def test_hdf5_layer(tmpdir):
td = tmpdir.mkdir('test_hdf5_layer')
path_h5 = td.join('h5layer.in.h5').ensure().strpath
n_sample = 5
names = ['data%02d' % i for i in xrange(n_sample)]
blob_name = 'blob1'
batch_size = 2
blob_shape = (1, 2, 3)
data = np.random.rand(*((n_sample, ) + blob_shape))
import h5py
with h5py.File(path_h5, 'w') as hd:
for i, name in enumerate(names):
hd.create_group(name)
hd[name][blob_name] = data[i]
import csv
lpath_source = td.join('source.txt').ensure()
csv.writer(lpath_source.open('w')).writerows(map(lambda x: [x], names))
t = caffe.Blob([])
bottom = []
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.data_layers"
lp.python_param.layer = "HDF5Layer"
lp.python_param.param_str = str(
dict(batch_size=batch_size,
source=lpath_source.strpath,
path_h5=path_h5,
column_id=0,
blob_name='blob1'))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
j = 0
for i in xrange(3):
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert top[0].shape == (batch_size, ) + blob_shape
for blob in top[0].data:
j %= n_sample
assert np.all(blob == data[j].astype(np.float32))
j += 1
# Shuffle: Values are not checked here so far.
lp.python_param.param_str = str(
dict(batch_size=batch_size,
source=lpath_source.strpath,
path_h5=path_h5,
column_id=0,
blob_name='blob1',
shuffle=True,
random_seed=313))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
for i in xrange(3):
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert top[0].shape == (batch_size, ) + blob_shape
def test_hdf5_layer(tmpdir):
td = tmpdir.mkdir('test_hdf5_layer')
path_h5 = td.join('h5layer.in.h5').ensure().strpath
n_sample = 5
names = ['data%02d' % i for i in xrange(n_sample)]
blob_name = 'blob1'
batch_size = 2
blob_shape = (1, 2, 3)
data = np.random.rand(*((n_sample,) + blob_shape))
import h5py
with h5py.File(path_h5, 'w') as hd:
for i, name in enumerate(names):
hd.create_group(name)
hd[name][blob_name] = data[i]
import csv
lpath_source = td.join('source.txt').ensure()
csv.writer(lpath_source.open('w')).writerows(map(lambda x: [x], names))
t = caffe.Blob([])
bottom = []
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.data_layers"
lp.python_param.layer = "HDF5Layer"
lp.python_param.param_str = str(dict(
batch_size=batch_size, source=lpath_source.strpath, path_h5=path_h5,
column_id=0, blob_name='blob1'))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
j = 0
for i in xrange(3):
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert top[0].shape == (batch_size,) + blob_shape
for blob in top[0].data:
j %= n_sample
assert np.all(blob == data[j].astype(np.float32))
