code stringlengths 3 6.57k |
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assert_almost_equal(p.coef, leg.legint([1,2,3], 1, 0, scl=.5) |
self.p2.integ(lbnd=0) |
assert_almost_equal(p(0) |
self.p2.integ(1, 1) |
assert_almost_equal(p.coef, leg.legint([1,2,3], 1, 1, scl=.5) |
self.p2.integ(2, [1, 2]) |
assert_almost_equal(p.coef, leg.legint([1,2,3], 2, [1,2], scl=.5) |
test_deriv(self) |
self.p2.integ(2, [1, 2]) |
assert_almost_equal(p.deriv(1) |
self.p2.integ(1, [1]) |
assert_almost_equal(p.deriv(2) |
test_roots(self) |
leg.Legendre(leg.poly2leg([0, -1, 0, 1]) |
p.roots() |
assert_almost_equal(res, tgt) |
test_linspace(self) |
np.linspace(0, 1, 20) |
self.p2(xdes) |
self.p2.linspace(20) |
assert_almost_equal(xres, xdes) |
assert_almost_equal(yres, ydes) |
test_fromroots(self) |
leg.Legendre.fromroots(roots, domain=[0, 1]) |
leg.poly2leg([0, -1, 0, 1]) |
assert_almost_equal(res, tgt) |
test_fit(self) |
f(x) |
np.linspace(0,3) |
f(x) |
leg.Legendre.fit(x, y, 3) |
assert_almost_equal(p.domain, [0,3]) |
leg.Legendre.fit(x, y, 3, None) |
assert_almost_equal(p(x) |
assert_almost_equal(p.domain, [0,3]) |
leg.Legendre.fit(x, y, 3, []) |
assert_almost_equal(p(x) |
assert_almost_equal(p.domain, [-1, 1]) |
np.zeros_like(x) |
y.copy() |
leg.Legendre.fit(x, yw, 3, w=w) |
assert_almost_equal(p(x) |
test_identity(self) |
np.linspace(0,3) |
leg.Legendre.identity() |
assert_almost_equal(p(x) |
leg.Legendre.identity([1,3]) |
assert_almost_equal(p(x) |
run_module_suite() |
method (Callable) |
request (:class:`google.ads.googleads.v6.services.types.SearchGoogleAdsFieldsRequest`) |
response (:class:`google.ads.googleads.v6.services.types.SearchGoogleAdsFieldsResponse`) |
metadata (Sequence[Tuple[str, str]]) |
__getattr__(self, name: str) |
getattr(self._response, name) |
__iter__(self) |
__repr__(self) |
format(self.__class__.__name__, self._response) |
_PSP1x1Conv(in_channels, out_channels) |
nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) |
nn.BatchNorm2d(out_channels) |
nn.ReLU(inplace=True) |
_PyramidPooling(nn.Module) |
__init__(self, in_channels) |
super(_PyramidPooling, self) |
__init__() |
_PSP1x1Conv(in_channels, out_channels) |
_PSP1x1Conv(in_channels, out_channels) |
_PSP1x1Conv(in_channels, out_channels) |
_PSP1x1Conv(in_channels, out_channels) |
pool(x, size) |
F.adaptive_avg_pool2d(x, output_size=size) |
upsample(x, h, w) |
F.interpolate(x, (h, w) |
forward(self, x) |
self.upsample(self.conv1(self.pool(x, 1) |
self.upsample(self.conv2(self.pool(x, 2) |
self.upsample(self.conv3(self.pool(x, 3) |
self.upsample(self.conv4(self.pool(x, 4) |
torch.cat([x, feat1, feat2, feat3, feat4], dim=1) |
_PSPHead(nn.Module) |
__init__(self, nclass, **kwargs) |
super(_PSPHead, self) |
__init__(**kwargs) |
_PyramidPooling(2048) |
list() |
self.block.append(nn.Conv2d(4096, 512, kernel_size=3, padding=1, bias=False) |
self.block.append(nn.BatchNorm2d(512) |
self.block.append(nn.ReLU(inplace=True) |
self.block.append(nn.Dropout(0.1) |
self.block.append(nn.Conv2d(512, nclass, kernel_size=1) |
nn.Sequential(*self.block) |
forward(self, x) |
self.psp(x) |
self.block(x) |
PSPNet(SegBaseModel) |
super(PSPNet, self) |
_PSPHead(nclass, **kwargs) |
_FCNHead(1024, nclass, **kwargs) |
self.__setattr__('others', ['head', 'auxlayer'] if self.aux else ['head']) |
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