| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| |
| """ |
| DNNL related test cases |
| """ |
| import sys |
| import os |
| import numpy as np |
| import mxnet as mx |
| import pytest |
| from mxnet.test_utils import rand_ndarray, assert_almost_equal |
| from mxnet import gluon, context, use_np |
| from mxnet.gluon import nn |
| from mxnet.test_utils import * |
| curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) |
| sys.path.append(os.path.join(curr_path, '../unittest/')) |
| import itertools |
| |
| @use_np |
| @pytest.mark.seed(1234) |
| def test_dnnl_ndarray_slice(): |
| ctx = mx.cpu() |
| net = gluon.nn.HybridSequential() |
| net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None)) |
| net.initialize(ctx=ctx) |
| x = mx.np.array(np.ones([32, 3, 224, 224]), ctx=ctx) |
| y = net(x) |
| |
| # trigger computation on ndarray slice |
| assert_almost_equal(y[0].asnumpy()[0, 0, 0], np.array(0.056331709)) |
| |
| |
| # In python3.8, functions are only pickable if they are defined in |
| # the top-level of a module |
| # https://docs.python.org/3/library/pickle.html#what-can-be-pickled-and-unpickled |
| class Dummy(gluon.data.Dataset): |
| def __len__(self): |
| return 2 |
| def __getitem__(self, key): |
| return key, np.ones((3, 224, 224)), np.ones((10, )) |
| |
| @use_np |
| @pytest.mark.seed(1234) |
| def test_dnnl_engine_threading(): |
| net = gluon.nn.HybridSequential() |
| net.add(gluon.nn.Conv2D(channels=32, kernel_size=3, activation=None)) |
| net.initialize(ctx=mx.cpu()) |
| |
| loader = gluon.data.DataLoader(Dummy(), batch_size=2, num_workers=1) |
| |
| X = (32, 3, 32, 32) |
| # trigger dnnl execution thread |
| y = net(mx.np.array(np.ones(X))).asnumpy() |
| |
| # Use Gluon dataloader to trigger different thread. |
| # below line triggers different execution thread |
| for _ in loader: |
| y = net(mx.np.array(np.ones(X))).asnumpy() |
| # output should be 056331709 (non-dnnl mode output) |
| assert_almost_equal(y[0, 0, 0, 0], np.array(0.056331709)) |
| break |
| |
| def test_dnnl_reshape(): |
| def test_reshape_after_conv(dst_shape): |
| shape = (1,1,4,4) |
| data = mx.symbol.Variable('data') |
| conv = mx.symbol.Convolution(data=data, num_filter=16, kernel=(1, 1), pad=(0, 0), stride=(1, 1)) |
| res = mx.symbol.reshape(data=conv, shape=dst_shape) |
| exe = res._simple_bind(mx.cpu(), data=shape, grad_req='null') |
| |
| val1 = np.random.uniform(-1, 1, shape) |
| val2 = np.random.uniform(-1, 1, (16, 1, 1, 1)) |
| val3 = np.random.uniform(-1 ,1, (1)) |
| |
| exe.arg_arrays[0][:] = val1 |
| exe.arg_arrays[1][:] = val2 |
| exe.arg_arrays[2][:] = val3 |
| outputs = exe.forward(is_train=False)[0].asnumpy() |
| |
| conv_exe = conv._simple_bind(mx.cpu(), data=shape, grad_req='null') |
| conv_exe.arg_arrays[0][:] = val1 |
| conv_exe.arg_arrays[1][:] = val2 |
| conv_exe.arg_arrays[2][:] = val3 |
| data_npy = conv_exe.forward(is_train=False)[0].asnumpy() |
| assert_almost_equal(outputs, data_npy.reshape(dst_shape)) |
| |
| |
| # Test dnnl reshape (Using shape) |
