Porting docs, examples, tutorials and galleries

android/test_app/make_assets.py

@@ -1,11 +1,18 @@
 import torch
-import torchvision
 from torch.utils.mobile_optimizer import optimize_for_mobile
+from torchvision.models.detection import (
+    fasterrcnn_mobilenet_v3_large_320_fpn,
+    FasterRCNN_MobileNet_V3_Large_320_FPN_Weights,
+)
 
 print(torch.__version__)
 
-model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(
-    pretrained=True, box_score_thresh=0.7, rpn_post_nms_top_n_test=100, rpn_score_thresh=0.4, rpn_pre_nms_top_n_test=150
+model = fasterrcnn_mobilenet_v3_large_320_fpn(
+    weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT,
+    box_score_thresh=0.7,
+    rpn_post_nms_top_n_test=100,
+    rpn_score_thresh=0.4,
+    rpn_pre_nms_top_n_test=150,
 )
 
 model.eval()

examples/cpp/hello_world/trace_model.py

@@ -6,7 +6,7 @@
 HERE = osp.dirname(osp.abspath(__file__))
 ASSETS = osp.dirname(osp.dirname(HERE))
 
-model = torchvision.models.resnet18(pretrained=False)
+model = torchvision.models.resnet18()
 model.eval()
 
 traced_model = torch.jit.script(model)

gallery/plot_optical_flow.py

@@ -19,7 +19,6 @@
 import torch
 import matplotlib.pyplot as plt
 import torchvision.transforms.functional as F
-import torchvision.transforms as T
 
 
 plt.rcParams["savefig.bbox"] = "tight"
@@ -88,24 +87,19 @@ def plot(imgs, **imshow_kwargs):
 # reduce the image sizes for the example to run faster. Image dimension must be
 # divisible by 8.
 
+from torchvision.models.optical_flow import Raft_Large_Weights
 
-def preprocess(batch):
-    transforms = T.Compose(
-        [
-            T.ConvertImageDtype(torch.float32),
-            T.Normalize(mean=0.5, std=0.5),  # map [0, 1] into [-1, 1]
-            T.Resize(size=(520, 960)),
-        ]
-    )
-    batch = transforms(batch)
-    return batch
+weights = Raft_Large_Weights.DEFAULT
+transforms = weights.transforms()
 
 
-# If you can, run this example on a GPU, it will be a lot faster.
-device = "cuda" if torch.cuda.is_available() else "cpu"
+def preprocess(img1_batch, img2_batch):
+    batch1 = F.resize(img1_batch, size=[520, 960])
+    batch2 = F.resize(img2_batch, size=[520, 960])
+    return transforms(img1_batch, img2_batch)
+
 
-img1_batch = preprocess(img1_batch).to(device)
-img2_batch = preprocess(img2_batch).to(device)
+img1_batch, img2_batch = preprocess(img1_batch, img2_batch)
 
 print(f"shape = {img1_batch.shape}, dtype = {img1_batch.dtype}")
 
@@ -121,7 +115,10 @@ def preprocess(batch):
 
 from torchvision.models.optical_flow import raft_large
 
-model = raft_large(pretrained=True, progress=False).to(device)
+# If you can, run this example on a GPU, it will be a lot faster.
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = raft_large(weights=Raft_Large_Weights.DEFAULT, progress=False).to(device)
 model = model.eval()
 
 list_of_flows = model(img1_batch.to(device), img2_batch.to(device))

gallery/plot_repurposing_annotations.py

@@ -139,12 +139,14 @@ def show(imgs):
 # Here is demo with a Faster R-CNN model loaded from
 # :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn`
 
-from torchvision.models.detection import fasterrcnn_resnet50_fpn
+from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_Weights
 
-model = fasterrcnn_resnet50_fpn(pretrained=True, progress=False)
+weights = FasterRCNN_ResNet50_FPN_Weights.DEFAULT
+model = fasterrcnn_resnet50_fpn(weights=weights, progress=False)
 print(img.size())
 
-img = F.convert_image_dtype(img, torch.float)
+tranforms = weights.transforms()
+img = tranforms(img)
 target = {}
 target["boxes"] = boxes
 target["labels"] = labels = torch.ones((masks.size(0),), dtype=torch.int64)

gallery/plot_scripted_tensor_transforms.py

@@ -85,20 +85,16 @@ def show(imgs):
 # Let's define a ``Predictor`` module that transforms the input tensor and then
 # applies an ImageNet model on it.
 
-from torchvision.models import resnet18
+from torchvision.models import resnet18, ResNet18_Weights
 
 
 class Predictor(nn.Module):
 
     def __init__(self):
         super().__init__()
-        self.resnet18 = resnet18(pretrained=True, progress=False).eval()
-        self.transforms = nn.Sequential(
-            T.Resize([256, ]),  # We use single int value inside a list due to torchscript type restrictions
-            T.CenterCrop(224),
-            T.ConvertImageDtype(torch.float),
-            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-        )
+        weights = ResNet18_Weights.DEFAULT
+        self.resnet18 = resnet18(weights=weights, progress=False).eval()
+        self.transforms = weights.transforms()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         with torch.no_grad():

gallery/plot_visualization_utils.py

@@ -73,14 +73,17 @@ def show(imgs):
 # :func:`~torchvision.models.detection.ssd300_vgg16`. For more details
 # on the output of such models, you may refer to :ref:`instance_seg_output`.
 
