Remove fasterrcnn_mobilenet_v3_large prototype and update expected file.

Author: datumbox

test/expect/ModelTester.test_fasterrcnn_mobilenet_v3_large_expect.pkl

@@ -37,7 +37,6 @@ def get_available_video_models():
     'googlenet': lambda x: x.logits,
     'inception_v3': lambda x: x.logits,
     "fasterrcnn_resnet50_fpn": lambda x: x[1],
-    "fasterrcnn_mobilenet_v3_large": lambda x: x[1],
     "fasterrcnn_mobilenet_v3_large_fpn": lambda x: x[1],
     "maskrcnn_resnet50_fpn": lambda x: x[1],
     "keypointrcnn_resnet50_fpn": lambda x: x[1],

test/expect/ModelTester.test_fasterrcnn_mobilenet_v3_large_fpn_expect.pkl

@@ -97,7 +97,7 @@ def test_assign_targets_to_proposals(self):
         self.assertEqual(labels[0].dtype, torch.int64)
 
     def test_forward_negative_sample_frcnn(self):
-        for name in ["fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large", "fasterrcnn_mobilenet_v3_large_fpn"]:
+        for name in ["fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large_fpn"]:
             model = torchvision.models.detection.__dict__[name](
                 num_classes=2, min_size=100, max_size=100)
 

test/test_models.py

@@ -1,10 +1,7 @@
-from collections import OrderedDict
-
 import torch
 from torch import nn
 import torch.nn.functional as F
 
-from torchvision.ops import misc as misc_nn_ops
 from torchvision.ops import MultiScaleRoIAlign
 
 from ._utils import overwrite_eps
@@ -19,7 +16,7 @@
 
 
 __all__ = [
-    "FasterRCNN", "fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large", "fasterrcnn_mobilenet_v3_large_fpn"
+    "FasterRCNN", "fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large_fpn"
 ]
 
 
@@ -291,8 +288,7 @@ def forward(self, x):
 model_urls = {
     'fasterrcnn_resnet50_fpn_coco':
         'https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth',
-    'fasterrcnn_mobilenet_v3_large_coco': None,
-    'fasterrcnn_mobilenet_v3_large_fpn_coco': None,
+    'fasterrcnn_mobilenet_v3_large_fpn_coco': None,  # TODO: Add the final model url
 }
 
 
@@ -371,48 +367,6 @@ def fasterrcnn_resnet50_fpn(pretrained=False, progress=True,
     return model
 
 
-def fasterrcnn_mobilenet_v3_large(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True,
-                                  trainable_backbone_layers=None, min_size=320, max_size=640, **kwargs):
-    """
-    Constructs a Faster R-CNN model with a MobileNetV3-Large backbone. It works similarly
-    to Faster R-CNN with ResNet-50 FPN backbone. See `fasterrcnn_resnet50_fpn` for more details.
-
-    Example::
-
-        >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large(pretrained=True)
-        >>> model.eval()
-        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
-        >>> predictions = model(x)
-
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on COCO train2017
-        progress (bool): If True, displays a progress bar of the download to stderr
-        num_classes (int): number of output classes of the model (including the background)
-        pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
-        trainable_backbone_layers (int): number of trainable (not frozen) resnet layers starting from final block.
-            Valid values are between 0 and 6, with 6 meaning all backbone layers are trainable.
-        min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
-        max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
-    """
-    trainable_backbone_layers = _validate_trainable_layers(
-        pretrained or pretrained_backbone, trainable_backbone_layers, 6, 3)
-
-    if pretrained:
-        pretrained_backbone = False
-    backbone = mobilenet_backbone("mobilenet_v3_large", pretrained_backbone, False,
-                                  trainable_layers=trainable_backbone_layers)
-
-    anchor_sizes = ((32, 64, 128, 256, 512, ), )
-    aspect_ratios = ((0.5, 1.0, 2.0), )
-
-    model = FasterRCNN(backbone, num_classes, rpn_anchor_generator=AnchorGenerator(anchor_sizes, aspect_ratios),
-                       min_size=min_size, max_size=max_size, **kwargs)
-    if pretrained:
-        state_dict = load_state_dict_from_url(model_urls['fasterrcnn_mobilenet_v3_large_coco'], progress=progress)
-        model.load_state_dict(state_dict)
-    return model
-
-
 def fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, progress=True, num_classes=91, pretrained_backbone=True,
                                       trainable_backbone_layers=None, min_size=320, max_size=640, rpn_score_thresh=0.05,
                                       **kwargs):