def run7(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype( np.float32) input_pd = fluid.dygraph.to_variable(input_np) res_pd = max_pool3d(input_pd, kernel_size=2, stride=2, padding="padding", data_format='NNNN')
def run10(): with fluid.dygraph.guard(): input_np = np.random.uniform(-1, 1, [2, 3, 32, 32, 32]).astype( np.float32) input_pd = fluid.dygraph.to_variable(input_np) res_pd = max_pool3d(input_pd, kernel_size=2, stride=2, padding=0, data_format='NDHWC', return_indices=True)
def forward(self, x): x = self._conv(x) x = self._bn(x) x = F.relu(x) x = F.max_pool3d(x=x, kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1], data_format="NCDHW") return x
def check_max_dygraph_padding(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) padding = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]] result = max_pool3d(input, kernel_size=2, stride=2, padding=padding) result_np = pool3D_forward_naive( input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='max') self.assertTrue(np.allclose(result.numpy(), result_np)) max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=2, padding=0) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np)) padding = [0, 0, 0, 0, 0, 0] result = max_pool3d(input, kernel_size=2, stride=2, padding=padding) self.assertTrue(np.allclose(result.numpy(), result_np))
def check_max_static_results(self, place): with fluid.program_guard(fluid.Program(), fluid.Program()): input = fluid.data(name="input", shape=[2, 3, 32, 32, 32], dtype="float32") result = max_pool3d(input, kernel_size=2, stride=2, padding=0) input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='max') exe = fluid.Executor(place) fetches = exe.run(fluid.default_main_program(), feed={"input": input_np}, fetch_list=[result]) self.assertTrue(np.allclose(fetches[0], result_np))
def forward(self, x): x = self.s1(x) #VideoModelStem x = self.s1_fuse(x) #FuseFastToSlow x = self.s2(x) #ResStage x = self.s2_fuse(x) for pathway in range(self.num_pathways): x[pathway] = F.max_pool3d( x=x[pathway], kernel_size=self.pool_size_ratio[pathway], stride=self.pool_size_ratio[pathway], padding=[0, 0, 0], data_format="NCDHW") x = self.s3(x) x = self.s3_fuse(x) x = self.s4(x) x = self.s4_fuse(x) x = self.s5(x) return x
def check_max_dygraph_padding_results(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) result = max_pool3d( input, kernel_size=2, stride=2, padding=1, ceil_mode=False) result_np = max_pool3D_forward_naive( input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[1, 1, 1], ceil_mode=False) self.assertTrue(np.allclose(result.numpy(), result_np)) max_pool3d_dg = paddle.nn.layer.MaxPool3D( kernel_size=2, stride=None, padding=1, ceil_mode=False) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np))
def check_max_dygraph_ndhwc_results(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable( np.transpose(input_np, [0, 2, 3, 4, 1])) result = max_pool3d(input, kernel_size=2, stride=2, padding=0, data_format="NDHWC", return_indices=False) result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='max') self.assertTrue( np.allclose(np.transpose(result.numpy(), [0, 4, 1, 2, 3]), result_np))
def check_max_dygraph_stride_is_none(self, place): with fluid.dygraph.guard(place): input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32") input = fluid.dygraph.to_variable(input_np) result, indices = max_pool3d(input, kernel_size=2, stride=None, padding="SAME", return_indices=True) result_np = pool3D_forward_naive(input_np, ksize=[2, 2, 2], strides=[2, 2, 2], paddings=[0, 0, 0], pool_type='max', padding_algorithm="SAME") self.assertTrue(np.allclose(result.numpy(), result_np)) max_pool3d_dg = paddle.nn.layer.MaxPool3d(kernel_size=2, stride=2, padding=0) result = max_pool3d_dg(input) self.assertTrue(np.allclose(result.numpy(), result_np))