def process_image(image_path):
""" Scales, crops, and normalizes a PIL image for a PyTorch model,
returns an Numpy array
"""
with Image.open(image_path) as image:
transform = get_test_transforms()
image = transform(image).numpy()
return image
map_location=device))
model2 = Darknet(os.path.join(
BASE_DIR, "yolo_v3/config/yolov3-custom.cfg")).to(device)
model2.load_state_dict(
torch.load(os.path.join(models_path, "yolo_v3_4_20.pt"),
map_location=device))
model3 = Darknet(os.path.join(
BASE_DIR, "yolo_v3/config/yolov3-custom.cfg")).to(device)
model3.load_state_dict(
torch.load(os.path.join(models_path, "yolo_v3_4_25.pt"),
map_location=device))
dataset = MyTestDataset(split='stage1_train',
transforms=get_test_transforms(rescale_size=(416,
416)))
test_loader = DataLoader(dataset,
batch_size=1,
num_workers=0,
shuffle=False)
model.eval()
model2.eval()
model3.eval()
for i, (image, targets) in enumerate(test_loader):
image = image[0].to(device=device)
name = targets["name"][0]
start_time = time.time()
with torch.no_grad():
outputs = model(image)
# image_copy.show()
# image_copy.save(os.path.join(images_path, f"faster_rcnn/{attempt}/images/{name}.png"))
print(f"{name}, time: {elapsed_time}")
plt.imshow(image_copy)
plt.show()
break
if __name__ == "__main__":
# torch.manual_seed(4)
from models import model
attempt = 7
model_name = "faster_rcnn_7_30.pt"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Running on {device}...")
model.load_state_dict(
torch.load(os.path.join(models_path, "faster_rcnn_7_30.pt"),
map_location=device))
dataset = MyTestDataset(split='stage1_test',
transforms=get_test_transforms(rescale_size=(256,
256)))
test_loader = DataLoader(dataset,
batch_size=1,
num_workers=0,
shuffle=True)
predict(model, test_loader)
from yolo_v3.models import Darknet
from unet.models import UNet
faster_name = "faster_rcnn.pt"
yolo_name = "yolo_v3.pt"
unet_name = "unet.pt"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Running on {device}")
print(f"Loading {faster_name}")
faster.load_state_dict(
torch.load(os.path.join(models_path, faster_name),
map_location=device))
faster.to(device=device)
dataset = MyDemoDataset(transforms=get_test_transforms(rescale_size=(256,
256)))
faster_loader = DataLoader(dataset,
batch_size=1,
num_workers=0,
shuffle=False)
print(f"Loading {yolo_name}")
yolo = Darknet(os.path.join(BASE_DIR, "yolo_v3/config/yolov3-custom.cfg"))
yolo.load_state_dict(
torch.load(os.path.join(models_path, yolo_name), map_location=device))
yolo.to(device=device)
dataset = MyDemoDataset(transforms=get_test_transforms(rescale_size=(416,
416)))
yolo_loader = DataLoader(dataset,
batch_size=1,
num_workers=0,