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Running
on
Zero
File size: 3,519 Bytes
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from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
import torch
import numpy as np
class ActPredScorer(torch.nn.Module):
def __init__(self, model_name = "MCG-NJU/videomae-base-finetuned-kinetics", num_frames = 16, device = 'cuda', dtype=torch.float32):
super().__init__()
self.model = VideoMAEForVideoClassification.from_pretrained(model_name, num_frames = num_frames, torch_dtype=dtype)
self.feature_extractor = VideoMAEFeatureExtractor.from_pretrained(model_name)
self.device = device
self.model.to(device)
def get_target_class_idx(self, target_action):
def mapping_func(x):
if 'piano' in x:
return 'playing piano'
if 'guitar' in x:
return 'playing guitar'
if 'doughnuts' in x:
return 'eating doughnuts'
if 'beer' in x:
return 'drinking beer'
if 'badminton' in x:
return 'playing badminton'
if 'cello' in x:
return 'playing cello'
if 'scooter' in x:
return 'riding scooter'
if 'ballet' in x:
return 'dancing ballet'
if 'pancake' in x:
return 'flipping pancake'
if 'violin' in x:
return 'playing violin'
if 'wood' in x:
return 'chopping wood'
if 'watermelon' in x:
return 'eating watermelon'
if 'jogging' in x:
return 'jogging'
else:
print(f"Please add your action mapping to ActPredScorer. Mapping not found for {x}")
raise NotImplementedError
try:
target_class_idx = self.model.config.label2id[target_action]
except:
target_class_idx = self.model.config.label2id[mapping_func(target_action)]
return target_class_idx
def get_loss_and_score(self, norm_vid, target_action):
''' video should be a torch array of dtype float, with values from 0-1, of dimension (num_frames, height, width, 3)'''
target_class_idx = self.get_target_class_idx(target_action)
outputs = self.model(norm_vid, labels = torch.tensor([target_class_idx]).to(self.device))
loss = outputs.loss
logits = outputs.logits
norm_logits = torch.exp(logits)/ (torch.exp(logits).sum())
norm_logits = norm_logits.squeeze()
score = norm_logits[target_class_idx]
return loss, score, self.get_pred_class(logits)
def get_pred_class(self, logits):
predicted_class_idx = logits.argmax(-1).item()
return self.model.config.id2label[predicted_class_idx]
def gen_rand_labels_file(labels_list, out_file, num_labels = 50):
idxs = np.random.choice(len(labels_list), num_labels, replace = False)
rand_labels = [labels_list[i] for i in idxs]
rand_labels.sort()
with open(out_file, 'w') as f:
for line in rand_labels:
f.write(f"{line}\n")
if __name__ == '__main__':
# import numpy as np
# scorer = ActPredScorer(num_frames = 7)
# video_torch = [torch.randn((3,256,256)).clamp(0,1) for _ in range(7)]
# encoding = scorer.feature_extractor(video_torch, do_rescale = False, return_tensors="pt")
# print(scorer.get_loss_and_score(video_torch))
scorer = ActPredScorer(num_frames = 7)
labels = scorer.model.config.id2label
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