def test_main_fc_ks(self):
img = torch.randn(size=(1, 3, 10, 10))
main_fc_ks = [1, 8, 10, 24, 32]
r = cond_resnet18()
r.eval()
_, intermediate_full = r(img,
return_intermediate=True,
main_fc_ks=main_fc_ks)
for n in main_fc_ks:
out = r(img, full_k=n)
assert_equal(out, intermediate_full[MAIN_FC_KS][n])
def test_fc_for_channels(self):
img = torch.randn(size=(1, 3, 10, 10))
fc_for_channels = [1, 8, 10, 24, 32]
r = cond_resnet18(fc_for_channels=fc_for_channels)
r.eval()
n_channels = 64
_, intermediate_full = r(img, return_intermediate=True)
for n in range(1, n_channels + 1):
_, intermediate_zero = r(img, full_k=n, return_intermediate=True)
for k, v in intermediate_zero[FC_FOR_CHANNELS].items():
self.assertLessEqual(k, n)
assert_equal(intermediate_full[FC_FOR_CHANNELS][k], v)
def test_resnet18(self):
img = torch.randn(size=(1, 3, 10, 10))
r = cond_resnet18()
r.eval()
n_channels = 64
_, intermediate_full = r(img, return_intermediate=True)
for n in range(1, n_channels + 1):
_, intermediate_zero = r(img, k=n, return_intermediate=True)
for (k, v) in intermediate_zero.items():
intermediate_n_channels = v.shape[1]
self.assertEqual(intermediate_n_channels // n_channels,
intermediate_n_channels / n_channels)
chan_mult = intermediate_n_channels // n_channels
n_out = n * chan_mult
assert_equal(v[0, n_out:], torch.zeros_like(v[0, n_out:]))
assert_equal(v[0, :n_out], intermediate_full[k][0, :n_out])
def _test_cond_conv_variable_channels(self, in_channels: int,
out_channels: int):
cc = CondConv(in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
padding=1)
img = torch.randn(size=(1, in_channels, 10, 10))
out_full = cc(img, k=in_channels)
print(
"The output of N-convolution on full image should be equal to N-convolution on N-image"
)
for n in range(1, in_channels + 1):
n_out = n * cc.channel_multiplication
mask = torch.ones_like(img)
mask[0, n:] = 0
img_zero = img * mask
out = cc(img, k=n)
out_zero = cc(img_zero, k=n)
assert_equal(out, out_zero)
assert_equal(out_zero[0, n_out:],
torch.zeros_like(out_zero[0, n_out:]))
assert_equal(out_zero[0, :n_out], out_full[0, :n_out])
def assert_packed_sequence_equal(actual: PackedSequence,
expected: PackedSequence,
check_device: bool = True,
check_dtype: bool = True,
check_stride: bool = True) -> None:
kwargs = dict(check_device=check_device,
check_dtype=check_dtype,
check_stride=check_stride)
assert_equal(actual.data, expected.data, **kwargs)
assert_close(actual.batch_sizes, expected.batch_sizes, **kwargs)
if actual.sorted_indices is None:
assert expected.sorted_indices is None
else:
assert_close(actual.sorted_indices, expected.sorted_indices, **kwargs)
if actual.unsorted_indices is None:
assert expected.unsorted_indices is None
else:
assert_close(actual.unsorted_indices, expected.unsorted_indices,
**kwargs)
def test_lukasz_marcin_approach_backward_pass(self):
img = torch.randn(size=(2, 3, 10, 10))
r1 = cond_resnet18(norm_layer=None)
r2 = cond_resnet18(norm_layer=None)
r2.load_state_dict(r1.state_dict())
assert_equal(r1.fc.weight, r2.fc.weight)
r1.eval()
r2.eval()
ks = list(range(1, 64, 6))
loss_fn = CrossEntropyLoss()
y_true = torch.tensor([1, 2])
# marcin approach
loss_1 = 0
for k in ks:
y_pred = r1(img, full_k=k)
l = loss_fn(y_pred, y_true)
loss_1 += l
# lukasz approach
loss_2 = 0
_, intermediate_full = r2(img, return_intermediate=True)
emb_full = intermediate_full["embedding"]
for k in ks:
mask = torch.ones_like(emb_full)
k_out = k * 8
mask[torch.arange(len(mask)), k_out:] = 0
emb_mask = emb_full * mask
