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import numpy as np
from hypothesis import given, settings, assume
import hypothesis.strategies as st
from caffe2.python import core, utils, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
class TestLocallyConnectedOp(serial.SerializedTestCase):
@given(N=st.integers(1, 3),
C=st.integers(1, 3),
H=st.integers(1, 5),
W=st.integers(1, 5),
M=st.integers(1, 3),
kernel=st.integers(1, 3),
op_name=st.sampled_from(["LC", "LC2D"]),
order=st.sampled_from(["NCHW", "NHWC"]),
use_bias=st.booleans(),
**hu.gcs)
@settings(deadline=10000)
def test_lc_2d(
self, N, C, H, W, M, kernel, op_name, order, use_bias, gc, dc):
if H < kernel:
kernel = H
if W < kernel:
kernel = W
assume(C == kernel * N)
op = core.CreateOperator(
op_name,
["X", "W", "b"] if use_bias else ["X", "W"],
["Y"],
kernels=[kernel, kernel],
order=order,
engine="",
)
Y_H = H - kernel + 1
Y_W = W - kernel + 1
if order == "NCHW":
X = np.random.rand(N, C, H, W).astype(np.float32) - 0.5
W = np.random.rand(Y_H, Y_W, M, C, kernel,
kernel).astype(np.float32) - 0.5
else:
X = np.random.rand(N, H, W, C).astype(np.float32) - 0.5
W = np.random.rand(Y_H, Y_W, M, kernel, kernel,
C).astype(np.float32) - 0.5
b = np.random.rand(Y_H, Y_W, M).astype(np.float32) - 0.5
inputs = [X, W, b] if use_bias else [X, W]
def lc_2d_nchw(X, W, b=None):
N, C, XH, XW = X.shape
YH, YW, M, _, KH, KW = W.shape
def conv(n, m, yh, yw):
sum = b[yh, yw, m] if b is not None else 0
for c in range(C):
for kh in range(KH):
for kw in range(KW):
hh = yh + kh
ww = yw + kw
sum += X[n, c, hh, ww] * W[yh, yw, m, c, kh, kw]
return sum
output = np.zeros((N, M, YH, YW), dtype=np.float32)
for n in range(N):
for m in range(M):
for yh in range(YH):
for yw in range(YW):
output[n, m, yh, yw] = conv(n, m, yh, yw)
return [output]
def lc_2d_nhwc(X, W, b=None):
XT = utils.NHWC2NCHW(X)
WT = np.transpose(W, [0, 1, 2, 5, 3, 4])
output = lc_2d_nchw(XT, WT, b)
return [utils.NCHW2NHWC(output[0])]
ref_op = lc_2d_nchw if order == "NCHW" else lc_2d_nhwc
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=inputs,
reference=ref_op,
)
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
@given(N=st.integers(1, 3),
C=st.integers(1, 3),
size=st.integers(1, 5),
M=st.integers(1, 3),
kernel=st.integers(1, 3),
op_name=st.sampled_from(["LC", "LC1D"]),
use_bias=st.booleans(),
**hu.gcs)
@settings(deadline=None)
# Increased timeout from 1 second to 5 for ROCM
def test_lc_1d(self, N, C, size, M, kernel, op_name, use_bias, gc, dc):
if size < kernel:
kernel = size
op = core.CreateOperator(
op_name,
["X", "W", "b"] if use_bias else ["X", "W"],
["Y"],
kernels=[kernel],
order="NCHW",
engine="",
)
L = size - kernel + 1
X = np.random.rand(N, C, size).astype(np.float32) - 0.5
W = np.random.rand(L, M, C, kernel).astype(np.float32) - 0.5
b = np.random.rand(L, M).astype(np.float32) - 0.5
inputs = [X, W, b] if use_bias else [X, W]
def lc_1d_nchw(X, W, b=None):
N, C, XL = X.shape
YL, M, _, KL = W.shape
def conv(n, m, yl):
sum = b[yl, m] if b is not None else 0
for c in range(C):
for kl in range(KL):
ll = yl + kl
sum += X[n, c, ll] * W[yl, m, c, kl]
return sum
output = np.zeros((N, M, YL), dtype=np.float32)
for n in range(N):
for m in range(M):
for yl in range(YL):
output[n, m, yl] = conv(n, m, yl)
return [output]
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=inputs,
reference=lc_1d_nchw,
)
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
@given(N=st.integers(1, 1),
C=st.integers(1, 1),
T=st.integers(2, 2),
H=st.integers(2, 2),
W=st.integers(2, 2),
M=st.integers(1, 1),
kernel=st.integers(2, 2),
op_name=st.sampled_from(["LC", "LC3D"]),
use_bias=st.booleans(),
**hu.gcs)
@settings(deadline=None)
def test_lc_3d(self, N, C, T, H, W, M, kernel, op_name, use_bias, gc, dc):
if T < kernel:
kernel = T
if H < kernel:
kernel = H
if W < kernel:
kernel = W
op = core.CreateOperator(
op_name,
["X", "W", "b"] if use_bias else ["X", "W"],
["Y"],
kernels=[kernel, kernel, kernel],
order="NCHW",
engine="",
)
Y_T = T - kernel + 1
Y_H = H - kernel + 1
Y_W = W - kernel + 1
X = np.random.rand(N, C, T, H, W).astype(np.float32) - 0.5
W = np.random.rand(Y_T, Y_H, Y_W, M, C, kernel,
kernel, kernel).astype(np.float32) - 0.5
b = np.random.rand(Y_T, Y_H, Y_W, M).astype(np.float32) - 0.5
inputs = [X, W, b] if use_bias else [X, W]
def lc_3d_nchw(X, W, b=None):
N, C, XT, XH, XW = X.shape
YT, YH, YW, M, _, KT, KH, KW = W.shape
def conv(n, m, yt, yh, yw):
sum = b[yt, yh, yw, m] if b is not None else 0
for c in range(C):
for kt in range(KT):
for kh in range(KH):
for kw in range(KW):
tt = yt + kt
hh = yh + kh
ww = yw + kw
sum += X[n, c, tt, hh, ww] * \
W[yt, yh, yw, m, c, kt, kh, kw]
return sum
output = np.zeros((N, M, YT, YH, YW), dtype=np.float32)
for n in range(N):
for m in range(M):
for yt in range(YT):
for yh in range(YH):
for yw in range(YW):
output[n, m, yt, yh, yw] = conv(
n, m, yt, yh, yw)
return [output]
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=inputs,
reference=lc_3d_nchw,
)
self.assertDeviceChecks(dc, op, inputs, [0])
for i in range(len(inputs)):
self.assertGradientChecks(gc, op, inputs, i, [0])
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