mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
db5e5781ad
Summary: Per title Test Plan: Fixes existing tests Reviewed By: robieta Differential Revision: D28690296 fbshipit-source-id: d7b5b5065517373b75d501872814c89b24ec8cfc
468 lines
16 KiB
Python
468 lines
16 KiB
Python
|
|
|
|
|
|
|
|
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()
|