Import oneflow as torch (#6076)

* add oneflow_pytorch_compatiblity_test

* add compatiblity test

* align init model

* add alexnet

* fix comments

* restruct code structure

* add resnet50 and restruct structure

* Delete loss_compare.png

* fix comment

* make dataset and modelzoo read only

* fix comments

* refine

* fix bug

* fix comments

* auto format by CI

* fix ci error

Co-authored-by: tsai <jackalcooper@gmail.com>
Co-authored-by: oneflow-ci-bot <69100618+oneflow-ci-bot@users.noreply.github.com>
Co-authored-by: oneflow-ci-bot <ci-bot@oneflow.org>

.github/workflows/test.yml

@@ -286,7 +286,7 @@ jobs:
         if: matrix.test_suite == 'cpu' && env.is_built != '1' && needs.changed_files.outputs.should_run_single_client_tests == '1'
         run: |
           set -x
-          docker run --shm-size=8g --rm -w $PWD -v $PWD:$PWD -v /dataset:/dataset -v /model_zoo:/model_zoo \
+          docker run --shm-size=8g --rm -w $PWD -v $PWD:$PWD -v /dataset:/dataset:ro -v /model_zoo:/model_zoo:ro \
             ${{ env.extra_docker_args }} ${{ env.pip_cache_docker_args }} \
             -v ${wheelhouse_dir}:${wheelhouse_dir} --env ONEFLOW_WHEEL_PATH=${wheelhouse_dir} \
             oneflow-manylinux2014-cuda10.2:0.1 \
@@ -407,7 +407,7 @@ jobs:
           echo "container_name=${container_name}" >> $GITHUB_ENV
 
           extra_docker_args+=" --shm-size=8g --rm -w $PWD -v $PWD:$PWD"
-          extra_docker_args+=" -v /dataset:/dataset -v /model_zoo:/model_zoo"
+          extra_docker_args+=" -v /dataset:/dataset:ro -v /model_zoo:/model_zoo:ro"
 
 
           oneflow_test_cache_dir="$HOME/ci-cache/${{ github.repository }}/test_cache"

python/oneflow/test/modules/_internally_replaced_utils.py

@@ -0,0 +1,70 @@
+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+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.
+"""
+import os
+import importlib.machinery
+
+
+def _download_file_from_remote_location(fpath: str, url: str) -> None:
+    pass
+
+
+def _is_remote_location_available() -> bool:
+    return False
+
+
+try:
+    from torch.hub import load_state_dict_from_url
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url
+
+
+def _get_extension_path(lib_name):
+
+    lib_dir = os.path.dirname(__file__)
+    if os.name == "nt":
+        # Register the main torchvision library location on the default DLL path
+        import ctypes
+        import sys
+
+        kernel32 = ctypes.WinDLL("kernel32.dll", use_last_error=True)
+        with_load_library_flags = hasattr(kernel32, "AddDllDirectory")
+        prev_error_mode = kernel32.SetErrorMode(0x0001)
+
+        if with_load_library_flags:
+            kernel32.AddDllDirectory.restype = ctypes.c_void_p
+
+        if sys.version_info >= (3, 8):
+            os.add_dll_directory(lib_dir)
+        elif with_load_library_flags:
+            res = kernel32.AddDllDirectory(lib_dir)
+            if res is None:
+                err = ctypes.WinError(ctypes.get_last_error())
+                err.strerror += f' Error adding "{lib_dir}" to the DLL directories.'
+                raise err
+
+        kernel32.SetErrorMode(prev_error_mode)
+
+    loader_details = (
+        importlib.machinery.ExtensionFileLoader,
+        importlib.machinery.EXTENSION_SUFFIXES,
+    )
+
+    extfinder = importlib.machinery.FileFinder(lib_dir, loader_details)
+    ext_specs = extfinder.find_spec(lib_name)
+    if ext_specs is None:
+        raise ImportError
+
+    return ext_specs.origin

python/oneflow/test/modules/pytorch_alexnet.py

@@ -0,0 +1,79 @@
+"""
+Copyright 2020 The OneFlow Authors. All rights reserved.
+
+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.
+"""
+import torch
+import torch.nn as nn
+from _internally_replaced_utils import load_state_dict_from_url
+from typing import Any
+
+
+__all__ = ["AlexNet", "alexnet"]
+
+
+model_urls = {
+    "alexnet": "https://download.pytorch.org/models/alexnet-owt-7be5be79.pth",
+}
+
+
+class AlexNet(nn.Module):
+    def __init__(self, num_classes: int = 1000) -> None:
+        super(AlexNet, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(64, 192, kernel_size=5, padding=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+            nn.Conv2d(192, 384, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(384, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2),
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(256 * 6 * 6, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(4096, 4096),
+            nn.ReLU(inplace=True),
+            nn.Linear(4096, num_classes),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.features(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.classifier(x)
+        return x
+
+
+def alexnet(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> AlexNet:
+    r"""AlexNet model architecture from the
+    `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
+    The required minimum input size of the model is 63x63.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    model = AlexNet(**kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(model_urls["alexnet"], progress=progress)
+        model.load_state_dict(state_dict)
+    return model