add regnet_y_128gf factory function

docs/source/models.rst

@@ -76,6 +76,7 @@ You can construct a model with random weights by calling its constructor:
     regnet_y_8gf = models.regnet_y_8gf()
     regnet_y_16gf = models.regnet_y_16gf()
     regnet_y_32gf = models.regnet_y_32gf()
+    regnet_y_128gf = models.regnet_y_128gf()
     regnet_x_400mf = models.regnet_x_400mf()
     regnet_x_800mf = models.regnet_x_800mf()
     regnet_x_1_6gf = models.regnet_x_1_6gf()
@@ -439,6 +440,7 @@ RegNet
     regnet_y_8gf
     regnet_y_16gf
     regnet_y_32gf
+    regnet_y_128gf
     regnet_x_400mf
     regnet_x_800mf
     regnet_x_1_6gf

hubconf.py

@@ -27,6 +27,7 @@
     regnet_y_8gf,
     regnet_y_16gf,
     regnet_y_32gf,
+    regnet_y_128gf,
     regnet_x_400mf,
     regnet_x_800mf,
     regnet_x_1_6gf,

test/test_models.py

@@ -1,13 +1,13 @@
 import contextlib
 import functools
-import io
 import operator
 import os
 import pkgutil
 import sys
 import traceback
 import warnings
 from collections import OrderedDict
+from tempfile import TemporaryDirectory
 
 import pytest
 import torch
@@ -126,16 +126,16 @@ def assert_export_import_module(m, args):
 
         def get_export_import_copy(m):
             """Save and load a TorchScript model"""
-            buffer = io.BytesIO()
-            torch.jit.save(m, buffer)
-            buffer.seek(0)
-            imported = torch.jit.load(buffer)
+            with TemporaryDirectory() as dir:
+                path = os.path.join(dir, "script.pt")
+                m.save(path)
+                imported = torch.jit.load(path)
             return imported
 
         m_import = get_export_import_copy(m)
-        with freeze_rng_state():
+        with torch.no_grad(), freeze_rng_state():
             results = m(*args)
-        with freeze_rng_state():
+        with torch.no_grad(), freeze_rng_state():
             results_from_imported = m_import(*args)
         tol = 3e-4
         torch.testing.assert_close(results, results_from_imported, atol=tol, rtol=tol)
@@ -156,10 +156,10 @@ def get_export_import_copy(m):
 
     sm = torch.jit.script(nn_module)
 
-    with freeze_rng_state():
+    with torch.no_grad(), freeze_rng_state():
         eager_out = nn_module(*args)
 
-    with freeze_rng_state():
+    with torch.no_grad(), freeze_rng_state():
         script_out = sm(*args)
         if unwrapper:
             script_out = unwrapper(script_out)

torchvision/models/regnet.py

@@ -26,6 +26,7 @@
     "regnet_y_8gf",
     "regnet_y_16gf",
     "regnet_y_32gf",
+    "regnet_y_128gf",
     "regnet_x_400mf",
     "regnet_x_800mf",
     "regnet_x_1_6gf",
@@ -505,6 +506,18 @@ def regnet_y_32gf(pretrained: bool = False, progress: bool = True, **kwargs: Any
     return _regnet("regnet_y_32gf", params, pretrained, progress, **kwargs)
 
 
+def regnet_y_128gf(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> RegNet:
+    """
+    Constructs a RegNetY_128GF architecture from
+    `"Designing Network Design Spaces" <https://arxiv.org/abs/2003.13678>`_.
+    NOTE: Pretrained weights are not available for this model.
+    """
+    params = BlockParams.from_init_params(
+        depth=27, w_0=456, w_a=160.83, w_m=2.52, group_width=264, se_ratio=0.25, **kwargs
+    )
+    return _regnet("regnet_y_128gf", params, pretrained, progress, **kwargs)
+
+
 def regnet_x_400mf(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> RegNet:
     """
     Constructs a RegNetX_400MF architecture from

torchvision/prototype/models/regnet.py

@@ -20,6 +20,7 @@
     "RegNet_Y_8GF_Weights",
     "RegNet_Y_16GF_Weights",
     "RegNet_Y_32GF_Weights",
+    "RegNet_Y_128GF_Weights",
     "RegNet_X_400MF_Weights",
     "RegNet_X_800MF_Weights",
     "RegNet_X_1_6GF_Weights",
@@ -34,6 +35,7 @@
     "regnet_y_8gf",
     "regnet_y_16gf",
     "regnet_y_32gf",
+    "regnet_y_128gf",
     "regnet_x_400mf",
     "regnet_x_800mf",
     "regnet_x_1_6gf",
@@ -253,6 +255,11 @@ class RegNet_Y_32GF_Weights(WeightsEnum):
     default = ImageNet1K_V2
 
 
+class RegNet_Y_128GF_Weights(WeightsEnum):
+    # weights are not available yet.
+    pass
+
+
 class RegNet_X_400MF_Weights(WeightsEnum):
     ImageNet1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_400mf-adf1edd5.pth",
@@ -501,6 +508,16 @@ def regnet_y_32gf(*, weights: Optional[RegNet_Y_32GF_Weights] = None, progress:
     return _regnet(params, weights, progress, **kwargs)
 
 
+@handle_legacy_interface(weights=("pretrained", None))
+def regnet_y_128gf(*, weights: Optional[RegNet_Y_128GF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
+    weights = RegNet_Y_128GF_Weights.verify(weights)
+
+    params = BlockParams.from_init_params(
+        depth=27, w_0=456, w_a=160.83, w_m=2.52, group_width=264, se_ratio=0.25, **kwargs
+    )
+    return _regnet(params, weights, progress, **kwargs)
+
+
 @handle_legacy_interface(weights=("pretrained", RegNet_X_400MF_Weights.ImageNet1K_V1))
 def regnet_x_400mf(*, weights: Optional[RegNet_X_400MF_Weights] = None, progress: bool = True, **kwargs: Any) -> RegNet:
     weights = RegNet_X_400MF_Weights.verify(weights)