Skip to main content

CnnConvolutionDescriptor

apple_mps::neural

Struct CnnConvolutionDescriptor 

Source 
pub struct CnnConvolutionDescriptor { /* private fields */ }
Expand description

Wraps MPSCNNConvolutionDescriptor.

Implementations§

Source§

impl CnnConvolutionDescriptor

Source

pub const fn as_ptr(&self) -> *mut c_void

Returns the retained Objective-C pointer backing this wrapper.

Source§

impl CnnConvolutionDescriptor

Source

pub fn new( kernel_width: usize, kernel_height: usize, input_feature_channels: usize, output_feature_channels: usize, ) -> Option<Self>

Wraps a constructor on MPSCNNConvolutionDescriptor.

Examples found in repository?
examples/08_cnn_convolution.rs (line 11)
7fn main() {
8    let device = MetalDevice::system_default().expect("no Metal device available");
9    let queue = device.new_command_queue().expect("command queue");
10
11    let descriptor = CnnConvolutionDescriptor::new(1, 1, 1, 1).expect("descriptor");
12    let convolution = CnnConvolution::new(
13        &device,
14        &descriptor,
15        &[2.0],
16        Some(&[0.5]),
17        cnn_convolution_flags::NONE,
18    )
19    .expect("convolution");
20    convolution.set_accumulator_precision_option(cnn_accumulator_precision_option::FLOAT);
21
22    let image_descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
23    let source = Image::new(&device, image_descriptor).expect("source image");
24    let destination = Image::new(&device, image_descriptor).expect("destination image");
25    source
26        .write_f32(&[1.0, 2.0, 3.0, 4.0])
27        .expect("write source");
28
29    let command_buffer = queue.new_command_buffer().expect("command buffer");
30    convolution.encode_image(&command_buffer, &source, &destination);
31    command_buffer.commit();
32    command_buffer.wait_until_completed();
33
34    let output = destination.read_f32().expect("output");
35    println!("{output:?}");
36}
More examples
Hide additional examples
examples/05_nn_graph_relu.rs (line 61)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn kernel_width(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 67)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn kernel_height(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 68)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn input_feature_channels(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Source

pub fn output_feature_channels(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Source

pub fn stride_in_pixels_x(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 69)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn set_stride_in_pixels_x(&self, value: usize)

Wraps the corresponding MPSCNNConvolutionDescriptor setter.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 62)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn stride_in_pixels_y(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 70)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn set_stride_in_pixels_y(&self, value: usize)

Wraps the corresponding MPSCNNConvolutionDescriptor setter.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 63)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn groups(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 71)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn set_groups(&self, value: usize)

Wraps the corresponding MPSCNNConvolutionDescriptor setter.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 64)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn dilation_rate_x(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 72)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn set_dilation_rate_x(&self, value: usize)

Wraps the corresponding MPSCNNConvolutionDescriptor setter.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 65)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn dilation_rate_y(&self) -> usize

Wraps the corresponding MPSCNNConvolutionDescriptor method.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 73)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}
Source

pub fn set_dilation_rate_y(&self, value: usize)

Wraps the corresponding MPSCNNConvolutionDescriptor setter.

