[GitHub] [incubator-tvm] masahi commented on a change in pull request #6449: [Frontend][Pytorch] Improve Pytorch frontend for object detection models

[GitBox <gi...@apache.org>]

masahi commented on a change in pull request #6449:
URL: https://github.com/apache/incubator-tvm/pull/6449#discussion_r488360061



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File path: tests/python/frontend/pytorch/test_object_detection.py
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@@ -0,0 +1,131 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint: disable=import-self, invalid-name, unused-argument
+"""Test torch vision fasterrcnn and maskrcnn models"""
+import torch
+import torchvision
+import cv2
+
+import tvm
+
+from tvm import relay
+from tvm.runtime.vm import VirtualMachine
+from tvm.contrib.download import download
+
+
+in_size = 512
+
+def process_image(img):
+    img = cv2.imread(img).astype("float32")
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = torch.from_numpy(img/255.).permute(2,0,1).float()
+    img = torch.unsqueeze(img, axis=0)
+
+    return img
+
+
+def do_trace(model, inp, in_size=in_size):
+    model_trace = torch.jit.trace(model, inp)
+    model_trace.eval()
+    return model_trace
+
+
+def dict_to_tuple(out_dict):
+    if "masks" in out_dict.keys():
+        return out_dict["boxes"], out_dict["scores"], out_dict["labels"], out_dict["masks"]
+    return out_dict["boxes"], out_dict["scores"], out_dict["labels"]
+
+
+class TraceWrapper(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, inp):
+        out = self.model(inp)
+        return dict_to_tuple(out[0])
+
+
+def generate_jit_model(index, img):
+    model_funcs = [torchvision.models.detection.fasterrcnn_resnet50_fpn,
+                   torchvision.models.detection.maskrcnn_resnet50_fpn]
+
+    model_func = model_funcs[index]
+    model = TraceWrapper(model_func(pretrained=True))
+
+    model.eval()
+    inp = process_image(img)
+
+    with torch.no_grad():
+        out = model(inp)
+
+        script_module = do_trace(model, inp)
+        script_out = script_module(inp)
+
+        assert len(out[0]) > 0 and len(script_out[0]) > 0
+        torch._C._jit_pass_inline(script_module.graph)

Review comment:
       this can be removed




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