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import numpy as np
from hypothesis import assume, given, settings
import hypothesis.strategies as st
import os
import unittest
from caffe2.python import core, utils, workspace
import caffe2.python.hip_test_util as hiputl
import caffe2.python.hypothesis_test_util as hu
class TestPooling(hu.HypothesisTestCase):
# CUDNN does NOT support different padding values and we skip it
@given(stride_h=st.integers(1, 3),
stride_w=st.integers(1, 3),
pad_t=st.integers(0, 3),
pad_l=st.integers(0, 3),
pad_b=st.integers(0, 3),
pad_r=st.integers(0, 3),
kernel=st.integers(3, 5),
size=st.integers(7, 9),
input_channels=st.integers(1, 3),
batch_size=st.integers(0, 3),
order=st.sampled_from(["NCHW", "NHWC"]),
op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool",
"MaxPool2D", "AveragePool2D"]),
**hu.gcs)
@settings(deadline=10000)
def test_pooling_separate_stride_pad(self, stride_h, stride_w,
pad_t, pad_l, pad_b,
pad_r, kernel, size,
input_channels,
batch_size, order,
op_type,
gc, dc):
assume(np.max([pad_t, pad_l, pad_b, pad_r]) < kernel)
op = core.CreateOperator(
op_type,
["X"],
["Y"],
stride_h=stride_h,
stride_w=stride_w,
pad_t=pad_t,
pad_l=pad_l,
pad_b=pad_b,
pad_r=pad_r,
kernel=kernel,
order=order,
)
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0])
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0])
# This test is to check if CUDNN works for bigger batch size or not
@unittest.skipIf(not os.getenv('CAFFE2_DEBUG'),
"This is a test that reproduces a cudnn error. If you "
"want to run it, set env variable CAFFE2_DEBUG=1.")
@given(**hu.gcs_cuda_only)
def test_pooling_big_batch(self, gc, dc):
op = core.CreateOperator(
"AveragePool",
["X"],
["Y"],
stride=1,
kernel=7,
pad=0,
order="NHWC",
engine="CUDNN",
)
X = np.random.rand(70000, 7, 7, 81).astype(np.float32)
self.assertDeviceChecks(dc, op, [X], [0])
@given(stride=st.integers(1, 3),
pad=st.integers(0, 3),
kernel=st.integers(1, 5),
size=st.integers(7, 9),
input_channels=st.integers(1, 3),
batch_size=st.integers(0, 3),
order=st.sampled_from(["NCHW", "NHWC"]),
op_type=st.sampled_from(["MaxPool", "AveragePool",
"MaxPool1D", "AveragePool1D"]),
**hu.gcs)
@settings(deadline=10000)
def test_pooling_1d(self, stride, pad, kernel, size, input_channels,
batch_size, order, op_type, gc, dc):
assume(pad < kernel)
op = core.CreateOperator(
op_type,
["X"],
["Y"],
strides=[stride],
kernels=[kernel],
pads=[pad, pad],
order=order,
engine="",
)
X = np.random.rand(
batch_size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0])
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(stride=st.integers(1, 3),
pad=st.integers(0, 2),
kernel=st.integers(1, 6),
size=st.integers(3, 5),
input_channels=st.integers(1, 3),
batch_size=st.integers(0, 3),
order=st.sampled_from(["NCHW", "NHWC"]),
op_type=st.sampled_from(["MaxPool", "AveragePool",
"MaxPool3D", "AveragePool3D"]),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
@settings(deadline=None, max_examples=50)
def test_pooling_3d(self, stride, pad, kernel, size, input_channels,
batch_size, order, op_type, engine, gc, dc):
assume(pad < kernel)
assume(size + pad + pad >= kernel)
# Currently MIOpen Pooling only supports pooling with NCHW order.
if hiputl.run_in_hip(gc, dc) and (workspace.GetHIPVersion() < 303 or order == "NHWC"):
assume(engine != "CUDNN")
