mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
3b1a5a1b8a
Summary: Followup to the [first refactor](https://github.com/pytorch/pytorch/pull/11350). Increase coverage of tests Pull Request resolved: https://github.com/pytorch/pytorch/pull/11811 Reviewed By: houseroad Differential Revision: D9923074 Pulled By: ajyu fbshipit-source-id: 0f899bb9e9a75bf7ed939e06cc9b028daa7f6bd9
673 lines
23 KiB
Python
673 lines
23 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from caffe2.python import core, workspace
|
|
from hypothesis import given
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
import caffe2.python.serialized_test.serialized_test_util as serial
|
|
import hypothesis.strategies as st
|
|
import numpy as np
|
|
|
|
import unittest
|
|
|
|
|
|
class TestSoftmaxOps(serial.SerializedTestCase):
|
|
|
|
@serial.given(n=st.sampled_from([0, 2, 4, 71, 103]),
|
|
D=st.sampled_from([4, 8, 64, 79, 256, 333]),
|
|
engine=st.sampled_from([None, 'CUDNN']),
|
|
**hu.gcs)
|
|
def test_softmax(self, n, D, engine, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
X = np.random.rand(n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax(X):
|
|
probs = np.zeros((n, D))
|
|
rowmax = np.zeros(n)
|
|
for i in range(n):
|
|
rowmax[i] = max(X[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
|
|
return [probs]
|
|
|
|
op = core.CreateOperator(
|
|
"Softmax",
|
|
["X"],
|
|
["probs"],
|
|
engine=engine
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X],
|
|
reference=label_softmax,
|
|
)
|
|
|
|
@serial.given(n=st.sampled_from([0, 2, 4, 71, 103, 555, 751, 1201]),
|
|
D=st.sampled_from([4, 8, 64, 79, 256, 333, 1000]),
|
|
engine=st.sampled_from([None, 'CUDNN']),
|
|
**hu.gcs)
|
|
def test_softmax_grad(self, n, D, engine, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
Y = np.random.rand(n, D).astype(np.float32)
|
|
dY = np.random.rand(n, D).astype(np.float32)
|
|
Y = Y + 1e-2
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax_grad(X, dY):
|
|
dX = Y * 0.0
|
|
for i in range(n):
|
|
d = np.dot(Y[i, :], dY[i, :])
|
|
dX[i, :] = Y[i, :] * (dY[i, :] - d)
|
|
return [dX]
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxGradient",
|
|
["Y", "dY"],
|
|
["dX"],
|
|
engine=engine
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[Y, dY],
|
|
reference=label_softmax_grad,
|
|
)
|
|
|
|
@given(axis=st.integers(min_value=1, max_value=4),
|
|
engine=st.sampled_from([None, 'CUDNN']),
|
|
**hu.gcs)
|
|
def test_softmax_axis(self, axis, engine, gc, dc):
|
|
np.random.seed(1)
|
|
X = np.random.randn(1, 2, 3, 2, 1).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
def prod(xs):
|
|
p = 1
|
|
for x in xs:
|
|
p *= x
|
|
return p
|
|
|
|
N = prod(list(X.shape)[:axis])
|
|
D = prod(list(X.shape)[axis:])
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax(X):
|
|
X_ = X.reshape(N, D)
|
|
probs = np.zeros((N, D))
|
|
rowmax = np.zeros(N)
|
|
for i in range(N):
|
|
rowmax[i] = max(X_[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X_[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
|
|
return [probs.reshape(*X.shape)]
|
|
|
|
op = core.CreateOperator(
|
|
"Softmax",
|
|
["X"],
|
|
["probs"],
|
|
axis=axis,
|
|
engine=engine,
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X],
|
|
reference=label_softmax,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2)
|
|
|
|
@serial.given(n=st.integers(2, 10), D=st.integers(4, 16),
|
|
only_loss=st.booleans(), **hu.gcs)
|
|
def test_softmax_with_loss(self, n, D, gc, only_loss, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
