[NumPy] loss for np array

src/operator/nn/ctc_loss.cc

@@ -50,6 +50,7 @@ DMLC_REGISTER_PARAMETER(CTCLossOpParam);
 
 NNVM_REGISTER_OP(CTCLoss)
 .add_alias("ctc_loss")
+.add_alias("_npx_ctc_loss")
 .add_alias("_contrib_CTCLoss")
 .add_alias("_contrib_ctc_loss")
 .describe(R"code(Connectionist Temporal Classification Loss.

src/operator/nn/ctc_loss.cu

@@ -51,6 +51,7 @@ namespace op {
 
 NNVM_REGISTER_OP(CTCLoss)
 .add_alias("ctc_loss")
+.add_alias("_npx_ctc_loss")
 .add_alias("_contrib_ctc_loss")
 .add_alias("_contrib_CTCLoss")
 .set_attr<FCompute>("FCompute<gpu>", CTCLossOpForward<gpu>);

src/operator/tensor/broadcast_reduce_norm_value.cc

@@ -87,6 +87,7 @@ Examples::
   norm(csr) = [5.47722578]
 
 )code" ADD_FILELINE)
+.add_alias("_npx_norm")
 .set_num_inputs(1)
 .set_num_outputs(1)
 .set_attr_parser(ParamParser<NormParam>)

tests/python/gpu/test_gluon_gpu.py

@@ -33,6 +33,7 @@
 from common import setup_module, with_seed, teardown_module, assert_raises_cudnn_not_satisfied, run_in_spawned_process
 from test_gluon import *
 from test_loss import *
+from test_numpy_loss import *
 from test_gluon_rnn import *
 
 set_default_context(mx.gpu(0))

tests/python/unittest/test_loss.py

@@ -56,16 +56,6 @@ def test_loss_ndarray():
     assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]), rtol=1e-3, atol=1e-4)
 
 
-def get_net(num_hidden, flatten=True):
-    data = mx.symbol.Variable('data')
-    fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128, flatten=flatten)
-    act1 = mx.symbol.Activation(fc1, name='relu1', act_type="relu")
-    fc2 = mx.symbol.FullyConnected(act1, name = 'fc2', num_hidden = 64, flatten=flatten)
-    act2 = mx.symbol.Activation(fc2, name='relu2', act_type="relu")
-    fc3 = mx.symbol.FullyConnected(act2, name='fc3', num_hidden=num_hidden, flatten=flatten)
-    return fc3
-
-
 @with_seed()
 def test_bce_equal_ce2():
     N = 100
@@ -163,7 +153,7 @@ def test_cosine_loss():
     denominator = mx.nd.sqrt(mx.nd.sum(input1**2, axis=1, keepdims=True)) \
     * mx.nd.sqrt(mx.nd.sum(input2**2, axis=1, keepdims=True))
     numpy_loss = mx.nd.where(label == 1, 1-numerator/denominator, \
-    mx.nd.broadcast_maximum(mx.nd.array([0]), numerator/denominator, axis=1))
+    mx.nd.broadcast_maximum(mx.nd.array([0]), numerator/denominator, axis=1)).reshape((-1,))
     assert_almost_equal(loss.asnumpy(), numpy_loss.asnumpy(), rtol=1e-3, atol=1e-5)
 
 @xfail_when_nonstandard_decimal_separator
@@ -186,25 +176,23 @@ def test_poisson_nllloss():
     #Calculating by brute formula for default value of from_logits = True
 
     # 1) Testing for flag logits = True
-    brute_loss = np.mean(np.exp(pred.asnumpy()) - target.asnumpy() * pred.asnumpy())
+    brute_loss = np.mean(np.exp(pred.asnumpy()) - target.asnumpy() * pred.asnumpy(), axis=1)
     loss_withlogits = Loss(pred, target)
-    assert_almost_equal(brute_loss, loss_withlogits.asscalar())
+    assert_almost_equal(brute_loss, loss_withlogits)
 