j += 1
# Shuffle: Values are not checked here so far.
lp.python_param.param_str = str(dict(
batch_size=batch_size, source=lpath_source.strpath, path_h5=path_h5,
column_id=0, blob_name='blob1', shuffle=True, random_seed=313))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
for i in xrange(3):
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert top[0].shape == (batch_size,) + blob_shape
def matrix_mult_layer(request):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("MatrixMultLayer requires GPU")
m1 = caffe.Blob((2, 4, 2))
m2 = caffe.Blob((2, 2, 3))
t = caffe.Blob([])
t1, t2 = request.param
if t1:
s = m1.shape
m1.reshape(s[0], s[2], s[1])
if t2:
s = m2.shape
m2.reshape(s[0], s[2], s[1])
rng = np.random.RandomState(313)
m1.data[...] = rng.randn(*m1.shape)
m2.data[...] = rng.randn(*m2.shape)
bottom = [m1, m2]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "MatrixMultLayer"
lp.python_param.param_str = str({'t1': t1, 't2': t2})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
return layer, bottom, top, request.param
def test_morphology_layer():
ba, c, h, w = (2, 3, 4, 4)
b = caffe.Blob([ba, c, h, w])
t = caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.vision_layers"
lp.python_param.layer = "MorphologyLayer"
lp.python_param.param_str = str(dict(op='erode', kernel='4nn'))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
b.data[1, ...] = [[
[0, 1, 1, 0],
[1, 1, 1, 1],
[1, 1, 1, 1],
[0, 1, 1, 0],
]]
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(top[0].data[1, ...] == [[
[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 1, 0],
[0, 0, 0, 0],
]])
def test_dssim_layer(request):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("DSSIMLayer requires GPU")
x, y = np.ogrid[:5, :5]
img1 = np.sin(x / 5.0 * np.pi) * np.cos(y / 5.0 * np.pi)
img1 = np.repeat(img1[..., np.newaxis], 3, 2)
img1 = (img1 - img1.min()) / (img1.max() - img1.min())
rng = np.random.RandomState(313)
img2 = img1 + rng.randn(*img1.shape) * 0.2
img2[img2 > 1] = 1
img2[img2 < 0] = 0
bottom = [caffe.Blob([]), caffe.Blob([])]
top = [caffe.Blob([])]
img1 = img1.transpose(2, 0, 1)
img2 = img2.transpose(2, 0, 1)
bottom[0].reshape(*((1,) + img1.shape))
bottom[1].reshape(*((1,) + img2.shape))
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.loss_layers"
lp.python_param.layer = "DSSIMLayer"
lp.python_param.param_str = str({'hsize': 3})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
bottom[0].data[...] = img1[np.newaxis]
bottom[1].data[...] = img2[np.newaxis]
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(
layer, bottom, top, check_bottom=[0, 1])
def test_crossent_layer(request):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("crossentropyLossLayer requires GPU")
pred = caffe.Blob((5, 8))
label = caffe.Blob((5, 1))
loss = caffe.Blob([])
bottom = [pred, label]
top = [loss]
# Fill
rng = np.random.RandomState(313)
pred.data[...] = rng.rand(*pred.shape) + 0.1
label.data[...] = rng.randint(0, 8, label.shape)
pred.data[...] = pred.data / pred.data.sum(axis=1, keepdims=True)
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.loss_layers"
lp.python_param.layer = "CrossEntropyLossLayer"
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
ref = -np.mean(
np.log(
np.maximum(np.finfo(np.float32).tiny, pred.data)
).reshape(pred.shape[0], -1)[
np.arange(pred.shape[0]), label.data.astype('int32')]
)
assert np.isclose(ref, loss.data)
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(
layer, bottom, top, check_bottom=[0])
def test_slice_by_array_layer(blob_4_2322, tmpdir):
path_indexes = tmpdir.join('indexes.mat').strpath
from scipy.io import savemat
indexes = np.array([2, 0])
savemat(path_indexes, {'indexes': indexes})
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "SliceByArrayLayer"
lp.python_param.param_str = str(
{'path_mat': path_indexes, 'key': 'indexes'})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(top[0].data == bottom[0].data[:, indexes, ...])