np.random.seed(2603)
|
|
X = np.random.rand(n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
# Initialize label
|
|
label = (np.random.rand(n) * D).astype(np.int32)
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax_crossent(X, label):
|
|
probs = np.zeros((n, D))
|
|
rowmax = np.zeros(n)
|
|
for i in range(n):
|
|
rowmax[i] = max(X[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
|
|
label_xent = [-np.log(max(probs[i][label[i]], 1e-20))
|
|
for i in range(n)]
|
|
avgloss = np.sum(label_xent) / float(n)
|
|
return (probs, avgloss)
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxWithLoss",
|
|
["X", "label"],
|
|
["probs", "avgloss"],
|
|
only_loss=only_loss,
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X, label],
|
|
reference=label_softmax_crossent,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, [X, label], 0, [1], stepsize=1e-4, threshold=1e-2)
|
|
|
|
@given(
|
|
n=st.integers(2, 5),
|
|
D=st.integers(4, 16),
|
|
only_loss=st.booleans(),
|
|
label_prob=st.booleans(),
|
|
**hu.gcs
|
|
)
|
|
def test_softmax_with_loss_axis_2(
|
|
self, n, D, only_loss, label_prob,
|
|
gc, dc
|
|
):
|
|
np.random.seed(2603)
|
|
X = np.random.rand(n, n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
if label_prob:
|
|
label = np.random.rand(n, n, D).astype(np.float32)
|
|
label /= label.sum(axis=2, keepdims=True)
|
|
else:
|
|
label = (np.random.rand(n, n) * D).astype(np.int32)
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax_crossent(X, label):
|
|
probs = np.zeros((n, n, D))
|
|
rowmax = np.zeros((n, n))
|
|
for i in range(n):
|
|
for j in range(n):
|
|
rowmax[i, j] = max(X[i, j, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i, j] = X[i, j] - rowmax[i, j]
|
|
exps = np.exp(probs[i, j, ])
|
|
norm = sum(exps)
|
|
probs[i, j, ] = exps / norm
|
|
label_xent = 0
|
|
for i in range(n):
|
|
for j in range(n):
|
|
if label_prob:
|
|
for k in range(D):
|
|
label_xent += (
|
|
-np.log(max(probs[i, j, k], 1e-20)) *
|
|
label[i, j, k]
|
|
)
|
|
else:
|
|
label_xent += -np.log(max(probs[i, j, label[i, j]], 1e-20))
|
|
|
|
avgloss = label_xent / float(n * n)
|
|
return (probs, avgloss)
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxWithLoss",
|
|
["X", "label"],
|
|
["probs", "avgloss"],
|
|
only_loss=only_loss,
|
|
label_prob=label_prob,
|
|
axis=2,
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X, label],
|
|
reference=label_softmax_crossent,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, [X, label], 0, [1], stepsize=1e-4, threshold=1e-2)
|
|
|
|
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
|
|
@given(**hu.gcs_gpu_only)
|
|
def test_softmax_with_loss_large(self, gc, dc):
|
|
np.random.seed(2603)
|
|
for n in [32]:
|
|
for D in [1000, 2000, 20000]:
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
X = np.random.rand(n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
# Initialize label
|
|
label = (np.random.rand(n) * D).astype(np.int32)
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax_crossent(X, label):
|
|
probs = np.zeros((n, D))
|
|
rowmax = np.zeros(n)
|
|
for i in range(n):
|
|
rowmax[i] = max(X[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
|
|
label_xent = [-np.log(max(probs[i][label[i]], 1e-20))
|
|
for i in range(n)]
|
|
avgloss = np.sum(label_xent) / float(n)
|
|
return (probs, avgloss)
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxWithLoss",
|
|
["X", "label"],
|
|
["probs", "avgloss"]
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X, label],
