[FxImporter] Add an e2e test example for FxImporter (#3331)

README.md

@@ -76,6 +76,23 @@ pip install torch-mlir -f https://github.com/llvm/torch-mlir-release/releases/ex
 
 ## Demos
 
+### FxImporter ResNet18
+```shell
+# Get the latest example if you haven't checked out the code
+wget https://raw.githubusercontent.com/llvm/torch-mlir/main/projects/pt1/examples/fximporter_resnet18.py
+
+# Run ResNet18 as a standalone script.
+python projects/pt1/examples/fximporter_resnet18.py
+
+# Output
+load image from https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg
+...
+PyTorch prediction
+[('Labrador retriever', 70.65674591064453), ('golden retriever', 4.988346099853516), ('Saluki, gazelle hound', 4.477451324462891)]
+torch-mlir prediction
+[('Labrador retriever', 70.6567153930664), ('golden retriever', 4.988325119018555), ('Saluki, gazelle hound', 4.477458477020264)]
+```
+
 ### TorchScript ResNet18
 
 Standalone script to Convert a PyTorch ResNet18 model to MLIR and run it on the CPU Backend:

projects/pt1/examples/_example_utils.py

@@ -0,0 +1,52 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+from PIL import Image
+import requests
+
+import torch
+from torchvision import transforms
+
+
+DEFAULT_IMAGE_URL = (
+    "https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
+)
+DEFAULT_LABEL_URL = (
+    "https://raw.githubusercontent.com/cathyzhyi/ml-data/main/imagenet-classes.txt"
+)
+
+
+def load_and_preprocess_image(url: str = DEFAULT_IMAGE_URL):
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36"
+    }
+    img = Image.open(requests.get(url, headers=headers, stream=True).raw).convert("RGB")
+    # preprocessing pipeline
+    preprocess = transforms.Compose(
+        [
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+    img_preprocessed = preprocess(img)
+    return torch.unsqueeze(img_preprocessed, 0)
+
+
+def load_labels(url: str = DEFAULT_LABEL_URL):
+    classes_text = requests.get(
+        url=url,
+        stream=True,
+    ).text
+    labels = [line.strip() for line in classes_text.splitlines()]
+    return labels
+
+
+def top3_possibilities(res, labels):
+    _, indexes = torch.sort(res, descending=True)
+    percentage = torch.nn.functional.softmax(res, dim=1)[0] * 100
+    top3 = [(labels[idx], percentage[idx].item()) for idx in indexes[0][:3]]
+    return top3

projects/pt1/examples/fximporter_resnet18.py

@@ -0,0 +1,59 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+import sys
+from pathlib import Path
+
+import torch
+import torch.utils._pytree as pytree
+import torchvision.models as models
+from torch_mlir import fx
+from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
+from torch_mlir_e2e_test.configs.utils import (
+    recursively_convert_to_numpy,
+)
+
+sys.path.append(str(Path(__file__).absolute().parent))
+from _example_utils import (
+    top3_possibilities,
+    load_and_preprocess_image,
+    load_labels,
+    DEFAULT_IMAGE_URL,
+)
+
+
+print("load image from " + DEFAULT_IMAGE_URL, file=sys.stderr)
+img = load_and_preprocess_image(DEFAULT_IMAGE_URL)
+labels = load_labels()
+
+resnet18 = models.resnet18(pretrained=True).eval()
+module = fx.export_and_import(
+    resnet18,
+    torch.ones(1, 3, 224, 224),
+    output_type="linalg-on-tensors",
+    func_name=resnet18.__class__.__name__,
+)
+backend = refbackend.RefBackendLinalgOnTensorsBackend()
+compiled = backend.compile(module)
+fx_module = backend.load(compiled)
+
+params = {
+    **dict(resnet18.named_buffers(remove_duplicate=False)),
+}
+params_flat, params_spec = pytree.tree_flatten(params)
+params_flat = list(params_flat)
+with torch.no_grad():
+    numpy_inputs = recursively_convert_to_numpy(params_flat + [img])
+
+golden_prediction = top3_possibilities(resnet18.forward(img), labels)
+print("PyTorch prediction")
+print(golden_prediction)
+
+prediction = top3_possibilities(
+    torch.from_numpy(getattr(fx_module, resnet18.__class__.__name__)(*numpy_inputs)),
+    labels,
+)
+print("torch-mlir prediction")
+print(prediction)

projects/pt1/examples/torchscript_resnet18.py

@@ -4,71 +4,36 @@
 # Also available under a BSD-style license. See LICENSE.
 
 import sys
-
-from PIL import Image
-import requests
+from pathlib import Path
 
 import torch
 import torchvision.models as models
-from torchvision import transforms
-
 from torch_mlir import torchscript
 from torch_mlir_e2e_test.linalg_on_tensors_backends import refbackend
 
-
-def load_and_preprocess_image(url: str):
-    headers = {
-        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36"
-    }
-    img = Image.open(requests.get(url, headers=headers, stream=True).raw).convert("RGB")
-    # preprocessing pipeline
-    preprocess = transforms.Compose(
-        [
-            transforms.Resize(256),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        ]
-    )
-    img_preprocessed = preprocess(img)
-    return torch.unsqueeze(img_preprocessed, 0)
-
-
-def load_labels():
-    classes_text = requests.get(
-        "https://raw.githubusercontent.com/cathyzhyi/ml-data/main/imagenet-classes.txt",
-        stream=True,
-    ).text
-    labels = [line.strip() for line in classes_text.splitlines()]
-    return labels
-
-
-def top3_possibilities(res):
-    _, indexes = torch.sort(res, descending=True)
-    percentage = torch.nn.functional.softmax(res, dim=1)[0] * 100
-    top3 = [(labels[idx], percentage[idx].item()) for idx in indexes[0][:3]]
-    return top3
+sys.path.append(str(Path(__file__).absolute().parent))
+from _example_utils import (
+    top3_possibilities,
+    load_and_preprocess_image,
+    load_labels,
+    DEFAULT_IMAGE_URL,
+)
 
 
 def predictions(torch_func, jit_func, img, labels):
-    golden_prediction = top3_possibilities(torch_func(img))
+    golden_prediction = top3_possibilities(torch_func(img), labels)
     print("PyTorch prediction")
     print(golden_prediction)
-    prediction = top3_possibilities(torch.from_numpy(jit_func(img.numpy())))
+    prediction = top3_possibilities(torch.from_numpy(jit_func(img.numpy())), labels)
     print("torch-mlir prediction")
     print(prediction)
 
 
-image_url = (
-    "https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg"
-)
-
-print("load image from " + image_url, file=sys.stderr)
-img = load_and_preprocess_image(image_url)
+print("load image from " + DEFAULT_IMAGE_URL, file=sys.stderr)
+img = load_and_preprocess_image(DEFAULT_IMAGE_URL)
 labels = load_labels()
 
-resnet18 = models.resnet18(pretrained=True)
-resnet18.train(False)
+resnet18 = models.resnet18(pretrained=True).eval()
 module = torchscript.compile(
     resnet18, torch.ones(1, 3, 224, 224), output_type="linalg-on-tensors"
 )