# some case here could be calculated with global pooling, but instead
# calculated with general implementation, slower but should still
# be correct.
op = core.CreateOperator(
op_type,
["X"],
["Y"],
strides=[stride] * 3,
kernels=[kernel] * 3,
pads=[pad] * 6,
order=order,
engine=engine,
)
X = np.random.rand(
batch_size, size, size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)
@given(kernel=st.integers(3, 6),
size=st.integers(3, 5),
input_channels=st.integers(1, 3),
batch_size=st.integers(0, 3),
order=st.sampled_from(["NCHW", "NHWC"]),
op_type=st.sampled_from(["MaxPool", "AveragePool",
"MaxPool3D", "AveragePool3D"]),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
@settings(deadline=10000)
def test_global_pooling_3d(self, kernel, size, input_channels,
batch_size, order, op_type, engine, gc, dc):
# Currently MIOpen Pooling only supports pooling with NCHW order.
if hiputl.run_in_hip(gc, dc) and (workspace.GetHIPVersion() < 303 or order == "NHWC"):
assume(engine != "CUDNN")
# pad and stride ignored because they will be inferred in global_pooling
op = core.CreateOperator(
op_type,
["X"],
["Y"],
kernels=[kernel] * 3,
order=order,
global_pooling=True,
engine=engine,
)
X = np.random.rand(
batch_size, size, size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0], threshold=0.001)
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0], threshold=0.001)
@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),
size=st.integers(7, 9),
input_channels=st.integers(1, 3),
batch_size=st.integers(0, 3),
**hu.gcs_gpu_only)
def test_pooling_with_index(self, stride, pad, kernel, size,
input_channels, batch_size, gc, dc):
assume(pad < kernel)
op = core.CreateOperator(
"MaxPoolWithIndex",
["X"],
["Y", "Y_index"],
stride=stride,
kernel=kernel,
pad=pad,
order="NCHW",
deterministic=1,
)
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32)
# transpose due to order = NCHW
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0])
@given(sz=st.integers(1, 20),
batch_size=st.integers(0, 4),
engine=st.sampled_from(["", "CUDNN"]),
op_type=st.sampled_from(["AveragePool", "AveragePool2D"]),
**hu.gcs)
@settings(max_examples=3, deadline=None)
def test_global_avg_pool_nchw(self, op_type, sz, batch_size, engine, gc, dc):
''' Special test to stress the fast path of NCHW average pool '''
op = core.CreateOperator(
op_type,
["X"],
["Y"],
stride=1,
kernel=sz,
pad=0,
order="NCHW",
engine=engine,
)
X = np.random.rand(
batch_size, 3, sz, sz).astype(np.float32)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(sz=st.integers(1, 20),
batch_size=st.integers(0, 4),
engine=st.sampled_from(["", "CUDNN"]),
op_type=st.sampled_from(["MaxPool", "MaxPool2D"]),
**hu.gcs)
@settings(max_examples=3, deadline=None)
def test_global_max_pool_nchw(self, op_type, sz,
batch_size, engine, gc, dc):
''' Special test to stress the fast path of NCHW max pool '''
# CuDNN 5 does not support deterministic max pooling.
assume(workspace.GetCuDNNVersion() >= 6000 or engine != "CUDNN")
op = core.CreateOperator(
op_type,
["X"],
["Y"],
stride=1,
kernel=sz,
pad=0,
order="NCHW",
engine=engine,
deterministic=1,
)
np.random.seed(1234)
X = np.random.rand(
batch_size, 3, sz, sz).astype(np.float32)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=1e-4)
@given(stride=st.integers(1, 3),
pad=st.integers(0, 3),
kernel=st.integers(1, 5),
size=st.integers(7, 9),
input_channels=st.integers(1, 3),
batch_size=st.integers(0, 3),
order=st.sampled_from(["NCHW", "NHWC"]),
op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool",
"MaxPool2D", "AveragePool2D"]),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
@settings(deadline=10000)
def test_pooling(self, stride, pad, kernel, size,
input_channels, batch_size,
order, op_type, engine, gc, dc):
assume(pad < kernel)
if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
assume(order == "NCHW" and op_type != "LpPool")
op = core.CreateOperator(
op_type,
["X"],
["Y"],
stride=stride,
kernel=kernel,
pad=pad,
order=order,
engine=engine,
)
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0])
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(size=st.integers(7, 9),
input_channels=st.integers(1, 3),
batch_size=st.integers(0, 3),
order=st.sampled_from(["NCHW", "NHWC"]),
op_type=st.sampled_from(["MaxPool", "AveragePool", "LpPool"]),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
@settings(deadline=10000)
def test_global_pooling(self, size, input_channels, batch_size,
order, op_type, engine, gc, dc):