     #2) Testing for flag logits = False
     loss_no_logits = Loss_no_logits(pred, target)
-    np_loss_no_logits = np.mean(pred.asnumpy() - target.asnumpy() * np.log(pred.asnumpy() + 1e-08))
-    if np.isnan(loss_no_logits.asscalar()):
-        assert_almost_equal(np.isnan(np_loss_no_logits), np.isnan(loss_no_logits.asscalar()))
-    else:
-        assert_almost_equal(np_loss_no_logits, loss_no_logits.asscalar())
+    np_loss_no_logits = np.mean(pred.asnumpy() - target.asnumpy() * np.log(pred.asnumpy() + 1e-08),
+                                axis=1)
+    assert_almost_equal(np_loss_no_logits, loss_no_logits.asnumpy())
 
     #3) Testing for Sterling approximation
     shape=(2, 3)
     np_pred = np.random.uniform(1, 5, shape)
     np_target = np.random.uniform(1, 5, shape)
     np_compute_full = np.mean((np_pred - np_target * np.log(np_pred + 1e-08)) + ((np_target * np.log(np_target)-\
-     np_target + 0.5 * np.log(2 * np_target * np.pi))*(np_target > 1)))
+     np_target + 0.5 * np.log(2 * np_target * np.pi))*(np_target > 1)), axis=1)
     Loss_compute_full = gluon.loss.PoissonNLLLoss(from_logits=False, compute_full=True)
     loss_compute_full = Loss_compute_full(mx.nd.array(np_pred), mx.nd.array(np_target))
-    assert_almost_equal(np_compute_full, loss_compute_full.asscalar())
+    assert_almost_equal(np_compute_full, loss_compute_full)
 

