batch += 1
_, _, _, _, c = correct_preds(probs, labels.squeeze())
if disp:
print(i, c)
correct.append(c)
PCE = np.mean(correct)
return PCE
if __name__ == "__main__":
split = 1
seq_length = 64
n_cpu = 6
model = EventDetector(
pretrain=True,
width_mult=1.0,
lstm_layers=1,
lstm_hidden=256,
bidirectional=True,
dropout=False,
)
save_dict = torch.load("models/swingnet_1800.pth.tar")
model.load_state_dict(save_dict["model_state_dict"])
model.cuda()
model.eval()
PCE = eval(model, split, seq_length, n_cpu, True)
print("Average PCE: {}".format(PCE))
if __name__ == '__main__':
split = 1
seq_length = 64
n_cpu = 6
model = EventDetector(pretrain=True,
width_mult=1,
lstm_layers=1,
lstm_hidden=256,
bidirectional=True,
dropout=False)
rgb_save_dict = torch.load('swingnet_1600.pth.tar')
model.load_state_dict(rgb_save_dict['model_state_dict'])
model.cuda()
model.eval()
_, _, rgb_probs, rgb_tols, rgb_events = myeval(model, split, seq_length,
n_cpu, False, 1)
optical_save_dict = torch.load('swingnet_1200.pth.tar')
model.load_state_dict(optical_save_dict['model_state_dict'])
model.cuda()
model.eval()
_, _, optical_probs, optical_tols, optical_events = myeval(
model, split, seq_length, n_cpu, False, 0)
if len(optical_probs) != len(rgb_probs):
print("there is error in fusion part")
if __name__ == '__main__':
seq_length = 64
n_cpu = 6
model = EventDetector(pretrain=True,
width_mult=1.,
lstm_layers=1,
lstm_hidden=256,
bidirectional=True,
dropout=False)
save_dict = torch.load('models/' + version_name + '_10000.pth.tar',
map_location=lambda storage, loc: storage)
model.load_state_dict(save_dict['model_state_dict'])
model.to(device)
model.eval()
PCE = eval(model, split, seq_length, n_cpu, True)
print('Average PCE: {}'.format(PCE))
if not os.path.exists('results'):
os.mkdir('results')
if bool_classical_loss:
output_file = 'results/classical.txt'
else:
output_file = 'results/softloc.txt'
with open(output_file, 'a') as f:
f.write(version_name)
dropout=False)
# model = torch.nn.parallel.DataParallel(model)
PCES = {}
PFCRS = {}
vNum = 0
for i in range(99, 100):
index = i * 100
print('swingnet_{}.pth.tar'.format(index))
save_dict = torch.load('./models/swingnet_{}.pth.tar'.format(index))
new_state_dict = save_dict['model_state_dict']
# from collections import OrderedDict
# new_state_dict = OrderedDict()
# for k, v in save_dict['model_state_dict'].items():
# name = k[7:] # remove `module.`
# new_state_dict[name] = v
model.load_state_dict(new_state_dict)
model.cuda()
model.eval()
PCE, vNum, _, _, _, PFCR = myeval(model, split, seq_length, n_cpu,
False, 0)
PCES[index] = PCE
PFCRS[index] = PFCR
if cfg.FRAME_13_OPEN:
print("13 frames")
print('Average PCE: {}'.format(PCES))
print("video file num:{}".format(vNum))
print("per frame correct ratio:{}".format(PFCRS))
else:
print("8 frames")
print('split:{} Average PCE: {}'.format(split, PCES))
print("video file num:{}".format(vNum))