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[TorchFX] Conformance test init #2841
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AlexanderDokuchaev
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openvinotoolkit:develop
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daniil-lyakhov:dl/torch_fx/conformance
Aug 7, 2024
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80 changes: 80 additions & 0 deletions
80
tests/post_training/pipelines/image_classification_base.py
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| @@ -0,0 +1,80 @@ | ||
| # Copyright (c) 2024 Intel Corporation | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| import copy | ||
| import os | ||
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| import numpy as np | ||
| import openvino as ov | ||
| import torch | ||
| from sklearn.metrics import accuracy_score | ||
| from torchvision import datasets | ||
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| import nncf | ||
| from nncf.common.logging.track_progress import track | ||
| from tests.post_training.pipelines.base import DEFAULT_VAL_THREADS | ||
| from tests.post_training.pipelines.base import PTQTestPipeline | ||
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| class ImageClassificationBase(PTQTestPipeline): | ||
| """Base pipeline for Image Classification models""" | ||
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| def prepare_calibration_dataset(self): | ||
| dataset = datasets.ImageFolder(root=self.data_dir / "imagenet" / "val", transform=self.transform) | ||
| loader = torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, num_workers=2, shuffle=False) | ||
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| self.calibration_dataset = nncf.Dataset(loader, self.get_transform_calibration_fn()) | ||
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| def _validate(self): | ||
| val_dataset = datasets.ImageFolder(root=self.data_dir / "imagenet" / "val", transform=self.transform) | ||
| val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, num_workers=2, shuffle=False) | ||
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| dataset_size = len(val_loader) | ||
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| # Initialize result tensors for async inference support. | ||
| predictions = np.zeros((dataset_size)) | ||
| references = -1 * np.ones((dataset_size)) | ||
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| core = ov.Core() | ||
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| if os.environ.get("INFERENCE_NUM_THREADS"): | ||
| # Set CPU_THREADS_NUM for OpenVINO inference | ||
| inference_num_threads = os.environ.get("INFERENCE_NUM_THREADS") | ||
| core.set_property("CPU", properties={"INFERENCE_NUM_THREADS": str(inference_num_threads)}) | ||
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| ov_model = core.read_model(self.path_compressed_ir) | ||
| compiled_model = core.compile_model(ov_model, device_name="CPU") | ||
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| jobs = int(os.environ.get("NUM_VAL_THREADS", DEFAULT_VAL_THREADS)) | ||
| infer_queue = ov.AsyncInferQueue(compiled_model, jobs) | ||
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| with track(total=dataset_size, description="Validation") as pbar: | ||
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| def process_result(request, userdata): | ||
| output_data = request.get_output_tensor().data | ||
| predicted_label = np.argmax(output_data, axis=1) | ||
| predictions[userdata] = predicted_label | ||
| pbar.progress.update(pbar.task, advance=1) | ||
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| infer_queue.set_callback(process_result) | ||
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| for i, (images, target) in enumerate(val_loader): | ||
| # W/A for memory leaks when using torch DataLoader and OpenVINO | ||
| image_copies = copy.deepcopy(images.numpy()) | ||
| infer_queue.start_async(image_copies, userdata=i) | ||
| references[i] = target | ||
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| infer_queue.wait_all() | ||
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| acc_top1 = accuracy_score(predictions, references) | ||
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| self.run_info.metric_name = "Acc@1" | ||
| self.run_info.metric_value = acc_top1 |
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121 changes: 121 additions & 0 deletions
121
tests/post_training/pipelines/image_classification_torchvision.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| # Copyright (c) 2024 Intel Corporation | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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||
| import numpy as np | ||
| import onnx | ||
| import openvino as ov | ||
| import torch | ||
| from torch._export import capture_pre_autograd_graph | ||
| from torchvision import models | ||
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| from nncf.torch import disable_patching | ||
| from tests.post_training.pipelines.base import PT_BACKENDS | ||
| from tests.post_training.pipelines.base import BackendType | ||
| from tests.post_training.pipelines.image_classification_base import ImageClassificationBase | ||