| test_cases = [(256), (16, 16), (4, 4, 16), (4, 4, 4, 4)] |
| for test_case in test_cases: |
| test_reshape_after_conv(test_case) |
| |
| |
| @use_np |
| def test_reshape_before_conv(): |
| class Net(gluon.HybridBlock): |
| """ |
| test Net |
| """ |
| def __init__(self, **kwargs): |
| super(Net, self).__init__(**kwargs) |
| self.conv0 = nn.Conv2D(10, (3, 3)) |
| self.conv1 = nn.Conv2D(5, (3, 3)) |
| |
| def forward(self, x, *args, **kwargs): |
| x_reshape = x.reshape((2, 4, 20, 5)) |
| y = self.conv0(x_reshape) |
| y_reshape = y.reshape((2, 10, 9, 6)) |
| out = self.conv1(y_reshape) |
| return out |
| |
| x = mx.np.random.uniform(size=(2, 4, 10, 10)) |
| x.attach_grad() |
| net = Net() |
| net.initialize() |
| with mx.autograd.record(): |
| out1 = net(x) |
| out1.backward() |
| dx1 = x.grad |
| net.hybridize() |
| with mx.autograd.record(): |
| out2 = net(x) |
| out2.backward() |
| assert_almost_equal(dx1, x.grad, rtol=1e-5, atol=1e-6) |
| assert_almost_equal(out1, out2, rtol=1e-5, atol=1e-6) |
| |
| |
| @use_np |
| def test_slice_before_conv(): |
| class Net(gluon.HybridBlock): |
| """ |
| test Net |
| """ |
| def __init__(self, **kwargs): |
| super(Net, self).__init__(**kwargs) |
| self.conv0 = nn.Conv2D(4, (3, 3)) |
| self.conv1 = nn.Conv2D(4, (3, 3)) |
| |
| def forward(self, x, *args, **kwargs): |
| x_slice = mx.npx.slice(x, begin=(0, 0, 0, 0), end=(2, 4, 10, 10)) |
| y = self.conv0(x_slice) |
| y_slice = mx.npx.slice(y, begin=(1, 0, 2, 2), end=(2, 1, 7, 7)) |
| out = self.conv1(y_slice) |
| return out |
| |
| x = mx.np.random.uniform(size=(2, 10, 10, 10)) |
| x.attach_grad() |
| net = Net() |
| net.initialize() |
| with mx.autograd.record(): |
| out1 = net(x) |
| out1.backward() |
| dx1 = x.grad |
| net.hybridize() |
| with mx.autograd.record(): |
| out2 = net(x) |
| out2.backward() |
| assert_almost_equal(dx1, x.grad, rtol=1e-5, atol=1e-6) |
| assert_almost_equal(out1, out2, rtol=1e-5, atol=1e-6) |
| |
| |
| @use_np |
| def test_slice_reshape_before_conv(): |
| class Net(gluon.HybridBlock): |
| """ |
| test Net |
| """ |
| def __init__(self, **kwargs): |
| super(Net, self).__init__(**kwargs) |
| self.conv0 = nn.Conv2D(4, (3, 3)) |
| self.conv1 = nn.Conv2D(4, (3, 3)) |
| |
| def forward(self, x, *args, **kwargs): |
| x_slice = mx.npx.slice(x, begin=(0, 0, 0, 0), end=(2, 4, 8, 9)) |
| y = self.conv0(x_slice) |
| y_reshape = y.reshape((2, 4, 14, 3)) |
| out = self.conv1(y_reshape) |
| return out |
| |
| x = mx.np.random.uniform(size=(2, 10, 10, 10)) |
| x.attach_grad() |
| net = Net() |
| net.initialize() |
| with mx.autograd.record(): |
| out1 = net(x) |
| out1.backward() |
| dx1 = x.grad |
| net.hybridize() |
| with mx.autograd.record(): |
| out2 = net(x) |
| out2.backward() |
| assert_almost_equal(dx1, x.grad, rtol=1e-5, atol=1e-6) |
| assert_almost_equal(out1, out2, rtol=1e-5, atol=1e-6) |
| |
| |
| def test_flatten_slice_after_conv(): |
| data = mx.symbol.Variable('data') |
| weight = mx.symbol.Variable('weight') |
| bias = mx.symbol.Variable('bias') |
