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