tests/python/unittest/test_numpy_loss.py

@@ -0,0 +1,235 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import mxnet as mx
+import numpy as np
+from mxnet import gluon, autograd
+from mxnet.test_utils import assert_almost_equal, default_context, use_np
+from common import setup_module, with_seed, teardown_module, xfail_when_nonstandard_decimal_separator
+import unittest
+
+
+@xfail_when_nonstandard_decimal_separator
+@with_seed()
+@use_np
+def test_loss_np_ndarray():
+    output = mx.np.array([1, 2, 3, 4])
+    label = mx.np.array([1, 3, 5, 7])
+    weighting = mx.np.array([0.5, 1, 0.5, 1])
+
+    loss = gluon.loss.L1Loss()
+    assert mx.np.sum(loss(output, label)) == 6.
+    loss = gluon.loss.L1Loss(weight=0.5)
+    assert mx.np.sum(loss(output, label)) == 3.
+    loss = gluon.loss.L1Loss()
+    assert mx.np.sum(loss(output, label, weighting)) == 5.
+
+    loss = gluon.loss.L2Loss()
+    assert mx.np.sum(loss(output, label)) == 7.
+    loss = gluon.loss.L2Loss(weight=0.25)
+    assert mx.np.sum(loss(output, label)) == 1.75
+    loss = gluon.loss.L2Loss()
+    assert mx.np.sum(loss(output, label, weighting)) == 6
+
+    loss = gluon.loss.HuberLoss()
+    assert mx.np.sum(loss(output, label)) == 4.5
+    loss = gluon.loss.HuberLoss(weight=0.25)
+    assert mx.np.sum(loss(output, label)) == 1.125
+    loss = gluon.loss.HuberLoss()
+    assert mx.np.sum(loss(output, label, weighting)) == 3.75
+
+    loss = gluon.loss.HingeLoss(margin=10)
+    assert mx.np.sum(loss(output, label)) == 13.
+    loss = gluon.loss.HingeLoss(margin=8, weight=0.25)
+    assert mx.np.sum(loss(output, label)) == 2.25
+    loss = gluon.loss.HingeLoss(margin=7)
+    assert mx.np.sum(loss(output, label, weighting)) == 4.
+
+    loss = gluon.loss.SquaredHingeLoss(margin=10)
+    assert mx.np.sum(loss(output, label)) == 97.
+    loss = gluon.loss.SquaredHingeLoss(margin=8, weight=0.25)
+    assert mx.np.sum(loss(output, label)) == 13.25
+    loss = gluon.loss.SquaredHingeLoss(margin=7)
+    assert mx.np.sum(loss(output, label, weighting)) == 19.
+
+    loss = gluon.loss.TripletLoss(margin=10)
+    assert mx.np.sum(loss(output, label, -label)) == 6.
+    loss = gluon.loss.TripletLoss(margin=8, weight=0.25)
+    assert mx.np.sum(loss(output, label, -label)) == 1.
+    loss = gluon.loss.TripletLoss(margin=7)
+    assert mx.np.sum(loss(output, label, -label, weighting)) == 1.5
+
+    output = mx.np.array([[0, 2], [1, 4]])
+    label = mx.np.array([0, 1])
+    weighting = mx.np.array([[0.5], [1.0]])
+
+    loss = gluon.loss.SoftmaxCrossEntropyLoss()
+    L = loss(output, label).asnumpy()
+    assert_almost_equal(L, np.array([ 2.12692809,  0.04858733]), rtol=1e-3, atol=1e-4)
+
+    L = loss(output, label, weighting).asnumpy()
+    assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]), rtol=1e-3, atol=1e-4)
+
+
+@with_seed()
+@use_np
+def test_bce_equal_ce2():
+    N = 100
+    loss1 = gluon.loss.SigmoidBCELoss(from_sigmoid=True)
+    loss2 = gluon.loss.SoftmaxCELoss(from_logits=True)
+    out1 = mx.np.random.uniform(0.1, 0.9, size=(N, 1))
+    out2 = mx.np.log(mx.np.concatenate((1-out1, out1), axis=1) + 1e-8)
+    label = mx.np.round(mx.np.random.uniform(0, 1, size=(N, 1)))
+    assert_almost_equal(loss1(out1, label).asnumpy(), loss2(out2, label).asnumpy())
+
+@use_np
+def test_logistic_loss_equal_bce():
+    N = 100
+    loss_binary = gluon.loss.LogisticLoss(label_format='binary')
+    loss_signed = gluon.loss.LogisticLoss(label_format='signed')
+    loss_bce = gluon.loss.SigmoidBCELoss(from_sigmoid=False)
+    data = mx.np.random.uniform(-10, 10, size=(N, 1))
+    label = mx.np.round(mx.np.random.uniform(0, 1, size=(N, 1)))
+    assert_almost_equal(loss_binary(data, label), loss_bce(data, label), atol=1e-6)
+    assert_almost_equal(loss_signed(data, 2 * label - 1), loss_bce(data, label), atol=1e-6)
+
+
+@with_seed()
+@use_np
+def test_ctc_loss():
+    loss = gluon.loss.CTCLoss()
+    l = loss(mx.np.ones((2,20,4)), mx.np.array([[1,0,-1,-1],[2,1,1,-1]]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
+
+    loss = gluon.loss.CTCLoss(layout='TNC')
+    l = loss(mx.np.ones((20,2,4)), mx.np.array([[1,0,-1,-1],[2,1,1,-1]]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
+
+    loss = gluon.loss.CTCLoss(layout='TNC', label_layout='TN')
+    l = loss(mx.np.ones((20,2,4)), mx.np.array([[1,0,-1,-1],[2,1,1,-1]]).T)
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
+
+    loss = gluon.loss.CTCLoss()
+    l = loss(mx.np.ones((2,20,4)), mx.np.array([[2,1,2,2],[3,2,2,2]]), None, mx.np.array([2,3]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