| conv1= mx.symbol.Convolution(data = data, weight=weight, bias=bias, name='conv1', num_filter=64, kernel=(3,3), stride=(1,1)) |
| flatten1 = mx.symbol.flatten(data = conv1) |
| slice1 = mx.symbol.slice(data = flatten1, begin=0, end=1) |
| |
| shape = (2, 16, 16, 16) |
| val = np.random.rand(2, 16, 16, 16).astype(np.float32) |
| exe = slice1._simple_bind(context.current_context(), data=shape) |
| exe.arg_arrays[0][:] = val |
| exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape) |
| exe.arg_arrays[2][:] = np.random.normal(size=exe.arg_arrays[2].shape) |
| p = exe.forward(is_train=False) |
| p[0].wait_to_read() |
| print(p[0]) |
| |
| |
| def test_dnnl_sum_with_dnnl_layout(): |
| |
| x_shape = (32, 3, 224, 224) |
| x_npy = np.ones(x_shape, dtype='float32') |
| w_shape = (32, 3, 3, 3) |
| w_npy = np.ones(w_shape, dtype='float32') |
| |
| x = mx.sym.Variable("x") |
| w = mx.sym.Variable("w") |
| z = mx.symbol.Convolution(data=x, weight=w, num_filter=32, kernel=(3, 3)) |
| num_inputs = [2, 3, 4, 5] |
| for i in num_inputs: |
| inputs = [] |
| for _ in range(i): |
| inputs.append(z) |
| y = mx.sym.add_n(*inputs) # (only DNNL data input) |
| exe = y._simple_bind(ctx=mx.cpu(), x=x_shape, w=w_shape) |
| out = exe.forward(is_train=False, x=x_npy, w=np.ones(w_shape))[0] |
| #conv with kernel (3,3) on ones should give result=27 |
| single_cov = 27.0 |
| assert_almost_equal(out[0].asnumpy()[0, 0, 0], single_cov*i) |
| |
| def test_dnnl_sum_inplace_with_cpu_layout(): |
| x_shape = (32, 3, 224, 224) |
| x_npy = np.ones(x_shape, dtype='float32') |
| y_shape = (32, 32, 222, 222) |
| y_npy = np.ones(y_shape, dtype='float32') |
| x = mx.sym.Variable("x") |
| y = mx.sym.Variable("y") |
| z = mx.symbol.Convolution(data=x, num_filter=32, kernel=(3, 3)) |
| z = mx.sym.add_n(z, y) # (DNNL data, cpu data) |
| exe = z._simple_bind(ctx=mx.cpu(), x=x_shape, y=y_shape) |
| out = exe.forward(is_train=False, x=x_npy, y=y_npy)[0] |
| assert_almost_equal(out[0].asnumpy()[0, 0, 0], 1.0) |
| |
| |
| def test_batchnorm(): |
| def check_batchnorm_training(stype): |
| for shape in [(2, 3), (2, 4), (2, 3, 2, 2), (2, 4, 2, 2)]: |
| data_tmp = np.random.normal(-0.1, 0.1, size=shape) |
| s = shape[1], |
| gamma = np.ones(s) |
| beta = np.ones(s) |
| gamma[1] = 3 |
| beta[0] = 3 |
| |
| rolling_mean = np.random.uniform(size=s) |
| rolling_std = np.random.uniform(size=s) |
| |
| data = mx.symbol.Variable('data', stype=stype) |
| in_location = [mx.nd.array(data_tmp).tostype(stype), mx.nd.array(gamma).tostype(stype), |
| mx.nd.array(beta).tostype(stype)] |
| mean_std = [mx.nd.array(rolling_mean).tostype(stype), mx.nd.array(rolling_std).tostype(stype)] |
| |
| test = mx.symbol.BatchNorm(data, fix_gamma=False) |
| check_numeric_gradient(test, in_location, mean_std, numeric_eps=1e-2, rtol=0.16, atol=1e-2) |
| |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_batchnorm_training(stype) |
| |
| |
| def test_softmax(): |
| def check_softmax_training(stype): |
| for shape in [(2, 3), (2, 3, 2, 2)]: |
| data_tmp = np.random.normal(-0.1, 0.1, size=shape) |
| |
