def resnet_v1_200(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, reuse=None, scope='resnet_v1_200'): """ResNet-200 model of [2]. See resnet_v1() for arg and return description.""" blocks = [ resnet_utils.Block('block1', bottleneck, [(256, 64, 1)] * 2 + [(256, 64, 2)]), resnet_utils.Block('block2', bottleneck, [(512, 128, 1)] * 23 + [(512, 128, 2)]), resnet_utils.Block('block3', bottleneck, [(1024, 256, 1)] * 35 + [(1024, 256, 2)]), resnet_utils.Block('block4', bottleneck, [(2048, 512, 1)] * 3) ] return resnet_v1(inputs, blocks, num_classes, is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=True, reuse=reuse, scope=scope)
def _resnet_small(self, inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, include_root_block=True, reuse=None, scope='resnet_v2_small'): """A shallow and thin ResNet v2 for faster tests.""" bottleneck = resnet_v2.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1)] * 2 + [(4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1)] * 2 + [(8, 2, 2)]), resnet_utils.Block('block3', bottleneck, [(16, 4, 1)] * 2 + [(16, 4, 2)]), resnet_utils.Block('block4', bottleneck, [(32, 8, 1)] * 2) ] return resnet_v2.resnet_v2(inputs, blocks, num_classes, is_training=is_training, global_pool=global_pool, output_stride=output_stride, include_root_block=include_root_block, reuse=reuse, scope=scope)
def testEndPointsV2(self): """Test the end points of a tiny v2 bottleneck network.""" bottleneck = resnet_v2.bottleneck blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 1)]) ] inputs = create_test_input(2, 32, 16, 3) with slim.arg_scope(resnet_utils.resnet_arg_scope()): _, end_points = self._resnet_plain(inputs, blocks, scope='tiny') expected = [ 'tiny/block1/unit_1/bottleneck_v2/shortcut', 'tiny/block1/unit_1/bottleneck_v2/conv1', 'tiny/block1/unit_1/bottleneck_v2/conv2', 'tiny/block1/unit_1/bottleneck_v2/conv3', 'tiny/block1/unit_2/bottleneck_v2/conv1', 'tiny/block1/unit_2/bottleneck_v2/conv2', 'tiny/block1/unit_2/bottleneck_v2/conv3', 'tiny/block2/unit_1/bottleneck_v2/shortcut', 'tiny/block2/unit_1/bottleneck_v2/conv1', 'tiny/block2/unit_1/bottleneck_v2/conv2', 'tiny/block2/unit_1/bottleneck_v2/conv3', 'tiny/block2/unit_2/bottleneck_v2/conv1', 'tiny/block2/unit_2/bottleneck_v2/conv2', 'tiny/block2/unit_2/bottleneck_v2/conv3' ] self.assertItemsEqual(expected, end_points)
def _atrousValues(self, bottleneck): """Verify the values of dense feature extraction by atrous convolution. Make sure that dense feature extraction by stack_blocks_dense() followed by subsampling gives identical results to feature extraction at the nominal network output stride using the simple self._stack_blocks_nondense() above. Args: bottleneck: The bottleneck function. """ blocks = [ resnet_utils.Block('block1', bottleneck, [(4, 1, 1), (4, 1, 2)]), resnet_utils.Block('block2', bottleneck, [(8, 2, 1), (8, 2, 2)]), resnet_utils.Block('block3', bottleneck, [(16, 4, 1), (16, 4, 2)]), resnet_utils.Block('block4', bottleneck, [(32, 8, 1), (32, 8, 1)]) ] nominal_stride = 8 # Test both odd and even input dimensions. height = 30 width = 31 with slim.arg_scope(resnet_utils.resnet_arg_scope()): with slim.arg_scope([slim.batch_norm], is_training=False): for output_stride in [1, 2, 4, 8, None]: with tf.Graph().as_default(): with self.test_session() as sess: tf.set_random_seed(0) inputs = create_test_input(1, height, width, 3) # Dense feature extraction followed by subsampling. output = resnet_utils.stack_blocks_dense( inputs, blocks, output_stride) if output_stride is None: factor = 1 else: factor = nominal_stride // output_stride output = resnet_utils.subsample(output, factor) # Make the two networks use the same weights. tf.get_variable_scope().reuse_variables() # Feature extraction at the nominal network rate. expected = self._stack_blocks_nondense( inputs, blocks) sess.run(tf.global_variables_initializer()) output, expected = sess.run([output, expected]) self.assertAllClose(output, expected, atol=1e-4, rtol=1e-4)
def resnet_v1_block(scope, base_depth, num_units, stride): """Helper function for creating a resnet_v1 bottleneck block. Args: scope: The scope of the block. base_depth: The depth of the bottleneck layer for each unit. num_units: The number of units in the block. stride: The stride of the block, implemented as a stride in the last unit. All other units have stride=1. Returns: A resnet_v1 bottleneck block. """ return resnet_utils.Block(scope, bottleneck, [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': 1 }] * (num_units - 1) + [{ 'depth': base_depth * 4, 'depth_bottleneck': base_depth, 'stride': stride }])