support resnet models, test failed models

machine-learning/export/ann/run.py

@@ -20,7 +20,8 @@ from shutil import rmtree
 # armnn only supports up to 4d tranposes, but the model has a 5d transpose due to a redundant unsqueeze
 # this function folds the unsqueeze+transpose+squeeze into a single 4d transpose
 # it also switches from gather ops to slices since armnn has different dimension semantics for gathers
-def onnx_transpose_4d(model_path: str):
+# also fixes batch normalization being in training mode
+def make_onnx_armnn_compatible(model_path: str):
     proto = onnx.load(model_path)
     graph = import_onnx(proto)
 
@@ -141,6 +142,60 @@ def onnx_transpose_4d(model_path: str):
                                     node2.inputs[idx] = squeeze_link
                                 except ValueError:
                                     pass
+            elif node.op == "Reshape":
+                for node1 in link1.outputs:
+                    if node1.op == "Gather":
+                        node2s = [n for l in node1.outputs for n in l.outputs]
+                        if any(n.op == "Abs" for n in node2s):
+                            axis = node1.attrs.get("axis", 0)
+                            index = node1.inputs[1].values
+                            slice_link = Variable(
+                                f"onnx::Slice_123{gather_idx}",
+                                dtype=node1.outputs[0].dtype,
+                                shape=[1] + node1.outputs[0].shape,
+                            )
+                            slice_node = Node(
+                                op="Slice",
+                                inputs=[
+                                    node1.inputs[0],
+                                    Constant(
+                                        f"SliceStart_123{gather_idx}",
+                                        np.array([index]),
+                                    ),
+                                    Constant(
+                                        f"SliceEnd_123{gather_idx}",
+                                        np.array([index + 1]),
+                                    ),
+                                    Constant(
+                                        f"SliceAxis_123{gather_idx}",
+                                        np.array([axis]),
+                                    ),
+                                ],
+                                outputs=[slice_link],
+                                name=f"Slice_123{gather_idx}",
+                            )
+                            graph.nodes.append(slice_node)
+                            gather_idx += 1
+                            
+                            squeeze_link = Variable(
+                                f"onnx::Squeeze_123{squeeze_idx}",
+                                dtype=node1.outputs[0].dtype,
+                                shape=node1.outputs[0].shape,
+                            )
+                            squeeze_node = Node(
+                                op="Squeeze",
+                                inputs=[slice_link, Constant(f"SqueezeAxis_123{squeeze_idx}",np.array([0]),)],
+                                outputs=[squeeze_link],
+                                name=f"Squeeze_123{squeeze_idx}",
+                            )
+                            graph.nodes.append(squeeze_node)
+                            squeeze_idx += 1
+                            for node2 in node2s:
+                                node2.inputs[0] = squeeze_link
+            elif node.op == "BatchNormalization":
+                if node.attrs.get("training_mode") == 1:
+                    node.attrs["training_mode"] = 0
+                    node.outputs = node.outputs[:1]
 
     graph.cleanup(remove_unused_node_outputs=True, recurse_subgraphs=True, recurse_functions=True)
     graph.toposort()
@@ -170,12 +225,19 @@ def onnx_make_fixed(input_path: str, output_path: str, input_shape: tuple[int, .
     simplified, success = onnxsim.simplify(input_path)
     if not success:
         raise RuntimeError(f"Failed to simplify {input_path}")
+    try:
+        onnx.save(simplified, output_path)
+    except:
         onnx.save(simplified, output_path, save_as_external_data=True, all_tensors_to_one_file=False)
     infer_shapes_path(output_path, check_type=True, strict_mode=True, data_prop=True)
     model = onnx.load_model(output_path)
     make_input_shape_fixed(model.graph, model.graph.input[0].name, input_shape)
     fix_output_shapes(model)
+    try:
+        onnx.save(model, output_path)
+    except:
         onnx.save(model, output_path, save_as_external_data=True, all_tensors_to_one_file=False)
+    onnx.save(model, output_path)
     infer_shapes_path(output_path, check_type=True, strict_mode=True, data_prop=True)
 