| data = mx.symbol.Variable('data', stype=stype) |
| in_location = [mx.nd.array(data_tmp).tostype(stype)] |
| |
| test = mx.symbol.softmax(data, axis=-1) |
| check_numeric_gradient(test, in_location, numeric_eps=1e-2, rtol=0.16, atol=1e-4) |
| |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_softmax_training(stype) |
| |
| |
| def test_pooling(): |
| def check_pooling_training(stype): |
| for shape in [(3, 3, 10), (3, 3, 20, 20), (3, 3, 10, 20, 20)]: |
| data_tmp = np.random.normal(-0.1, 0.1, size=shape) |
| data = mx.symbol.Variable('data', stype=stype) |
| in_location = [mx.nd.array(data_tmp).tostype(stype)] |
| |
| if np.array(shape).shape[0] == 3: |
| test = mx.symbol.Pooling(data=data, kernel=(3), stride=(2), pool_type='avg') |
| elif np.array(shape).shape[0] == 4: |
| test = mx.symbol.Pooling(data=data, kernel=(3, 3), stride=(2, 2), pool_type='avg') |
| elif np.array(shape).shape[0] == 5: |
| test = mx.symbol.Pooling(data=data, kernel=(3, 3, 3), stride=(2, 2, 2), pool_type='avg') |
| else: |
| return 0 |
| check_numeric_gradient(test, in_location, numeric_eps=1e-2, rtol=0.16, atol=1e-4) |
| |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_pooling_training(stype) |
| |
| @pytest.mark.parametrize('num_filter', [4, 8, 16]) |
| @pytest.mark.parametrize('output_size', [4, 5, 8, 16]) |
| @pytest.mark.parametrize('stype', ['row_sparse', 'default']) |
| @pytest.mark.parametrize('shape', [(3, 3, 8, 8), (3, 3, 20, 20), (3, 3, 32, 32)]) |
| def test_adaptive_pooling(num_filter, output_size, stype, shape): |
| data_tmp = mx.nd.random.uniform(shape=shape) |
| data = mx.sym.var('data', stype=stype) |
| in_channels = shape[1] |
| |
| data = mx.sym.Convolution(data=data, kernel=(3, 3), pad=(1,1), num_filter=num_filter) |
| data = mx.sym.contrib.AdaptiveAvgPooling2D(data=data, output_size=output_size) |
| |
| weight_tmp = np.random.normal(-0.1, 0.1, size=(num_filter, in_channels, 3, 3)) |
| bias_tmp = np.random.normal(0.1, 0.1, size=(num_filter,)) |
| |
| in_location = [mx.nd.array(data_tmp).tostype(stype), mx.nd.array(weight_tmp).tostype(stype), |
| mx.nd.array(bias_tmp).tostype(stype)] |
| |
| check_numeric_gradient(data, in_location, numeric_eps=1e-2, rtol=0.16, atol=1e-4) |
| |
| |
| def test_activation(): |
| def check_activation_training(stype): |
| for shape in [(2, 3, 3), (2, 3, 2, 2)]: |
| eps = 1e-5 |
| data_tmp = np.random.normal(-0.1, 1, size=shape) |
| # Avoid finite difference method inaccuracies due to discontinuous gradient at the origin. |
| # Here we replace small problematic inputs with 1.0. Repro issue with seed 851486559. |
| data_tmp[abs(data_tmp) < eps] = 1.0 |
| |
| data = mx.symbol.Variable('data', stype=stype) |
| in_location = [mx.nd.array(data_tmp).tostype(stype)] |
| |
| test = mx.symbol.Activation(data, act_type="relu") |
| check_numeric_gradient(test, in_location, numeric_eps=eps, rtol=0.16, atol=1e-4) |
| |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_activation_training(stype) |
| |
| |
| def test_convolution(): |
| def check_convolution_training(stype): |