Examples found in repository?
examples/05_nn_graph_relu.rs (line 66)
8fn main() {
9    let device = MetalDevice::system_default().expect("no Metal device available");
10    let queue = device.new_command_queue().expect("command queue");
11
12    let input_node = NNImageNode::new().expect("input node");
13    input_node.set_format(feature_channel_format::FLOAT32);
14    let relu = CnnNeuronReluNode::new(&input_node, 0.0).expect("relu node");
15    let relu_result = relu.result_image().expect("relu result image");
16    relu_result.set_format(feature_channel_format::FLOAT32);
17    relu_result.set_synchronize_resource(true);
18    relu_result.use_default_allocator();
19    let pooling = CnnPoolingMaxNode::new(&relu_result, 2, 2).expect("pooling node");
20    assert!(
21        pooling.result_image().is_some(),
22        "pooling result image should exist"
23    );
24    let softmax = CnnSoftMaxNode::new(&relu_result).expect("softmax node");
25    assert!(
26        softmax.result_image().is_some(),
27        "softmax result image should exist"
28    );
29    let upsampling = CnnUpsamplingNearestNode::new(&relu_result, 2, 2).expect("upsampling node");
30    assert!(
31        upsampling.result_image().is_some(),
32        "upsampling result image should exist"
33    );
34
35    let graph = NNGraph::new(&device, &relu_result, true).expect("graph");
36    graph.set_format(feature_channel_format::FLOAT32);
37    graph.use_default_destination_image_allocator();
38
39    let descriptor = ImageDescriptor::new(2, 2, 1, feature_channel_format::FLOAT32);
40    let source = Image::new(&device, descriptor).expect("source image");
41    source
42        .write_f32(&[-1.0, 0.5, 2.0, -0.25])
43        .expect("write source image");
44
45    let command_buffer = queue.new_command_buffer().expect("command buffer");
46    let result = graph
47        .encode(&command_buffer, &[&source])
48        .expect("graph encode");
49    command_buffer.commit();
50    command_buffer.wait_until_completed();
51
52    let output = result.read_f32().expect("read graph output");
53    let expected = [0.0_f32, 0.5, 2.0, 0.0];
54    for (actual, expected_value) in output.iter().zip(expected) {
55        assert!(
56            (actual - expected_value).abs() < 1.0e-4,
57            "unexpected relu graph output: {output:?}"
58        );
59    }
60
61    let convolution = CnnConvolutionDescriptor::new(3, 3, 1, 4).expect("convolution descriptor");
62    convolution.set_stride_in_pixels_x(2);
63    convolution.set_stride_in_pixels_y(1);
64    convolution.set_groups(1);
65    convolution.set_dilation_rate_x(1);
66    convolution.set_dilation_rate_y(2);
67    assert_eq!(convolution.kernel_width(), 3);
68    assert_eq!(convolution.kernel_height(), 3);
69    assert_eq!(convolution.stride_in_pixels_x(), 2);
70    assert_eq!(convolution.stride_in_pixels_y(), 1);
71    assert_eq!(convolution.groups(), 1);
72    assert_eq!(convolution.dilation_rate_x(), 1);
73    assert_eq!(convolution.dilation_rate_y(), 2);
74
75    let rnn = RnnSingleGateDescriptor::new(3, 5).expect("rnn descriptor");
76    rnn.set_use_layer_input_unit_transform_mode(true);
77    rnn.set_use_float32_weights(true);
78    rnn.set_layer_sequence_direction(rnn_sequence_direction::BACKWARD);
79    assert_eq!(rnn.input_feature_channels(), 3);
80    assert_eq!(rnn.output_feature_channels(), 5);
81    assert!(rnn.use_layer_input_unit_transform_mode());
82    assert!(rnn.use_float32_weights());
83    assert_eq!(
84        rnn.layer_sequence_direction(),
85        rnn_sequence_direction::BACKWARD,
86        "expected backward RNN sequence direction"
87    );
88
89    println!(
90        "nn smoke passed: relu={output:?} source_images={}",
91        graph.source_image_count()
92    );
93}

Trait Implementations§

Source§

impl Drop for CnnConvolutionDescriptor

Source§

fn drop(&mut self)

Executes the destructor for this type. Read more
Source§

fn pin_drop(self: Pin<&mut Self>)

🔬This is a nightly-only experimental API. (pin_ergonomics)
Execute the destructor for this type, but different to Drop::drop, it requires self to be pinned. Read more
Source§

impl Send for CnnConvolutionDescriptor

Source§

impl Sync for CnnConvolutionDescriptor

Auto Trait Implementations§

Blanket Implementations§

Source§

impl<T> Any for T
where T: 'static + ?Sized,

Source§

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
Source§

impl<T> Borrow<T> for T
where T: ?Sized,

Source§

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
Source§

impl<T> BorrowMut<T> for T
where T: ?Sized,

Source§

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
Source§

impl<T> From<T> for T

Source§

fn from(t: T) -> T

Returns the argument unchanged.

Source§

impl<T, U> Into<U> for T
where U: From<T>,

Source§

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

Source§

impl<T, U> TryFrom<U> for T
where U: Into<T>,

Source§

type Error = Infallible

The type returned in the event of a conversion error.
Source§

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
Source§

impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

Source§

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
Source§

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.