y_pred = r2.fc(emb_mask)
l = loss_fn(y_pred, y_true)
loss_2 += l
assert_equal(loss_1, loss_2)
loss_1.backward()
loss_2.backward()
for ((n1, p1), (n2, p2)) in zip(r1.named_parameters(),
r2.named_parameters()):
self.assertEqual(n1, n2)
assert_equal(p1, p2, msg=n1)
if p1.grad is None:
self.assertIsNone(p2.grad)
else:
assert_close(p1.grad, p2.grad, msg=n1)
def _test_cond_linear_var_channels(self, in_size: int, out_size: int):
cl = CondLinear(in_features=in_size, out_features=out_size)
v = torch.randn(size=(1, in_size))
out_full = cl(v, k=in_size)
for n in range(1, in_size + 1):
n_out = n * cl.feature_multiplication
mask = torch.ones_like(v)
mask[n:] = 0
v_zero = v * mask
out = cl(v, k=n)
out_zero = cl(v_zero, k=n)
assert_equal(out, out_zero)
assert_equal(out_zero[0, n_out:],
torch.zeros_like(out_zero[0, n_out:]))
assert_equal(out_zero[0, :n_out], out_full[0, :n_out])
def test_cond_linear_same_in_out(self):
in_size = 5
cl = CondLinear(in_features=in_size, out_features=in_size)
v = torch.randn(size=(1, in_size))
out_full = cl(v, k=in_size)
for n in range(1, in_size + 1):
mask = torch.ones_like(v)
mask[n:] = 0
v_zero = v * mask
out = cl(v, k=n)
out_zero = cl(v_zero, k=n)
assert_equal(out, out_zero)
assert_equal(out_zero[0, n:], torch.zeros_like(out_zero[0, n:]))
assert_equal(out_zero[0, :n], out_full[0, :n])
def test_cond_batchnorm(self):
n_channels = 5
cb = CondBatchNorm(num_features=n_channels)
img = torch.randn(size=(1, n_channels, 10, 10))
out_full = cb(img, k=n_channels)
for n in range(1, n_channels + 1):
mask = torch.zeros_like(img)
mask[0, :n] = 1
img_zero = img * mask
out = cb(img, k=n)
out_zero = cb(img_zero, k=n)
assert_equal(out, out_zero)
assert_equal(out_zero[0, n:], torch.zeros_like(out_zero[0, n:]))
assert_equal(out_zero[0, :n], out_full[0, :n])
def test_lukasz_marcin_approach_forward_pass(self):
"""Test if passing image through resnet with K channels is the same as
zeroing out K*8 channels before the final FC layer"""
img = torch.randn(size=(1, 3, 10, 10))
r = cond_resnet18()
r.eval()
n_channels = 64
_, intermediate_full = r(img, return_intermediate=True)
emb_full = intermediate_full["embedding"]
for k in range(1, n_channels + 1):
k_cls, k_intermediate = r(img, k=k, return_intermediate=True)
emb_k = k_intermediate["embedding"]
mask = torch.ones_like(emb_full)
k_out = k * 8
mask[0, k_out:] = 0
emb_mask = emb_full * mask
assert_equal(emb_mask, emb_k)
assert_equal(k_cls, r.fc(emb_k))
assert_equal(k_cls, r.fc(emb_mask))
def test_tril_repeated(self):
x = torch.tril(torch.ones(3, 3))
assert_equal(x,
torch.tensor([[1, 0, 0], [1, 1, 0], [1, 1, 1]]).float())
x_rep_0 = x.repeat_interleave(2, 0)
assert_equal(
x_rep_0,
torch.tensor([
[1, 0, 0],
[1, 0, 0],
[1, 1, 0],
[1, 1, 0],
[1, 1, 1],
[1, 1, 1],
]).float())
x_rep_1 = x.repeat_interleave(2, 1)
assert_equal(
x_rep_1,
torch.tensor([[1, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 1]]).float())
def test_condconv_same_in_out(self):
n_channels = 5
cc = CondConv(in_channels=n_channels,
out_channels=n_channels,
kernel_size=3,
padding=1)
img = torch.randn(size=(1, n_channels, 10, 10))
out_full = cc(img, k=n_channels)
print(
"The output of N-convolution on full image should be equal to N-convolution on N-image"
)
for n in range(1, n_channels + 1):
mask = torch.ones_like(img)
mask[0, n:] = 0
img_zero = img * mask
out = cc(img, k=n)
out_zero = cc(img_zero, k=n)
assert_equal(out, out_zero)
assert_equal(out_zero[0, n:], torch.zeros_like(out_zero[0, n:]))
assert_equal(out_zero[0, :n], out_full[0, :n])
