152 lines
3.7 KiB
Plaintext
152 lines
3.7 KiB
Plaintext
[package]
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name = "rtx-vision-advanced"
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version = "0.1.0"
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edition = "2021"
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authors = ["RustyTorch++ Team"]
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license = "Apache-2.0"
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description = "State-of-the-art computer vision capabilities with medical imaging, autonomous vehicle support, and production-ready deployment"
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repository = "https://github.com/rustytorch/rustytorch"
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[dependencies]
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# Core RTX dependencies
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rtx-tensor = { path = "../rtx-tensor" }
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rtx-autograd = { path = "../rtx-autograd" }
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rtx-transformers = { path = "../rtx-transformers" }
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rtx-flash-attention = { path = "../rtx-flash-attention" }
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rtx-vision = { path = "../rtx-vision" }
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rtx-memory = { path = "../rtx-memory" }
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rtx-inference = { path = "../rtx-inference" }
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# Core utilities
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anyhow = "1.0"
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thiserror = "1.0"
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tracing = "0.1"
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log = "0.4"
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# Image processing and computer vision
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image = "0.24"
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imageproc = "0.23"
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opencv = { version = "0.88", optional = true }
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rerun = { version = "0.17", optional = true }
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# Medical imaging
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dicom = { version = "0.6", optional = true }
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dicom-object = { version = "0.6", optional = true }
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medical-imaging-toolkit = { version = "0.1", optional = true }
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# Point cloud processing for autonomous vehicles
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pcd-rs = { version = "0.8", optional = true }
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las = { version = "0.8", optional = true }
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nalgebra.workspace = true
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nalgebra-glm = "0.18"
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# Advanced numeric computing
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ndarray = "0.15"
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ndarray-stats = "0.5"
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ndarray-linalg = "0.16"
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approx-float = "0.1"
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num-traits = "0.2"
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half = "2.3"
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rand = "0.8"
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rand_distr = "0.4"
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# Machine learning utilities
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candle-core = { version = "0.5", optional = true }
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candle-nn = { version = "0.5", optional = true }
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ort = { version = "2.0", optional = true }
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tch = { version = "0.13", optional = true }
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# Serialization and I/O
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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bincode = "1.3"
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hdf5 = { version = "0.8", optional = true }
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npy = "0.7"
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# Concurrency and async
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tokio = { version = "1.35", features = ["full"] }
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rayon = "1.8"
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crossbeam = "0.8"
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parking_lot = "0.12"
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# GPU computing and optimization
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cudarc = { version = "0.9", optional = true }
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wgpu = { version = "0.19", optional = true }
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metal = { version = "0.27", optional = true }
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# Network models and pre-trained weights
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safetensors = "0.4"
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memmap2 = "0.9"
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reqwest = { version = "0.11", features = ["json", "stream"] }
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futures = "0.3"
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# Evaluation and benchmarking
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criterion = { version = "0.5", optional = true }
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indicatif = "0.17"
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console = "0.15"
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# Geometric and mathematical operations
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geo = { version = "0.27", optional = true }
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rstar = { version = "0.11", optional = true }
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kiddo = { version = "4.1", optional = true }
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[dev-dependencies]
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# Testing framework
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proptest = "1.4"
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criterion = "0.5"
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approx = "0.5"
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tempfile = "3.8"
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tokio-test = "0.4"
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# Benchmarking
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divan = "0.1"
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[features]
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default = ["gpu"]
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gpu = ["cudarc", "wgpu"]
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cuda = ["cudarc", "rtx-tensor/cuda"]
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metal = ["dep:metal"]
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opencv = ["dep:opencv"]
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medical = ["dicom", "dicom-object", "medical-imaging-toolkit", "hdf5"]
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autonomous = ["pcd-rs", "las", "geo", "rstar", "kiddo"]
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onnx = ["ort"]
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pytorch = ["tch"]
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candle = ["candle-core", "candle-nn"]
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visualization = ["rerun"]
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benchmarks = ["criterion"]
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[lib]
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name = "rtx_vision_advanced"
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path = "src/lib.rs"
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[[bench]]
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name = "detection_bench"
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harness = false
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required-features = ["benchmarks"]
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[[bench]]
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name = "segmentation_bench"
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harness = false
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required-features = ["benchmarks"]
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[[bench]]
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name = "medical_bench"
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harness = false
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required-features = ["benchmarks", "medical"]
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[[bench]]
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name = "autonomous_bench"
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harness = false
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required-features = ["benchmarks", "autonomous"]
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[[example]]
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name = "yolo_detection"
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required-features = ["gpu"]
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[[example]]
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name = "medical_segmentation"
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required-features = ["medical"]
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[[example]]
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name = "lidar_processing"
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required-features = ["autonomous"] |