abhinavs95 commented on a change in pull request #14405: [MXNet-1343][Fit API]Add CNN integration test for fit() API URL: https://github.com/apache/incubator-mxnet/pull/14405#discussion_r270512099
########## File path: tests/nightly/estimator/test_estimator_cnn.py ########## @@ -0,0 +1,156 @@ +# 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. + +# Test gluon estimator on CNN models + +import argparse +import numpy as np +import mxnet as mx +from mxnet import gluon, init, nd +from mxnet.gluon import data +from mxnet.gluon.estimator import estimator +from mxnet.gluon.model_zoo import vision + +def load_data_mnist(batch_size, resize=None, num_workers=4): + ''' + Load MNIST dataset + ''' + transformer = [] + if resize: + transformer += [data.vision.transforms.Resize(resize)] + transformer += [data.vision.transforms.ToTensor()] + transformer = data.vision.transforms.Compose(transformer) + mnist_train = data.vision.MNIST(train=True) + mnist_test = data.vision.MNIST(train=False) + train_iter = data.DataLoader( + mnist_train.transform_first(transformer), batch_size, shuffle=True, + num_workers=num_workers) + test_iter = data.DataLoader( + mnist_test.transform_first(transformer), batch_size, shuffle=False, + num_workers=num_workers) + return train_iter, test_iter + +def bilinear_kernel(in_channels, out_channels, kernel_size): + ''' + Bilinear interpolation using transposed convolution + https://github.com/d2l-ai/d2l-en/blob/master/chapter_computer-vision/fcn.md + ''' + factor = (kernel_size + 1) // 2 + if kernel_size % 2 == 1: + center = factor - 1 + else: + center = factor - 0.5 + og = np.ogrid[:kernel_size, :kernel_size] + filt = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor) + weight = np.zeros((in_channels, out_channels, kernel_size, kernel_size), dtype='float32') + weight[range(in_channels), range(out_channels), :, :] = filt + return nd.array(weight) + +def get_net(model_name, context): + if model_name == 'FCN': + num_classes = 21 + pretrained_net = vision.resnet18_v2(pretrained=True, ctx=context) + net = gluon.nn.HybridSequential() + for layer in pretrained_net.features[:-2]: + net.add(layer) + net.add(gluon.nn.Conv2D(num_classes, kernel_size=1), + gluon.nn.Conv2DTranspose(num_classes, kernel_size=64, padding=16, strides=32)) + net[-1].initialize(init.Constant(bilinear_kernel(num_classes, num_classes, 64)), ctx=context) + net[-2].initialize(init=init.Xavier(), ctx=context) + input_shape = (1, 3, 320, 480) + label_shape = (1, 320, 480) + loss_axis = 1 + else: + net = vision.get_model(model_name, classes=10) + net.initialize(mx.init.Xavier(), ctx=context) + input_shape = (1, 1, 224, 224) + label_shape = 1 + loss_axis = -1 + return net, input_shape, label_shape, loss_axis + +def test_estimator_cpu(): + ''' + Test estimator by doing one pass over each model with synthetic data + ''' + models = ['resnet18_v1', + 'FCN' + ] + context = mx.cpu() + for model_name in models: + net, input_shape, label_shape, loss_axis = get_net(model_name, context) + train_dataset = gluon.data.dataset.ArrayDataset(mx.nd.random.uniform(shape=input_shape), + mx.nd.zeros(shape=label_shape)) + val_dataset = gluon.data.dataset.ArrayDataset(mx.nd.random.uniform(shape=input_shape), + mx.nd.zeros(shape=label_shape)) + loss = gluon.loss.SoftmaxCrossEntropyLoss(axis=loss_axis) + train_data = gluon.data.DataLoader(train_dataset, batch_size=1) + val_data = gluon.data.DataLoader(val_dataset, batch_size=1) + net.hybridize() + trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.001}) + # Define estimator + est = estimator.Estimator(net=net, + loss=loss, + metrics=mx.metric.Accuracy(), + trainers=trainer, + context=context) + # Call fit() + est.fit(train_data=train_data, + val_data=val_data, + epochs=1, + batch_size=1) + +def test_estimator_gpu(): + ''' + Test estimator by training resnet18_v1 for 5 epochs on MNIST and verify accuracy + ''' + model_name = 'resnet18_v1' + batch_size = 128 + num_epochs = 5 + if mx.context.num_gpus() > 0: Review comment: I do not want the test to fail if GPU is not available, it should pass on either context. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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