.adjW
local output_height = (input_height - 1) * module.dH - 2 * module.padW + module.kH + module.adjH
local bias_ops = output_width * output_height * module.nOutputPlane
return batch_size * (sample_kernel_ops + bias_ops)
end
local function ops_dilatedconvolution(module, input)
assert(input:dim() == 4, "ops_convolution supports only batched inputs!")
assert(input:size(2) == module.nInputPlane, "number of input planes doesn't match!")
local batch_size = input:size(1)
local input_planes = input:size(2)
local input_height = input:size(3)
local input_width = input:size(4)
local dilW = module.dilationW
local dilH = module.dilationH
-- ops per output element
local kernel_ops = module.kH * module.kW * input_planes * (multiply_adds and 1 or 2)
local bias_ops = 1
local ops_per_element = kernel_ops + bias_ops
local output_width = math.floor(
(input_width + 2 * module.padW - dilW * (module.kW - 1) + 1)
/module.dW + 1)
local output_height = math.floor(
(input_height + 2 * module.padH - dilH * (module.kH - 1) + 1)
/module.dH + 1)
return batch_size * module.nOutputPlane * output_width * output_height * ops_per_element
end
local function ops_pooling(module, input)
assert(input:dim() == 4, "ops_averagepooling supports only batched inputs!")
local batch_size = input:size(1)
local input_planes = input:size(2)
local input_height = input:size(3)
local input_width = input:size(4)
local kernel_ops = module.kH * module.kW
local output_width = math.floor((input_width + 2 * module.padW - module.kW) / module.dW + 1)
local output_height = math.floor((input_height + 2 * module.padH - module.kH) / module.dH + 1)
return batch_size * input_planes * output_width * output_height * kernel_ops
end
local function ops_unpooling(module, input)
assert(input:dim() == 4, "ops_unpooling supports only batched inputs!")
local batch_size = input:size(1)
local input_planes = input:size(2)
local input_height = input:size(3)
local input_width = input:size(4)
local output_width = (input_width - 1) * module.pooling.dW - (2 * module.pooling.padW - module.pooling.kW)
local output_height = (input_height - 1) * module.pooling.dH - (2 * module.pooling.padH - module.pooling.kH)
return batch_size * input_planes * output_width * output_height
end
local function ops_caddtable(module, input)
assert(torch.type(input) == 'table', "ops_caddtable input should be a table!")
return input[1]:nElement() * #input
end
local function ops_batchnorm(module, input)
return input:nElement() * (multiply_adds and 1 or 2)
end
local function ops_sum(module, input)
assert(not module.nInputDims, 'nInputDims mode of nn.Sum not supported.')
local ops = 1
for d = 1, input:dim() do
local s = input:size(d)
ops = d ~= module.dimension and ops * s or ops * (s - 1)
end
return ops
end
local function ops_mulconstant(module, input)
local ops = 1
for d = 1, input:dim() do
ops = ops * input:size(d)
end
return ops
end
module_handlers = {
-- Containers
['nn.Sequential'] = ops_nothing,
['nn.Parallel'] = ops_nothing,
['nn.Concat'] = ops_nothing,
['nn.gModule'] = ops_nothing,
['nn.Identity'] = ops_nothing,
['nn.DataParallelTable'] = ops_nothing,
['nn.Contiguous'] = ops_nothing,
['nn.ConcatTable'] = ops_nothing,
['nn.JoinTable'] = ops_nothing,
['nn.Padding'] = ops_nothing,
-- Nonlinearities
['nn.ReLU'] = ops_nonlinearity,
['nn.PReLU'] = ops_nonlinearity,
['nn.Threshold'] = ops_nonlinearity,
['nn.LogSoftMax'] = ops_logsoftmax,
['nn.SoftMax'] = ops_logsoftmax, --TODO Update it with correct ops calculator
['cudnn.ReLU'] = ops_nonlinearity,
['cudnn.PReLU'] = ops_nonlinearity,
-- Basic modules
['nn.Linear'] = ops_linear,
['nn.Sum'] = ops_sum,
['nn.MulConstant'] = ops_mulconstant,
-- Spatial Modules
['nn.SpatialConvolution'] = ops_convolution,
['nn.SpatialConvolutionMM'] = ops_convolution,
['nn.SpatialDilatedConvolution'] = ops_dilatedconvolution,
['nn.SpatialFullConvolution'] = ops_fullconvolution,
['nn.SpatialMaxPooling'] = ops_pooling,
['nn.SpatialAveragePooling'] = ops_pooling,
['nn.SpatialMaxUnpooling'] = ops_unpooling,
['nn.SpatialZeroPadding'] = ops_nothing,
['nn.BatchNormalization'] = ops_nothing, -- Can be squashed
['nn.SpatialBatchNormalization'] = ops_nothing, -- Can be squashed
['cudnn.SpatialConvolution'] = ops_convolution,
['cudnn.SpatialConvolutionMM'] = ops_convolution,
['cudnn.SpatialDilatedConvolution'] = ops_dilatedconvolution,
['cudnn.SpatialMaxPooling'] = ops_pooling,
['cudnn.SpatialAveragePooling'] = ops_pooling,
['cudnn.SpatialBatchNormalization'] = ops_nothing, -- Can be squashed
-- Table modules
['nn.CAddTable'] = ops_caddtable,
-- Various modules
['nn.View'] = ops_nothing,
['nn.Reshape'] = ops_nothing,
['nn.Dropout'] = ops_nothing, -- Is turned off in inference
['nn.SpatialDropout'] = ops_nothing, -- Is turned off in inference
['nn.Concat'] = ops_nothing,
}