checker = GradientChecker(1e-2, 1e-5)
checker.check_gradient_exhaustive(
layer, bottom, top)
def test_graident_4_layer():
ba, c, h, w = (2, 3, 4, 4)
b = caffe.Blob([ba, c, h, w])
t = caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.vision_layers"
lp.python_param.layer = "Gradient4Layer"
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert top[0].shape == (ba, 2, c, h, w)
assert np.all(
top[0].data[:, 0, :, :, :-1] ==
bottom[0].data[..., :, :-1] - bottom[0].data[..., :, 1:])
assert np.all(
top[0].data[:, 1, :, :-1, :] ==
bottom[0].data[..., :-1, :] - bottom[0].data[..., 1:, :])
checker = GradientChecker(1e-2, 1e-4)
checker.check_gradient_exhaustive(
layer, bottom, top)
def matrix_mult_layer(request):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("MatrixMultLayer requires GPU")
m1 = caffe.Blob((2, 4, 2))
m2 = caffe.Blob((2, 2, 3))
t = caffe.Blob([])
t1, t2 = request.param
if t1:
s = m1.shape
m1.reshape(s[0], s[2], s[1])
if t2:
s = m2.shape
m2.reshape(s[0], s[2], s[1])
rng = np.random.RandomState(313)
m1.data[...] = rng.randn(*m1.shape)
m2.data[...] = rng.randn(*m2.shape)
bottom = [m1, m2]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "MatrixMultLayer"
lp.python_param.param_str = str({'t1': t1, 't2': t2})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
return layer, bottom, top, request.param
def test_tile_layer():
ba, c, h, w = [2, 3, 3, 4]
b, t = caffe.Blob([ba, c, h, w]), caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "TileLayer"
axis = 1
num = 5
lp.python_param.param_str = str(
{'axis': axis, 'num': num})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert t.shape == (ba, c * num, h, w)
reps = [1 for _ in t.shape]
reps[axis] = num
assert np.all(np.tile(b.data, reps) == t.data)
checker = GradientChecker(1e-2, 1e-5)
checker.check_gradient_exhaustive(
layer, bottom, top)
def test_tile_layer():
ba, c, h, w = [2, 3, 3, 4]
b, t = caffe.Blob([ba, c, h, w]), caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "TileLayer"
axis = 1
num = 5
lp.python_param.param_str = str({'axis': axis, 'num': num})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert t.shape == (ba, c * num, h, w)
reps = [1 for _ in t.shape]
reps[axis] = num
assert np.all(np.tile(b.data, reps) == t.data)
checker = GradientChecker(1e-2, 1e-5)
checker.check_gradient_exhaustive(layer, bottom, top)
def test_logit_loss_layer(request):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("LogitLossLayer requires GPU")
# data
y = np.random.rand(2, 3, 4, 5)
t = (np.random.rand(2, 3, 4, 5) > 0.5).astype(np.float)\
- (np.random.rand(2, 3, 4, 5) > 0.5).astype(np.float)
# setting up blobs
bottom = [caffe.Blob([]), caffe.Blob([])]
top = [caffe.Blob([])]
bottom[0].reshape(*y.shape)
bottom[1].reshape(*t.shape)
bottom[0].data[...] = y
bottom[1].data[...] = t
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.loss_layers"
lp.python_param.layer = "LogitLossLayer"
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
# reference computation
l = np.sum(np.abs(t) * np.log(1 + np.exp(-y * t))) / np.sum(np.abs(t))
assert np.isclose(top[0].data, l)
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(
layer, bottom, top, check_bottom=[0])
def test_create_conv_layer(self):
# Setting layer parameter for convolution
layer_param = caffe_pb2.LayerParameter()
layer_param.type = 'Convolution'
layer_param.name = 'conv1'
cparam = layer_param.convolution_param
cparam.num_output = 3
cparam.kernel_size.append(2)
wfiller = cparam.weight_filler
wfiller.type = "uniform"
wfiller.max = 3
wfiller.min = 1.5
# Create layer
conv_layer = caffe.create_layer(layer_param)
self.assertEqual(conv_layer.type, 'Convolution')
# Set up layer
conv_layer.SetUp([self.blobi], [self.blobo])
weights = conv_layer.blobs[0]
self.assertTrue(np.all(weights.data >= 1.5))
self.assertTrue(np.all(weights.data <= 3.0))
# Reshape out blobs
conv_layer.Reshape([self.blobi], [self.blobo])
shapei = self.shapei
shapeo = self.blobo.data.shape
self.assertEqual(
shapeo,
(shapei[0], cparam.num_output, shapei[2] - cparam.kernel_size[0] +
1, shapei[3] - cparam.kernel_size[0] + 1))
# Forward, Backward
conv_layer.Forward([self.blobi], [self.blobo])
conv_layer.Backward([self.blobo], [True], [self.blobi])
def test_slice_by_array_layer(blob_4_2322, tmpdir):
path_indexes = tmpdir.join('indexes.mat').strpath
from scipy.io import savemat
indexes = np.array([2, 0])
savemat(path_indexes, {'indexes': indexes})
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "SliceByArrayLayer"
lp.python_param.param_str = str({
'path_mat': path_indexes,
'key': 'indexes'
})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(top[0].data == bottom[0].data[:, indexes, ...])