| for shape in [(3, 3, 10), (3, 3, 10, 10), (3, 3, 10, 10, 10)]: |
| data_tmp = np.random.normal(-0.1, 1, size=shape) |
| data = mx.symbol.Variable('data', stype=stype) |
| |
| if np.array(shape).shape[0] == 3: |
| test = mx.symbol.Convolution(data=data, kernel=(3,), stride=(2), num_filter=4) |
| weight_tmp = np.random.normal(-0.1, 0.1, size=(4, 3, 3)) |
| elif np.array(shape).shape[0] == 4: |
| test = mx.symbol.Convolution(data=data, kernel=(3, 3), stride=(2, 2), num_filter=4) |
| weight_tmp = np.random.normal(-0.1, 0.1, size=(4, 3, 3, 3)) |
| elif np.array(shape).shape[0] == 5: |
| test = mx.symbol.Convolution(data=data, kernel=(3, 3, 3), stride=(2, 2, 2), num_filter=4) |
| weight_tmp = np.random.normal(-0.1, 0.1, size=(4, 3, 3, 3, 3)) |
| else: |
| return 0 |
| bias_tmp = np.random.normal(0.1, 0.1, size=(4,)) |
| in_location = [mx.nd.array(data_tmp).tostype(stype), mx.nd.array(weight_tmp).tostype(stype), |
| mx.nd.array(bias_tmp).tostype(stype)] |
| check_numeric_gradient(test, in_location, numeric_eps=1e-2, rtol=0.16, atol=1e-4) |
| |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_convolution_training(stype) |
| |
| |
| def test_Deconvolution(): |
| def check_Deconvolution_training(stype): |
| for shape in [(3, 3, 10), (3, 3, 10, 10), (3, 3, 3, 10, 10)]: |
| data_tmp = np.random.normal(-0.1, 1, size=shape) |
| data = mx.symbol.Variable('data', stype=stype) |
| |
| if np.array(shape).shape[0] == 3: |
| test = mx.symbol.Deconvolution(data=data, kernel=(3,), stride=(2), num_filter=4) |
| weight_tmp = np.random.normal(-0.1, 0.1, size=(3, 4, 3)) |
| elif np.array(shape).shape[0] == 4: |
| test = mx.symbol.Deconvolution(data=data, kernel=(3, 3), stride=(2, 2), num_filter=4) |
| weight_tmp = np.random.normal(-0.1, 0.1, size=(3, 4, 3, 3)) |
| elif np.array(shape).shape[0] == 5: |
| test = mx.symbol.Deconvolution(data=data, kernel=(3,3,3), stride=(2,2,2), num_filter=4) |
| weight_tmp = np.random.normal(-0.1, 0.1, size=(3, 4, 3, 3, 3)) |
| else: |
| return 0 |
| bias_tmp = np.random.normal(0.1, 0.1, size=(4,)) |
| in_location = [mx.nd.array(data_tmp).tostype(stype), mx.nd.array(weight_tmp).tostype(stype), |
| mx.nd.array(bias_tmp).tostype(stype)] |
| check_numeric_gradient(test, in_location, numeric_eps=1e-2, rtol=0.16, atol=1e-4) |
| |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_Deconvolution_training(stype) |
| |
| |
| def test_LRN(): |
| def check_LRN_training(stype): |
| for shape in [(3, 4, 5, 5)]: |
| data_tmp = np.random.normal(-0.1, 0.1, size=shape) |
| data = mx.symbol.Variable('data', stype=stype) |
| in_location = [mx.nd.array(data_tmp).tostype(stype)] |
| |
| test = mx.symbol.LRN(data, nsize=3) |
| check_numeric_gradient(test, in_location, numeric_eps=1e-2, rtol=0.16, atol=1e-4) |
| |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_LRN_training(stype) |
| |
| |
| def test_fullyconnected(): |
| def check_fullyconnected_training(stype): |
| data_shape = rand_shape_nd(2) |
| weight_shape = rand_shape_nd(2) |
| weight_shape = (weight_shape[0], data_shape[1]) |
| for density in [1.0, 0.5, 0.0]: |