-from torchvision.models.detection import fasterrcnn_resnet50_fpn
-from torchvision.transforms.functional import convert_image_dtype
+from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_Weights
 
 
 batch_int = torch.stack([dog1_int, dog2_int])
-batch = convert_image_dtype(batch_int, dtype=torch.float)
 
-model = fasterrcnn_resnet50_fpn(pretrained=True, progress=False)
+weights = FasterRCNN_ResNet50_FPN_Weights.DEFAULT
+transforms = weights.transforms()
+
+batch = transforms(batch_int)
+
+model = fasterrcnn_resnet50_fpn(weights=weights, progress=False)
 model = model.eval()
 
 outputs = model(batch)
@@ -120,13 +123,15 @@ def show(imgs):
 # images must be normalized before they're passed to a semantic segmentation
 # model.
 
-from torchvision.models.segmentation import fcn_resnet50
+from torchvision.models.segmentation import fcn_resnet50, FCN_ResNet50_Weights
 
+weights = FCN_ResNet50_Weights.DEFAULT
+transforms = weights.transforms()
 
-model = fcn_resnet50(pretrained=True, progress=False)
+model = fcn_resnet50(weights=weights, progress=False)
 model = model.eval()
 
-normalized_batch = F.normalize(batch, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+normalized_batch = transforms(batch)
 output = model(normalized_batch)['out']
 print(output.shape, output.min().item(), output.max().item())
 
@@ -262,8 +267,14 @@ def show(imgs):
 #     of them may not have masks, like
 #     :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn`.
 
-from torchvision.models.detection import maskrcnn_resnet50_fpn
-model = maskrcnn_resnet50_fpn(pretrained=True, progress=False)
+from torchvision.models.detection import maskrcnn_resnet50_fpn, MaskRCNN_ResNet50_FPN_Weights
+
+weights = MaskRCNN_ResNet50_FPN_Weights.DEFAULT
+transforms = weights.transforms()
+
+batch = transforms(batch_int)
+
+model = maskrcnn_resnet50_fpn(weights=weights, progress=False)
 model = model.eval()
 
 output = model(batch)
@@ -378,13 +389,17 @@ def show(imgs):
 # Note that the keypoint detection model does not need normalized images.
 #
 
-from torchvision.models.detection import keypointrcnn_resnet50_fpn
+from torchvision.models.detection import keypointrcnn_resnet50_fpn, KeypointRCNN_ResNet50_FPN_Weights
 from torchvision.io import read_image
 
 person_int = read_image(str(Path("assets") / "person1.jpg"))
-person_float = convert_image_dtype(person_int, dtype=torch.float)
 
-model = keypointrcnn_resnet50_fpn(pretrained=True, progress=False)
+weights = KeypointRCNN_ResNet50_FPN_Weights.DEFAULT
+transforms = weights.transforms()
+
+person_float = transforms(person_int)
+
+model = keypointrcnn_resnet50_fpn(weights=weights, progress=False)
 model = model.eval()
 
 outputs = model([person_float])

ios/VisionTestApp/make_assets.py

@@ -1,11 +1,18 @@
 import torch
-import torchvision
 from torch.utils.mobile_optimizer import optimize_for_mobile
+from torchvision.models.detection import (
+    fasterrcnn_mobilenet_v3_large_320_fpn,
+    FasterRCNN_MobileNet_V3_Large_320_FPN_Weights,
+)
 
 print(torch.__version__)
 
-model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(
-    pretrained=True, box_score_thresh=0.7, rpn_post_nms_top_n_test=100, rpn_score_thresh=0.4, rpn_pre_nms_top_n_test=150
+model = fasterrcnn_mobilenet_v3_large_320_fpn(
+    weights=FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.DEFAULT,
+    box_score_thresh=0.7,
+    rpn_post_nms_top_n_test=100,
+    rpn_score_thresh=0.4,
+    rpn_pre_nms_top_n_test=150,
 )
 
 model.eval()

test/tracing/frcnn/trace_model.py

@@ -6,7 +6,7 @@
 HERE = osp.dirname(osp.abspath(__file__))
 ASSETS = osp.dirname(osp.dirname(HERE))
 
-model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False)
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn()
 model.eval()
 
 traced_model = torch.jit.script(model)

torchvision/transforms/_presets.py

@@ -107,7 +107,7 @@ def forward(self, img: Tensor, target: Optional[Tensor] = None) -> Tuple[Tensor,
 
 class OpticalFlowEval(nn.Module):
     def forward(
-        self, img1: Tensor, img2: Tensor, flow: Optional[Tensor], valid_flow_mask: Optional[Tensor]
+        self, img1: Tensor, img2: Tensor, flow: Optional[Tensor] = None, valid_flow_mask: Optional[Tensor] = None
     ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]:
 
         img1, img2, flow, valid_flow_mask = self._pil_or_numpy_to_tensor(img1, img2, flow, valid_flow_mask)