checker = GradientChecker(1e-2, 1e-5)
checker.check_gradient_exhaustive(layer, bottom, top)
def test_dssim_layer(request):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("DSSIMLayer requires GPU")
x, y = np.ogrid[:5, :5]
img1 = np.sin(x / 5.0 * np.pi) * np.cos(y / 5.0 * np.pi)
img1 = np.repeat(img1[..., np.newaxis], 3, 2)
img1 = (img1 - img1.min()) / (img1.max() - img1.min())
rng = np.random.RandomState(313)
img2 = img1 + rng.randn(*img1.shape) * 0.2
img2[img2 > 1] = 1
img2[img2 < 0] = 0
bottom = [caffe.Blob([]), caffe.Blob([])]
top = [caffe.Blob([])]
img1 = img1.transpose(2, 0, 1)
img2 = img2.transpose(2, 0, 1)
bottom[0].reshape(*((1,) + img1.shape))
bottom[1].reshape(*((1,) + img2.shape))
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.loss_layers"
lp.python_param.layer = "DSSIMLayer"
lp.python_param.param_str = str({'hsize': 3})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
bottom[0].data[...] = img1[np.newaxis]
bottom[1].data[...] = img2[np.newaxis]
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(
layer, bottom, top, check_bottom=[0, 1])
def test_create_conv_layer(self):
# Setting layer parameter for convolution
layer_param = caffe_pb2.LayerParameter()
layer_param.type = 'Convolution'
layer_param.name = 'conv1'
cparam = layer_param.convolution_param
cparam.num_output = 3
cparam.kernel_size = 2
wfiller = cparam.weight_filler
wfiller.type = "uniform"
wfiller.max = 3
wfiller.min = 1.5
# Create layer
conv_layer = caffe.create_layer(layer_param)
self.assertEqual(conv_layer.type, 'Convolution')
# Set up layer
conv_layer.SetUp([self.blobi], [self.blobo])
weights = conv_layer.blobs[0]
self.assertTrue(np.all(weights.data >= 1.5))
self.assertTrue(np.all(weights.data <= 3.0))
# Reshape out blobs
conv_layer.Reshape([self.blobi], [self.blobo])
shapei = self.shapei
shapeo = self.blobo.data.shape
self.assertEqual(
shapeo,
(shapei[0], cparam.num_output,
shapei[2] - cparam.kernel_size + 1,
shapei[3] - cparam.kernel_size + 1))
# Forward, Backward
conv_layer.Forward([self.blobi], [self.blobo])
conv_layer.Backward([self.blobo], [True], [self.blobi])
def test_morphology_layer():
ba, c, h, w = (2, 3, 4, 4)
b = caffe.Blob([ba, c, h, w])
t = caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.vision_layers"
lp.python_param.layer = "MorphologyLayer"
lp.python_param.param_str = str(dict(op='erode', kernel='4nn'))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
b.data[1, ...] = [[
[0, 1, 1, 0],
[1, 1, 1, 1],
[1, 1, 1, 1],
[0, 1, 1, 0],
]]
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(
top[0].data[1, ...] == [[
[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 1, 0],
[0, 0, 0, 0],
]])
def upsample_creator(factor, num_in):
kernel = get_kernel_size(factor)
stride = factor
lp = caffe.LayerParameter(
"""name: "upsample", type: "Deconvolution"
convolution_param { kernel_size: %d stride: %d num_output: %d group: %d pad: %d
weight_filler: { type: "bilinear_upsampling" } bias_term: false }""" %
(kernel, stride, num_in, num_in, get_pad(factor)))
return caffe.create_layer(lp.to_python())
def upsample_creator(factor, num_in):
kernel = get_kernel_size(factor)
stride = factor
lp = caffe.LayerParameter("""name: "upsample", type: "Deconvolution"
convolution_param { kernel_size: %d stride: %d num_output: %d group: %d pad: %d
weight_filler: { type: "bilinear_upsampling" } bias_term: false }""" % (
kernel, stride, num_in, num_in, get_pad(factor)
))
return caffe.create_layer(lp.to_python())
def test_mask_loss(self):
