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from caffe2.python import core
from hypothesis import assume, given, settings
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class TestReduceFrontSum(hu.HypothesisTestCase):
@given(batch_size=st.integers(1, 3),
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),
channels=st.integers(1, 8),
**hu.gcs)
def test_im2col_layout(self, batch_size, stride, pad, kernel, dilation,
size, channels, gc, dc):
dkernel = (dilation * (kernel - 1) + 1)
assume(size >= dkernel)
NCHW_TO_NHWC = (0, 2, 3, 1)
NHWC_TO_NCHW = (0, 3, 1, 2)
COL_NHWC_TO_NCHW = (4, 2, 3, 0, 1)
N = batch_size
C = channels
H = size
W = size
out_h = int((H + (2 * pad) - dkernel) / stride + 1)
out_w = int((W + (2 * pad) - dkernel) / stride + 1)
im_nchw = np.random.rand(N, C, H, W).astype(np.float32) - 0.5
im_nhwc = im_nchw.transpose(NCHW_TO_NHWC)
op_im2col_nchw = core.CreateOperator(
"Im2Col",
["im_nchw"], ["col_nchw"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NCHW",
device_option=gc)
op_im2col_nhwc = core.CreateOperator(
"Im2Col",
["im_nhwc"], ["col_nhwc"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NHWC",
device_option=gc)
self.ws.create_blob("im_nchw").feed(im_nchw, device_option=gc)
self.ws.create_blob("im_nhwc").feed(im_nhwc, device_option=gc)
self.ws.run(op_im2col_nchw)
self.ws.run(op_im2col_nhwc)
# there is probably a clever way to spell this in np
col_nchw = self.ws.blobs["col_nchw"].fetch()
col_nhwc = self.ws.blobs["col_nhwc"].fetch()
col_nchw_ = col_nchw.reshape(N, C, kernel, kernel, out_h, out_w)
col_nhwc_ = col_nhwc.reshape(N, out_h, out_w, kernel, kernel, C)
for i in range(0, N):
np.testing.assert_allclose(
col_nchw_[i],
col_nhwc_[i].transpose(COL_NHWC_TO_NCHW),
atol=1e-4,
rtol=1e-4)
op_col2im_nchw = core.CreateOperator(
"Col2Im",
["col_nchw", "im_nchw"],
["out_nchw"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NCHW",
device_option=gc)
op_col2im_nhwc = core.CreateOperator(
"Col2Im",
["col_nhwc", "im_nhwc"],
["out_nhwc"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order="NHWC",
device_option=gc)
self.ws.run(op_col2im_nchw)
self.ws.run(op_col2im_nhwc)
out_nchw = self.ws.blobs["out_nchw"].fetch()
out_nhwc = self.ws.blobs["out_nhwc"].fetch()
np.testing.assert_allclose(
out_nchw,
out_nhwc.transpose(NHWC_TO_NCHW),
atol=1e-4,
rtol=1e-4)
@given(batch_size=st.integers(1, 3),
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),
channels=st.integers(1, 8),
order=st.sampled_from(["NCHW"]),
**hu.gcs)
@settings(deadline=10000)
def test_col2im_gradients(self, batch_size, stride, pad, kernel,
dilation, size, channels, order, gc, dc):
assume(size >= dilation * (kernel - 1) + 1)
op = core.CreateOperator(
"Im2Col",
["X"], ["Y"],
stride=stride,
kernel=kernel,
dilation=dilation,
pad=pad,
order=order,
device_option=gc)
X = np.random.rand(batch_size, channels, size, size).astype(np.float32)
self.assertGradientChecks(gc, op, [X], 0, [0])
return
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