|
|
reference=label_softmax_crossent,
|
|
)
|
|
|
|
@given(n=st.integers(2, 10), D=st.integers(4, 16), **hu.gcs)
|
|
def test_softmax_with_loss_label_prob(self, n, D, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
np.random.seed(2603)
|
|
X = np.random.rand(n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
# Initialize label
|
|
label = np.random.rand(D, n).astype(np.float32)
|
|
|
|
# normalize labels to sum to 1
|
|
label /= np.sum(label, axis=0)
|
|
label = label.transpose()
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax_crossent(X, label):
|
|
probs = np.zeros((n, D))
|
|
rowmax = np.zeros(n)
|
|
for i in range(n):
|
|
rowmax[i] = max(X[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
|
|
label_xent = np.zeros(X.shape)
|
|
for i in range(n):
|
|
for j in range(D):
|
|
label_xent[i][j] = -np.log(
|
|
max(probs[i, j], 1e-20)) * label[i, j]
|
|
avgloss = np.sum(label_xent) / float(n)
|
|
return (probs, avgloss)
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxWithLoss",
|
|
["X", "label"],
|
|
["probs", "avgloss"],
|
|
label_prob=1
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X, label],
|
|
reference=label_softmax_crossent,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, [X, label], 0, [1], stepsize=1e-4, threshold=1e-2)
|
|
|
|
@given(
|
|
n=st.integers(2, 10),
|
|
D=st.integers(4, 16),
|
|
only_loss=st.booleans(),
|
|
**hu.gcs)
|
|
def test_softmax_with_loss_weighted(self, n, D, only_loss, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
np.random.seed(2603)
|
|
X = np.random.rand(n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
# Initialize label
|
|
label = (np.random.rand(n) * D).astype(np.int32)
|
|
|
|
# Init weights (weight by sample)
|
|
weights = np.random.rand(n).astype(np.float32)
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax_crossent_weighted(X, label, weights):
|
|
probs = np.zeros((n, D))
|
|
rowmax = np.zeros(n)
|
|
for i in range(n):
|
|
rowmax[i] = max(X[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
|
|
label_xent = [-weights[i] * np.log(max(probs[i][label[i]], 1e-20))
|
|
for i in range(n)]
|
|
avgloss = np.sum(label_xent) / sum(weights)
|
|
return (probs, avgloss)
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxWithLoss",
|
|
["X", "label", "weights"],
|
|
["probs", "avgloss"],
|
|
only_loss=only_loss,
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X, label, weights],
|
|
reference=label_softmax_crossent_weighted,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, [X, label, weights], 0, [1], stepsize=1e-4, threshold=1e-2)
|
|
|
|
@given(n=st.integers(2, 10), D=st.integers(4, 16), **hu.gcs)
|
|
def test_softmax_with_loss_label_prob_weighted(self, n, D, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
X = np.random.rand(n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
# Initialize label
|
|
label = np.random.rand(D, n).astype(np.float32)
|
|
|
|
# normalize labels to sum to 1
|
|
label /= np.sum(label, axis=0)
|
|
label = label.transpose()
|
|
|
|
# Init weights (weight by sample)
|
|
weights = np.random.rand(n).astype(np.float32)
|
|
|
|
# Reference implementation of cross entropy with soft labels
|
|
def label_softmax_crossent_weighted(X, label, weights):
|
|
probs = np.zeros((n, D))
|
|
rowmax = np.zeros(n)
|
|
for i in range(n):
|
|
rowmax[i] = max(X[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
|
|
label_xent = np.zeros(X.shape)
|
|
for i in range(n):
|
|
for j in range(D):
|
|
label_xent[i][j] = -np.log(
|
|