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module='mask_reg_layer.layer'
lp.python_param.layer='MaskLossLayer'
layer = caffe.create_layer(lp)
checker = GradientChecker(1e-2, 1e-1)
checker.check_gradient_exhaustive(
layer, self.bottom, self.top, check_bottom=[0])
def view_sigmoid_output(self):
net = self.solver.net
lp = caffe_pb2.LayerParameter()
lp.type = "Sigmoid"
layer = caffe.create_layer(lp)
bottom = [net.blobs['cls_score']]
top = [caffe.Blob([])]
labels = net.blobs['labels'].data
layer.SetUp(bottom, top)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
np.set_printoptions(precision=3, suppress=True)
print("Sigmoid output v.s. Labels: ")
print(np.c_[top[0].data, labels])
def test_euclidean(self):
lp = caffe_pb2.LayerParameter()
lp.type = "EuclideanLoss"
layer = caffe.create_layer(lp)
layer.SetUp(self.bottom, self.top)
layer.Reshape(self.bottom, self.top)
layer.Forward(self.bottom, self.top)
# manual computation
loss = np.sum((self.bottom[0].data - self.bottom[1].data) ** 2) \
/ self.bottom[0].shape[0] / 2.0
self.assertAlmostEqual(float(self.top[0].data), loss, 5)
checker = GradientChecker(1e-2, 1e-2)
checker.check_gradient_exhaustive(
layer, self.bottom, self.top, check_bottom='all')
def reshape_layer(blob_4_2322):
b, _, _, t = blob_4_2322
t.reshape(*b.shape)
bottom = [t]
top = [t]
reshape = (2, 12)
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "ReshapeLayer"
lp.python_param.param_str = str({'shape': reshape})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
return layer
def reshape_layer(blob_4_2322):
b, _, _, t = blob_4_2322
t.reshape(*b.shape)
bottom = [t]
top = [t]
reshape = (2, 12)
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "ReshapeLayer"
lp.python_param.param_str = str({'shape': reshape})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
return layer
def view_softmax_output(self):
net = self.solver.net
lp = caffe_pb2.LayerParameter()
lp.type = "Softmax"
layer = caffe.create_layer(lp)
bottom = [net.blobs['cls_score']]
top = [caffe.Blob([])]
labels = net.blobs['labels'].data
layer.SetUp(bottom, top)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
np.set_printoptions(precision=3, suppress=True)
print("Softmax output v.s. Labels: ")
# print(np.sum(top[0].data,axis=1))
# print(np.sum(top[0].data,axis=0))
# print(top[0].data.shape)
# print(np.c_[top[0].data, labels])
print(np.c_[np.argmax(top[0].data, axis=1), labels])
def test_reduction_layer_sum(blob_4_2322):
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "ReductionLayer"
lp.python_param.param_str = str({'axis': 1, 'op': 'sum'})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(b.data.sum(layer.axis_, keepdims=True) == t.data)
checker = GradientChecker(1e-2, 1e-4)
checker.check_gradient_exhaustive(layer, bottom, top)
def test_reduction_layer_sum(blob_4_2322):
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "ReductionLayer"
lp.python_param.param_str = str({'axis': 1, 'op': 'sum'})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(b.data.sum(layer.axis_, keepdims=True) == t.data)
checker = GradientChecker(1e-2, 1e-4)
checker.check_gradient_exhaustive(
layer, bottom, top)
def sil2_loss_layer(request, blob_4_2322):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("ScaleInvariantL2LossLayer requires GPU")
print "Call:", request.fixturename
# Create blobs
pred, label, mask, top = blob_4_2322
bottom = [pred, label, mask]
top = [top]
lam = 0.5
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.loss_layers"