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| class ImageClassificationTorchvision(ImageClassificationBase): | ||
| """Pipeline for Image Classification model from torchvision repository""" | ||
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| models_vs_imagenet_weights = { | ||
| models.resnet18: models.ResNet18_Weights.DEFAULT, | ||
| models.mobilenet_v3_small: models.MobileNet_V3_Small_Weights.DEFAULT, | ||
| models.vit_b_16: models.ViT_B_16_Weights.DEFAULT, | ||
| models.swin_v2_s: models.Swin_V2_S_Weights.DEFAULT, | ||
| } | ||
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| def __init__(self, *args, **kwargs): | ||
| super().__init__(*args, **kwargs) | ||
| self.model_weights: models.WeightsEnum = None | ||
| self.input_name: str = None | ||
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| def prepare_model(self) -> None: | ||
| model_cls = models.__dict__.get(self.model_id) | ||
| self.model_weights = self.models_vs_imagenet_weights[model_cls] | ||
| model = model_cls(weights=self.model_weights) | ||
| model.eval() | ||
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| self.static_input_size = [self.batch_size, 3, 224, 224] | ||
| self.input_size = self.static_input_size.copy() | ||
| if self.batch_size > 1: # Dynamic batch_size shape export | ||
| self.input_size[0] = -1 | ||
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| self.dummy_tensor = torch.rand(self.static_input_size) | ||
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| if self.backend == BackendType.FX_TORCH: | ||
| with torch.no_grad(): | ||
| with disable_patching(): | ||
| self.model = capture_pre_autograd_graph(model, (self.dummy_tensor,)) | ||
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| elif self.backend in PT_BACKENDS: | ||
| self.model = model | ||
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| if self.backend == BackendType.ONNX: | ||
| onnx_path = self.fp32_model_dir / "model_fp32.onnx" | ||
| additional_kwargs = {} | ||
| if self.batch_size > 1: | ||
| additional_kwargs["input_names"] = ["image"] | ||
| additional_kwargs["dynamic_axes"] = {"image": {0: "batch"}} | ||
| torch.onnx.export( | ||
| model, self.dummy_tensor, onnx_path, export_params=True, opset_version=13, **additional_kwargs | ||
| ) | ||
| self.model = onnx.load(onnx_path) | ||
| self.input_name = self.model.graph.input[0].name | ||
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| elif self.backend in [BackendType.OV, BackendType.FP32]: | ||
| with torch.no_grad(): | ||
| self.model = ov.convert_model(model, example_input=self.dummy_tensor, input=self.input_size) | ||
| self.input_name = list(inp.get_any_name() for inp in self.model.inputs)[0] | ||
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| self._dump_model_fp32() | ||
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| # Set device after dump fp32 model | ||
| if self.backend == BackendType.CUDA_TORCH: | ||
| self.model.cuda() | ||
| self.dummy_tensor = self.dummy_tensor.cuda() | ||
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| def _dump_model_fp32(self) -> None: | ||
| """Dump IRs of fp32 models, to help debugging.""" | ||
| if self.backend in PT_BACKENDS: | ||
| with disable_patching(): | ||
| ov_model = ov.convert_model( | ||
| torch.export.export(self.model, args=(self.dummy_tensor,)), | ||
| example_input=self.dummy_tensor, | ||
| input=self.input_size, | ||
| ) | ||
| ov.serialize(ov_model, self.fp32_model_dir / "model_fp32.xml") | ||
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| if self.backend == BackendType.FX_TORCH: | ||
| exported_model = torch.export.export(self.model, (self.dummy_tensor,)) | ||
| ov_model = ov.convert_model(exported_model, example_input=self.dummy_tensor, input=self.input_size) | ||
| ov.serialize(ov_model, self.fp32_model_dir / "fx_model_fp32.xml") | ||
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| if self.backend in [BackendType.FP32, BackendType.OV]: | ||
| ov.serialize(self.model, self.fp32_model_dir / "model_fp32.xml") | ||
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| def prepare_preprocessor(self) -> None: | ||
| self.transform = self.model_weights.transforms() | ||
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| def get_transform_calibration_fn(self): | ||
| if self.backend in [BackendType.FX_TORCH] + PT_BACKENDS: | ||
| device = torch.device("cuda" if self.backend == BackendType.CUDA_TORCH else "cpu") | ||
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| def transform_fn(data_item): | ||
| images, _ = data_item | ||
| return images.to(device) | ||
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| else: | ||
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| def transform_fn(data_item): | ||
| images, _ = data_item | ||
| return {self.input_name: np.array(images, dtype=np.float32)} | ||
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| return transform_fn |
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What differ between torchvision and timm models?
If it's same but timm can not be exported to FX, timm/resnet18 can be removed
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I've removed timm/resnet18 and replaced with torchvision/resnet18