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Improve type-stability for metrices #233

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Apr 15, 2017
Merged

Improve type-stability for metrices #233

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1697522
update MSE to be agnostic to dimensions.
vchuravy Mar 28, 2017
e12052c
convert single outputs to array so that inference has it easier
vchuravy Mar 30, 2017
def129f
convert eps to eltype of array
vchuravy Apr 14, 2017
c2044d5
adds THTT to enable metrics to support NDArray or Array
vchuravy Apr 14, 2017
349d2a7
convert MSE to use NDArray as an example for async
vchuravy Apr 14, 2017
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255 changes: 142 additions & 113 deletions src/metric.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,16 @@ interfaces:
"""
abstract AbstractEvalMetric

"""
hasNDArraySupport(metric) -> Val{true/false}

Trait for `_update_single_output` should return `Val{true}() if metric can handle `NDArray`
directly and `Val{false}()i` if requires `Array`. Metric that work with NDArrays can be
async, while native Julia arrays require that we copy the output of the network, which is
a blocking operation.
"""
hasNDArraySupport(::AbstractEvalMetric) = Val{true}()

"""
update!(metric, labels, preds)

Expand All @@ -21,6 +31,10 @@ Update and accumulate metrics.
* `preds::Vector{NDArray}`: the outputs (predictions) of the network.
"""
function update!{T <: AbstractEvalMetric}(metric :: T, labels :: Vector{NDArray}, preds :: Vector{NDArray})
_update!(metric, labels, preds, hasNDArraySupport(metric))
end

function _update!{T<: AbstractEvalMetric}(metric :: T, labels :: Vector{NDArray}, preds :: Vector{NDArray}, :: Val{true})
if length(labels) != length(preds)
Base.warn_once(
"The number of labels ($(length(labels))) does not correspond to the\
Expand All @@ -31,6 +45,21 @@ function update!{T <: AbstractEvalMetric}(metric :: T, labels :: Vector{NDArray}
end
end

function _update!{T<: AbstractEvalMetric}(metric :: T, labels :: Vector{NDArray}, preds :: Vector{NDArray}, :: Val{false})
if length(labels) != length(preds)
Base.warn_once(
"The number of labels ($(length(labels))) does not correspond to the\
number of outputs ($(length(preds))). The calculated metric might not be accuracte.")
end
for (label, pred) in zip(labels, preds)
@nd_as_jl ro=(label, pred) begin
# This is a dynamic dispatch since the conversion from NDArray to
# Array is not type-stable.
_update_single_output(metric, label, pred)
end
end
end

"""
reset!(metric)

Expand Down Expand Up @@ -154,37 +183,37 @@ type Accuracy <: AbstractEvalMetric
Accuracy() = new(0.0, 0)
end

function _update_single_output(metric :: Accuracy, label :: NDArray, pred :: NDArray)
@nd_as_jl ro=(label,pred) begin
# Samples are stored in the last dimension
@assert size(label, ndims(label)) == size(pred, ndims(pred))
hasNDArraySupport(::Accuracy) = Val{false}()

function _update_single_output(metric :: Accuracy, label :: Array, pred :: Array)
# Samples are stored in the last dimension
@assert size(label, ndims(label)) == size(pred, ndims(pred))

if ndims(pred) == 4 # Multidimensional case
# Reshape label to be of the same shape as pred.
# Except for the third dimension where the predictions are stored.
labels = reshape(label, size(pred, 1, 2)..., 1, size(pred, 4))
if ndims(pred) == 4 # Multidimensional case
# Reshape label to be of the same shape as pred.
# Except for the third dimension where the predictions are stored.
labels = reshape(label, size(pred, 1, 2)..., 1, size(pred, 4))

for sample in 1:size(labels, 4)
for j in 1:size(labels, 2)
for i in 1:size(labels, 1)
label = labels[i, j, 1, sample]
klasses = view(pred, i, j, :, sample)
klass = indmax(klasses) - 1 # Classes start at 0...k-1
for sample in 1:size(labels, 4)
for j in 1:size(labels, 2)
for i in 1:size(labels, 1)
label = labels[i, j, 1, sample]
klasses = view(pred, i, j, :, sample)
klass = indmax(klasses) - 1 # Classes start at 0...k-1

metric.acc_sum += klass == label
metric.n_sample += 1
end
metric.acc_sum += klass == label
metric.n_sample += 1
end
end
elseif ndims(pred) == 2 # 1-dimensional case
for sample in 1:size(label, 1)
klass = indmax(view(pred, :, sample)) - 1
metric.acc_sum += klass == label[sample]
metric.n_sample += 1
end
else
error("Can't handle prediction with dimensions $(ndims(pred)).")
end
elseif ndims(pred) == 2 # 1-dimensional case
for sample in 1:size(label, 1)
klass = indmax(view(pred, :, sample)) - 1
metric.acc_sum += klass == label[sample]
metric.n_sample += 1
end
else
error("Can't handle prediction with dimensions $(ndims(pred)).")
end
end

Expand All @@ -200,36 +229,37 @@ end
"""
MSE

Mean Squared Error. TODO: add support for multi-dimensional outputs.
Mean Squared Error.