 
@@ -192,7 +254,6 @@ class ExportBase:
         super().__init__()
         self.name = name
         self.optimize = optimization_level
-        self.nchw_transpose = False
         self.input_shape = input_shape
         self.pretrained = pretrained
         self.cache_dir = os.path.join(os.environ["CACHE_DIR"], self.model_name)
@@ -213,7 +274,7 @@ class ExportBase:
         if not os.path.isfile(static_path):
             print(f"Making {self.model_name} ({self.task}) static")
             onnx_make_fixed(onnx_path_original, static_path, self.input_shape)
-            onnx_transpose_4d(static_path)
+            make_onnx_armnn_compatible(static_path)
         static_model = onnx.load_model(static_path)
         self.inputs = [input_.name for input_ in static_model.graph.input]
         self.outputs = [output_.name for output_ in static_model.graph.output]
@@ -247,10 +308,10 @@ class ExportBase:
         armnn_fp16 = os.path.join(fp16_dir, "model.armnn")
         
         args = ["./armnnconverter", "-f", "tflite-binary"]
-        for input_ in self.inputs:
+        args.append("-i")
-            args.extend(["-i", input_])
+        args.extend(self.inputs)
-        for output_ in self.outputs:
+        args.append("-o")
-            args.extend(["-o", output_])
+        args.extend(self.outputs)
             
         fp32_args = args.copy()
         fp32_args.extend(["-m", tflite_fp32, "-p", armnn_fp32])
@@ -320,32 +381,28 @@ def main() -> None:
     failed: list[Callable[[], ExportBase]] = [
         lambda: OpenClipVisual("ViT-H-14-378-quickgelu", (1, 3, 378, 378), pretrained="dfn5b"), # flatbuffers: cannot grow buffer beyond 2 gigabytes (will probably work with fp16)
         lambda: OpenClipVisual("ViT-H-14-quickgelu", (1, 3, 224, 224), pretrained="dfn5b"), # flatbuffers: cannot grow buffer beyond 2 gigabytes (will probably work with fp16)
-        lambda: OpenClipTextual("nllb-clip-base-siglip", (1, 77), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::logical_not
-        lambda: OpenClipTextual("nllb-clip-large-siglip", (1, 77), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::logical_not
-        lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e31"),
-        lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e31"),
-        lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e32"),
-        lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e32"),
-        lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b82k"),
-        lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion2b-s32b-b82k"),
         lambda: OpenClipVisual("ViT-H-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b79k"),
         lambda: OpenClipTextual("ViT-H-14", (1, 77), pretrained="laion2b-s32b-b79k"),
         lambda: OpenClipVisual("ViT-g-14", (1, 3, 224, 224), pretrained="laion2b-s12b-b42k"),
         lambda: OpenClipTextual("ViT-g-14", (1, 77), pretrained="laion2b-s12b-b42k"),
         lambda: OpenClipVisual("XLM-Roberta-Large-Vit-B-16Plus", (1, 3, 240, 240)),
         lambda: OpenClipVisual("XLM-Roberta-Large-ViT-H-14", (1, 3, 224, 224), pretrained="frozen_laion5b_s13b_b90k"),
-        lambda: OpenClipVisual("nllb-clip-base-siglip", (1, 3, 384, 384), pretrained="v1"),
-        lambda: OpenClipVisual("nllb-clip-large-siglip", (1, 3, 384, 384), pretrained="v1"),
-        lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="yfcc15m"), # BatchNorm operation with mean/var output is not implemented
-        lambda: OpenClipTextual("RN50", (1, 77), pretrained="yfcc15m"), # BatchNorm operation with mean/var output is not implemented
-        lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="cc12m"), # BatchNorm operation with mean/var output is not implemented
-        lambda: OpenClipTextual("RN50", (1, 77), pretrained="cc12m"), # BatchNorm operation with mean/var output is not implemented
         lambda: MClipTextual("XLM-Roberta-Large-Vit-L-14", (1, 77)), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
         lambda: MClipTextual("XLM-Roberta-Large-Vit-B-16Plus", (1, 77)), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
         lambda: MClipTextual("LABSE-Vit-L-14", (1, 77)), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
         lambda: OpenClipTextual("XLM-Roberta-Large-ViT-H-14", (1, 77), pretrained="frozen_laion5b_s13b_b90k"), # Expected normalized_shape to be at least 1-dimensional, i.e., containing at least one element, but got normalized_shape = []
     ]
     
