# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import contextlib import numpy as np from decorator_helper import prog_scope import inspect from six.moves import filter import paddle import paddle.fluid as fluid from paddle.fluid.layers.device import get_places import paddle.fluid.nets as nets from paddle.fluid.framework import Program, program_guard, default_main_program from paddle.fluid.param_attr import ParamAttr from paddle.fluid import core from paddle.fluid.initializer import Constant import paddle.fluid.layers as layers from test_imperative_base import new_program_scope from paddle.fluid.dygraph import nn from paddle.fluid.dygraph import base class LayerTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.seed = 111 @classmethod def tearDownClass(cls): pass def _get_place(self, force_to_use_cpu=False): # this option for ops that only have cpu kernel if force_to_use_cpu: return core.CPUPlace() else: if core.is_compiled_with_cuda(): return core.CUDAPlace(0) return core.CPUPlace() @contextlib.contextmanager def static_graph(self): with new_program_scope(): fluid.default_startup_program().random_seed = self.seed fluid.default_main_program().random_seed = self.seed yield def get_static_graph_result(self, feed, fetch_list, with_lod=False, force_to_use_cpu=False): exe = fluid.Executor(self._get_place(force_to_use_cpu)) exe.run(fluid.default_startup_program()) return exe.run(fluid.default_main_program(), feed=feed, fetch_list=fetch_list, return_numpy=(not with_lod)) @contextlib.contextmanager def dynamic_graph(self, force_to_use_cpu=False): with fluid.dygraph.guard( self._get_place(force_to_use_cpu=force_to_use_cpu)): fluid.default_startup_program().random_seed = self.seed fluid.default_main_program().random_seed = self.seed yield class TestLayer(LayerTest): def test_custom_layer_with_kwargs(self): class CustomLayer(fluid.Layer): def __init__(self, name_scope, fc1_size=4): super(CustomLayer, self).__init__(name_scope) self.fc1 = nn.FC('fc1', size=fc1_size, bias_attr=False, num_flatten_dims=1) self.fc2 = nn.FC('fc2', size=1, bias_attr=False, num_flatten_dims=1) def forward(self, x, do_fc2=False): ret = self.fc1(x) if do_fc2: ret = self.fc2(ret) return ret with self.dynamic_graph(): inp = np.ones([3, 3], dtype='float32') x = base.to_variable(inp) custom = CustomLayer('custom', fc1_size=2) ret = custom(x, do_fc2=False) self.assertTrue(np.array_equal(ret.numpy().shape, [3, 2])) ret = custom(x, do_fc2=True) self.assertTrue(np.array_equal(ret.numpy().shape, [3, 1])) def test_linear(self): inp = np.ones([3, 32, 32], dtype='float32') with self.static_graph(): t = layers.data( name='data', shape=[3, 32, 32], dtype='float32', append_batch_size=False) linear = nn.Linear( 32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1)) ret = linear(t) static_ret = self.get_static_graph_result( feed={'data': inp}, fetch_list=[ret])[0] with self.dynamic_graph(): t = base.to_variable(inp) linear = nn.Linear( 32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1)) dy_ret = linear(t) dy_ret_value = dy_ret.numpy() self.assertTrue(np.array_equal(static_ret, dy_ret_value)) inp = np.ones([3, 32], dtype='float32') with self.dynamic_graph(): t = base.to_variable(inp) linear = nn.Linear(32, 4, bias_attr=False) dy_ret = linear(t) dy_ret_value = dy_ret.numpy() with self.dynamic_graph(): t = base.to_variable(inp) fc = nn.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1) dy_ret2 = fc(t) dy_ret_value2 = dy_ret2.numpy() self.assertTrue(np.array_equal(dy_ret_value, dy_ret_value2)) def test_fc(self): inp = np.ones([3, 32, 32], dtype='float32') with self.static_graph(): t = layers.data( name='data', shape=[3, 32, 32], dtype='float32', append_batch_size=False) ret = layers.fc(t, size=4, bias_attr=False, num_flatten_dims=1) ret2 = layers.fc(ret, size=4) static_ret = self.get_static_graph_result( feed={'data': inp}, fetch_list=[ret2])[0] with self.static_graph(): t = layers.data( name='data', shape=[3, 32, 32], dtype='float32', append_batch_size=False) fc1 = nn.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1) fc2 = nn.FC('fc2', size=4) ret = fc1(t) ret2 = fc2(ret) static_ret2 = self.get_static_graph_result( feed={'data': inp}, fetch_list=[ret2])[0] with self.dynamic_graph(): t = base.to_variable(inp) fc1 = nn.FC('fc1', size=4, bias_attr=False, num_flatten_dims=1) fc2 = nn.FC('fc2', size=4) ret = fc1(t) dy_ret = fc2(ret) dy_ret_value = dy_ret.numpy() self.assertTrue(np.array_equal(static_ret, static_ret2)) self.assertTrue(np.array_equal(static_ret, dy_ret_value)) with self.dynamic_graph(): custom_weight = np.random.randn(1024, 4).astype("float32") weight_attr1 = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) fc1 = fluid.dygraph.FC("fc1", 4, num_flatten_dims=1, param_attr=weight_attr1) out1 = fc1(base.to_variable(inp)) loss1 = fluid.layers.reduce_mean(out1) fc1_weight_init = fc1.weight.detach() fc1_bias_init = fc1.bias.detach() loss1.backward() optimizer1 = fluid.optimizer.SGD(learning_rate=0.1) optimizer1.minimize(loss1) fc1_weight_updated = fc1.weight.detach() with self.dynamic_graph(): weight_attr2 = fluid.ParamAttr( initializer=fluid.initializer.Uniform()) fc2 = fluid.dygraph.FC("fc2", 4, num_flatten_dims=1, param_attr=weight_attr2) out2 = fc2(base.to_variable(inp)) self.assertFalse( np.array_equal(fc1_weight_init.numpy(), fc2.weight.numpy())) self.assertFalse(np.array_equal(out1.numpy(), out2.numpy())) mismatched_weight = np.random.randn(4, 4).astype("float32") with self.assertRaises(AssertionError): fc2.weight.set_value(mismatched_weight) fc2.weight.set_value(fc1_weight_init) fc2.bias.set_value(fc1_bias_init) out2 = fc2(base.to_variable(inp)) loss2 = fluid.layers.reduce_mean(out2) loss2.backward() optimizer2 = fluid.optimizer.SGD(learning_rate=0.1) optimizer2.minimize(loss2) self.assertTrue( np.array_equal(fc2.weight.numpy(), fc1_weight_updated.numpy())) self.assertTrue(np.array_equal(out1.numpy(), out2.numpy())) fc2.weight = fc1.weight fc2.bias = fc1.bias self.assertTrue( np.array_equal(fc2.weight.numpy(), fc1.weight.numpy())) self.assertTrue(np.array_equal(fc2.bias.numpy(), fc1.bias.numpy())) def test_layer_norm(self): inp = np.ones([3, 32, 32], dtype='float32') with self.static_graph(): t = layers.data( name='data', shape=[3, 32, 32], dtype='float32', append_batch_size=False) ret = layers.layer_norm( t, bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') static_ret = self.get_static_graph_result( feed={'data': inp}, fetch_list=[ret])[0] with self.static_graph(): t = layers.data( name='data', shape=[3, 32, 32], dtype='float32', append_batch_size=False) lm = nn.LayerNorm( normalized_shape=[32, 32], bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') ret = lm(t) static_ret2 = self.get_static_graph_result( feed={'data': inp}, fetch_list=[ret])[0] with self.dynamic_graph(): lm = nn.LayerNorm( normalized_shape=[32, 32], bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') dy_ret = lm(base.to_variable(inp)) dy_ret_value = dy_ret.numpy() with self.dynamic_graph(): lm = nn.LayerNorm( normalized_shape=[32, 32], shift=False, scale=False, param_attr=fluid.initializer.ConstantInitializer(value=1), bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') lm(base.to_variable(inp)) self.assertFalse(hasattr(lm, "_scale_w")) self.assertFalse(hasattr(lm, "_bias_w")) self.assertTrue(np.array_equal(static_ret, static_ret2)) self.assertTrue(np.array_equal(dy_ret_value, static_ret2)) with self.dynamic_graph(): lm = nn.LayerNorm( normalized_shape=[16, 32], bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') with self.assertRaises(ValueError): lm(base.to_variable(inp)) def test_relu(self): with self.static_graph(): t = layers.data(name='t', shape=[3, 3], dtype='float32') ret = layers.relu(t) static_ret = self.get_static_graph_result( feed={'t': np.ones( [3, 3], dtype='float32')}, fetch_list=[ret])[0] with self.dynamic_graph(): t = np.ones([3, 3], dtype='float32') dy_ret = layers.relu(base.to_variable(t)) dy_ret_value = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, dy_ret_value)) def test_matmul(self): with self.static_graph(): t = layers.data(name='t', shape=[3, 3], dtype='float32') t2 = layers.data(name='t2', shape=[3, 3], dtype='float32') ret = layers.matmul(t, t2) static_ret = self.get_static_graph_result( feed={ 't': np.ones( [3, 3], dtype='float32'), 't2': np.ones( [3, 3], dtype='float32') }, fetch_list=[ret])[0] with self.dynamic_graph(): t = np.ones([3, 3], dtype='float32') t2 = np.ones([3, 3], dtype='float32') dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2)) dy_ret_value = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, dy_ret_value)) def test_conv2d(self): with self.static_graph(): images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32') ret = layers.conv2d(input=images, num_filters=3, filter_size=[2, 2]) static_ret = self.get_static_graph_result( feed={'pixel': np.ones( [2, 3, 5, 5], dtype='float32')}, fetch_list=[ret])[0] with self.static_graph(): images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32') conv2d = nn.Conv2D( num_channels=3, num_filters=3, filter_size=[2, 2]) ret = conv2d(images) static_ret2 = self.get_static_graph_result( feed={'pixel': np.ones( [2, 3, 5, 5], dtype='float32')}, fetch_list=[ret])[0] with self.dynamic_graph(): images = np.ones([2, 3, 5, 5], dtype='float32') conv2d = nn.Conv2D( num_channels=3, num_filters=3, filter_size=[2, 2]) dy_ret = conv2d(base.to_variable(images)) dy_ret_value = dy_ret.numpy() with self.dynamic_graph(): images = np.ones([2, 3, 5, 5], dtype='float32') conv2d = nn.Conv2D( num_channels=3, num_filters=3, filter_size=[2, 2], bias_attr=False) dy_ret = conv2d(base.to_variable(images)) self.assertTrue(conv2d._bias_param is None) self.assertTrue(np.allclose(static_ret, dy_ret_value)) self.assertTrue(np.allclose(static_ret, static_ret2)) with self.dynamic_graph(): images = np.ones([2, 3, 5, 5], dtype='float32') custom_weight = np.random.randn(3, 3, 2, 2).