max(probs[i, j], 1e-20)) * label[i, j] * weights[i]
|
|
avgloss = np.sum(label_xent) / sum(weights)
|
|
return (probs, avgloss)
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxWithLoss",
|
|
["X", "label", "weights"],
|
|
["probs", "avgloss"],
|
|
label_prob=1,
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X, label, weights],
|
|
reference=label_softmax_crossent_weighted,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, [X, label, weights], 0, [1], stepsize=1e-4, threshold=1e-2)
|
|
|
|
@serial.given(n=st.integers(2, 5), D=st.integers(2, 4),
|
|
weighted=st.booleans(), **hu.gcs)
|
|
def test_spatial_softmax_with_loss(self, n, D, weighted, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
W = 18
|
|
H = 12
|
|
np.random.seed(2603)
|
|
X = np.random.rand(n, D, H, W).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
weighted = True
|
|
weights = None
|
|
if weighted:
|
|
weights = np.random.rand(n, H, W).astype(np.float32)
|
|
|
|
# Initialize label. Some of the labels are (-1), i.e "DONT CARE"
|
|
label = (np.random.rand(n, H, W) * (D + 1)).astype(np.int32) - 1
|
|
|
|
def label_softmax_crossent_spatial(X, label, weights=None):
|
|
probs = np.zeros((n, D, H, W))
|
|
rowmax = np.zeros((n, H, W))
|
|
label_xent = np.zeros((n, H, W))
|
|
for i in range(n):
|
|
for x in range(W):
|
|
for y in range(H):
|
|
rowmax[i, y, x] = max(X[i, :, y, x])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i, :, y, x] = X[i, :, y, x] - rowmax[i, y, x]
|
|
exps = np.exp(probs[i, :, y, x])
|
|
probs[i, :, y, x] = exps / sum(exps)
|
|
|
|
label_xent[:, y, x] = \
|
|
[-np.log(max(probs[j, label[i, y, x], y, x], 1e-20))
|
|
for j in range(n)]
|
|
|
|
total_xent = 0.0
|
|
total_weight = 0.0
|
|
for y in range(H):
|
|
for x in range(W):
|
|
for i in range(n):
|
|
l = label[i, y, x]
|
|
if (l != (-1)):
|
|
w = 1.0 if weights is None else weights[i, y, x]
|
|
total_xent += \
|
|
-np.log(max(probs[i, l, y, x], 1e-20)) * w
|
|
total_weight += w
|
|
print("Total weight {}".format(total_weight))
|
|
|
|
return (probs, total_xent / total_weight)
|
|
|
|
op = core.CreateOperator(
|
|
"SpatialSoftmaxWithLoss",
|
|
["X", "label"] + ([] if weights is None else ["weights"]),
|
|
["probs", "avgloss"],
|
|
)
|
|
|
|
inputs = [X, label] + ([] if weights is None else [weights])
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=inputs,
|
|
reference=label_softmax_crossent_spatial,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, inputs, 0, [1], stepsize=1e-4, threshold=1e-2)
|
|
|
|
@given(n=st.integers(4, 5), D=st.integers(3, 4),
|
|
weighted=st.booleans(), **hu.gcs)
|
|
def test_spatial_softmax_with_loss_allignore(self, n, D, weighted, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
W = 18
|
|
H = 12
|
|
np.random.seed(2603)
|
|
X = np.random.rand(n, D, H, W).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
weighted = True
|
|
weights = None
|
|
if weighted:
|
|
weights = np.random.rand(n, H, W).astype(np.float32)
|
|
|
|
# Initialize label. All labels as "DONT CARE"
|
|
label = np.zeros((n, H, W)).astype(np.int32) - 1
|
|
print(label)
|
|
|
|
def label_softmax_crossent_spatial(X, label, weights=None):
|
|
probs = np.zeros((n, D, H, W))
|
|
rowmax = np.zeros((n, H, W))
|
|
label_xent = np.zeros((n, H, W))
|
|
for i in range(n):
|
|
for x in range(W):
|
|
for y in range(H):
|
|
rowmax[i, y, x] = max(X[i, :, y, x])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i, :, y, x] = X[i, :, y, x] - rowmax[i, y, x]
|
|
exps = np.exp(probs[i, :, y, x])
|
|
