fastforks 0.1.0

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README.md

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+---
+library_name: "fastforks"
+pipeline_tag: "image-classification"
+license: "other"
+license_name: "bsd-3-clause-code-weights-unstated"
+license_link: "https://github.com/pytorch/vision/blob/1f1a920d685d46dfbe187934b785b7ecb7a182f2/LICENSE"
+tags:
+- "classification"
+- "image-classification"
+- "cuda"
+- "pytorch"
+- "fastforks"
+---
+# ResNet (fastforks)
+
+ResNet (He et al. 2015): residual basic / bottleneck blocks.
+
+This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: <https://github.com/kleinicke/fastforks>. Kernels for the `kernels` backend: <https://huggingface.co/fastforks/kernels>. Original code: <https://github.com/pytorch/vision> at `1f1a920d685d`.
+
+Task: classification. Tier: stable. Outputs: `logits`. Backends: `portable`, `kernels`.
+
+Input: one RGB image. Output: ImageNet-1k class logits.
+
+## Variants
+
+Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in [`card.toml`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/classification/models/resnet/card.toml).
+
+| variant | weights (author link) | SHA-256 | weights licence | commercial use |
+|---|---|---|---|---|
+| `resnet152.tv2_in1k@1` | [resnet152-f82ba261.pth](https://download.pytorch.org/models/resnet152-f82ba261.pth) | `f82ba2614b9f` | BSD-3-Clause (assumed from code) | yes |
+| `resnet18.tv_in1k@1` | [resnet18-f37072fd.pth](https://download.pytorch.org/models/resnet18-f37072fd.pth) | `f37072fd47e8` | BSD-3-Clause (assumed from code) | yes |
+| `resnet50.tv2_in1k@1` | [resnet50-11ad3fa6.pth](https://download.pytorch.org/models/resnet50-11ad3fa6.pth) | `11ad3fa62ca7` | BSD-3-Clause (assumed from code) | yes |
+
+## Licence
+
+Original-code strings, recorded as they are (research-use package; check them before any other use). Code: BSD-3-Clause. Files: .
+
+Code licence: BSD-3-Clause; the authors do not state a separate weights licence.
+
+Training data: ImageNet-1k (ILSVRC 2012 classification).
+
+Licence evidence: <https://github.com/pytorch/vision/blob/1f1a920d685d46dfbe187934b785b7ecb7a182f2/LICENSE>
+
+## Input size
+
+- `resnet152.tv2_in1k`, `resnet50.tv2_in1k`: fixed 224x224: the input is resized (short side 232) and centre-cropped to it
+- `resnet18.tv_in1k`: fixed 224x224: the input is resized (short side 256) and centre-cropped to it
+Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `resnet152.tv2_in1k` 224x224 (0.2 GiB peak allocated, 0.2 reserved); `resnet18.tv_in1k` 224x224 (0.1 GiB peak allocated, 0.1 reserved); `resnet50.tv2_in1k` 224x224 (0.1 GiB peak allocated, 0.1 reserved)
+
+## Speed and memory
+
+### Classification, synthetic, 224x224
+
+| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `resnet152.tv2_in1k` | 12.79 | 16.27 | 8.36 | 2.17 | 5.9x | 3.9x | 378 / 390 | 155 / 186 | 184 / 248 |
+| `resnet18.tv_in1k` | 1.91 | 2.44 | 1.48 | 0.55 | 3.5x | 2.7x | 105 / 130 | 61 / 90 | 91 / 154 |
+| `resnet50.tv2_in1k` | 4.74 | 5.84 | 3.15 | 0.99 | 4.8x | 3.2x | 180 / 192 | 89 / 142 | 118 / 204 |
+
+### Startup and first call
+
+| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
+| --- | --- | --- | --- | --- | --- | --- |
+| `resnet152.tv2_in1k` | 2.34 | 0.43 | 0.49† | 0.24 | 0.02 | 0.00 |
+| `resnet18.tv_in1k` | 0.27 | 0.07 | 0.08† | 0.00 | 0.00 | 0.00 |
+| `resnet50.tv2_in1k` | 0.51 | 0.16 | 0.18† | 0.01 | 0.00 | 0.00 |
+
+### Fixed-size speed and peak memory, all models
+
+| model | mode | 224x224 |
+| --- | --- | --- |
+| `resnet152.tv2_in1k` | portable | 8.4 ms / 157 / 192 |
+| `resnet152.tv2_in1k` | kernels | 2.2 ms / 188 / 270 |
+| `resnet18.tv_in1k` | portable | 1.4 ms / 60 / 68 |
+| `resnet18.tv_in1k` | kernels | 0.6 ms / 91 / 144 |
+| `resnet50.tv2_in1k` | portable | 3.1 ms / 91 / 110 |
+| `resnet50.tv2_in1k` | kernels | 1.0 ms / 121 / 190 |
+
+Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
+
+Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or `(fp32)` where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
+
+## Accuracy
+
+### Parity with the original code
+
+Every variant is checked against golden outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.02 px and p99 <= 0.08 px.
+
+## Usage
+
+```python
+from fastforks import classification
+
+model = classification.load("resnet152.tv2_in1k")  # portable PyTorch on the GPU if there is one, else the CPU
+result = model("photo.jpg")
+result.top(5)  # top-5 classes
+result.backend  # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
+```
+
+Install with `pip install fastforks` (see <https://github.com/kleinicke/fastforks>). Other variants: `resnet152.tv2_in1k`, `resnet18.tv_in1k`, `resnet50.tv2_in1k`.
+
+## Kernels backend
+
+`classification.load("resnet152.tv2_in1k", mode="kernels")` runs our CUDA kernels (`cudnn_hacc`, `fvm_conv`, `fvm_extra`, `fvm_mb`, `fvm_r3`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.