+
+    loss = gluon.loss.CTCLoss()
+    l = loss(mx.np.ones((2,25,4)), mx.np.array([[2,1,-1,-1],[3,2,2,-1]]), mx.np.array([20,20]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
+
+    loss = gluon.loss.CTCLoss()
+    l = loss(mx.np.ones((2,25,4)), mx.np.array([[2,1,3,3],[3,2,2,3]]), mx.np.array([20,20]), mx.np.array([2,3]))
+    assert_almost_equal(l, np.array([18.82820702, 16.50581741]))
+
+
+@xfail_when_nonstandard_decimal_separator
+@with_seed()
+@use_np
+def test_sdml_loss():
+
+    N = 5 # number of samples
+    DIM = 10 # Dimensionality
+    EPOCHS = 20
+
+    # Generate randomized data and 'positive' samples
+    data = mx.np.random.uniform(-1, 1, size=(N, DIM))
+    pos = data + mx.np.random.uniform(-0.1, 0.1, size=(N, DIM)) # correlated paired data
+    data_iter = mx.io.NDArrayIter({'data' : data, 'pos' : pos}, batch_size=N)
+
+    # Init model and trainer
+    sdml_loss = gluon.loss.SDMLLoss()
+    model = gluon.nn.Dense(DIM, activation='tanh') # Simple NN encoder
+    model.initialize(mx.init.Xavier(), ctx=mx.current_context())
+    trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate' : 0.1})
+
+    for i in range(EPOCHS): # Training loop
+        data_iter.reset()
+        for iter_batch in data_iter:
+            batch = [datum.as_in_ctx(mx.current_context()).as_np_ndarray() for datum in iter_batch.data]
+            with autograd.record():
+                data, pos = batch
+                z_data, z_pos = model(data), model(pos)
+                loss = sdml_loss(z_data, z_pos)
+                loss.backward()
+            trainer.step(1)
+
+    # After training euclidean distance between aligned pairs should be lower than all non-aligned pairs
+    avg_loss = loss.sum()/len(loss)
+    assert(avg_loss < 0.05)
+
+@with_seed()
+@use_np
+def test_cosine_loss():
+    #Generating samples
+    input1 = mx.np.random.randn(3, 2)
+    input2 = mx.np.random.randn(3, 2)
+    label = mx.np.sign(mx.np.random.randn(input1.shape[0]))
+    #Calculating loss from cosine embedding loss function in Gluon
+    Loss = gluon.loss.CosineEmbeddingLoss()
+    loss = Loss(input1, input2, label)
+
+    # Calculating the loss Numpy way
+    numerator = mx.np.sum(input1 * input2, keepdims=True, axis=1)
+    denominator = mx.np.sqrt(mx.np.sum(input1**2, axis=1, keepdims=True)) \
+    * mx.np.sqrt(mx.np.sum(input2**2, axis=1, keepdims=True))
+    x = numerator/denominator
+    label = mx.npx.reshape(label, (-1, 1))
+    numpy_loss = mx.npx.reshape(
+        mx.np.where(label == 1, 1-x, mx.npx.relu(x)), (-1,))
+    assert_almost_equal(loss.asnumpy(), numpy_loss.asnumpy(), rtol=1e-3, atol=1e-5)
+
+@xfail_when_nonstandard_decimal_separator
+@use_np
+def test_poisson_nllloss():
+    shape=(3, 4)
+    not_axis0 = tuple(range(1, len(shape)))
+    pred = mx.np.random.normal(size=shape)
+    min_pred = mx.np.min(pred)
+    #This is necessary to ensure only positive random values are generated for prediction,
+    # to avoid ivalid log calculation
+    pred[:] = pred + mx.np.abs(min_pred)
+    target = mx.np.random.normal(size=shape)
+    min_target = mx.np.min(target)
+    #This is necessary to ensure only positive random values are generated for prediction,
+    # to avoid ivalid log calculation
+    target[:] += mx.np.abs(min_target)
+
+    Loss = gluon.loss.PoissonNLLLoss(from_logits=True)
+    Loss_no_logits = gluon.loss.PoissonNLLLoss(from_logits=False)
+    #Calculating by brute formula for default value of from_logits = True
+
+    # 1) Testing for flag logits = True
+    brute_loss = mx.np.mean(mx.np.exp(pred) - target * pred, axis=1)
+    loss_withlogits = Loss(pred, target)
+    assert_almost_equal(brute_loss, loss_withlogits)
+
+    #2) Testing for flag logits = False
+    loss_no_logits = Loss_no_logits(pred, target)
+    np_loss_no_logits = mx.np.mean(pred - target * mx.np.log(pred + 1e-08),
+                                   axis=1)
+    assert_almost_equal(np_loss_no_logits, loss_no_logits)
+
+    #3) Testing for Sterling approximation
+    shape=(2, 3)
+    np_pred = mx.np.random.uniform(1, 5, shape)
+    np_target = mx.np.random.uniform(1, 5, shape)
+    np_compute_full = mx.np.mean((np_pred - np_target * mx.np.log(np_pred + 1e-08)) + ((np_target * np.log(np_target)-\
+     np_target + 0.5 * np.log(2 * np_target * np.pi))*(np_target > 1)), axis=1)
+    Loss_compute_full = gluon.loss.PoissonNLLLoss(from_logits=False, compute_full=True)
+    loss_compute_full = Loss_compute_full(np_pred, np_target)
+    assert_almost_equal(np_compute_full, loss_compute_full)
+