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) conv2d1 = nn.Conv2D( num_channels=3, num_filters=3, filter_size=[2, 2]) conv2d2 = nn.Conv2D( num_channels=3, num_filters=3, filter_size=[2, 2], param_attr=weight_attr) dy_ret1 = conv2d1(base.to_variable(images)) dy_ret2 = conv2d2(base.to_variable(images)) self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv2d1_weight_np = conv2d1.weight.numpy() conv2d1_bias = conv2d1.bias self.assertFalse( np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy())) conv2d2.weight.set_value(conv2d1_weight_np) self.assertTrue( np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy())) conv2d2.bias.set_value(conv2d1_bias) dy_ret1 = conv2d1(base.to_variable(images)) dy_ret2 = conv2d2(base.to_variable(images)) self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv2d2.weight = conv2d1.weight conv2d2.bias = conv2d1.bias self.assertTrue( np.array_equal(conv2d1.weight.numpy(), conv2d2.weight.numpy())) self.assertTrue( np.array_equal(conv2d1.bias.numpy(), conv2d2.bias.numpy())) def test_gru_unit(self): lod = [[2, 4, 3]] D = 5 T = sum(lod[0]) N = len(lod[0]) input = np.random.rand(T, 3 * D).astype('float32') hidden_input = np.random.rand(T, D).astype('float32') with self.static_graph(): x = layers.data(name='x', shape=[-1, D * 3], dtype='float32') hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32') updated_hidden, reset_hidden_pre, gate = layers.gru_unit( input=x, hidden=hidden, size=D * 3) static_ret = self.get_static_graph_result( feed={'x': input, 'hidden': hidden_input}, fetch_list=[updated_hidden, reset_hidden_pre, gate]) with self.static_graph(): x = layers.data(name='x', shape=[-1, D * 3], dtype='float32') hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32') updated_hidden, reset_hidden_pre, gate = layers.gru_unit( input=x, hidden=hidden, size=D * 3) gru = nn.GRUUnit(size=D * 3) updated_hidden, reset_hidden_pre, gate = gru(x, hidden) static_ret2 = self.get_static_graph_result( feed={'x': input, 'hidden': hidden_input}, fetch_list=[updated_hidden, reset_hidden_pre, gate]) with self.dynamic_graph(): gru = nn.GRUUnit(size=D * 3) dy_ret = gru( base.to_variable(input), base.to_variable(hidden_input)) dy_ret_value = [] for i in range(len(static_ret)): dy_ret_value.append(dy_ret[i].numpy()) for i in range(len(static_ret)): self.assertTrue(np.allclose(static_ret[i], static_ret2[i])) self.assertTrue(np.allclose(static_ret[i], dy_ret_value[i])) with self.dynamic_graph(): custom_weight = np.random.randn(D, D * 3).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) gru1 = nn.GRUUnit(size=D * 3) gru2 = nn.GRUUnit(size=D * 3, param_attr=weight_attr) dy_ret1 = gru1( base.to_variable(input), base.to_variable(hidden_input)) dy_ret2 = gru2( base.to_variable(input), base.to_variable(hidden_input)) self.assertFalse( np.array_equal(gru1.weight.numpy(), gru2.weight.numpy())) for o1, o2 in zip(dy_ret1, dy_ret2): self.assertFalse(np.array_equal(o1.numpy(), o2.numpy())) gru2.weight.set_value(gru1.weight.numpy()) gru2.bias.set_value(gru1.bias) dy_ret1 = gru1( base.to_variable(input), base.to_variable(hidden_input)) dy_ret2 = gru2( base.to_variable(input), base.to_variable(hidden_input)) for o1, o2 in zip(dy_ret1, dy_ret2): self.assertTrue(np.array_equal(o1.numpy(), o2.numpy())) gru2.weight = gru1.weight gru2.bias = gru1.bias self.assertTrue( np.array_equal(gru1.weight.numpy(), gru2.weight.numpy())) self.assertTrue( np.array_equal(gru1.bias.numpy(), gru2.bias.numpy())) def test_elementwise_math(self): n = np.ones([3, 3], dtype='float32') n2 = np.ones([3, 3], dtype='float32') * 1.1 n3 = np.ones([3, 3], dtype='float32') * 2 n4 = np.ones([3, 3], dtype='float32') * 3 n5 = np.ones([3, 3], dtype='float32') * 4 n6 = np.ones([3, 3], dtype='float32') * 5 with self.static_graph(): t = layers.data(name='t', shape=[3, 3], dtype='float32') t2 = layers.data(name='t2', shape=[3, 3], dtype='float32') t3 = layers.data(name='t3', shape=[3, 3], dtype='float32') t4 = layers.data(name='t4', shape=[3, 3], dtype='float32') t5 = layers.data(name='t5', shape=[3, 3], dtype='float32') t6 = layers.data(name='t6', shape=[3, 3], dtype='float32') ret = layers.elementwise_add(t, t2) ret = layers.elementwise_pow(ret, t3) ret = layers.elementwise_div(ret, t4) ret = layers.elementwise_sub(ret, t5) ret = layers.elementwise_mul(ret, t6) static_ret = self.get_static_graph_result( feed={ 't': n, 't2': n2, 't3': n3, 't4': n4, 't5': n5, 't6': n6 }, fetch_list=[ret])[0] with self.dynamic_graph(): ret = layers.elementwise_add(n, n2) ret = layers.elementwise_pow(ret, n3) ret = layers.elementwise_div(ret, n4) ret = layers.elementwise_sub(ret, n5) dy_ret = layers.elementwise_mul(ret, n6) dy_ret_value = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, dy_ret_value)) def test_elementwise_minmax(self): n = np.ones([3, 3], dtype='float32') n2 = np.ones([3, 3], dtype='float32') * 2 with self.dynamic_graph(): min_ret = layers.elementwise_min(n, n2) max_ret = layers.elementwise_max(n, n2) min_ret_value = min_ret.numpy() max_ret_value = max_ret.numpy() self.assertTrue(np.allclose(n, min_ret_value)) self.assertTrue(np.allclose(n2, max_ret_value)) def test_sequence_conv(self): inp_np = np.arange(12).reshape([3, 4]).astype('float32') if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() with self.static_graph(): seq = layers.data( name='seq_in', shape=[3, 4], dtype='float32', lod_level=1, append_batch_size=False) out = layers.sequence_conv(seq, 2, act='sigmoid') static_rlt = self.get_static_graph_result( feed={ "seq_in": fluid.create_lod_tensor( data=inp_np, recursive_seq_lens=[[1, 1, 1]], place=place) }, fetch_list=[out], with_lod=True)[0] with self.static_graph(): seq = layers.data( name='seq_in', shape=[3, 4], dtype='float32', lod_level=1, append_batch_size=False) seq_conv = nn.SequenceConv('seq_conv', num_filters=2, act='sigmoid') out = seq_conv(seq) static_rlt2 = self.get_static_graph_result( feed={ "seq_in": fluid.create_lod_tensor( data=inp_np, recursive_seq_lens=[[1, 1, 1]], place=place) }, fetch_list=[out], with_lod=True)[0] self.assertTrue( np.array_equal(np.array(static_rlt), np.array(static_rlt2))) def test_conv2d_transpose(self): inp_np = np.arange(0, 24).reshape([2, 3, 2, 2]).astype('float32') with self.static_graph(): img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32') out = layers.conv2d_transpose( input=img, num_filters=10, filter_size=27, act='sigmoid', bias_attr=fluid.initializer.ConstantInitializer(value=1)) static_rlt = self.get_static_graph_result( feed={'pixel': inp_np}, fetch_list=[out])[0] with self.static_graph(): img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32') conv2d_transpose = nn.Conv2DTranspose( num_channels=3, num_filters=10, filter_size=27, act='sigmoid', bias_attr=fluid.initializer.ConstantInitializer(value=1)) out = conv2d_transpose(img) static_rlt2 = self.get_static_graph_result( feed={'pixel': inp_np}, fetch_list=[out])[0] with self.dynamic_graph(): conv2d_transpose = nn.Conv2DTranspose( num_channels=3, num_filters=10, filter_size=27, act='sigmoid', bias_attr=fluid.initializer.ConstantInitializer(value=1)) dy_rlt = conv2d_transpose(base.to_variable(inp_np)) dy_rlt_value = dy_rlt.numpy() self.assertTrue(np.allclose(static_rlt2, static_rlt)) self.assertTrue(np.allclose(dy_rlt_value, static_rlt2)) with self.dynamic_graph(): images = np.ones([2, 3, 5, 5], dtype='float32') custom_weight = np.random.randn(3, 3, 2, 2).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) conv2d1 = nn.Conv2DTranspose( num_channels=3, num_filters=3, filter_size=[2, 2]) conv2d2 = nn.Conv2DTranspose( num_channels=3, num_filters=3, filter_size=[2, 2], param_attr=weight_attr) dy_ret1 = conv2d1(base.to_variable(images)) dy_ret2 = conv2d2(base.to_variable(images)) self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv2d1_weight_np = conv2d1.weight.numpy() conv2d1_bias = conv2d1.bias self.assertFalse( np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy())) conv2d2.weight.set_value(conv2d1_weight_np) self.assertTrue( np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy())) conv2d2.bias.set_value(conv2d1_bias) dy_ret1 = conv2d1(base.to_variable(images)) dy_ret2 = conv2d2(base.to_variable(images)) self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv2d2.weight = conv2d1.weight conv2d2.bias = conv2d1.bias self.assertTrue( np.array_equal(conv2d1.weight.numpy(), conv2d2.weight.numpy())) self.assertTrue( np.array_equal(conv2d1.bias.numpy(), conv2d2.bias.numpy())) def test_bilinear_tensor_product(self): inp_np_x = np.array([[1, 2, 3]]).astype('float32') inp_np_y = np.array([[4, 5, 6]]).astype('float32') with self.static_graph(): data_x = layers.data( name='x', shape=[1, 3], dtype="float32", append_batch_size=False) data_y = layers.data( name='y', shape=[1, 3], dtype="float32", append_batch_size=False) out = layers.bilinear_tensor_product( data_x, data_y, 6, bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') static_rlt = self.get_static_graph_result( feed={'x': inp_np_x, 'y': inp_np_y}, fetch_list=[out])[0] with self.static_graph(): data_x = layers.data( name='x', shape=[1, 3], dtype="float32", append_batch_size=False) data_y = layers.data( name='y', shape=[1, 3], dtype="float32", append_batch_size=False) btp = nn.BilinearTensorProduct( 3, 3, 6, bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') out = btp(data_x, data_y) static_rlt2 = self.get_static_graph_result( feed={'x': inp_np_x, 'y': inp_np_y}, fetch_list=[out])[0] with self.dynamic_graph(): btp = nn.BilinearTensorProduct( 3, 3, 6, bias_attr=fluid.initializer.ConstantInitializer(value=1), act='sigmoid') dy_rlt = btp(base.to_variable(inp_np_x), base.to_variable(inp_np_y)) dy_rlt_value = dy_rlt.numpy() with self.dynamic_graph(): btp2 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid') dy_rlt2 = btp2( base.to_variable(inp_np_x), base.to_variable(inp_np_y)) dy_rlt2_value = dy_rlt2.numpy() with self.static_graph(): data_x2 = layers.data( name='x', shape=[1, 3], dtype="float32", append_batch_size=False) data_y2 = layers.data( name='y', shape=[1, 3], dtype="float32", append_batch_size=False) out2 = layers.bilinear_tensor_product( data_x2, data_y2, 6, act='sigmoid') static_rlt3 = self.get_static_graph_result( feed={'x': inp_np_x, 'y': inp_np_y}, fetch_list=[out2])[0] self.assertTrue(np.array_equal(dy_rlt2_value, static_rlt3)) self.assertTrue(np.array_equal(static_rlt2, static_rlt)) self.assertTrue(np.array_equal(dy_rlt_value, static_rlt)) with self.dynamic_graph(): custom_weight = np.random.randn(6, 3, 3).