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RnnSingleGateDescriptor

apple_mps::neural

Struct RnnSingleGateDescriptor 

Source 
pub struct RnnSingleGateDescriptor { /* private fields */ }

Implementations§

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impl RnnSingleGateDescriptor

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

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impl RnnSingleGateDescriptor

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

Examples found in repository?
examples/09_rnn_image_inference.rs (line 8)
4fn main() {
5    let device = MetalDevice::system_default().expect("no Metal device available");
6    let queue = device.new_command_queue().expect("command queue");
7
8    let single_gate = RnnSingleGateDescriptor::new(1, 1).expect("single gate descriptor");
9    single_gate.set_use_layer_input_unit_transform_mode(true);
10    let descriptor = single_gate.as_descriptor().expect("base descriptor");
11    let layer = RnnImageInferenceLayer::new(&device, &descriptor).expect("rnn layer");
12
13    let image_descriptor = ImageDescriptor::new(1, 1, 1, feature_channel_format::FLOAT32);
14    let src0 = Image::new(&device, image_descriptor).expect("src0");
15    let src1 = Image::new(&device, image_descriptor).expect("src1");
16    let dst0 = Image::new(&device, image_descriptor).expect("dst0");
17    let dst1 = Image::new(&device, image_descriptor).expect("dst1");
18    src0.write_f32(&[0.25]).expect("write src0");
19    src1.write_f32(&[0.75]).expect("write src1");
20
21    let command_buffer = queue.new_command_buffer().expect("command buffer");
22    let recurrent_state = layer
23        .encode_sequence(&command_buffer, &[&src0, &src1], &[&dst0, &dst1], None)
24        .expect("recurrent state");
25    command_buffer.commit();
26    command_buffer.wait_until_completed();
27
28    let recurrent_output = recurrent_state
29        .recurrent_output_image_for_layer_index(0)
30        .expect("recurrent output image");
31    println!(
32        "{:?} {}x{}x{}",
33        dst1.read_f32().expect("dst1 output"),
34        recurrent_output.width(),
35        recurrent_output.height(),
36        recurrent_output.feature_channels()
37    );
38}
More examples
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examples/05_nn_graph_relu.rs (line 75)
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

Examples found in repository?
examples/05_nn_graph_relu.rs (line 79)
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_input_feature_channels(&self, value: usize)

Source

pub fn output_feature_channels(&self) -> usize

Examples found in repository?
examples/05_nn_graph_relu.rs (line 80)
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_output_feature_channels(&self, value: usize)

Source

pub fn use_layer_input_unit_transform_mode(&self) -> bool

Examples found in repository?
examples/05_nn_graph_relu.rs (line 81)
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_use_layer_input_unit_transform_mode(&self, value: bool)

Examples found in repository?
examples/09_rnn_image_inference.rs (line 9)
4fn main() {
5    let device = MetalDevice::system_default().expect("no Metal device available");
6    let queue = device.new_command_queue().expect("command queue");
7
8    let single_gate = RnnSingleGateDescriptor::new(1, 1).expect("single gate descriptor");
9    single_gate.set_use_layer_input_unit_transform_mode(true);
10    let descriptor = single_gate.as_descriptor().expect("base descriptor");
11    let layer = RnnImageInferenceLayer::new(&device, &descriptor).expect("rnn layer");
12
13    let image_descriptor = ImageDescriptor::new(1, 1, 1, feature_channel_format::FLOAT32);
14    let src0 = Image::new(&device, image_descriptor).expect("src0");
15    let src1 = Image::new(&device, image_descriptor).expect("src1");
16    let dst0 = Image::new(&device, image_descriptor).expect("dst0");
17    let dst1 = Image::new(&device, image_descriptor).expect("dst1");
18    src0.write_f32(&[0.25]).expect("write src0");
19    src1.write_f32(&[0.75]).expect("write src1");
20
21    let command_buffer = queue.new_command_buffer().expect("command buffer");
22    let recurrent_state = layer
23        .encode_sequence(&command_buffer, &[&src0, &src1], &[&dst0, &dst1], None)
24        .expect("recurrent state");
25    command_buffer.commit();
26    command_buffer.wait_until_completed();
27
28    let recurrent_output = recurrent_state
29        .recurrent_output_image_for_layer_index(0)
30        .expect("recurrent output image");
31    println!(
32        "{:?} {}x{}x{}",
33        dst1.read_f32().expect("dst1 output"),
34        recurrent_output.width(),
35        recurrent_output.height(),
36        recurrent_output.feature_channels()
37    );
38}
More examples
Hide additional examples
examples/05_nn_graph_relu.rs (line 76)
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 use_float32_weights(&self) -> bool

Examples found in repository?
examples/05_nn_graph_relu.rs (line 82)
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_use_float32_weights(&self, value: bool)

Examples found in repository?
examples/05_nn_graph_relu.rs (line 77)
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 layer_sequence_direction(&self) -> usize

Examples found in repository?
examples/05_nn_graph_relu.rs (line 84)
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_layer_sequence_direction(&self, value: usize)

Examples found in repository?
examples/05_nn_graph_relu.rs (line 78)
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 as_descriptor(&self) -> Option<RnnDescriptor>

Examples found in repository?
examples/09_rnn_image_inference.rs (line 10)
4fn main() {
5    let device = MetalDevice::system_default().expect("no Metal device available");
6    let queue = device.new_command_queue().expect("command queue");
7
8    let single_gate = RnnSingleGateDescriptor::new(1, 1).expect("single gate descriptor");
9    single_gate.set_use_layer_input_unit_transform_mode(true);
10    let descriptor = single_gate.as_descriptor().expect("base descriptor");
11    let layer = RnnImageInferenceLayer::new(&device, &descriptor).expect("rnn layer");
12
13    let image_descriptor = ImageDescriptor::new(1, 1, 1, feature_channel_format::FLOAT32);
14    let src0 = Image::new(&device, image_descriptor).expect("src0");
15    let src1 = Image::new(&device, image_descriptor).expect("src1");
16    let dst0 = Image::new(&device, image_descriptor).expect("dst0");
17    let dst1 = Image::new(&device, image_descriptor).expect("dst1");
18    src0.write_f32(&[0.25]).expect("write src0");
19    src1.write_f32(&[0.75]).expect("write src1");
20
21    let command_buffer = queue.new_command_buffer().expect("command buffer");
22    let recurrent_state = layer
23        .encode_sequence(&command_buffer, &[&src0, &src1], &[&dst0, &dst1], None)
24        .expect("recurrent state");
25    command_buffer.commit();
26    command_buffer.wait_until_completed();
27
28    let recurrent_output = recurrent_state
29        .recurrent_output_image_for_layer_index(0)
30        .expect("recurrent output image");
31    println!(
32        "{:?} {}x{}x{}",
33        dst1.read_f32().expect("dst1 output"),
34        recurrent_output.width(),
35        recurrent_output.height(),
36        recurrent_output.feature_channels()
37    );
38}

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impl Drop for RnnSingleGateDescriptor

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fn drop(&mut self)

Executes the destructor for this type. Read more
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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
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impl Send for RnnSingleGateDescriptor

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impl Sync for RnnSingleGateDescriptor

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