Use PyTorch 1.7 (openvinotoolkit#223)

README.md

@@ -78,7 +78,7 @@ See [third_party_integration](./third_party_integration) for examples of code mo
 - Ubuntu\* 18.04 or later (64-bit)
 - Python\* 3.6 or later
 - NVidia CUDA\* Toolkit 10.2
-- PyTorch\* 1.5.0
+- PyTorch\* 1.7 or later.
 
 ## Installation
 We suggest to install or use the package in the [Python virtual environment](https://docs.python.org/3/tutorial/venv.html).

examples/classification/main.py

@@ -530,7 +530,7 @@ def accuracy(output, target, topk=(1,)):
 
         res = []
         for k in topk:
-            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
             res.append(correct_k.mul_(100.0 / batch_size).item())
         return res
 

examples/common/utils.py

@@ -246,6 +246,7 @@ def is_pretrained_model_requested(config: SampleConfig) -> bool:
 class MockDataset(data.Dataset):
     def __init__(self, img_size: Tuple[int, int] = (32, 32), num_images: int = 1000,
                  transform=None):
+        super().__init__()
         self._img_size = img_size
         self._num_images = num_images
         self._transform = transform

examples/object_detection/datasets/coco.py

@@ -77,6 +77,7 @@ class COCODataset(data.Dataset):
 
     def __init__(self, annotation_file, images_folder, transform=None, target_transform=None, scale_bboxes=True,
                  return_image_info=False, rgb=True):
+        super().__init__()
         self.rgb = rgb
         self.target_transform = target_transform
         self.return_image_info = return_image_info

examples/object_detection/datasets/voc0712.py

@@ -107,6 +107,7 @@ class VOCDetection(data.Dataset):
     def __init__(self, root, image_sets=(('2007', 'trainval'), ('2012', 'trainval')), transform=None,
                  target_transform=VOCAnnotationTransform(keep_difficult=False), dataset_name='VOC2012',
                  return_image_info=False, rgb=True):
+        super().__init__()
         self.rgb = rgb
         self.root = root
         self.image_set = image_sets

examples/semantic_segmentation/datasets/camvid.py

@@ -71,6 +71,7 @@ def __init__(self,
                  image_set='train',
                  transforms=None,
                  loader=data_utils.pil_loader):
+        super().__init__()
         self.root_dir = root
         self.mode = image_set
         self.transforms = transforms

examples/semantic_segmentation/datasets/cityscapes.py

@@ -85,6 +85,7 @@ def __init__(self,
                  image_set='train',
                  transforms=None,
                  loader=data_utils.pil_loader):
+        super().__init__()
         self.root_dir = root
         self.mode = image_set
         self.transforms = transforms

examples/semantic_segmentation/datasets/mapillary.py

@@ -80,6 +80,7 @@ def __init__(self,
                  image_set='train',
                  transforms=None,
                  loader=data_utils.pil_loader):
+        super().__init__()
         self.root_dir = root
         self.mode = image_set
         self.transforms = transforms

nncf/compression_method_api.py

@@ -243,7 +243,8 @@ def export_model(self, filename, input_names: List[str] = None, output_names: Li
                               filename, input_names=input_names,
                               output_names=output_names,
                               enable_onnx_checker=False,
-                              opset_version=10)
+                              opset_version=10,
+                              training=True)  # Do not fuse Conv+BN in ONNX. May cause dropout nodes to appear in ONNX
         model.forward = original_forward
 
 

nncf/dynamic_graph/trace_tensor.py

@@ -51,6 +51,17 @@ def from_torch_tensor(tensor, tensor_meta: TensorMeta):
         tensor.__class__ = TracedTensor
         return tensor
 
+    def as_subclass(self, cls: 'TracedTensor') -> 'TracedTensor':
+        """Required for PyTorch 1.7.0 compatibility - the handle_torch_function and __torch_function__
+        API in general calls this after a wrapped function call; need to preserve the tensor_meta extensions"""
+
+        return self
+
+    # NOTE: This disables the __torch_function__ API altogether when using NNCF.
+    # TODO: make NNCF utilize the __torch_function__ API instead.
+    #pylint:disable=protected-access
+    __torch_function__ = torch._C._disabled_torch_function_impl
+
 
 def is_iterable(item):
     non_iterable_types = (str, bytes, bytearray, torch.Tensor, np.ndarray)

nncf/utils.py

@@ -11,7 +11,6 @@
  limitations under the License.
 """
 from collections import OrderedDict
-from contextlib import contextmanager
 from typing import Dict, Callable, Any, Mapping, Sequence, Set, List
 
 import numpy as np
@@ -23,6 +22,7 @@
 
 from nncf.dynamic_graph.graph_builder import GraphBuilder, ModelInputInfo, create_dummy_forward_fn
 from nncf.layer_utils import _NNCFModuleMixin
+from contextlib import contextmanager
 
 def scopes_matched(scope_stack_0, scope_stack_1):
     from nncf.layers import NNCF_MODULES_MAP
@@ -198,15 +198,15 @@ def is_tracing_state():
     return torch._C._get_tracing_state()
 
 
-@contextmanager
-def no_jit_trace():
-    # pylint: disable=protected-access
-    disable_tracing = torch.jit._disable_tracing()
-    disable_tracing.__enter__()
-    yield disable_tracing
-    disable_tracing.__exit__()
-
+class no_jit_trace:
+    def __enter__(self):
+        # pylint: disable=protected-access
+        self.state = torch._C._get_tracing_state()
+        torch._C._set_tracing_state(None)
 
+    def __exit__(self, *args):
+        torch._C._set_tracing_state(self.state)
+        self.state = None
 
 
 def sum_like(tensor_to_sum, ref_tensor):

setup.py

@@ -81,8 +81,8 @@ def find_version(*file_paths):
 
 INSTALL_REQUIRES.extend(["torch", "torchvision"])
 
-TORCH_VERSION = "1.5.0"
-TORCHVISION_VERSION = "0.6.0"
+TORCH_VERSION = "1.7.0"
+TORCHVISION_VERSION = "0.8.1"
 CUDA_VERSION = "102"
 IS_CUDA_VER_DEFAULT_FOR_CURRENT_TORCH_VER = True