|
@ -41,12 +41,14 @@ class Omnivore(NNOperator): |
|
|
def __init__(self, |
|
|
def __init__(self, |
|
|
model_name: str = 'omnivore_swinT', |
|
|
model_name: str = 'omnivore_swinT', |
|
|
framework: str = 'pytorch', |
|
|
framework: str = 'pytorch', |
|
|
|
|
|
input_type: str = 'video', |
|
|
skip_preprocess: bool = False, |
|
|
skip_preprocess: bool = False, |
|
|
classmap: dict = None, |
|
|
classmap: dict = None, |
|
|
topk: int = 5, |
|
|
topk: int = 5, |
|
|
): |
|
|
): |
|
|
super().__init__(framework=framework) |
|
|
super().__init__(framework=framework) |
|
|
self.model_name = model_name |
|
|
self.model_name = model_name |
|
|
|
|
|
self.input_type = input_type |
|
|
self.skip_preprocess = skip_preprocess |
|
|
self.skip_preprocess = skip_preprocess |
|
|
self.topk = topk |
|
|
self.topk = topk |
|
|
self.dataset_name = 'kinetics_400' |
|
|
self.dataset_name = 'kinetics_400' |
|
@ -99,11 +101,14 @@ class Omnivore(NNOperator): |
|
|
) |
|
|
) |
|
|
inputs = data.to(self.device)[None, ...] |
|
|
inputs = data.to(self.device)[None, ...] |
|
|
|
|
|
|
|
|
outs = self.model(inputs,input_type="video") |
|
|
|
|
|
|
|
|
feats = self.model.forward_features(inputs ,input_type = self.input_type) |
|
|
|
|
|
features = feats.to('cpu').squeeze(0).detach().numpy() |
|
|
|
|
|
|
|
|
|
|
|
outs = self.model.head(feats) |
|
|
post_act = torch.nn.Softmax(dim=1) |
|
|
post_act = torch.nn.Softmax(dim=1) |
|
|
preds = post_act(outs) |
|
|
preds = post_act(outs) |
|
|
pred_scores, pred_classes = preds.topk(k=self.topk) |
|
|
pred_scores, pred_classes = preds.topk(k=self.topk) |
|
|
labels = [self.classmap[int(i)] for i in pred_classes[0]] |
|
|
labels = [self.classmap[int(i)] for i in pred_classes[0]] |
|
|
scores = [round(float(x), 5) for x in pred_scores[0]] |
|
|
scores = [round(float(x), 5) for x in pred_scores[0]] |
|
|
print(labels,scores) |
|
|
|
|
|
return labels, scores |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
return labels, scores, features |
|
|