| x = rand_ndarray(shape=data_shape, stype=stype, density=density) |
| w = rand_ndarray(shape=weight_shape, stype=stype, density=density) |
| x_sym = mx.sym.Variable("data") |
| w_sym = mx.sym.Variable("weight") |
| sym = mx.sym.FullyConnected(data=x_sym, weight=w_sym, num_hidden=weight_shape[0], no_bias=True) |
| in_location = [x, w] |
| check_numeric_gradient(sym, in_location, numeric_eps=1e-3, rtol=1e-3, atol=5e-3) |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_fullyconnected_training(stype) |
| |
| def test_softmax_with_large_inputs(): |
| def softmax_forward(input_data, true_output): |
| data = mx.sym.Variable('data') |
| out1 = data.softmax(axis=1) |
| exec1 = out1._bind(mx.cpu(), args={'data': input_data}) |
| exec1.forward()[0].wait_to_read() |
| ndarr = exec1.outputs[0][0][0][0] |
| nparr = ndarr.asnumpy() |
| assert_almost_equal(nparr, true_output, rtol=1e-5, atol=1e-5) |
| |
| softmax_forward(mx.nd.array([[[[-1e30,-1e30]]]]), np.array([1.0,1.0])) |
| softmax_forward(mx.nd.array([[[[1e30,1e30]]]]), np.array([1.0,1.0])) |
| softmax_forward(mx.nd.array([[[[-3.4e38,-3.4e38]]]]), np.array([1.0,1.0])) |
| softmax_forward(mx.nd.array([[[[3.4e38,3.4e38]]]]), np.array([1.0,1.0])) |
| |
| def test_non_dnnl_fcomputeex(): |
| # test special case where DNNL formatted NDArray feeds into non-dnnl fcomputeex operator |
| # conv is example where DNNL NDArray is created from regular NDArrays |
| # CustomOps is example of non-dnnl fcomputeex operator |
| |
| @mx.operator.register("custom") |
| class CustomProp(mx.operator.CustomOpProp): |
| def __int__(self): |
| super(CustomProp, self).__init__(need_top_grad=False) |
| |
| def list_arguments(self): |
| return ['data'] |
| |
| def list_outputs(self): |
| return ['output'] |
| |
| def infer_shape(self, in_shape): |
| data_shape = in_shape[0] |
| output_shape = in_shape[0] |
| return [data_shape], [output_shape], [] |
| |
| def infer_type(self, in_type): |
| dtype = in_type[0] |
| return [dtype], [dtype], [] |
| |
| def create_operator(self, ctx, shapes, dtypes): |
| return Custom() |
| |
| |
| class Custom(mx.operator.CustomOp): |
| def forward(self, is_train, req, in_data, out_data, aux): |
| print(in_data[0]) |
| self.assign(out_data[0], req[0], in_data[0]) |
| |
| def backward(self, req, out_grad, in_data, out_data, in_grad, aux): |
| self.assign(in_grad[0], req[0], out_grad) |
| |
| data = mx.symbol.Variable('data') |
| conv = mx.sym.Convolution(data=data, kernel=(5, 5), pad=(1, 1), stride=(1,1), num_filter=8, name="conv", no_bias=True) |
| custom = mx.symbol.Custom(name='custom', data=conv, op_type='custom') |
| exec1 = custom._bind(mx.cpu(), args={'data': mx.nd.ones([10,3,96,96]), 'conv_weight': mx.nd.ones([8,3,5,5])}) |
| exec1.forward()[0].wait_to_read() |
| |
| def test_conv_transpose(): |
| axes = [(0,2,1,3), (0,2,3,1), (1,2,3,0), (3,2,1,0)] |
| a = np.random.rand(10, 16, 50, 50) |
| b = np.random.rand(32, 16, 3, 3) |
| x = mx.nd.array(a) |
| w = mx.nd.array(b) |
| y = mx.nd.Convolution(data=x, weight=w, kernel=(3, 3), num_group=1, num_filter=32, no_bias=True) |
