[GitHub] [incubator-mxnet] nswamy commented on a change in pull request #14405: [MXNet-1343][Fit API]Add CNN integration test for fit() API

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nswamy 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_r269730077
 
 

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 File path: tests/nightly/estimator/test_estimator_cnn_cpu.py
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+# 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 CPU using CNN models
+
+import numpy as np
+import mxnet as mx
+from mxnet import gluon, init, nd
+from mxnet.gluon.estimator import estimator, event_handler
+from mxnet.gluon.model_zoo import vision
+
+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 FCN(num_classes=21, ctx=None):
+    '''
+    FCN model for semantic segmentation
+    '''
+    pretrained_net = vision.resnet18_v2(pretrained=True, ctx=ctx)
+
+    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))
+    return net
+
+def test_estimator():
+    '''
+    Test estimator by doing one pass over each model with synthetic data
+    '''
+    models = ['resnet18_v1',
+              'alexnet',
+              'FCN'
+              ]
+    context = mx.cpu()
+    for model_name in models:
+        batch_size = 1
+        num_epochs = 1
+        lr = 0.001
+        # Get model and initialize, define loss
+        if model_name is 'FCN':
+            num_classes = 21
+            net = FCN(num_classes=num_classes, ctx=context)
+            train_dataset = gluon.data.dataset.ArrayDataset(mx.nd.random.uniform(shape=(batch_size, 3, 320, 480)),
+                                                      mx.nd.zeros(shape=(batch_size, 320, 480)))
+            val_dataset = gluon.data.dataset.ArrayDataset(mx.nd.random.uniform(shape=(batch_size, 3, 320, 480)),
+                                                      mx.nd.zeros(shape=(batch_size, 320, 480)))
+            loss = gluon.loss.SoftmaxCrossEntropyLoss(axis=1)
+            net[-1].initialize(init.Constant(bilinear_kernel(num_classes, num_classes, 64)), ctx=context)
 
 Review comment:
   why not get the net from directly from FCN method?, why split the logic?. 

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