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Fixes types annotation #3059

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Merged
merged 8 commits into from
Nov 30, 2020
Merged

Fixes types annotation #3059

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9a7dfed
Correcting incorrect types
zhiqwang Nov 30, 2020
7f660c0
Add missing type statement
zhiqwang Nov 30, 2020
ee922e6
Fix type annotations in unittest
zhiqwang Nov 30, 2020
825f776
Fix TypeError
zhiqwang Nov 30, 2020
0f6e35d
Fix TypeError
zhiqwang Nov 30, 2020
f5f9aef
Fix type equality judgment
zhiqwang Nov 30, 2020
25cb032
Fix recursive compile
zhiqwang Nov 30, 2020
ed80970
Use string for class name annotation.
datumbox Nov 30, 2020
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24 changes: 12 additions & 12 deletions test/test_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,16 +70,16 @@ def _test_classification_model(self, name, input_shape, dev):
# RNG always on CPU, to ensure x in cuda tests is bitwise identical to x in cpu tests
x = torch.rand(input_shape).to(device=dev)
out = model(x)
self.assertExpected(out.cpu(), prec=0.1, strip_suffix="_" + dev)
self.assertExpected(out.cpu(), prec=0.1, strip_suffix=f"_{dev}")
self.assertEqual(out.shape[-1], 50)
self.check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(name, None))

if dev == "cuda":
if dev == torch.device("cuda"):
with torch.cuda.amp.autocast():
out = model(x)
# See autocast_flaky_numerics comment at top of file.
if name not in autocast_flaky_numerics:
self.assertExpected(out.cpu(), prec=0.1, strip_suffix="_" + dev)
self.assertExpected(out.cpu(), prec=0.1, strip_suffix=f"_{dev}")
self.assertEqual(out.shape[-1], 50)

def _test_segmentation_model(self, name, dev):
Expand All @@ -94,7 +94,7 @@ def _test_segmentation_model(self, name, dev):
self.assertEqual(tuple(out["out"].shape), (1, 50, 300, 300))
self.check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(name, None))

if dev == "cuda":
if dev == torch.device("cuda"):
with torch.cuda.amp.autocast():
out = model(x)
self.assertEqual(tuple(out["out"].shape), (1, 50, 300, 300))
Expand Down Expand Up @@ -143,7 +143,7 @@ def compute_mean_std(tensor):

output = map_nested_tensor_object(out, tensor_map_fn=compact)
prec = 0.01
strip_suffix = "_" + dev
strip_suffix = f"_{dev}"
try:
# We first try to assert the entire output if possible. This is not
# only the best way to assert results but also handles the cases
Expand All @@ -169,7 +169,7 @@ def compute_mean_std(tensor):
full_validation = check_out(out)
self.check_jit_scriptable(model, ([x],), unwrapper=script_model_unwrapper.get(name, None))

if dev == "cuda":
if dev == torch.device("cuda"):
with torch.cuda.amp.autocast():
out = model(model_input)
# See autocast_flaky_numerics comment at top of file.
Expand Down Expand Up @@ -220,7 +220,7 @@ def _test_video_model(self, name, dev):
self.check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(name, None))
self.assertEqual(out.shape[-1], 50)

if dev == "cuda":
if dev == torch.device("cuda"):
with torch.cuda.amp.autocast():
out = model(x)
self.assertEqual(out.shape[-1], 50)
Expand Down Expand Up @@ -380,7 +380,7 @@ def test_generalizedrcnn_transform_repr(self):
self.assertEqual(t.__repr__(), expected_string)


_devs = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"]
_devs = [torch.device("cpu"), torch.device("cuda")] if torch.cuda.is_available() else [torch.device("cpu")]


for model_name in get_available_classification_models():
Expand All @@ -393,7 +393,7 @@ def do_test(self, model_name=model_name, dev=dev):
input_shape = (1, 3, 299, 299)
self._test_classification_model(model_name, input_shape, dev)

setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)
setattr(ModelTester, f"test_{model_name}_{dev}", do_test)


for model_name in get_available_segmentation_models():
Expand All @@ -403,7 +403,7 @@ def do_test(self, model_name=model_name, dev=dev):
def do_test(self, model_name=model_name, dev=dev):
self._test_segmentation_model(model_name, dev)

setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)
setattr(ModelTester, f"test_{model_name}_{dev}", do_test)


for model_name in get_available_detection_models():
Expand All @@ -413,7 +413,7 @@ def do_test(self, model_name=model_name, dev=dev):
def do_test(self, model_name=model_name, dev=dev):
self._test_detection_model(model_name, dev)

setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)
setattr(ModelTester, f"test_{model_name}_{dev}", do_test)

def do_validation_test(self, model_name=model_name):
self._test_detection_model_validation(model_name)
Expand All @@ -426,7 +426,7 @@ def do_validation_test(self, model_name=model_name):
def do_test(self, model_name=model_name, dev=dev):
self._test_video_model(model_name, dev)

setattr(ModelTester, "test_" + model_name + "_" + dev, do_test)
setattr(ModelTester, f"test_{model_name}_{dev}", do_test)

if __name__ == '__main__':
unittest.main()
6 changes: 2 additions & 4 deletions torchvision/models/detection/image_list.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,7 @@ class ImageList(object):
and storing in a field the original sizes of each image
"""

def __init__(self, tensors, image_sizes):
# type: (Tensor, List[Tuple[int, int]]) -> None
def __init__(self, tensors: Tensor, image_sizes: List[Tuple[int, int]]):
"""
Arguments:
tensors (tensor)
Expand All @@ -22,7 +21,6 @@ def __init__(self, tensors, image_sizes):
self.tensors = tensors
self.image_sizes = image_sizes

def to(self, device):
# type: (Device) -> ImageList # noqa
def to(self, device: torch.device) -> 'ImageList':
cast_tensor = self.tensors.to(device)
return ImageList(cast_tensor, self.image_sizes)
2 changes: 1 addition & 1 deletion torchvision/models/detection/retinanet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import torch
import torch.nn as nn
from torch import Tensor
from torch.jit.annotations import Dict, List, Tuple
from torch.jit.annotations import Dict, List, Tuple, Optional
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nit: is this needed?

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It is used in here

vision/torchvision/models/detection/retinanet.py

Line 459 in 8e24479

# type: (List[Tensor], Optional[List[Dict[str, Tensor]]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]

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Ok. Let's keep this here then, as when we move the # type: annotations to be inline the import will be necessary


from ._utils import overwrite_eps
from ..utils import load_state_dict_from_url
Expand Down