def test_errors(self):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.hardsigmoid, 1)
# The input dtype must be float16, float32, float64.
x_int32 = paddle.fluid.data(name='x_int32',
shape=[12, 10],
dtype='int32')
self.assertRaises(TypeError, F.hardsigmoid, x_int32)
# support the input dtype is float16
x_fp16 = paddle.fluid.data(name='x_fp16',
shape=[12, 10],
dtype='float16')
F.hardsigmoid(x_fp16)
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
return paddle.multiply(x=inputs, y=outputs)
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = hardsigmoid(outputs)
return paddle.multiply(x=inputs, y=outputs)
def forward(self, inputs):
outputs = self.avg_pool(inputs)
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
return inputs * outputs
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.hardsigmoid(x)
m = paddle.nn.Hardsigmoid()
out2 = m(x)
out_ref = ref_hardsigmoid(self.x_np)
for r in [out1, out2]:
self.assertTrue(np.allclose(out_ref, r.numpy()))
paddle.enable_static()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.static.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.hardsigmoid(x)
m = paddle.nn.Hardsigmoid()
out2 = m(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_hardsigmoid(self.x_np)
for r in res:
self.assertTrue(np.allclose(out_ref, r))
def _scale(self, input, inplace):
scale = F.adaptive_avg_pool2d(input, 1)
scale = self.fc1(scale)
scale = self.relu(scale)
scale = self.fc2(scale)
return F.hardsigmoid(scale, inplace=inplace)