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) btp1 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid') btp2 = nn.BilinearTensorProduct( 3, 3, 6, act='sigmoid', param_attr=weight_attr) dy_rlt1 = btp1( base.to_variable(inp_np_x), base.to_variable(inp_np_y)) dy_rlt2 = btp2( base.to_variable(inp_np_x), base.to_variable(inp_np_y)) self.assertFalse(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())) btp2.weight.set_value(btp1.weight.numpy()) btp2.bias.set_value(btp1.bias) dy_rlt1 = btp1( base.to_variable(inp_np_x), base.to_variable(inp_np_y)) dy_rlt2 = btp2( base.to_variable(inp_np_x), base.to_variable(inp_np_y)) self.assertTrue(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())) btp2.weight = btp1.weight btp2.bias = btp1.bias self.assertTrue( np.array_equal(btp1.weight.numpy(), btp2.weight.numpy())) self.assertTrue( np.array_equal(btp1.bias.numpy(), btp2.bias.numpy())) def prelu_test(self, mode): inp_np = np.ones([5, 200, 100, 100]).astype('float32') with self.static_graph(): data_t = layers.data( name="input", shape=[5, 200, 100, 100], dtype="float32", append_batch_size=False) out = layers.prelu( data_t, mode, param_attr=ParamAttr(initializer=Constant(1.0))) static_rlt = self.get_static_graph_result( feed={"input": inp_np}, fetch_list=[out])[0] with self.static_graph(): data_t = layers.data( name="input", shape=[5, 200, 100, 100], dtype="float32", append_batch_size=False) prelu = nn.PRelu( mode=mode, input_shape=data_t.shape, param_attr=ParamAttr(initializer=Constant(1.0))) out = prelu(data_t) static_rlt2 = self.get_static_graph_result( feed={"input": inp_np}, fetch_list=[out])[0] with self.dynamic_graph(): prelu = nn.PRelu( mode=mode, input_shape=inp_np.shape, param_attr=ParamAttr(initializer=Constant(1.0))) dy_rlt = prelu(base.to_variable(inp_np)) dy_rlt_value = dy_rlt.numpy() self.assertTrue(np.allclose(static_rlt2, static_rlt)) self.assertTrue(np.allclose(dy_rlt_value, static_rlt)) with self.dynamic_graph(): inp_np = np.random.randn(5, 200, 100, 100).astype("float32") inp = base.to_variable(inp_np) prelu1 = nn.PRelu( mode=mode, input_shape=inp_np.shape, param_attr=ParamAttr(initializer=Constant(2.0))) prelu2 = nn.PRelu( mode=mode, input_shape=inp_np.shape, param_attr=ParamAttr(initializer=Constant(1.0))) dy_rlt1 = prelu1(inp) dy_rlt2 = prelu2(inp) self.assertFalse( np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy())) self.assertFalse(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())) prelu2.weight.set_value(prelu1.weight.numpy()) dy_rlt1 = prelu1(inp) dy_rlt2 = prelu2(inp) self.assertTrue(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy())) prelu2.weight = prelu1.weight self.assertTrue( np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy())) def test_prelu(self): self.prelu_test("channel") self.prelu_test("element") self.prelu_test("all") def test_embeding(self): inp_word = np.array([[[1]]]).astype('int64') dict_size = 20 with self.static_graph(): data_t = layers.data(name='word', shape=[1], dtype='int64') emb = layers.embedding( input=data_t, size=[dict_size, 32], param_attr='emb.w', is_sparse=False) static_rlt = self.get_static_graph_result( feed={'word': inp_word}, fetch_list=[emb])[0] with self.static_graph(): data_t = layers.data(name='word', shape=[1], dtype='int64') emb2 = nn.Embedding( size=[dict_size, 32], param_attr='emb.w', is_sparse=False) emb_rlt = emb2(data_t) static_rlt2 = self.get_static_graph_result( feed={'word': inp_word}, fetch_list=[emb_rlt])[0] with self.dynamic_graph(): emb2 = nn.Embedding( size=[dict_size, 32], param_attr='emb.w', is_sparse=False) dy_rlt = emb2(base.to_variable(inp_word)) dy_rlt_value = dy_rlt.numpy() self.assertTrue(np.allclose(static_rlt2, static_rlt)) self.assertTrue(np.allclose(dy_rlt_value, static_rlt)) with self.dynamic_graph(): custom_weight = np.random.randn(dict_size, 32).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) emb1 = nn.Embedding(size=[dict_size, 32], is_sparse=False) emb2 = nn.Embedding( size=[dict_size, 32], param_attr=weight_attr, is_sparse=False) rep1 = emb1(base.to_variable(inp_word)) rep2 = emb2(base.to_variable(inp_word)) self.assertFalse(np.array_equal(emb1.weight.numpy(), custom_weight)) self.assertTrue(np.array_equal(emb2.weight.numpy(), custom_weight)) self.assertFalse(np.array_equal(rep1.numpy(), rep2.numpy())) emb2.weight.set_value(emb1.weight.numpy()) rep2 = emb2(base.to_variable(inp_word)) self.assertTrue(np.array_equal(rep1.numpy(), rep2.numpy())) emb2.weight = emb1.weight self.assertTrue( np.array_equal(emb1.weight.numpy(), emb2.weight.numpy())) def test_nce(self): window_size = 5 dict_size = 20 label_word = int(window_size // 2) + 1 inp_word = np.array([[1], [2], [3], [4], [5]]).astype('int64') nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32') seed = 1 with self.static_graph(): words = [] for i in range(window_size): words.append( layers.data( name='word_{0}'.format(i), shape=[None], dtype='int64')) sample_weights = layers.fill_constant( shape=[5, 1], dtype='float32', value=1) embs = [] for i in range(window_size): if i == label_word: continue emb = fluid.embedding( input=words[i], size=[dict_size, 32], param_attr='emb.w', is_sparse=False) embs.append(emb) embs = layers.concat(input=embs, axis=1) wl = fluid.layers.unsqueeze(words[label_word], axes=[0]) nce_loss = layers.nce(input=embs, label=wl, num_total_classes=dict_size, num_neg_samples=2, sampler="custom_dist", custom_dist=nid_freq_arr.tolist(), seed=seed, param_attr='nce.w', bias_attr='nce.b', sample_weight=sample_weights) feed_dict = dict() for i in range(window_size): feed_dict['word_{0}'.format(i)] = inp_word[i] static_rlt = self.get_static_graph_result( feed=feed_dict, fetch_list=[nce_loss])[0] with self.static_graph(): words = [] for i in range(window_size): words.append( layers.data( name='word_{0}'.format(i), shape=[None], dtype='int64')) sample_weights = layers.fill_constant( shape=[5, 1], dtype='float32', value=1) emb = nn.Embedding( size=[dict_size, 32], param_attr='emb.w', is_sparse=False) embs2 = [] for i in range(window_size): if i == label_word: continue emb_rlt = emb(words[i]) embs2.append(emb_rlt) embs2 = layers.concat(input=embs2, axis=1) nce = nn.NCE(num_total_classes=dict_size, dim=embs2.shape[1], num_neg_samples=2, sampler="custom_dist", custom_dist=nid_freq_arr.tolist(), seed=seed, param_attr='nce.w', bias_attr='nce.b', sample_weight=sample_weights) wl = fluid.layers.unsqueeze(words[label_word], axes=[0]) nce_loss2 = nce(embs2, wl) feed_dict = dict() for i in range(len(words)): feed_dict['word_{0}'.format(i)] = inp_word[i] static_rlt2 = self.get_static_graph_result( feed=feed_dict, fetch_list=[nce_loss2])[0] with self.dynamic_graph(force_to_use_cpu=True): words = [] for i in range(window_size): words.append(base.to_variable(inp_word[i])) sample_weights = layers.fill_constant( shape=[5, 1], dtype='float32', value=1) emb = nn.Embedding( size=[dict_size, 32], param_attr='emb.w', is_sparse=False) embs3 = [] for i in range(window_size): if i == label_word: continue emb_rlt = emb(words[i]) embs3.append(emb_rlt) embs3 = layers.concat(input=embs3, axis=1) nce = nn.NCE(num_total_classes=dict_size, dim=embs3.shape[1], num_neg_samples=2, sampler="custom_dist", custom_dist=nid_freq_arr.tolist(), seed=seed, param_attr='nce.w', bias_attr='nce.b', sample_weight=sample_weights) wl = fluid.layers.unsqueeze(words[label_word], axes=[0]) dy_rlt = nce(embs3, wl) dy_rlt_value = dy_rlt.numpy() self.assertTrue(np.allclose(static_rlt2, static_rlt)) self.assertTrue(np.allclose(dy_rlt_value, static_rlt)) with self.dynamic_graph(force_to_use_cpu=True): custom_weight = np.random.randn(dict_size, 128).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) words = [] for i in range(window_size): words.append(base.to_variable(inp_word[i])) sample_weights = layers.fill_constant( shape=[5, 1], dtype='float32', value=1) emb = nn.Embedding( size=[dict_size, 32], param_attr='emb.w', is_sparse=False) embs3 = [] for i in range(window_size): if i == label_word: continue emb_rlt = emb(words[i]) embs3.append(emb_rlt) embs3 = layers.concat(input=embs3, axis=1) nce1 = nn.NCE(num_total_classes=dict_size, dim=embs3.shape[1], num_neg_samples=2, sampler="custom_dist", custom_dist=nid_freq_arr.tolist(), seed=seed, param_attr='nce1.w', bias_attr='nce1.b', sample_weight=sample_weights) nce2 = nn.NCE(num_total_classes=dict_size, dim=embs3.shape[1], num_neg_samples=2, sampler="custom_dist", custom_dist=nid_freq_arr.tolist(), seed=seed, param_attr=weight_attr, bias_attr='nce2.b', sample_weight=sample_weights) wl = fluid.layers.unsqueeze(words[label_word], axes=[0]) nce1_loss = nce1(embs3, wl) nce2_loss = nce2(embs3, wl) self.assertFalse( np.array_equal(nce1_loss.numpy(), nce2_loss.numpy())) nce2.weight.set_value(nce1.weight.numpy()) nce2.bias.set_value(nce1.bias) nce1_loss = nce1(embs3, wl) nce2_loss = nce2(embs3, wl) self.assertTrue( np.array_equal(nce1_loss.numpy(), nce2_loss.numpy())) nce2.weight = nce1.weight nce2.bias = nce1.bias self.assertTrue( np.array_equal(nce1.weight.numpy(), nce2.weight.numpy())) self.assertTrue( np.array_equal(nce1.bias.numpy(), nce2.bias.numpy())) def test_conv3d(self): with self.static_graph(): images = layers.data( name='pixel', shape=[3, 6, 6, 6], dtype='float32') ret = layers.conv3d(input=images, num_filters=3, filter_size=2) static_ret = self.get_static_graph_result( feed={'pixel': np.ones( [2, 3, 6, 6, 6], dtype='float32')}, fetch_list=[ret])[0] with self.static_graph(): images = layers.data( name='pixel', shape=[3, 6, 6, 6], dtype='float32') conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2) ret = conv3d(images) static_ret2 = self.get_static_graph_result( feed={'pixel': np.ones( [2, 3, 6, 6, 6], dtype='float32')}, fetch_list=[ret])[0] with self.dynamic_graph(): images = np.ones([2, 3, 6, 6, 6], dtype='float32') conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2) dy_ret = conv3d(base.to_variable(images)) dy_rlt_value = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, dy_rlt_value)) self.assertTrue(np.allclose(static_ret, static_ret2)) with self.dynamic_graph(): images = np.ones([2, 3, 6, 6, 6], dtype='float32') custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) conv3d1 = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2) conv3d2 = nn.Conv3D( num_channels=3, num_filters=3, filter_size=2, param_attr=weight_attr) dy_ret1 = conv3d1(base.to_variable(images)) dy_ret2 = conv3d2(base.to_variable(images)) self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv3d1_weight_np = conv3d1.weight.numpy() conv3d1_bias = conv3d1.bias self.assertFalse( np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())) conv3d2.weight.set_value(conv3d1_weight_np) self.assertTrue( np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())) conv3d1.bias.set_value(conv3d1_bias) dy_ret1 = conv3d1(base.to_variable(images)) dy_ret2 = conv3d2(base.to_variable(images)) self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv3d2.weight = conv3d1.weight conv3d2.bias = conv3d1.bias