| for axis in axes: |
| t = mx.nd.transpose(y, axis) |
| t.wait_to_read() |
| s = y.asnumpy() |
| n = np.transpose(s, axis) |
| np.allclose(t.asnumpy(), n) |
| |
| |
| # This test case is contributed by @awsbillz in https://github.com/apache/mxnet/issues/14766 |
| @use_np |
| def test_reshape_transpose_6d(): |
| class Reshape2D(gluon.HybridBlock): |
| def __init__(self, factor): |
| super(Reshape2D, self).__init__() |
| self._factors = (int(factor),) * 2 |
| |
| def forward(self, x): |
| f1, f2 = self._factors |
| N = 1 |
| C = 2 |
| H = W = 596 |
| # (N, f1*f2*C, H, W) |
| x = mx.np.reshape(x, (N, C, f1 * f2, H, W)) # (N, C, f1*f2, H, W) |
| x = mx.np.reshape(x, (N, C, f1, f2, H, W)) # (N, C, f1, f2, H, W) |
| x = mx.np.transpose(x, (0, 1, 4, 2, 5, 3)) # (N, C, H, f1, W, f2) |
| x = mx.np.reshape(x, (N, C, H*f1, W*f2)) # (N, C, H*f1, W*f2) |
| return x |
| |
| |
| class Net(gluon.HybridBlock): |
| def __init__(self, **kwargs): |
| super(Net, self).__init__(**kwargs) |
| self.conv1 = nn.Conv2D(8, kernel_size=5) |
| self.reshape2D = Reshape2D(2) |
| |
| def forward(self, x): |
| x = self.conv1(x) |
| x = self.reshape2D(x) |
| return x |
| |
| net = Net() |
| net.initialize(mx.init.Xavier(), ctx=mx.cpu()) |
| net.hybridize() |
| data = mx.np.random.normal(size=(1, 3, 600, 600)) |
| output = net(data) |
| a = output.asnumpy() |
| |
| def test_concat(): |
| def ref_concat(a, b, axis): |
| return np.concatenate((a, b), axis=axis) |
| |
| a_sym = mx.sym.Variable("a") |
| b_sym = mx.sym.Variable("b") |
| dshape = rand_shape_nd(4) |
| a_shape = tuple(dshape) |
| b_shape = tuple(dshape) |
| |
| for axis in range(0, 4): |
| z = mx.sym.concat(a_sym, b_sym, dim=axis) |
| a = np.random.uniform(-1, 1, a_shape) |
| b = np.random.uniform(-1, 1, b_shape) |
| exe = z._simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape) |
| out = exe.forward(is_train=False, a=a, b=b) |
| ref_out = ref_concat(a, b, axis=axis) |
| out = out[0].asnumpy() |
| assert_almost_equal(out, ref_out) |
| |
| def check_concat_training(stype): |
| data_shape = rand_shape_nd(4) |
| for density in [1.0, 0.5, 0.0]: |
| a_sym = mx.sym.Variable('a') |
| b_sym = mx.sym.Variable('b') |
| sym = mx.sym.concat(a_sym, b_sym, dim=1) |
| a = rand_ndarray(shape=data_shape, stype=stype, density=density) |
| b = rand_ndarray(shape=data_shape, stype=stype, density=density) |
| in_location = [a, b] |
| check_numeric_gradient(sym, in_location, numeric_eps=1e-3, rtol=1e-3, atol=5e-3) |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_concat_training(stype) |
| |
| def test_concat_blocked(): |
| ctx = mx.cpu() |
| axis = 1 |
| filters = 32 # must be a multiple of 16 |
| kernel = (3, 3) |
| for in_dim_size in range(1, 17): # check cases with and without padding |
| in_shape = (1, in_dim_size, 64, 64) |
| in_data = mx.nd.random.uniform(-1, 1, in_shape, ctx=ctx) |
| conv_weights = mx.nd.random.uniform(-1, 1, (filters, in_shape[1], kernel[0], kernel[1]), ctx=ctx) |
| |
| def calc_output_of_layer(layer): |
