/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/deform_conv_test.py (19276B)
import unittest
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.proto import caffe2_pb2
from caffe2.python import core, utils, workspace
from hypothesis import assume, given
def _cudnn_supports(dilation=False, nhwc=False):
"""Return True if cuDNN supports this configuration."""
v = workspace.GetCuDNNVersion()
if dilation and v < 6000:
# Dilation not supported until v6
return False
if dilation and nhwc:
# Dilation and NHWC not supported together
return False
return True
def _conv_1d_output_size(size, kernel, pad, dilation, stride):
return max(1, int((size + pad * 2 - (dilation * (kernel - 1) + 1)) / stride) + 1)
def _conv_2d_output_size(size, kernel, pad_h, pad_w, dilation, stride_h, stride_w):
return [
_conv_1d_output_size(size, kernel, pad_h, dilation, stride_h),
_conv_1d_output_size(size, kernel, pad_w, dilation, stride_w),
]
def _conv_2d_offsets_dims(
batch_size,
size,
kernel,
pad_h,
pad_w,
dilation,
stride_h,
stride_w,
deformable_group,
):
dims = [batch_size, 2 * kernel * kernel * deformable_group]
dims.extend(
_conv_2d_output_size(size, kernel, pad_h, pad_w, dilation, stride_h, stride_w)
)
return dims
def _conv_2d_random_offsets(batch_size, kernel, dims, num_deformable_group):
o = []
for y0 in range(0, kernel):
for x0 in range(0, kernel):
# stay away from integer offsets which correspond to "ridges" on the
# interpolated surface resulting in less precise estimates
x = np.random.randint(0, kernel) + np.random.uniform(0.05, 0.95)
y = np.random.randint(0, kernel) + np.random.uniform(0.05, 0.95)
o.append(y - y0)
o.append(x - x0)
o = o * num_deformable_group
e = []
for v in o:
e.append([[v] * dims[1]] * dims[0])
return np.array([e] * batch_size).astype(np.float32)
def _conv_2d_shuffle_offsets(
batch_size, kernel, dims, num_deformable_group, input_channels, output_channels
):
o = []
w0 = [[0 for x in range(kernel)] for y in range(kernel)]
for y0 in range(0, kernel):
for x0 in range(0, kernel):
x = np.random.randint(0, kernel)
y = np.random.randint(0, kernel)
o.append(y - y0)
o.append(x - x0)
w0[y][x] += 1
o = o * num_deformable_group
e = []
for v in o:
e.append([[v] * int(dims[1])] * int(dims[0]))
w0 = [[w0] * input_channels] * output_channels
return (
np.array([e] * batch_size).astype(np.float32),
utils.NCHW2NHWC(np.array(w0).astype(np.float32)),
)
class TestConvolution(hu.HypothesisTestCase):
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
@given(
stride=st.integers(1, 3),
pad=st.integers(0, 3),
kernel=st.integers(1, 5),
dilation=st.integers(1, 3),
size=st.integers(7, 10),
input_channels=st.integers(1, 8),
output_channels=st.integers(1, 8),
batch_size=st.integers(1, 3),
order=st.sampled_from(["NCHW"]),
engine=st.sampled_from(["", "CUDNN", "MKLDNN"]),
use_bias=st.booleans(),
deformable_group=st.integers(1, 3),
**hu.gcs_gpu_only
)
def test_null_offset_convolution(
self,
stride,
pad,
kernel,
dilation,
size,
input_channels,
output_channels,
batch_size,
order,
engine,
use_bias,
deformable_group,
gc,
dc,
):
dkernel = dilation * (kernel - 1) + 1
if gc.device_type == caffe2_pb2.CUDA and engine == "CUDNN":
assume(_cudnn_supports(dilation=(dilation > 1), nhwc=(order == "NHWC")))
assume(engine != "MKLDNN" or use_bias is True)
op = core.CreateOperator(
"DeformConv",
["X", "o", "w", "b"] if use_bias else ["X", "o", "w"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
deformable_group=deformable_group,
)
offset_dims = _conv_2d_offsets_dims(
batch_size,
size,
kernel,
pad,
pad,
dilation,
stride,
stride,
deformable_group,
)
X = (
np.random.rand(batch_size, size, size, input_channels).astype(np.float32)
- 0.5
)
o = np.zeros(tuple(offset_dims), np.float32)
w = (
np.random.rand(output_channels, kernel, kernel, input_channels).astype(
np.float32
)
- 0.5
)
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, o, w, b] if use_bias else [X, o, w]