self.assertTrue( np.array_equal(conv3d1.weight.numpy(), conv3d2.weight.numpy())) self.assertTrue( np.array_equal(conv3d1.bias.numpy(), conv3d2.bias.numpy())) def test_row_conv(self): input = np.arange(15).reshape([3, 5]).astype('float32') if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() with self.static_graph(): x = layers.data( name='X', shape=[3, 5], dtype='float32', lod_level=1, append_batch_size=False) ret = layers.row_conv(input=x, future_context_size=2) static_ret = self.get_static_graph_result( feed={ 'X': fluid.create_lod_tensor( data=input, recursive_seq_lens=[[1, 1, 1]], place=place) }, fetch_list=[ret], with_lod=True)[0] with self.static_graph(): x = layers.data( name='X', shape=[3, 5], dtype='float32', lod_level=1, append_batch_size=False) rowConv = nn.RowConv('RowConv', future_context_size=2) ret = rowConv(x) static_ret2 = self.get_static_graph_result( feed={ 'X': fluid.create_lod_tensor( data=input, recursive_seq_lens=[[1, 1, 1]], place=place) }, fetch_list=[ret], with_lod=True)[0] # TODO: dygraph can't support LODTensor self.assertTrue(np.allclose(static_ret, static_ret2)) def test_group_norm(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() shape = (2, 4, 3, 3) input = np.random.random(shape).astype('float32') with self.static_graph(): X = fluid.layers.data( name='X', shape=shape, dtype='float32', lod_level=1, append_batch_size=False) ret = layers.group_norm(input=X, groups=2) static_ret = self.get_static_graph_result( feed={ 'X': fluid.create_lod_tensor( data=input, recursive_seq_lens=[[1, 1]], place=place) }, fetch_list=[ret], with_lod=True)[0] with self.static_graph(): X = fluid.layers.data( name='X', shape=shape, dtype='float32', lod_level=1, append_batch_size=False) groupNorm = nn.GroupNorm(channels=shape[1], groups=2) ret = groupNorm(X) static_ret2 = self.get_static_graph_result( feed={ 'X': fluid.create_lod_tensor( data=input, recursive_seq_lens=[[1, 1]], place=place) }, fetch_list=[ret], with_lod=True)[0] with self.dynamic_graph(): groupNorm = nn.GroupNorm(channels=shape[1], groups=2) dy_ret = groupNorm(base.to_variable(input)) dy_rlt_value = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, dy_rlt_value)) self.assertTrue(np.allclose(static_ret, static_ret2)) def test_spectral_norm(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() shape = (2, 4, 3, 3) input = np.random.random(shape).astype('float32') with self.static_graph(): Weight = fluid.layers.data( name='Weight', shape=shape, dtype='float32', lod_level=1, append_batch_size=False) ret = layers.spectral_norm(weight=Weight, dim=1, power_iters=2) static_ret = self.get_static_graph_result( feed={ 'Weight': fluid.create_lod_tensor( data=input, recursive_seq_lens=[[1, 1]], place=place), }, fetch_list=[ret], with_lod=True)[0] with self.static_graph(): Weight = fluid.layers.data( name='Weight', shape=shape, dtype='float32', lod_level=1, append_batch_size=False) spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2) ret = spectralNorm(Weight) static_ret2 = self.get_static_graph_result( feed={ 'Weight': fluid.create_lod_tensor( data=input, recursive_seq_lens=[[1, 1]], place=place) }, fetch_list=[ret], with_lod=True)[0] with self.dynamic_graph(): spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2) dy_ret = spectralNorm(base.to_variable(input)) dy_rlt_value = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, dy_rlt_value)) self.assertTrue(np.allclose(static_ret, static_ret2)) def test_tree_conv(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() adj_array = [1, 2, 1, 3, 1, 4, 1, 5, 2, 6, 2, 7, 2, 8, 4, 9, 4, 10] adj = np.array(adj_array).reshape((1, 9, 2)).astype('int32') adj = np.tile(adj, (1, 1, 1)) vectors = np.random.random((1, 10, 5)).astype('float32') with self.static_graph(): NodesVector = fluid.layers.data( name='NodesVector', shape=(1, 10, 5), dtype='float32', lod_level=1, append_batch_size=False) EdgeSet = fluid.layers.data( name='EdgeSet', shape=(1, 9, 2), dtype='int32', lod_level=1, append_batch_size=False) ret = fluid.contrib.layers.tree_conv( nodes_vector=NodesVector, edge_set=EdgeSet, output_size=6, num_filters=1, max_depth=2) static_ret = self.get_static_graph_result( feed={ 'NodesVector': fluid.create_lod_tensor( data=vectors, recursive_seq_lens=[[1]], place=place), 'EdgeSet': fluid.create_lod_tensor( data=adj, recursive_seq_lens=[[1]], place=place) }, fetch_list=[ret], with_lod=False)[0] with self.static_graph(): NodesVector = fluid.layers.data( name='NodesVector', shape=(1, 10, 5), dtype='float32', lod_level=1, append_batch_size=False) EdgeSet = fluid.layers.data( name='EdgeSet', shape=(1, 9, 2), dtype='int32', lod_level=1, append_batch_size=False) treeConv = nn.TreeConv( feature_size=5, output_size=6, num_filters=1, max_depth=2) ret = treeConv(NodesVector, EdgeSet) static_ret2 = self.get_static_graph_result( feed={ 'NodesVector': fluid.create_lod_tensor( data=vectors, recursive_seq_lens=[[1]], place=place), 'EdgeSet': fluid.create_lod_tensor( data=adj, recursive_seq_lens=[[1]], place=place) }, fetch_list=[ret], with_lod=False)[0] with self.dynamic_graph(): treeConv = nn.TreeConv( feature_size=5, output_size=6, num_filters=1, max_depth=2) dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj)) dy_rlt_value = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, static_ret2)) self.assertTrue(np.allclose(static_ret, dy_rlt_value)) with self.dynamic_graph(): custom_weight = np.random.randn(5, 3, 6, 1).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) treeConv1 = nn.TreeConv( feature_size=5, output_size=6, num_filters=1, max_depth=2, bias_attr='tc1_b') treeConv2 = nn.TreeConv( feature_size=5, output_size=6, num_filters=1, max_depth=2, param_attr=weight_attr, bias_attr='tc2_b') dy_ret1 = treeConv1( base.to_variable(vectors), base.to_variable(adj)) dy_ret2 = treeConv2( base.to_variable(vectors), base.to_variable(adj)) self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) treeConv2.weight.set_value(treeConv1.weight.numpy()) treeConv2.bias.set_value(treeConv1.bias) dy_ret1 = treeConv1( base.to_variable(vectors), base.to_variable(adj)) dy_ret2 = treeConv2( base.to_variable(vectors), base.to_variable(adj)) self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) treeConv2.weight = treeConv1.weight treeConv2.bias = treeConv1.bias self.assertTrue( np.array_equal(treeConv1.weight.numpy(), treeConv2.weight.numpy())) self.assertTrue( np.array_equal(treeConv1.bias.numpy(), treeConv2.bias.numpy())) def test_conv3d_transpose(self): input_array = np.arange(0, 48).reshape( [2, 3, 2, 2, 2]).astype('float32') with self.static_graph(): img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32') out = layers.conv3d_transpose( input=img, num_filters=12, filter_size=12, use_cudnn=False) static_rlt = self.get_static_graph_result( feed={'pixel': input_array}, fetch_list=[out])[0] with self.static_graph(): img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32') conv3d_transpose = nn.Conv3DTranspose( num_channels=3, num_filters=12, filter_size=12, use_cudnn=False) out = conv3d_transpose(img) static_rlt2 = self.get_static_graph_result( feed={'pixel': input_array}, fetch_list=[out])[0] with self.dynamic_graph(): conv3d_transpose = nn.Conv3DTranspose( num_channels=3, num_filters=12, filter_size=12, use_cudnn=False) dy_rlt = conv3d_transpose(base.to_variable(input_array)) dy_rlt_value = dy_rlt.numpy() self.assertTrue(np.allclose(static_rlt2, static_rlt)) self.assertTrue(np.allclose(dy_rlt_value, static_rlt)) with self.dynamic_graph(): images = np.ones([2, 3, 6, 6, 6], dtype='float32') custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32") weight_attr = fluid.ParamAttr( initializer=fluid.initializer.NumpyArrayInitializer( custom_weight)) conv3d1 = nn.Conv3DTranspose( num_channels=3, num_filters=3, filter_size=2, bias_attr='conv3d1_b', use_cudnn=False) conv3d2 = nn.Conv3DTranspose( num_channels=3, num_filters=3, filter_size=2, param_attr=weight_attr, bias_attr='conv3d2_b', use_cudnn=False) dy_ret1 = conv3d1(base.to_variable(images)) dy_ret2 = conv3d2(base.to_variable(images)) self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv3d1_weight_np = conv3d1.weight.numpy() conv3d1_bias = conv3d1.bias self.assertFalse( np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())) conv3d2.weight.set_value(conv3d1_weight_np) self.assertTrue( np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy())) conv3d1.bias.set_value(conv3d1_bias) dy_ret1 = conv3d1(base.to_variable(images)) dy_ret2 = conv3d2(base.to_variable(images)) self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy())) conv3d2.weight = conv3d1.weight conv3d2.bias = conv3d1.bias self.assertTrue( np.array_equal(conv3d1.weight.numpy(), conv3d2.weight.numpy())) self.assertTrue( np.array_equal(conv3d1.bias.numpy(), conv3d2.bias.numpy())) def test_eye_op(self): np_eye = np.eye(3, 2) array_rlt1 = [np_eye for _ in range(3)] stack_rlt1 = np.stack(array_rlt1, axis=0) array_rlt2 = [stack_rlt1 for _ in range(4)] stack_rlt2 = np.stack(array_rlt2, axis=0) with self.dynamic_graph(): eye_tensor = layers.eye(num_rows=3, num_columns=2) eye_tensor_rlt1 = layers.eye(num_rows=3, num_columns=2, batch_shape=[3]) eye_tensor_rlt2 = layers.eye(num_rows=3, num_columns=2, batch_shape=[4, 3]) diag_tensor = layers.eye(20) eye_tensor_value = eye_tensor.numpy() eye_tensor_rlt1_value = eye_tensor_rlt1.numpy() eye_tensor_rlt2_value = eye_tensor_rlt2.numpy() diag_tensor_value = diag_tensor.numpy() self.assertTrue(np.allclose(eye_tensor_value, np_eye)) self.assertTrue(np.allclose(eye_tensor_rlt1_value, stack_rlt1)) self.assertTrue(np.allclose(eye_tensor_rlt2_value, stack_rlt2)) self.assertTrue(np.allclose(diag_tensor_value, np.eye(20))) with self.assertRaises(TypeError): layers.eye(num_rows=3.1) with self.assertRaises(TypeError): layers.eye(num_rows=3, num_columns=2.2) with self.assertRaises(TypeError): layers.eye(num_rows=3, batch_shape=2) with self.assertRaises(TypeError): layers.eye(num_rows=3, batch_shape=[-1]) def test_hard_swish(self): with self.static_graph(): t = layers.data(name='t', shape=[3, 3], dtype='float32') ret = layers.hard_swish(t) static_ret = self.get_static_graph_result( feed={'t': np.ones( [3, 3], dtype='float32')}, fetch_list=[ret])[0] with self.dynamic_graph(): t = np.ones([3, 3], dtype='float32') dy_ret = layers.hard_swish(base.to_variable(t)) dy_ret_rlt = dy_ret.numpy() self.assertTrue(np.allclose(static_ret, dy_ret_rlt)) def test_compare(self): value_a = np.arange(3) value_b = np.arange(3) # less