Calculates the mean squared error regression loss in one dimension.
Calculates the mean squared error regression loss.
Requires that label and prediction have the same shape.
"""

type MSE <: AbstractEvalMetric
mse_sum :: Float64
mse_sum :: Vector{NDArray}
n_sample :: Int

MSE() = new(0.0, 0)
MSE() = new(Vector{NDArray}(), 0)
end

function _update_single_output(metric :: MSE, label :: NDArray, pred :: NDArray)
label = copy(label)
pred = copy(pred)

n_sample = size(pred)[end]
metric.n_sample += n_sample
hasNDArraySupport(::MSE) = Val{true}()

for i = 1:n_sample
metric.mse_sum += (label[i] - pred[i])^2
end
function _update_single_output(metric :: MSE, label :: NDArray, pred :: NDArray)
@assert size(label) == size(pred)
metric.n_sample += length(label)
mse_sum = mx.sum(mx._PowerScalar(label - pred,scalar=2))
push!(metric.mse_sum, mse_sum)
return nothing
end

function get(metric :: MSE)
return [(:MSE, metric.mse_sum / metric.n_sample)]
# Delay copy until last possible moment
mse_sum = mapreduce(nda->copy(nda)[1], +, 0.0, metric.mse_sum)
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My understanding is that each NDArray in mse_sum at this point are actually scalars? Maybe using asscalar will be more efficient than the generic copy function. Otherwise, LGTM.

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asscalar only exist on the python side and is uses the same mechanism. We could probably write a more effective mechanism with an unsafe_load.

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I see. OK. Sounds good then.

return [(:MSE, mse_sum / metric.n_sample)]
end

function reset!(metric :: MSE)
metric.mse_sum = 0.0
metric.mse_sum = Vector{NDArray}()
metric.n_sample = 0
end

Expand Down Expand Up @@ -287,10 +317,9 @@ type NMSE <: AbstractEvalMetric
NMSE() = new(0.0, 0)
end

function _update_single_output(metric :: NMSE, label :: NDArray, pred :: NDArray)
label = copy(label)
pred = copy(pred)
hasNDArraySupport(::NMSE) = Val{false}()

function _update_single_output(metric :: NMSE, label :: Array, pred :: Array)
n_sample = size(pred)[end]
metric.n_sample += n_sample

Expand Down Expand Up @@ -337,42 +366,42 @@ function reset!(metric :: ACE)
metric.n_sample = 0
end

function _update_single_output(metric :: ACE, label :: NDArray, pred :: NDArray)
@nd_as_jl ro=(label,pred) begin
eps = metric.eps
# Samples are stored in the last dimension
@assert size(label, ndims(label)) == size(pred, ndims(pred))
if size(label) == size(pred) # simply calculate the cross entropy of the probabilities
for (q, p) in zip(pred, label)
# p == true probability
# q == "unnatural" probability
metric.ace_sum += p * log(q + eps)
hasNDArraySupport(::ACE) = Val{false}()

function _update_single_output{T}(metric :: ACE, label :: Array{T}, pred :: Array{T})
eps = convert(T, metric.eps)
# Samples are stored in the last dimension
@assert size(label, ndims(label)) == size(pred, ndims(pred))
if size(label) == size(pred) # simply calculate the cross entropy of the probabilities
for (q, p) in zip(pred, label)
# p == true probability
# q == "unnatural" probability
metric.ace_sum += p * log(q + eps)
metric.n_sample += 1
end
elseif ndims(pred) == 4
labels = reshape(label, size(pred, 1, 2)..., 1, size(pred, 4))
for sample in 1:size(labels, 4)
for j in 1:size(labels, 2)
for i in 1:size(labels, 1)
# Cross-entropy reduces to -(ln(p_1)*0 + ln(p_2)*1) for classification
# Since we can only target labels right now this is the only thing we can do.
target = Int(labels[i, j, 1, sample]) + 1 # klasses are 0...k-1 => julia indexing
p_k = pred[i, j, target, sample]
metric.ace_sum += log(p_k + eps)
metric.n_sample += 1
end
elseif ndims(pred) == 4
labels = reshape(label, size(pred, 1, 2)..., 1, size(pred, 4))
for sample in 1:size(labels, 4)
for j in 1:size(labels, 2)
for i in 1:size(labels, 1)
# Cross-entropy reduces to -(ln(p_1)*0 + ln(p_2)*1) for classification
# Since we can only target labels right now this is the only thing we can do.
target = Int(labels[i, j, 1, sample]) + 1 # klasses are 0...k-1 => julia indexing
p_k = pred[i, j, target, sample]
metric.ace_sum += log(p_k + eps)
metric.n_sample += 1
end
end
end
elseif ndims(pred) == 2 # 1-dimensional case
for sample in 1:size(label, 1)
target = Int(label[sample]) + 1 # 0-based indexing => 1-based indexing
p_k = pred[target, sample]
metric.ace_sum += log(p_k + eps)
metric.n_sample += 1
end
else
error("Can't handle prediction with dimensions $(ndims(pred)).")
end
elseif ndims(pred) == 2 # 1-dimensional case
for sample in 1:size(label, 1)
target = Int(label[sample]) + 1 # 0-based indexing => 1-based indexing
p_k = pred[target, sample]
metric.ace_sum += log(p_k +eps)
metric.n_sample += 1
end
else
error("Can't handle prediction with dimensions $(ndims(pred)).")
end
end