# Error handling path.
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if input_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if output_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
def reference_conv_op(*args):
reference_op = core.CreateOperator(
"Conv",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y0"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
device_option=gc,
)
workspace.RunOperatorOnce(reference_op)
reference_blob = workspace.FetchBlob("Y0")
return (reference_blob,)
self.assertReferenceChecks(gc, op, inputs, reference_conv_op)
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
@given(
stride=st.integers(1, 3),
pad=st.integers(0, 0),
kernel=st.integers(1, 5),
dilation=st.integers(1, 3),
size=st.integers(7, 10),
input_channels=st.integers(1, 8),
output_channels=st.integers(1, 8),
batch_size=st.integers(1, 3),
order=st.sampled_from(["NCHW"]),
engine=st.sampled_from(["", "CUDNN", "MKLDNN"]),
use_bias=st.booleans(),
deformable_group=st.integers(1, 4),
**hu.gcs_gpu_only
)
def test_flat_input_convolution(
self,
stride,
pad,
kernel,
dilation,
size,
input_channels,
output_channels,
batch_size,
order,
engine,
use_bias,
deformable_group,
gc,
dc,
):
dkernel = dilation * (kernel - 1) + 1
if gc.device_type == caffe2_pb2.CUDA and engine == "CUDNN":
assume(_cudnn_supports(dilation=(dilation > 1), nhwc=(order == "NHWC")))
assume(engine != "MKLDNN" or use_bias is True)
op = core.CreateOperator(
"DeformConv",
["X", "o", "w", "b"] if use_bias else ["X", "o", "w"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
deformable_group=deformable_group,
)
X = np.ones((batch_size, size, size, input_channels), np.float32) - 0.5
output_size = _conv_2d_output_size(
size, kernel, pad, pad, dilation, stride, stride
)
o = _conv_2d_random_offsets(batch_size, kernel, output_size, deformable_group)
w = np.ones((output_channels, kernel, kernel, input_channels), np.float32) - 0.5
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, o, w, b] if use_bias else [X, o, w]
# Error handling path.
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if input_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if output_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
def reference_conv_op(*args):
reference_op = core.CreateOperator(
"Conv",
["X", "w", "b"] if use_bias else ["X", "w"],
["Y0"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
device_option=gc,
)
workspace.RunOperatorOnce(reference_op)
reference_blob = workspace.FetchBlob("Y0")
return (reference_blob,)
self.assertReferenceChecks(gc, op, inputs, reference_conv_op)
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
@given(
stride=st.integers(1, 1),
pad=st.integers(0, 0),
kernel=st.integers(1, 5),
dilation=st.integers(1, 1),
size=st.integers(7, 10),
input_channels=st.integers(1, 8),
output_channels=st.integers(1, 8),
batch_size=st.integers(1, 3),
order=st.sampled_from(["NCHW"]),
engine=st.sampled_from(["", "CUDNN", "MKLDNN"]),
use_bias=st.booleans(),
deformable_group=st.integers(1, 4),
**hu.gcs_gpu_only
)
def test_shuffle_input_convolution(
self,
stride,
pad,
kernel,
dilation,
size,
input_channels,
output_channels,
batch_size,
order,
engine,
use_bias,
deformable_group,
gc,
dc,
):
dkernel = dilation * (kernel - 1) + 1
if gc.device_type == caffe2_pb2.CUDA and engine == "CUDNN":
assume(_cudnn_supports(dilation=(dilation > 1), nhwc=(order == "NHWC")))
assume(engine != "MKLDNN" or use_bias is True)
op = core.CreateOperator(
"DeformConv",
["X", "o", "w", "b"] if use_bias else ["X", "o", "w"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
deformable_group=deformable_group,
)
X = (
np.random.rand(batch_size, size, size, input_channels).astype(np.float32)
- 0.5
)
output_size = _conv_2d_output_size(
size, kernel, pad, pad, dilation, stride, stride
)
o, w0 = _conv_2d_shuffle_offsets(
batch_size,
kernel,
output_size,
deformable_group,
input_channels,
output_channels,
)
w = np.ones((output_channels, kernel, kernel, input_channels), np.float32)
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
w0 = utils.NHWC2NCHW(w0)
inputs = [X, o, w, b] if use_bias else [X, o, w]