| ex = layer._simple_bind(ctx, x=in_shape) |
| in_data.copyto(ex.arg_arrays[0]) |
| conv_weights.copyto(ex.arg_arrays[1]) |
| return ex.forward()[0].asnumpy() |
| |
| x = mx.sym.Variable('x') |
| w = mx.sym.Variable('w') |
| # convolution, so a blocked format is selected |
| conv = mx.sym.Convolution(data=x, weight=w, num_filter=filters, kernel=kernel, pad=(1, 1), no_bias=True) |
| conc = mx.sym.concat(conv, x, dim=axis) |
| |
| # first calculate the output of the convolution to determine ref_out |
| conv_out = calc_output_of_layer(conv) |
| ref_out = np.concatenate((conv_out, in_data.asnumpy()), axis=axis) |
| |
| out = calc_output_of_layer(conc) |
| assert_almost_equal(out, ref_out) |
| |
| def test_elemwise_add(): |
| def ref_add(a, b): |
| return np.add(a, b) |
| |
| a_sym = mx.sym.Variable("a") |
| b_sym = mx.sym.Variable("b") |
| dshape = rand_shape_nd(4) |
| a_shape = tuple(dshape) |
| b_shape = tuple(dshape) |
| z = mx.sym.elemwise_add(a_sym, b_sym) |
| a = np.random.uniform(-1, 1, a_shape) |
| b = np.random.uniform(-1, 1, b_shape) |
| exe = z._simple_bind(ctx=mx.cpu(), a=a_shape, b=b_shape) |
| out = exe.forward(is_train=False, a=a, b=b) |
| ref_out = ref_add(a, b) |
| out = out[0].asnumpy() |
| assert_almost_equal(out, ref_out, rtol=1e-6, atol=1e-6) |
| |
| def check_elemwise_add_training(stype): |
| data_shape = rand_shape_nd(4) |
| for density in [1.0, 0.5, 0.0]: |
| a_sym = mx.sym.Variable('a') |
| b_sym = mx.sym.Variable('b') |
| sym = mx.sym.elemwise_add(a_sym, b_sym) |
| a = rand_ndarray(shape=data_shape, stype=stype, density=density) |
| b = rand_ndarray(shape=data_shape, stype=stype, density=density) |
| in_location = [a, b] |
| check_numeric_gradient(sym, in_location, numeric_eps=1e-3, rtol=1e-3, atol=5e-3) |
| stypes = ['row_sparse', 'default'] |
| for stype in stypes: |
| check_elemwise_add_training(stype) |
| |
| def test_rnn(): |
| SEQ_LENGTH = [2**10, 2**5] |
| STATE_SIZE = [1, 2] |
| BATCH_SIZE = [4] |
| INPUT_SIZE = [4] |
| def batch_check(seq_length, state_size, batch_size, input_size): |
| modes_params = [('rnn_relu', mx.np.random.normal(0, 1, ((input_size + state_size + 2)*state_size),)), |
| ('rnn_tanh', mx.np.random.normal(0, 1, ((input_size + state_size + 2)*state_size),)), |
| ('gru', mx.np.random.normal(0, 1, ((input_size + state_size + 2)*state_size*3),)) |
| ] |
| for m, p in modes_params: |
| data = mx.np.random.normal(0, 1, (seq_length, batch_size, input_size)) |
| state = mx.np.random.normal(0, 1, (1, batch_size, state_size)) |
| data.attach_grad() |
| state.attach_grad() |
| |
| with mx.autograd.record(): |
| y = mx.npx.rnn(data=data, parameters=p, mode=m, \ |
| state=state, state_size=state_size, num_layers=1) |
| assert y.shape == (seq_length, batch_size, state_size) |
| assert type(y[0]).__name__ == 'ndarray' |
| y.backward() |
| assert state.shape == (1, batch_size, state_size) |
| assert type(state[0]).__name__ == 'ndarray' |
| |
| for sl, ss, bs, in_s in itertools.product(SEQ_LENGTH, STATE_SIZE, BATCH_SIZE, INPUT_SIZE): |
| batch_check(sl, ss, bs, in_s) |