than with self.static_graph(): a = layers.data(name='a', shape=[1], dtype='int64') b = layers.data(name='b', shape=[1], dtype='int64') cond = layers.less_than(x=a, y=b) static_ret = self.get_static_graph_result( feed={"a": value_a, "b": value_b}, fetch_list=[cond])[0] with self.dynamic_graph(): da = base.to_variable(value_a) db = base.to_variable(value_b) dcond = layers.less_than(x=da, y=db) for i in range(len(static_ret)): self.assertTrue(dcond.numpy()[i] == static_ret[i]) # less equal with self.static_graph(): a1 = layers.data(name='a1', shape=[1], dtype='int64') b1 = layers.data(name='b1', shape=[1], dtype='int64') cond1 = layers.less_equal(x=a1, y=b1) static_ret1 = self.get_static_graph_result( feed={"a1": value_a, "b1": value_b}, fetch_list=[cond1])[0] with self.dynamic_graph(): da1 = base.to_variable(value_a) db1 = base.to_variable(value_b) dcond1 = layers.less_equal(x=da1, y=db1) for i in range(len(static_ret1)): self.assertTrue(dcond1.numpy()[i] == static_ret1[i]) #greater than with self.static_graph(): a2 = layers.data(name='a2', shape=[1], dtype='int64') b2 = layers.data(name='b2', shape=[1], dtype='int64') cond2 = layers.greater_than(x=a2, y=b2) static_ret2 = self.get_static_graph_result( feed={"a2": value_a, "b2": value_b}, fetch_list=[cond2])[0] with self.dynamic_graph(): da2 = base.to_variable(value_a) db2 = base.to_variable(value_b) dcond2 = layers.greater_than(x=da2, y=db2) for i in range(len(static_ret2)): self.assertTrue(dcond2.numpy()[i] == static_ret2[i]) #greater equal with self.static_graph(): a3 = layers.data(name='a3', shape=[1], dtype='int64') b3 = layers.data(name='b3', shape=[1], dtype='int64') cond3 = layers.greater_equal(x=a3, y=b3) static_ret3 = self.get_static_graph_result( feed={"a3": value_a, "b3": value_b}, fetch_list=[cond3])[0] with self.dynamic_graph(): da3 = base.to_variable(value_a) db3 = base.to_variable(value_b) dcond3 = layers.greater_equal(x=da3, y=db3) for i in range(len(static_ret3)): self.assertTrue(dcond3.numpy()[i] == static_ret3[i]) # equal with self.static_graph(): a4 = layers.data(name='a4', shape=[1], dtype='int64') b4 = layers.data(name='b4', shape=[1], dtype='int64') cond4 = layers.equal(x=a4, y=b4) static_ret4 = self.get_static_graph_result( feed={"a4": value_a, "b4": value_b}, fetch_list=[cond4])[0] with self.dynamic_graph(): da4 = base.to_variable(value_a) db4 = base.to_variable(value_b) dcond4 = layers.equal(x=da4, y=db4) for i in range(len(static_ret4)): self.assertTrue(dcond4.numpy()[i] == static_ret4[i]) # not equal with self.static_graph(): a5 = layers.data(name='a5', shape=[1], dtype='int64') b5 = layers.data(name='b5', shape=[1], dtype='int64') cond5 = layers.equal(x=a5, y=b5) static_ret5 = self.get_static_graph_result( feed={"a5": value_a, "b5": value_b}, fetch_list=[cond5])[0] with self.dynamic_graph(): da5 = base.to_variable(value_a) db5 = base.to_variable(value_b) dcond5 = layers.equal(x=da5, y=db5) for i in range(len(static_ret5)): self.assertTrue(dcond5.numpy()[i] == static_ret5[i]) def test_crop_tensor(self): with self.static_graph(): x = fluid.layers.data(name="x1", shape=[6, 5, 8]) dim1 = fluid.layers.data( name="dim1", shape=[1], append_batch_size=False) dim2 = fluid.layers.data( name="dim2", shape=[1], append_batch_size=False) crop_shape1 = (1, 2, 4, 4) crop_shape2 = fluid.layers.data( name="crop_shape", shape=[4], append_batch_size=False) crop_shape3 = [-1, dim1, dim2, 4] crop_offsets1 = [0, 0, 1, 0] crop_offsets2 = fluid.layers.data( name="crop_offset", shape=[4], append_batch_size=False) crop_offsets3 = [0, dim1, dim2, 0] out1 = fluid.layers.crop_tensor( x, shape=crop_shape1, offsets=crop_offsets1) out2 = fluid.layers.crop_tensor( x, shape=crop_shape2, offsets=crop_offsets2) out3 = fluid.layers.crop_tensor( x, shape=crop_shape3, offsets=crop_offsets3) self.assertIsNotNone(out1) self.assertIsNotNone(out2) self.assertIsNotNone(out3) class TestBook(LayerTest): def test_all_layers(self): attrs = (getattr(self, name) for name in dir(self)) methods = filter(inspect.ismethod, attrs) for method in methods: if not method.__name__.startswith('make_'): continue self._low_data_bound = 0 self._high_data_bound = 2 self._batch_size = 2 self._feed_dict = {} self._force_to_use_cpu = False with self.static_graph(): static_var = method() if isinstance(static_var, tuple): static_var = static_var[0] if static_var is not None: fetch_list = [static_var.name] static_result = self.get_static_graph_result( feed=self._feed_dict, fetch_list=fetch_list, force_to_use_cpu=self._force_to_use_cpu) else: assert method.__name__ in ('make_get_places') continue with self.dynamic_graph(self._force_to_use_cpu): dy_result = method() if isinstance(dy_result, tuple): dy_result = dy_result[0] dy_result_value = dy_result.numpy() self.assertTrue(np.array_equal(static_result[0], dy_result_value)) def _get_np_data(self, shape, dtype, append_batch_size=True): np.random.seed(self.seed) if append_batch_size: shape = [self._batch_size] + shape if dtype == 'float32': return np.random.random(shape).astype(dtype) elif dtype == 'float64': return np.random.random(shape).astype(dtype) elif dtype == 'int32': return np.random.randint(self._low_data_bound, self._high_data_bound, shape).astype(dtype) elif dtype == 'int64': return np.random.randint(self._low_data_bound, self._high_data_bound, shape).astype(dtype) def _get_data(self, name, shape, dtype, set_feed_dict=True, append_batch_size=True): if base.enabled(): return base.to_variable( value=self._get_np_data(shape, dtype, append_batch_size), name=name) else: if set_feed_dict: self._feed_dict[name] = self._get_np_data(shape, dtype, append_batch_size) return layers.data( name=name, shape=shape, dtype=dtype, append_batch_size=append_batch_size) def make_sampled_softmax_with_cross_entropy(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): logits = self._get_data(name='Logits', shape=[256], dtype='float32') label = self._get_data(name='Label', shape=[1], dtype='int64') num_samples = 25 output = layers.sampled_softmax_with_cross_entropy(logits, label, num_samples) return (output) def make_fit_a_line(self): with program_guard( fluid.default_main_program(), startup_program=fluid.default_startup_program()): x = self._get_data(name='x', shape=[13], dtype='float32') y_predict = layers.fc(input=x, size=1, act=None) y = self._get_data(name='y', shape=[1], dtype='float32') cost = layers.square_error_cost(input=y_predict, label=y) avg_cost = layers.mean(cost) return (avg_cost) def make_recognize_digits_mlp(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): # Change g_program, so the rest layers use `g_program` images = self._get_data(name='pixel', shape=[784], dtype='float32') label = self._get_data(name='label', shape=[1], dtype='int64') hidden1 = layers.fc(input=images, size=128, act='relu') hidden2 = layers.fc(input=hidden1, size=64, act='relu') predict = layers.fc(input=[hidden2, hidden1], size=10, act='softmax', param_attr=["sftmax.w1", "sftmax.w2"]) cost = layers.cross_entropy(input=predict, label=label) avg_cost = layers.mean(cost) return (avg_cost) def make_conv2d_transpose(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): img = self._get_data(name='pixel', shape=[3, 2, 2], dtype='float32') return layers.conv2d_transpose( input=img, num_filters=10, output_size=28) def make_recognize_digits_conv(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): images = self._get_data( name='pixel', shape=[1, 28, 28], dtype='float32') label = self._get_data(name='label', shape=[1], dtype='int64') conv_pool_1 = nets.simple_img_conv_pool( input=images, filter_size=5, num_filters=2, pool_size=2, pool_stride=2, act="relu") conv_pool_2 = nets.simple_img_conv_pool( input=conv_pool_1, filter_size=5, num_filters=4, pool_size=2, pool_stride=2, act="relu") predict = layers.fc(input=conv_pool_2, size=10, act="softmax") cost = layers.cross_entropy(input=predict, label=label) avg_cost = layers.mean(cost) return avg_cost def make_word_embedding(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): dict_size = 10000 embed_size = 32 first_word = self._get_data(name='firstw', shape=[1], dtype='int64') second_word = self._get_data( name='secondw', shape=[1], dtype='int64') third_word = self._get_data(name='thirdw', shape=[1], dtype='int64') forth_word = self._get_data(name='forthw', shape=[1], dtype='int64') next_word = self._get_data(name='nextw', shape=[1], dtype='int64') embed_first = layers.embedding( input=first_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') embed_second = layers.embedding( input=second_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') embed_third = layers.embedding( input=third_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') embed_forth = layers.embedding( input=forth_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') concat_embed = layers.concat( input=[embed_first, embed_second, embed_third, embed_forth], axis=1) hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid') predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax') cost = layers.cross_entropy(input=predict_word, label=next_word) avg_cost = layers.mean(cost) return (avg_cost) def make_sigmoid_cross_entropy(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): dat = self._get_data(name='data', shape=[10], dtype='float32') lbl = self._get_data(name='label', shape=[10], dtype='float32') ignore_index = -1 return (layers.sigmoid_cross_entropy_with_logits( x=dat, label=lbl, ignore_index=ignore_index)) def make_hsigmoid(self): self._force_to_use_cpu = True with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()): x = self._get_data(name='x', shape=[2], dtype='float32') y = self._get_data(name='y', shape=[2], dtype='int64') return (layers.hsigmoid(input=x, label=y, num_classes=2)) # test hsigmod with custom tree structure program2 = Program() with program_guard(program2): x2 = self._get_data(name='x2', shape=[4, 8], dtype='float32') y2 = self._get_data(name='y2', shape=[4], dtype='int64') path_table = self._get_data( name='path_table', shape=[4, 6], dtype='int64') path_code = self._get_data( name='path_code', shape=[4, 6], dtype='int64') return (layers.hsigmoid( input=x2, label=y2, num_classes=6, path_table=path_table, path_code=path_code, is_custom=True)) def make_pool2d(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 224, 224], dtype='float32') return (layers.pool2d( x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1))) def make_pool2d_infershape(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): theta = self._get_data("theta", shape=[2, 3], dtype='float32') x = fluid.layers.affine_grid(theta, out_shape=[2, 3, 244, 244]) return (layers.pool2d( x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1))) def make_pool3d(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data( name='x', shape=[3, 244, 244, 244], dtype='float32') return (layers.pool3d( x, pool_size=[5, 3, 2], pool_stride=[1, 2, 3], pool_padding=(2, 1, 1))) def make_adaptive_pool2d(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 224, 224], dtype='float32') return (layers.adaptive_pool2d(x, [3, 3], pool_type='avg')) pool, mask = layers.adaptive_pool2d(x, [3, 3], require_index=True) return (pool) return (mask) return (layers.adaptive_pool2d(x, 3, pool_type='avg')) pool, mask = layers.adaptive_pool2d(x, 3, require_index=True) return (pool) return (mask) def make_adaptive_pool3d(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data( name='x', shape=[3, 244, 224, 224], dtype='float32') return (layers.adaptive_pool3d(x, [3, 3, 3], pool_type='avg')) pool, mask = layers.adaptive_pool3d( x, [3, 3, 3], require_index=True) return (pool) return (mask) return (layers.adaptive_pool3d(x, 3, pool_type='avg')) pool, mask = layers.adaptive_pool3d(x, 3, require_index=True) return (pool) return (mask) def make_lstm_unit(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x_t_data = self._get_data( name='x_t_data', shape=[10, 10], dtype='float32') x_t = layers.fc(input=x_t_data, size=10) prev_hidden_data = self._get_data( name='prev_hidden_data', shape=[10, 30], dtype='float32') prev_hidden = layers.fc(input=prev_hidden_data, size=30) prev_cell_data = self._get_data( name='prev_cell', shape=[10, 30], dtype='float32') prev_cell = layers.fc(input=prev_cell_data, size=30) return (layers.lstm_unit( x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell)) def make_softmax(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data(name='data', shape=[10], dtype='float32') hid = layers.fc(input=data, size=20) return (layers.softmax(hid, axis=1)) def make_space_to_depth(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data( name='data', shape=[32, 9, 6, 6], append_batch_size=False, dtype='float32') return (layers.space_to_depth(data, 3)) def make_lrn(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data(name='data', shape=[6, 2, 2], dtype='float32') return (layers.lrn(data)) def make_get_places(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): get_places(device_count=1) @prog_scope() def make_nce(self): window_size = 5 words = [] for i in range(window_size): words.append( self._get_data( name='word_{0}'.format(i), shape=[1], dtype='int64')) dict_size = 10000 label_word = int(window_size // 2) + 1 embs = [] for i in range(window_size): if i == label_word: continue emb = layers.embedding( input=words[i], size=[dict_size, 32], param_attr='emb.w', is_sparse=True) embs.append(emb) embs = layers.concat(input=embs, axis=1) loss = layers.nce(input=embs, label=words[label_word], num_total_classes=dict_size, param_attr='nce.w', bias_attr='nce.b') avg_loss = layers.mean(loss) return (avg_loss) def make_multiplex(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x1 = self._get_data(name='x1', shape=[4], dtype='float32') x2 = self._get_data(name='x2', shape=[4], dtype='float32') index = self._get_data(name='index', shape=[1], dtype='int32') out = layers.multiplex(inputs=[x1, x2], index=index) return (out) def make_softmax_with_cross_entropy(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[16], dtype='float32') y = self._get_data(name='label', shape=[1], dtype='int64') loss, softmax = layers.softmax_with_cross_entropy( x, y, return_softmax=True) self.assertIsNotNone(loss) self.assertIsNotNone(softmax) loss = layers.softmax_with_cross_entropy(x, y) self.assertIsNotNone(loss) x1 = self._get_data(name='x1', shape=[16, 32, 64], dtype='float32') y1 = self._get_data(name='label1', shape=[1, 32, 64], dtype='int64') y2 = self._get_data(name='label2', shape=[16, 1, 64], dtype='int64') y3 = self._get_data(name='label3', shape=[16, 32, 1], dtype='int64') loss1 = layers.softmax_with_cross_entropy(x1, y1, axis=1) loss2 = layers.softmax_with_cross_entropy(x1, y2, axis=2) loss3 = layers.softmax_with_cross_entropy(x1, y3, axis=3) loss4 = layers.softmax_with_cross_entropy(x1, y3, axis=-1) self.assertIsNotNone(loss1) self.assertIsNotNone(loss2) self.assertIsNotNone(loss3) self.assertIsNotNone(loss4) return (loss4) def make_smooth_l1(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[4], dtype='float32') y = self._get_data(name='label', shape=[4], dtype='float32') loss = layers.smooth_l1(x, y) return (loss) def make_scatter(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data( name='x', shape=[3, 3], append_batch_size=False, dtype='float32') idx = self._get_data( name='idx', shape=[2], append_batch_size=False, dtype='int32') updates = self._get_data( name='updates', shape=[2, 3], append_batch_size=False, dtype='float32') out = layers.scatter(input=x, index=idx, updates=updates) return (out) def make_one_hot(self): with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()): label = self._get_data(name="label", shape=[1], dtype="int32") one_hot_label = layers.one_hot(input=label, depth=10) return (one_hot_label) def make_label_smooth(self): # TODO(minqiyang): support gpu ut self._force_to_use_cpu = True with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()): label = self._get_data(name="label", shape=[1], dtype="int32") one_hot_label = layers.one_hot(input=label, depth=10) smooth_label = layers.label_smooth( label=one_hot_label, epsilon=0.1, dtype="int32") return (smooth_label) def make_topk(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data(name="label", shape=[200], dtype="float32") values, indices = layers.topk(data, k=5) return (values) return (indices) def make_resize_bilinear(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32") output = layers.resize_bilinear(x, out_shape=[12, 12]) return (output) def make_resize_bilinear_by_scale(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32") output = layers.resize_bilinear(x, scale=1.5) return (output) def make_resize_nearest(self): try: with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32") output = layers.resize_nearest(x, out_shape=[12, 12]) except ValueError: pass try: with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data( name='x2', shape=[3, 9, 6, 7], dtype="float32") output = layers.resize_nearest(x, out_shape=[12, 12, 12]) except ValueError: pass with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32") output = layers.resize_nearest(x, out_shape=[12, 12]) return (output) def make_resize_nearest_by_scale(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32") output = layers.resize_nearest(x, scale=1.8) return (output) def make_resize_trilinear(self): try: with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x2', shape=[3, 9, 6], dtype="float32") output = layers.resize_trilinear(x, out_shape=[12, 12, 12]) except ValueError: pass try: with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data( name='x', shape=[3, 9, 6, 7], dtype="float32") output = layers.resize_trilinear(x, out_shape=[12, 12]) except ValueError: pass with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32") output = layers.resize_trilinear(x, out_shape=[12, 12, 12]) return (output) def make_resize_trilinear_by_scale(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32") output = layers.resize_trilinear(x, scale=2.1) return (output) def make_polygon_box_transform(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[8, 4, 4], dtype="float32") output = layers.polygon_box_transform(input=x) return (output) def make_l2_normalize(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[8, 7, 10], dtype="float32") output = layers.l2_normalize(x, axis=1) return output def make_maxout(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data(name='x', shape=[8, 6, 6], dtype="float32") output = layers.maxout(x=data, groups=2) return (output) def make_crop(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 5], dtype="float32") y = self._get_data(name='y', shape=[2, 3], dtype="float32") output = layers.crop(x, shape=y) return (output) def make_mean_iou(self): with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()): x = self._get_data(name='x', shape=[16], dtype='int32') y = self._get_data(name='label', shape=[16], dtype='int32') iou = layers.mean_iou(x, y, self._high_data_bound) return (iou) def make_argsort(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data(name='x', shape=[2, 3, 3], dtype="float32") out, ids = layers.argsort(input=data, axis=1) return (out) return (ids) def make_rank_loss(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): label = self._get_data( name='label', append_batch_size=False, shape=[16, 1], dtype="float32") left = self._get_data( name='left', append_batch_size=False, shape=[16, 1], dtype="float32") right = self._get_data( name='right', append_batch_size=False, shape=[16, 1], dtype="float32") out = layers.rank_loss(label, left, right, name="rank_loss") return (out) def make_shape(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[3, 100, 100], dtype="float32") out = layers.shape(input) return (out) def make_pad2d(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[3, 100, 100], dtype="float32") paddings = layers.fill_constant(shape=[4], dtype='int32', value=1) out = layers.pad2d( input, paddings=[1, 2, 3, 4], mode='reflect', data_format='NCHW', name="shape") out_1 = layers.pad2d( input, paddings=paddings, mode='reflect', data_format='NCHW', name="shape") return (out) return (out_1) def make_prelu(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[5, 200, 100, 100], dtype="float32") mode = 'channel' out = layers.prelu( input, mode, param_attr=ParamAttr(initializer=Constant(1.0)), name='prelu') return (out) def make_brelu(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.brelu(input, t_min=1.0, t_max=20.0, name='brelu') return (out) def make_leaky_relu(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.leaky_relu(input, alpha=0.1, name='leaky_relu') return (out) def make_soft_relu(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.soft_relu(input, threshold=30.0, name='soft_relu') return (out) def make_sigmoid(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.sigmoid(input, name='sigmoid') return (out) def make_logsigmoid(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.logsigmoid(input, name='logsigmoid') return (out) def make_exp(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.exp(input, name='exp') return (out) def make_tanh(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.tanh(input, name='tanh') return (out) def make_tanh_shrink(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.tanh_shrink(input, name='tanh_shrink') return (out) def make_sqrt(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.sqrt(input, name='sqrt') return (out) def make_abs(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.abs(input, name='abs') return (out) def make_ceil(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.ceil(input, name='ceil') return (out) def make_floor(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.floor(input, name='floor') return (out) def make_cos(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.cos(input, name='cos') return (out) def make_sin(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.sin(input, name='sin') return (out) def make_round(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.round(input, name='round') return (out) def make_reciprocal(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.reciprocal(input, name='reciprocal') return (out) def make_square(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.square(input, name='square') return (out) def make_softplus(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.softplus(input, name='softplus') return (out) def make_softsign(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.softsign(input, name='softsign') return (out) def make_cross_entropy(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="x", shape=[30, 10], dtype="float32") label = self._get_data(name="label", shape=[30, 1], dtype="int64") mode = 'channel' out = layers.cross_entropy(x, label, False, 4) return (out) def make_bpr_loss(self): self._force_to_use_cpu = True with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()): x = self._get_data(name="x", shape=[30, 10], dtype="float32") label = self._get_data(name="label", shape=[30, 1], dtype="int64") out = layers.bpr_loss(x, label) return (out) def make_expand(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="input", shape=[10], dtype='int32') out = layers.expand(x, [1, 2]) return out def make_uniform_random_batch_size_like(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[13, 11], dtype='float32') out = layers.uniform_random_batch_size_like(input, [-1, 11]) return (out) def make_gaussian_random(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): out = layers.gaussian_random(shape=[20, 30]) return (out) def make_sampling_id(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data( name="X", shape=[13, 11], dtype='float32', append_batch_size=False) out = layers.sampling_id(x) return (out) def make_gaussian_random_batch_size_like(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[13, 11], dtype='float32') out = layers.gaussian_random_batch_size_like( input, shape=[-1, 11], mean=1.0, std=2.0) return (out) def make_sum(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[13, 11], dtype='float32') out = layers.sum(input) return (out) def make_slice(self): starts = [1, 0, 2] ends = [3, 3, 4] axes = [0, 1, 2] with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[3, 4, 5, 6], dtype='float32') out = layers.slice(input, axes=axes, starts=starts, ends=ends) return out def make_scale_variable(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data( name="input", shape=[3, 4, 5, 6], dtype='float32') scale_var = self._get_data( name="scale", shape=[1], dtype='float32', append_batch_size=False) out = layers.scale(input, scale=scale_var) return out def make_softshrink(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = self._get_data(name="input", shape=[16], dtype="float32") out = layers.softshrink(input, alpha=0.3) return (out) def make_iou_similarity(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="x", shape=[4], dtype="float32") y = self._get_data(name="y", shape=[4], dtype="float32") out = layers.iou_similarity(x, y, name='iou_similarity') return (out) def make_grid_sampler(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name='x', shape=[3, 5, 7], dtype='float32') grid = self._get_data(name='grid', shape=[5, 7, 2], dtype='float32') out = layers.grid_sampler(x, grid) return (out) def make_bilinear_tensor_product_layer(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data(name='data', shape=[4], dtype="float32") theta = self._get_data(name="theta", shape=[5], dtype="float32") out = layers.bilinear_tensor_product(data, theta, 6) return (out) def make_batch_norm(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data( name='data', shape=[32, 128, 128], dtype="float32") out = layers.batch_norm(data) return (out) def make_batch_norm_momentum_variable(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): data = self._get_data( name='data', shape=[32, 128, 128], dtype="float32") momentum = self._get_data( name='momentum', shape=[1], dtype='float32', append_batch_size=False) out = layers.batch_norm(data, momentum=momentum) return (out) def make_range(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): layers.range(0, 10, 2, 'int32') y = layers.range(0.1, 10.0, 0.2, 'float32') return y def make_spectral_norm(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): weight = self._get_data( name='weight', shape=[2, 3, 32, 32], dtype="float32", append_batch_size=False) out = layers.spectral_norm(weight, dim=1, power_iters=1) return (out) def make_kldiv_loss(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data( name='x', shape=[32, 128, 128], dtype="float32", append_batch_size=False) target = self._get_data( name='target', shape=[32, 128, 128], dtype="float32", append_batch_size=False) loss = layers.kldiv_loss(x=x, target=target, reduction='batchmean') return (loss) def make_temporal_shift(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32") out = layers.temporal_shift(x, seg_num=2, shift_ratio=0.2) return (out) def make_shuffle_channel(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32") out = layers.shuffle_channel(x, group=4) return (out) def make_fsp_matrix(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32") y = self._get_data(name="Y", shape=[8, 4, 4], dtype="float32") out = layers.fsp_matrix(x, y) return (out) def make_pixel_shuffle(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="X", shape=[9, 4, 4], dtype="float32") out = layers.pixel_shuffle(x, upscale_factor=3) return (out) def make_mse_loss(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="X", shape=[1], dtype="float32") y = self._get_data(name="Y", shape=[1], dtype="float32") out = layers.mse_loss(input=x, label=y) return (out) def make_square_error_cost(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): x = self._get_data(name="X", shape=[1], dtype="float32") y = self._get_data(name="Y", shape=[1], dtype="float32") out = layers.square_error_cost(input=x, label=y) return (out) def test_dynamic_lstmp(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): hidden_dim, proj_dim = 16, 8 seq_data = layers.data( name='seq_data', shape=[10, 10], dtype='float32', lod_level=1) fc_out = layers.fc(input=seq_data, size=4 * hidden_dim) self.assertIsNotNone( layers.dynamic_lstmp( input=fc_out, size=4 * hidden_dim, proj_size=proj_dim)) def test_linear_chain_crf(self): with self.static_graph(): label_dict_len = 10 feature = layers.data(name='feature', shape=[784], dtype='float32') label = layers.data(name='label', shape=[1], dtype='int64') emission = layers.fc(input=feature, size=10) crf = layers.linear_chain_crf( input=emission, label=label, param_attr=ParamAttr(name="crfw")) crf_decode = layers.crf_decoding( input=emission, param_attr=ParamAttr(name="crfw")) self.assertFalse(crf is None) self.assertFalse(crf_decode is None) return layers.chunk_eval( input=crf_decode, label=label, chunk_scheme="IOB", num_chunk_types=(label_dict_len - 1) // 2) def test_linear_chain_crf_padding(self): with self.static_graph(): label_dict_len, max_len = 10, 20 feature = layers.data( name='feature', shape=[max_len, 784], dtype='float32') label = layers.data(name='label', shape=[max_len], dtype='int64') length = layers.data(name='length', shape=[1], dtype='int64') emission = layers.fc(input=feature, size=10, num_flatten_dims=2) crf = layers.linear_chain_crf( input=emission, label=label, length=length, param_attr=ParamAttr(name="crfw")) crf_decode = layers.crf_decoding( input=emission, length=length, param_attr=ParamAttr(name="crfw")) self.assertFalse(crf is None) self.assertFalse(crf_decode is None) return layers.chunk_eval( input=crf_decode, label=label, seq_length=length, chunk_scheme="IOB", num_chunk_types=(label_dict_len - 1) // 2) def test_im2sequence(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name='x', shape=[3, 128, 128], dtype='float32') y = layers.data(name='y', shape=[], dtype='float32') output = layers.im2sequence( input=x, input_image_size=y, stride=[1, 1], filter_size=[2, 2], out_stride=[1, 1]) return (output) def test_lod_reset(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): # case 1 x = layers.data(name='x', shape=[10], dtype='float32') y = layers.data( name='y', shape=[10, 20], dtype='float32', lod_level=2) z = layers.lod_reset(x=x, y=y) self.assertTrue(z.lod_level == 2) # case 2 lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int64') z = layers.lod_reset(x=x, y=lod_tensor_in) self.assertTrue(z.lod_level == 1) # case 3 z = layers.lod_reset(x=x, target_lod=[1, 2, 3]) self.assertTrue(z.lod_level == 1) return z def test_affine_grid(self): with self.static_graph(): data = layers.data(name='data', shape=[2, 3, 3], dtype="float32") out, ids = layers.argsort(input=data, axis=1) theta = layers.data(name="theta", shape=[2, 3], dtype="float32") out_shape = layers.data( name="out_shape", shape=[-1], dtype="float32") data_0 = layers.affine_grid(theta, out_shape) data_1 = layers.affine_grid(theta, [5, 3, 28, 28]) self.assertIsNotNone(data_0) self.assertIsNotNone(data_1) def test_stridedslice(self): axes = [0, 1, 2] starts = [1, 0, 2] ends = [3, 3, 4] strides = [1, 1, 1] with self.static_graph(): x = layers.data(name="x", shape=[245, 30, 30], dtype="float32") out = layers.strided_slice( x, axes=axes, starts=starts, ends=ends, strides=strides) return out def test_fill_constant_batch_size_like(self): with self.static_graph(): like = fluid.layers.fill_constant( shape=[1, 200], value=10, dtype='int64') out = layers.fill_constant_batch_size_like( input=like, shape=[2, 3300], value=1315454564656, dtype='int64') return out def test_psroi_pool(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name="x", shape=[245, 30, 30], dtype="float32") rois = layers.data( name="rois", shape=[4], dtype="float32", lod_level=1) output = layers.psroi_pool(x, rois, 5, 0.25, 7, 7) return (output) def test_sequence_expand(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name='x', shape=[10], dtype='float32') y = layers.data( name='y', shape=[10, 20], dtype='float32', lod_level=2) return (layers.sequence_expand(x=x, y=y, ref_level=1)) def test_sequence_reshape(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1) out = layers.sequence_reshape(input=x, new_dim=16) return (out) def test_sequence_unpad(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name='x', shape=[10, 5], dtype='float32') length = layers.data(name='length', shape=[], dtype='int64') return (layers.sequence_unpad(x=x, length=length)) def test_sequence_softmax(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): seq_data = layers.data( name='seq_data', shape=[10, 10], dtype='float32', lod_level=1) seq = layers.fc(input=seq_data, size=20) return (layers.sequence_softmax(seq)) def test_sequence_unsqueeze(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name='x', shape=[8, 2], dtype='float32') out = layers.unsqueeze(input=x, axes=[1]) return (out) def test_sequence_scatter(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data( name='x', shape=[3, 6], append_batch_size=False, dtype='float32') idx = layers.data( name='idx', shape=[12, 1], append_batch_size=False, dtype='int32', lod_level=1) updates = layers.data( name='updates', shape=[12, 1], append_batch_size=False, dtype='float32', lod_level=1) out = layers.sequence_scatter(input=x, index=idx, updates=updates) return (out) def test_sequence_slice(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): import numpy as np seqs = layers.data( name='x', shape=[10, 5], dtype='float32', lod_level=1) offset = layers.assign(input=np.array([[0, 1]]).astype('int32')) length = layers.assign(input=np.array([[2, 1]]).astype('int32')) out = layers.sequence_slice( input=seqs, offset=offset, length=length) return (out) def test_filter_by_instag(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x1 = layers.data( name='Ins', shape=[32, 1], dtype='float32', lod_level=0) x2 = layers.data( name='Ins_tag', shape=[32, 1], dtype='int64', lod_level=0, stop_gradient=True) x3 = layers.create_global_var( shape=[1, 1], value=20, dtype='int64', persistable=True, force_cpu=True, name='Filter_tag') out1, out2 = layers.filter_by_instag(x1, x2, x3, is_lod=True) def test_roi_pool(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name="x", shape=[256, 30, 30], dtype="float32") rois = layers.data( name="rois", shape=[4], dtype="float32", lod_level=1) output = layers.roi_pool(x, rois, 7, 7, 0.6) return (output) def test_sequence_enumerate(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1) out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0) def test_roi_align(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name="x", shape=[256, 30, 30], dtype="float32") rois = layers.data( name="rois", shape=[4], dtype="float32", lod_level=1) output = layers.roi_align(x, rois, 14, 14, 0.5, 2) return (output) def test_roi_perspective_transform(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name="x", shape=[256, 30, 30], dtype="float32") rois = layers.data( name="rois", shape=[8], dtype="float32", lod_level=1) output = layers.roi_perspective_transform(x, rois, 7, 7, 0.6) return (output) def test_row_conv(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1) out = layers.row_conv(input=x, future_context_size=2) return (out) def test_simple_conv2d(self): # TODO(minqiyang): dygraph do not support layers with param now with self.static_graph(): images = layers.data( name='pixel', shape=[3, 48, 48], dtype='float32') return layers.conv2d( input=images, num_filters=3, filter_size=[4, 4]) def test_squeeze(self): # TODO(minqiyang): dygraph do not support layers with param now with self.static_graph(): x = layers.data(name='x', shape=[1, 1, 4], dtype='float32') out = layers.squeeze(input=x, axes=[2]) return (out) def test_flatten(self): # TODO(minqiyang): dygraph do not support op without kernel now with self.static_graph(): x = layers.data( name='x', append_batch_size=False, shape=[4, 4, 3], dtype="float32") out = layers.flatten(x, axis=1, name="flatten") return (out) def test_linspace(self): program = Program() with program_guard(program): out = layers.linspace(20, 10, 5, 'float64') self.assertIsNotNone(out) print(str(program)) def test_deformable_conv(self): with self.static_graph(): input = layers.data( name='input', append_batch_size=False, shape=[2, 3, 32, 32], dtype="float32") offset = layers.data( name='offset', append_batch_size=False, shape=[2, 18, 32, 32], dtype="float32") mask = layers.data( name='mask', append_batch_size=False, shape=[2, 9, 32, 32], dtype="float32") out = layers.deformable_conv( input=input, offset=offset, mask=mask, num_filters=2, filter_size=3, padding=1) return (out) def test_deformable_conv2(self): with self.static_graph(): input = fluid.data( name='input', shape=[None, 3, None, None], dtype="float32") offset = fluid.data( name='offset', shape=[None, 18, None, None], dtype="float32") mask = fluid.data( name='mask', shape=[None, 9, None, None], dtype="float32") out = layers.deformable_conv( input=input, offset=offset, mask=mask, num_filters=2, filter_size=3, padding=1) return (out) def test_unfold(self): with self.static_graph(): x = layers.data(name='x', shape=[3, 20, 20], dtype='float32') out = layers.unfold(x, [3, 3], 1, 1, 1) return (out) def test_deform_roi_pooling(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = layers.data( name='input', shape=[2, 3, 32, 32], dtype='float32', append_batch_size=False) rois = layers.data( name="rois", shape=[4], dtype='float32', lod_level=1) trans = layers.data( name="trans", shape=[2, 3, 32, 32], dtype='float32', append_batch_size=False) out = layers.deformable_roi_pooling( input=input, rois=rois, trans=trans, no_trans=False, spatial_scale=1.0, group_size=(1, 1), pooled_height=8, pooled_width=8, part_size=(8, 8), sample_per_part=4, trans_std=0.1) return (out) def test_deformable_conv_v1(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = layers.data( name='input', append_batch_size=False, shape=[2, 3, 32, 32], dtype="float32") offset = layers.data( name='offset', append_batch_size=False, shape=[2, 18, 32, 32], dtype="float32") out = layers.deformable_conv( input=input, offset=offset, mask=None, num_filters=2, filter_size=3, padding=1, modulated=False) return (out) def test_retinanet_target_assign(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): bbox_pred = layers.data( name='bbox_pred', shape=[1, 100, 4], append_batch_size=False, dtype='float32') cls_logits = layers.data( name='cls_logits', shape=[1, 100, 10], append_batch_size=False, dtype='float32') anchor_box = layers.data( name='anchor_box', shape=[100, 4], append_batch_size=False, dtype='float32') anchor_var = layers.data( name='anchor_var', shape=[100, 4], append_batch_size=False, dtype='float32') gt_boxes = layers.data( name='gt_boxes', shape=[10, 4], append_batch_size=False, dtype='float32') gt_labels = layers.data( name='gt_labels', shape=[10, 1], append_batch_size=False, dtype='float32') is_crowd = layers.data( name='is_crowd', shape=[1], append_batch_size=False, dtype='float32') im_info = layers.data( name='im_info', shape=[1, 3], append_batch_size=False, dtype='float32') return (layers.retinanet_target_assign( bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes, gt_labels, is_crowd, im_info, 10)) def test_sigmoid_focal_loss(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): input = layers.data( name='data', shape=[10, 80], append_batch_size=False, dtype='float32') label = layers.data( name='label', shape=[10, 1], append_batch_size=False, dtype='int32') fg_num = layers.data( name='fg_num', shape=[1], append_batch_size=False, dtype='int32') out = fluid.layers.sigmoid_focal_loss( x=input, label=label, fg_num=fg_num, gamma=2., alpha=0.25) return (out) def test_retinanet_detection_output(self): with program_guard(fluid.default_main_program(), fluid.default_startup_program()): bboxes = layers.data( name='bboxes', shape=[1, 21, 4], append_batch_size=False, dtype='float32') scores = layers.data( name='scores', shape=[1, 21, 10], append_batch_size=False, dtype='float32') anchors = layers.data( name='anchors', shape=[21, 4], append_batch_size=False, dtype='float32') im_info = layers.data( name="im_info", shape=[1, 3], append_batch_size=False, dtype='float32') nmsed_outs = layers.retinanet_detection_output( bboxes=[bboxes, bboxes], scores=[scores, scores], anchors=[anchors, anchors], im_info=im_info, score_threshold=0.05, nms_top_k=1000, keep_top_k=100, nms_threshold=0.3, nms_eta=1.) return (nmsed_outs) def test_warpctc_with_padding(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): input_length = layers.data( name='logits_length', shape=[11], dtype='int64') label_length = layers.data( name='labels_length', shape=[12], dtype='int64') label = layers.data(name='label', shape=[12, 1], dtype='int32') predict = layers.data( name='predict', shape=[4, 4, 8], dtype='float32') output = layers.warpctc( input=predict, label=label, input_length=input_length, label_length=label_length) return (output) def test_edit_distance(self): with self.static_graph(): predict = layers.data( name='predict', shape=[-1, 1], dtype='int64', lod_level=1) label = layers.data( name='label', shape=[-1, 1], dtype='int64', lod_level=1) evaluator = fluid.evaluator.EditDistance(predict, label) return evaluator.metrics def test_basic_gru(self): input_size = 128 hidden_size = 256 with self.static_graph(): input = fluid.data( name="input", shape=[None, None, input_size], dtype='float32') pre_hidden = fluid.data( name="pre_hidden", shape=[None, hidden_size], dtype='float32') sequence_length = fluid.data( name="sequence_length", shape=[None], dtype='int32') for bidirectional in [True, False]: for batch_first in [True, False]: rnn_out, last_hidden = fluid.contrib.layers.basic_gru( input, pre_hidden, hidden_size=256, num_layers=2, sequence_length=sequence_length, dropout_prob=0.5, bidirectional=bidirectional, batch_first=batch_first) if __name__ == '__main__': unittest.main()