def test_infer(self):
# Infer over an image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
tensor = im2tensor(image)
self.assertEqual(tensor.ndim, 4)
for model_name in supported_tv_models:
model = cnn.create_vision_cnn(model_name, 10, pretrained=None)
model = model.eval()
out = model(tensor)
self.assertEqual(out.shape[1], 10)
self.assertEqual(out.ndim, 2)
def test_train(self):
# Read Image using PIL Here
# Do forward over image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
tensor = im2tensor(image)
self.assertEqual(tensor.ndim, 4)
for model_name in supported_tv_models:
model = cnn.create_vision_cnn(model_name, 10, pretrained=None)
out = model(tensor)
self.assertEqual(out.shape[1], 10)
self.assertEqual(out.ndim, 2)
def test_train(self):
# Read Image using PIL Here
# Do forward over image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
img_tensor = im2tensor(image)
self.assertEqual(img_tensor.ndim, 4)
# Detr Input format is (xc, yc, w, h) Normalized to the image.
boxes = torch.tensor([[0, 0, 100, 100], [0, 1, 2, 2],
[10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
labels = torch.tensor([1, 2, 3, 4], dtype=torch.int64)
targets = [{"boxes": boxes, "labels": labels}]
return True
def test_infer(self):
# Infer over an image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
tensor = im2tensor(image)
self.assertEqual(tensor.ndim, 4)
frcnn_model = faster_rcnn.create_vision_fastercnn()
frcnn_model.eval()
out = frcnn_model(tensor)
self.assertIsInstance(out, list)
self.assertIsInstance(out[0], Dict)
self.assertIsInstance(out[0]["boxes"], torch.Tensor)
self.assertIsInstance(out[0]["labels"], torch.Tensor)
self.assertIsInstance(out[0]["scores"], torch.Tensor)
def test_infer(self):
# Infer over an image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
tensor = im2tensor(image)
self.assertEqual(tensor.ndim, 4)
retina_model = retinanet.create_retinanet()
retina_model = retina_model.cpu()
retina_model.eval()
out = retina_model(tensor)
self.assertIsInstance(out, list)
self.assertIsInstance(out[0], Dict)
self.assertIsInstance(out[0]["boxes"], torch.Tensor)
self.assertIsInstance(out[0]["labels"], torch.Tensor)
self.assertIsInstance(out[0]["scores"], torch.Tensor)
def test_train(self):
# Read Image using PIL Here
# Do forward over image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
img_tensor = im2tensor(image)
self.assertEqual(img_tensor.ndim, 4)
boxes = torch.tensor([[0, 0, 100, 100], [0, 1, 2, 2], [10, 15, 30, 35],
[23, 35, 93, 95]],
dtype=torch.float)
labels = torch.tensor([1, 2, 3, 4], dtype=torch.int64)
targets = [{"boxes": boxes, "labels": labels}]
retina_model = retinanet.create_vision_retinanet(num_classes=5)
out = retina_model(img_tensor, targets)
self.assertIsInstance(out, Dict)
self.assertIsInstance(out["classification"], torch.Tensor)
self.assertIsInstance(out["bbox_regression"], torch.Tensor)
def test_train(self):
# Read Image using PIL Here
# Do forward over image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
img_tensor = im2tensor(image)
self.assertEqual(img_tensor.ndim, 4)
boxes = torch.tensor([[0, 0, 100, 100], [0, 1, 2, 2],
[10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
labels = torch.tensor([1, 2, 3, 4], dtype=torch.int64)
targets = [{"boxes": boxes, "labels": labels}]
frcnn_model = faster_rcnn.create_vision_fastercnn(num_classes=5)
out = frcnn_model(img_tensor, targets)
self.assertIsInstance(out, Dict)
self.assertIsInstance(out["loss_classifier"], torch.Tensor)
self.assertIsInstance(out["loss_box_reg"], torch.Tensor)
self.assertIsInstance(out["loss_objectness"], torch.Tensor)
self.assertIsInstance(out["loss_rpn_box_reg"], torch.Tensor)
def test_infer(self):
# Infer over an image
image = Image.open("tests/assets/grace_hopper_517x606.jpg")
tensor = im2tensor(image)
self.assertEqual(tensor.ndim, 4)
return True