+    oom = [
+        lambda: OpenClipVisual("nllb-clip-base-siglip", (1, 3, 384, 384), pretrained="v1"),
+        lambda: OpenClipTextual("nllb-clip-base-siglip", (1, 77), pretrained="v1"),
+        lambda: OpenClipVisual("nllb-clip-large-siglip", (1, 3, 384, 384), pretrained="v1"),
+        lambda: OpenClipTextual("nllb-clip-large-siglip", (1, 77), pretrained="v1"), # ERROR (tinynn.converter.base) Unsupported ops: aten::logical_not
+        # lambda: OpenClipTextual("ViT-H-14-quickgelu", (1, 77), pretrained="dfn5b"),
+        # lambda: OpenClipTextual("ViT-H-14-378-quickgelu", (1, 77), pretrained="dfn5b"),
+        # lambda: OpenClipVisual("XLM-Roberta-Large-Vit-L-14", (1, 3, 224, 224)),
+    ]
+    
     succeeded: list[Callable[[], ExportBase]] = [
         # lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="laion2b_e16"),
         # lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="laion2b_e16"),
@@ -363,18 +420,25 @@ def main() -> None:
         # lambda: OpenClipTextual("ViT-B-16-plus-240", (1, 77), pretrained="laion400m_e31"),
         # lambda: OpenClipVisual("ViT-B-32", (1, 3, 224, 224), pretrained="openai"),
         # lambda: OpenClipTextual("ViT-B-32", (1, 77), pretrained="openai"),
-        lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="openai"),
+        # lambda: OpenClipVisual("ViT-B-16", (1, 3, 224, 224), pretrained="openai"),
-        lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="openai"),
+        # lambda: OpenClipTextual("ViT-B-16", (1, 77), pretrained="openai"),
-        # lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="openai"),
-        # lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="openai"),
-        # lambda: OpenClipVisual("ViT-L-14-336", (1, 3, 336, 336), pretrained="openai"),
-        # lambda: OpenClipTextual("ViT-L-14-336", (1, 77), pretrained="openai"),
         # lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="openai"),
         # lambda: OpenClipTextual("RN50", (1, 77), pretrained="openai"),
-        # lambda: OpenClipTextual("ViT-H-14-quickgelu", (1, 77), pretrained="dfn5b"),
+        # lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="yfcc15m"),
-        # lambda: OpenClipTextual("ViT-H-14-378-quickgelu", (1, 77), pretrained="dfn5b"),
+        # lambda: OpenClipTextual("RN50", (1, 77), pretrained="yfcc15m"),
-        # lambda: OpenClipVisual("XLM-Roberta-Large-Vit-L-14", (1, 3, 224, 224)),
+        # lambda: OpenClipVisual("RN50", (1, 3, 224, 224), pretrained="cc12m"),
+        # lambda: OpenClipTextual("RN50", (1, 77), pretrained="cc12m"),
         # lambda: OpenClipVisual("XLM-Roberta-Large-Vit-B-32", (1, 3, 224, 224)),
+        # lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="openai"),
+        # lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="openai"),
+        lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e31"),
+        lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e31"),
+        lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion400m_e32"),
+        lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion400m_e32"),
+        lambda: OpenClipVisual("ViT-L-14", (1, 3, 224, 224), pretrained="laion2b-s32b-b82k"),
+        lambda: OpenClipTextual("ViT-L-14", (1, 77), pretrained="laion2b-s32b-b82k"),
+        # lambda: OpenClipVisual("ViT-L-14-336", (1, 3, 336, 336), pretrained="openai"),
+        # lambda: OpenClipTextual("ViT-L-14-336", (1, 77), pretrained="openai"),
         # lambda: ArcFace("buffalo_s", (1, 3, 112, 112), optimization_level=3),
         # lambda: RetinaFace("buffalo_s", (1, 3, 640, 640), optimization_level=3),
         # lambda: ArcFace("buffalo_m", (1, 3, 112, 112), optimization_level=3),