# CuDNN 5 does not support deterministic max pooling.
assume(workspace.GetCuDNNVersion() >= 6000 or op_type != "MaxPool")
if hiputl.run_in_hip(gc, dc) and engine == "CUDNN":
assume(order == "NCHW" and op_type != "LpPool")
op = core.CreateOperator(
op_type,
["X"],
["Y"],
order=order,
engine=engine,
global_pooling=True,
)
X = np.random.rand(
batch_size, size, size, input_channels).astype(np.float32)
if order == "NCHW":
X = utils.NHWC2NCHW(X)
self.assertDeviceChecks(dc, op, [X], [0])
if 'MaxPool' not in op_type:
self.assertGradientChecks(gc, op, [X], 0, [0])
@given(op_type=st.sampled_from(["MaxPool", "MaxPoolND"]),
dim=st.integers(1, 3),
N=st.integers(1, 3),
C=st.integers(1, 3),
D=st.integers(3, 5),
H=st.integers(3, 5),
W=st.integers(3, 5),
kernel=st.integers(1, 3),
stride=st.integers(1, 3),
pad=st.integers(0, 2),
order=st.sampled_from(["NCHW", "NHWC"]),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
@settings(deadline=None, max_examples=50)
def test_max_pool_grad(
self, op_type, dim, N, C, D, H, W, kernel, stride, pad, order,
engine, gc, dc):
assume(pad < kernel)
assume(dim > 1 or engine == "")
if hiputl.run_in_hip(gc, dc):
if dim != 2:
assume(engine != "CUDNN")
elif engine == "CUDNN":
assume(order == "NCHW")
if op_type.endswith("ND"):
op_type = op_type.replace("N", str(dim))
op = core.CreateOperator(
op_type,
["X"],
["Y"],
kernels=[kernel] * dim,
strides=[stride] * dim,
pads=[pad] * dim * 2,
order=order,
engine=engine,
)
if dim == 1:
size = W
dims = [N, C, W]
axes = [0, 2, 1]
elif dim == 2:
size = H * W
dims = [N, C, H, W]
axes = [0, 2, 3, 1]
else:
size = D * H * W
dims = [N, C, D, H, W]
axes = [0, 2, 3, 4, 1]
X = np.zeros((N * C, size)).astype(np.float32)
for i in range(N * C):
X[i, :] = np.arange(size, dtype=np.float32) / size
np.random.shuffle(X[i, :])
X = X.reshape(dims)
if order == "NHWC":
X = np.transpose(X, axes)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(
gc, op, [X], 0, [0], threshold=0.05, stepsize=0.005)
@given(op_type=st.sampled_from(["AveragePool", "AveragePoolND"]),
dim=st.integers(1, 3),
N=st.integers(1, 3),
C=st.integers(1, 3),
D=st.integers(3, 5),
H=st.integers(3, 5),
W=st.integers(3, 5),
kernel=st.integers(1, 3),
stride=st.integers(1, 3),
pad=st.integers(0, 2),
count_include_pad=st.booleans(),
order=st.sampled_from(["NCHW", "NHWC"]),
engine=st.sampled_from(["", "CUDNN"]),
**hu.gcs)
@settings(deadline=10000)
def test_avg_pool_count_include_pad(
self, op_type, dim, N, C, D, H, W, kernel, stride, pad,
count_include_pad, order, engine, gc, dc):
assume(pad < kernel)
if hiputl.run_in_hip(gc, dc):
if dim != 2:
assume(engine != "CUDNN")
elif engine == "CUDNN":
assume(order == "NCHW")
if op_type.endswith("ND"):
op_type = op_type.replace("N", str(dim))
op = core.CreateOperator(
op_type,
["X"],
["Y"],
kernels=[kernel] * dim,
strides=[stride] * dim,
pads=[pad] * dim * 2,
count_include_pad=count_include_pad,
order=order,
engine=engine,
)
if dim == 1:
dims = [N, C, W]
axes = [0, 2, 1]
elif dim == 2:
dims = [N, C, H, W]
axes = [0, 2, 3, 1]
else:
dims = [N, C, D, H, W]
axes = [0, 2, 3, 4, 1]
X = np.random.randn(*dims).astype(np.float32)
if order == "NHWC":
X = np.transpose(X, axes)
self.assertDeviceChecks(dc, op, [X], [0])
self.assertGradientChecks(gc, op, [X], 0, [0])
if __name__ == "__main__":
import unittest
unittest.main()
|