probs[i, :, y, x] = exps / sum(exps)
|
|
|
|
label_xent[:, y, x] = \
|
|
[-np.log(max(probs[j, label[i, y, x], y, x], 1e-20))
|
|
for j in range(n)]
|
|
|
|
return (probs, 0.0)
|
|
|
|
op = core.CreateOperator(
|
|
"SpatialSoftmaxWithLoss",
|
|
["X", "label"] + ([] if weights is None else ["weights"]),
|
|
["probs", "avgloss"],
|
|
)
|
|
|
|
inputs = [X, label] + ([] if weights is None else [weights])
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=inputs,
|
|
reference=label_softmax_crossent_spatial,
|
|
)
|
|
|
|
@given(n=st.integers(4, 5), D=st.integers(3, 4),
|
|
weighted=st.booleans(), **hu.gcs)
|
|
def test_softmax_with_loss_zero_weight(self, n, D, weighted, gc, dc):
|
|
# n = number of examples, D = |labels|
|
|
# Initialize X and add 1e-2 for numerical stability
|
|
np.random.seed(2603)
|
|
X = np.random.rand(n, D).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
weights = np.zeros(n).astype(np.float32)
|
|
|
|
# Initialize label
|
|
label = (np.random.rand(n) * D).astype(np.int32)
|
|
|
|
def label_softmax_crossent(X, label, weights=None):
|
|
probs = np.zeros((n, D))
|
|
rowmax = np.zeros((n))
|
|
for i in range(n):
|
|
rowmax[i] = max(X[i, ])
|
|
# We need to subtract the max to avoid numerical issues
|
|
probs[i] = X[i] - rowmax[i]
|
|
exps = np.exp(probs[i, ])
|
|
norm = sum(exps)
|
|
probs[i, ] = exps / norm
|
|
return (probs, 0.0)
|
|
|
|
op = core.CreateOperator(
|
|
"SoftmaxWithLoss",
|
|
["X", "label", "weights"],
|
|
["probs", "avgloss"]
|
|
)
|
|
|
|
inputs = [X, label] + ([] if weights is None else [weights])
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=inputs,
|
|
reference=label_softmax_crossent,
|
|
)
|
|
|
|
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
|
|
def test_compare_cpugpu(self):
|
|
'''
|
|
Additional test that checks CPU and GPU returns same values
|
|
with larger examples. This is mainly to test the more complex
|
|
GPU implementation is correct.
|
|
'''
|
|
from caffe2.proto import caffe2_pb2
|
|
|
|
for _j in range(3):
|
|
gpuop = core.CreateOperator(
|
|
"SpatialSoftmaxWithLoss",
|
|
["X_gpu", "label_gpu"],
|
|
["probs_gpu", "avgloss_gpu"],
|
|
device_option=core.DeviceOption(caffe2_pb2.CUDA, 0)
|
|
)
|
|
|
|
cpuop = core.CreateOperator(
|
|
"SpatialSoftmaxWithLoss",
|
|
["X_cpu", "label_cpu"],
|
|
["probs_cpu", "avgloss_cpu"],
|
|
device_option=core.DeviceOption(caffe2_pb2.CPU)
|
|
)
|
|
|
|
n = 8
|
|
D = 4
|
|
W = 64 + int(np.random.rand(1) * 1024)
|
|
H = 64 + int(np.random.rand(1) * 1024)
|
|
|
|
print("W: {} H: {}".format(W, H))
|
|
|
|
X = np.random.rand(n, D, H, W).astype(np.float32)
|
|
X = X + 1e-2
|
|
|
|
# Initialize label. Some of the labels are (-1), i.e "DONT CARE"
|
|
label = (np.random.rand(n, H, W) * (D + 1)).astype(np.int32) - 1
|
|
|
|
gpu0 = core.DeviceOption(caffe2_pb2.CUDA, 0)
|
|
workspace.FeedBlob("X_cpu", X)
|
|
workspace.FeedBlob("label_cpu", label)
|
|
workspace.FeedBlob("X_gpu", X, device_option=gpu0)
|
|
workspace.FeedBlob("label_gpu", label, device_option=gpu0)
|
|
|
|
workspace.RunOperatorOnce(gpuop)
|
|
workspace.RunOperatorOnce(cpuop)
|
|
|
|
probs_gpu = workspace.FetchBlob("probs_gpu")
|
|
probs_cpu = workspace.FetchBlob("probs_cpu")
|
|
loss_gpu = workspace.FetchBlob("avgloss_gpu")
|
|
loss_cpu = workspace.FetchBlob("avgloss_cpu")
|
|
|
|
np.testing.assert_allclose(probs_gpu, probs_cpu, rtol=1e-4)
|
|
np.testing.assert_allclose(loss_gpu, loss_cpu, rtol=1e-1)
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
import random
|
|
random.seed(2603)
|
|
unittest.main()
|