def test_imagenet_labels():
# Compare first five labels for quick check
IMAGENET_LABELS_FIRST_FIVE = (
"tench",
"goldfish",
"great_white_shark",
"tiger_shark",
"hammerhead",
)
labels = imagenet_labels()
for i in range(5):
assert labels[i] == IMAGENET_LABELS_FIRST_FIVE[i]
# Check total number of labels
assert len(labels) == 1000
def model_to_learner(model: nn.Module,
im_size: int = IMAGENET_IM_SIZE) -> Learner:
"""Create Learner based on pyTorch ImageNet model.
Args:
model (nn.Module): Base ImageNet model. E.g. models.resnet18()
im_size (int): Image size the model will expect to have.
Returns:
Learner: a model trainer for prediction
"""
# Currently, fast.ai api requires to pass a DataBunch to create a model trainer (learner).
# To use the learner for prediction tasks without retraining, we have to pass an empty DataBunch.
# single_from_classes is deprecated, but this is the easiest go-around method.
# Create ImageNet data spec as an empty DataBunch.
# Related thread: https://forums.fast.ai/t/solved-get-prediction-from-the-imagenet-model-without-creating-a-databunch/36197/5
empty_data = ImageDataBunch.single_from_classes(
"", classes=imagenet_labels(), size=im_size).normalize(imagenet_stats)
return Learner(empty_data, model)
if args.model == "list":
for m in all_models: print(m)
sys.exit(0)
elif args.model == None:
modeln = ["resnet152"]
elif args.model == "all":
modeln = all_models
if not len(args.path):
sys.stderr.write("Cannot utilise all models from the webcam. " +
"Do not choose --model=all.\n")
sys.exit(1)
else:
modeln = [args.model]
try:
labels = imagenet_labels() # The 1000 labels.
except:
sys.stderr.write("Failed to obtain labels probably because of " +
"a network connection error.\n")
sys.exit(1)
# ----------------------------------------------------------------------
# Load the pre-built model
# ----------------------------------------------------------------------
for path in args.path:
if is_url(path):
tempdir = tempfile.gettempdir()
imfile = os.path.join(tempdir, "temp.jpg")
urllib.request.urlretrieve(path, imfile)
# Prepare processing function
# ----------------------------------------------------------------------
# Webcam classification
def classify_frame(capture, learner, label):
"""Use the learner to predict the class label.
"""
_, frame = capture.read() # Capture frame-by-frame
_, ind, prob = learner.predict(Image(utils.cv2torch(frame)))
utils.put_text(frame, f"{label[ind]} ({prob[ind]:.2f})")
return utils.cv2matplotlib(frame)
labels = imagenet_labels() # Load model labels
# Load ResNet model
# * https://download.pytorch.org/models/resnet18-5c106cde.pth -> ~/.cache/torch/checkpoints/resnet18-5c106cde.pth
learn = model_to_learner(models.resnet18(pretrained=True), IMAGENET_IM_SIZE)
#learn = model_to_learner(models.resnet152(pretrained=True), IMAGENET_IM_SIZE)
#learn = model_to_learner(models.xresnet152(pretrained=True), IMAGENET_IM_SIZE)
# Want to load from local copy rather than from ~/.torch? Maybe
#learn = load_learner(file="resnet18-5c106cde.pth")
#model = untar_data("resnet18-5c106cde.pth")
#learn = load_learner(model)
func = partial(classify_frame, learner=learn, label=labels)
sys.stderr = stderr
option_parser = argparse.ArgumentParser(add_help=False)
option_parser.add_argument('path', nargs="+", help='path or url to image')
#option_parser.add_argument(
# '--model',
# help="use this model instead of '{}'.".format(RESNET18))
args = option_parser.parse_args()
sys.stderr = devnull
# ----------------------------------------------------------------------
labels = imagenet_labels()
# Convert a pretrained imagenet model into Learner for prediction.
# You can load an exported model by learn = load_learner(path) as well.
# --model=
# models.BasicBlock models.Darknet models.DynamicUnet
# models.ResLayer models.ResNet models.SqueezeNet
# models.UnetBlock models.WideResNet models.XResNet
# models.alexnet models.darknet models.densenet121
# models.densenet161 models.densenet169 models.densenet201
# models.resnet101 models.resnet152 models.resnet18
# models.resnet34 models.resnet50 models.squeezenet1_0
# models.squeezenet1_1 models.unet models.vgg16_bn
# models.vgg19_bn models.wrn models.wrn_22
# models.xception models.xresnet models.xresnet101
# models.xresnet152 models.xresnet18 models.xresnet34