Expand Down Expand Up @@ -401,48 +430,48 @@ function reset!(metric :: MultiACE)
metric.counts = Base.zero(metric.counts)
end

function _update_single_output(metric :: MultiACE, label :: NDArray, pred :: NDArray)
@nd_as_jl ro=(label,pred) begin
eps = metric.eps
# Samples are stored in the last dimension
@assert size(label, ndims(label)) == size(pred, ndims(pred))
@assert size(metric.aces) == size(metric.counts)
if size(label) == size(pred) # simply calculate the cross entropy of the probabilities
for k in 1:length(metric.aces)
kpred = view(pred, ntuple(d->:, ndims(pred) - 2)..., k, :)
klabel = view(label, ntuple(d->:, ndims(label) - 2)..., k, :)
for (q, p) in zip(kpred, klabel)
# p == true probability
# q == "unnatural" probability
metric.aces[k] += p * log(q + eps)
metric.counts[k] += 1
end
hasNDArraySupport(::MultiACE) = Val{false}()

function _update_single_output{T}(metric :: MultiACE, label :: Array{T}, pred :: Array{T})
eps = convert(T, metric.eps)
# Samples are stored in the last dimension
@assert size(label, ndims(label)) == size(pred, ndims(pred))
@assert size(metric.aces) == size(metric.counts)
if size(label) == size(pred) # simply calculate the cross entropy of the probabilities
for k in 1:length(metric.aces)
kpred = view(pred, ntuple(d->:, ndims(pred) - 2)..., k, :)
klabel = view(label, ntuple(d->:, ndims(label) - 2)..., k, :)
for (q, p) in zip(kpred, klabel)
# p == true probability
# q == "unnatural" probability
metric.aces[k] += p * log(q + eps)
metric.counts[k] += 1
end
elseif ndims(pred) == 4
labels = reshape(label, size(pred, 1, 2)..., 1, size(pred, 4))
for sample in 1:size(labels, 4)
for j in 1:size(labels, 2)
for i in 1:size(labels, 1)
# Cross-entropy reduces to -(ln(p_1)*0 + ln(p_2)*1) for classification
# Since we can only target labels right now this is the only thing we can do.
target = Int(labels[i, j, 1, sample]) + 1 # klasses are 0...k-1 => julia indexing
p_k = pred[i, j, target, sample]

metric.aces[target] += log(p_k + eps)
metric.counts[target] += 1
end
end
elseif ndims(pred) == 4
labels = reshape(label, size(pred, 1, 2)..., 1, size(pred, 4))
for sample in 1:size(labels, 4)
for j in 1:size(labels, 2)
for i in 1:size(labels, 1)
# Cross-entropy reduces to -(ln(p_1)*0 + ln(p_2)*1) for classification
# Since we can only target labels right now this is the only thing we can do.
target = Int(labels[i, j, 1, sample]) + 1 # klasses are 0...k-1 => julia indexing
p_k = pred[i, j, target, sample]

metric.aces[target] += log(p_k + eps)
metric.counts[target] += 1
end
end
elseif ndims(pred) == 2
for sample in 1:size(label, 1)
target = Int(label[sample]) + 1
p_k = pred[target, sample]
metric.aces[target] += log(p_k + eps)
metric.counts[target] += 1
end
else
error("Can't handle prediction with dimensions $(ndims(pred)).")
end
elseif ndims(pred) == 2
for sample in 1:size(label, 1)
target = Int(label[sample]) + 1
p_k = pred[target, sample]
metric.aces[target] += log(p_k + eps)
metric.counts[target] += 1
end
else
error("Can't handle prediction with dimensions $(ndims(pred)).")
end
end

4 changes: 2 additions & 2 deletions test/unittest/metric.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ end

function loglikelihood{T <: AbstractFloat}(labels::Vector{T}, probs::Array{T, 2})
LL = 0.0
eps = 1.0e-8
eps = convert(T, 1.0e-8)
for i = 1:size(labels, 1)
LL += log(probs[Int(labels[i]) + 1, i] + eps) # labels are zero-based
end
Expand All @@ -48,7 +48,7 @@ function test_ace()
probs = convert(Array{Float32}, generate_probs(n_categories, n_observations))
LL = loglikelihood(labels, probs)
metric = mx.ACE() # For categorical variables, ACE == -LL
mx._update_single_output(metric, mx.NDArray(labels), mx.NDArray(probs))
mx._update_single_output(metric, labels, probs)
LL_v2 = metric.ace_sum / metric.n_sample
@static if VERSION >= v"0.6.0-dev.2075"
@test LL ≈ LL_v2 atol=1e-12
Expand Down