# Error handling path.
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if input_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if output_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
def reference_conv_op(*args):
with core.DeviceScope(gc):
workspace.FeedBlob("w0", w0)
reference_op = core.CreateOperator(
"Conv",
["X", "w0", "b"] if use_bias else ["X", "w0"],
["Y0"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
device_option=gc,
)
workspace.RunOperatorOnce(reference_op)
reference_blob = workspace.FetchBlob("Y0")
return (reference_blob,)
self.assertReferenceChecks(gc, op, inputs, reference_conv_op)
# CUDNN does NOT support different padding values and we skip it
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
@given(
stride_h=st.integers(1, 3),
stride_w=st.integers(1, 3),
pad_h=st.integers(0, 3),
pad_w=st.integers(0, 3),
kernel=st.integers(2, 5),
size=st.integers(1, 8),
input_channels=st.integers(1, 3),
output_channels=st.integers(1, 3),
batch_size=st.integers(1, 3),
order=st.sampled_from(["NCHW"]),
shared_buffer=st.booleans(),
use_bias=st.booleans(),
deformable_group=st.integers(1, 3),
**hu.gcs_gpu_only
)
def test_conv_separate_stride_pad_gradients(
self,
stride_h,
stride_w,
pad_h,
pad_w,
kernel,
size,
input_channels,
output_channels,
batch_size,
order,
shared_buffer,
use_bias,
deformable_group,
gc,
dc,
):
op = core.CreateOperator(
"DeformConv",
["X", "o", "w", "b"] if use_bias else ["X", "o", "w"],
["Y"],
stride_h=stride_h,
stride_w=stride_w,
pad_t=pad_h,
pad_l=pad_w,
pad_b=pad_h,
pad_r=pad_w,
kernel=kernel,
order=order,
shared_buffer=int(shared_buffer),
deformable_group=deformable_group,
)
X = (
np.random.rand(batch_size, size, size, input_channels).astype(np.float32)
- 0.5
)
output_size = _conv_2d_output_size(
size, kernel, pad_h, pad_w, 1, stride_h, stride_w
)
o = _conv_2d_random_offsets(batch_size, kernel, output_size, deformable_group)
w = (
np.random.rand(output_channels, kernel, kernel, input_channels).astype(
np.float32
)
- 0.5
)
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, o, w, b] if use_bias else [X, o, w]
# Error handling path.
if size + pad_h * 2 < kernel or size + pad_w * 2 < kernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if input_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if output_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
@given(
stride=st.integers(1, 3),
pad=st.integers(0, 3),
kernel=st.integers(1, 5),
dilation=st.integers(1, 3),
size=st.integers(7, 10),
input_channels=st.integers(1, 8),
output_channels=st.integers(1, 8),
batch_size=st.integers(1, 3),
order=st.sampled_from(["NCHW"]),
engine=st.sampled_from(["", "CUDNN", "MKLDNN"]),
use_bias=st.booleans(),
deformable_group=st.integers(1, 3),
**hu.gcs_gpu_only
)
def test_conv_gradients(
self,
stride,
pad,
kernel,
dilation,
size,
input_channels,
output_channels,
batch_size,
order,
engine,
use_bias,
deformable_group,
gc,
dc,
):
dkernel = dilation * (kernel - 1) + 1
if gc.device_type == caffe2_pb2.CUDA and engine == "CUDNN":
assume(_cudnn_supports(dilation=(dilation > 1), nhwc=(order == "NHWC")))
assume(engine != "MKLDNN" or use_bias is True)
op = core.CreateOperator(
"DeformConv",
["X", "o", "w", "b"] if use_bias else ["X", "o", "w"],
["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
engine=engine,
deformable_group=deformable_group,
)
X = (
np.random.rand(batch_size, size, size, input_channels).astype(np.float32)
- 0.5
)
output_size = _conv_2d_output_size(
size, kernel, pad, pad, dilation, stride, stride
)
o = _conv_2d_random_offsets(batch_size, kernel, output_size, deformable_group)
w = (
np.random.rand(output_channels, kernel, kernel, input_channels).astype(
np.float32
)
- 0.5
)
b = np.random.rand(output_channels).astype(np.float32) - 0.5
if order == "NCHW":
X = utils.NHWC2NCHW(X)
w = utils.NHWC2NCHW(w)
inputs = [X, o, w, b] if use_bias else [X, o, w]
# Error handling path.
if size + pad + pad < dkernel or size + pad + pad < dkernel:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if input_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
if output_channels % deformable_group != 0:
with self.assertRaises(RuntimeError):
self.assertDeviceChecks(dc, op, inputs, [0])
return
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
if __name__ == "__main__":
import unittest
unittest.main()