lp.python_param.layer = "ScaleInvariantL2LossLayer"
lp.python_param.param_str = str({'lambda': lam})
# caffe.set_mode_gpu()
# caffe.set_device(0)
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
return layer
def sil2_loss_layer(request, blob_4_2322):
if request.config.getoption('caffe_cpu'):
raise pytest.skip("ScaleInvariantL2LossLayer requires GPU")
print "Call:", request.fixturename
# Create blobs
pred, label, mask, top = blob_4_2322
bottom = [pred, label, mask]
top = [top]
lam = 0.5
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.loss_layers"
lp.python_param.layer = "ScaleInvariantL2LossLayer"
lp.python_param.param_str = str({'lambda': lam})
# caffe.set_mode_gpu()
# caffe.set_device(0)
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
return layer
def test_inner_product(self):
lp = caffe_pb2.LayerParameter()
lp.type = "InnerProduct"
lp.inner_product_param.num_output = 3
layer = caffe.create_layer(lp)
layer.SetUp([self.bottom[0]], self.top)
w = self.rng.randn(*layer.blobs[0].shape)
b = self.rng.randn(*layer.blobs[1].shape)
layer.blobs[0].data[...] = w
layer.blobs[1].data[...] = b
layer.Reshape([self.bottom[0]], self.top)
layer.Forward([self.bottom[0]], self.top)
assert np.allclose(
self.top[0].data,
np.dot(
self.bottom[0].data.reshape(self.bottom[0].shape[0], -1), w.T
) + b
)
checker = GradientChecker(1e-2, 1e-1)
checker.check_gradient_exhaustive(
layer, [self.bottom[0]], self.top, check_bottom=[0])
def test_parameter_layer():
t = caffe.Blob([])
bottom = []
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "ParameterLayer"
lp.python_param.param_str = str(
dict(shape=(2, 3, 2, 2),
filler="lambda shape, rng: rng.randn(*shape) * 0.01"))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
assert len(layer.blobs) == 1
assert layer.blobs[0].shape == (2, 3, 2, 2)
param_copy = layer.blobs[0].data.copy()
layer.Forward(bottom, top)
assert np.allclose(top[0].data, param_copy)
checker = GradientChecker(1e-3, 1e-5)
checker.check_gradient_exhaustive(layer, bottom, top)
def test_axpb_layer(blob_4_2322):
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
va = 0.7
vb = -0.3
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "AXPBLayer"
lp.python_param.param_str = str({'a': va, 'b': vb})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(va * b.data + vb == t.data)
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(layer, bottom, top)
def test_broadcast_layer():
ba, c, h, w = [2, 1, 3, 4]
b, t = caffe.Blob([ba, c, h, w]), caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "BroadcastLayer"
lp.python_param.param_str = str({'axis': 1, 'num': 3})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert t.shape == (ba, 3, h, w)
for i in xrange(3):
assert np.all(b.data == t.data[:, i:i + 1])
checker = GradientChecker(1e-2, 1e-5)
checker.check_gradient_exhaustive(layer, bottom, top)
def test_parameter_layer():
t = caffe.Blob([])
bottom = []
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "ParameterLayer"
lp.python_param.param_str = str(dict(
shape=(2, 3, 2, 2),
filler="lambda shape, rng: rng.randn(*shape) * 0.01"))
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
assert len(layer.blobs) == 1
assert layer.blobs[0].shape == (2, 3, 2, 2)
param_copy = layer.blobs[0].data.copy()
layer.Forward(bottom, top)
assert np.allclose(top[0].data, param_copy)
checker = GradientChecker(1e-3, 1e-5)
checker.check_gradient_exhaustive(
layer, bottom, top)
def test_axpb_layer(blob_4_2322):
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
va = 0.7
vb = -0.3
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "AXPBLayer"
lp.python_param.param_str = str({'a': va, 'b': vb})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert np.all(va * b.data + vb == t.data)
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(
layer, bottom, top)
def test_SoftmaxLoss(self):
net = self.solver.net
#print(net.blobs.keys())
# for i in range(30):
# print("shape @ {}: {}".format(i,net.blobs[i].shape))
# print(dir(net))
# print(len(net.blobs))
# print(dir(net.layer_dict['cls_score']))
# sys.exit()
#print(net.layer_dict.keys())
lp = caffe_pb2.LayerParameter()
lp.type = "Sigmoid"
layer = caffe.create_layer(lp)
data = net.blobs['cls_score'].data
bottom = [caffe.Blob(data.shape)]
bottom[0].data[...] = data
top = [caffe.Blob([])]
layer.SetUp(bottom, top)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
print("data", data, "top[0].data", top[0].data)
print(net.blobs['labels'].data - top[0].data)
def test_lp_normalization_layer(blob_4_2322, lpnorm_params):
axis, p = lpnorm_params
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "LpNormalizationLayer"
lp.python_param.param_str = str({'axis': axis, 'p': p})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.rand(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
if axis is None:
axis = tuple(range(1, len(bottom[0].shape)))
test_top = b.data / ((b.data**p).sum(axis, keepdims=True)**(1. / p))
assert np.allclose(test_top, t.data)
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(layer, bottom, top)
def test_broadcast_layer():
ba, c, h, w = [2, 1, 3, 4]
b, t = caffe.Blob([ba, c, h, w]), caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "BroadcastLayer"
lp.python_param.param_str = str(
{'axis': 1, 'num': 3})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert t.shape == (ba, 3, h, w)
for i in xrange(3):
assert np.all(b.data == t.data[:, i:i + 1])
checker = GradientChecker(1e-2, 1e-5)
checker.check_gradient_exhaustive(
layer, bottom, top)
def test_lp_normalization_layer(blob_4_2322, lpnorm_params):
axis, p = lpnorm_params
b, _, _, t = blob_4_2322
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.common_layers"
lp.python_param.layer = "LpNormalizationLayer"
lp.python_param.param_str = str({'axis': axis, 'p': p})
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.rand(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
if axis is None:
axis = tuple(range(1, len(bottom[0].shape)))
test_top = b.data / ((b.data**p).sum(axis, keepdims=True) ** (1./p))
assert np.allclose(test_top, t.data)
checker = GradientChecker(1e-3, 1e-2)
checker.check_gradient_exhaustive(
layer, bottom, top)
def test_graident_4_layer():
ba, c, h, w = (2, 3, 4, 4)
b = caffe.Blob([ba, c, h, w])
t = caffe.Blob([])
bottom = [b]
top = [t]
# Create Layer
lp = caffe_pb2.LayerParameter()
lp.type = "Python"
lp.python_param.module = "caffe_helper.layers.vision_layers"
lp.python_param.layer = "Gradient4Layer"
layer = caffe.create_layer(lp)
layer.SetUp(bottom, top)
rng = np.random.RandomState(313)
b.data[...] = rng.randn(*b.shape)
layer.Reshape(bottom, top)
layer.Forward(bottom, top)
assert top[0].shape == (ba, 2, c, h, w)
assert np.all(top[0].data[:, 0, :, :, :-1] == bottom[0].data[..., :, :-1] -
bottom[0].data[..., :, 1:])
assert np.all(top[0].data[:, 1, :, :-1, :] == bottom[0].data[..., :-1, :] -
bottom[0].data[..., 1:, :])
checker = GradientChecker(1e-2, 1e-4)
checker.check_gradient_exhaustive(layer, bottom, top)
