Running a test locally with webcam

[AmitHaritan2525]

Hi, i didn't understand what i need to do in order to run a simulation locally using my own webcam. I tried running the online_test.py with the predefined models in the wiki but i got this output: |Total number of trainable parameters: 33409104 [INFO]: RGB model is used for init model Model 1 DataParallel( (module): ResNet( (conv1): Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), padding=(1, 3, 3), bias=False) (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): BasicBlock( (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): BasicBlock( (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer2): Sequential( (0): BasicBlock( (conv1): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False) (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer3): Sequential( (0): BasicBlock( (conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False) (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer4): Sequential( (0): BasicBlock( (conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False) (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (avgpool): AvgPool3d(kernel_size=(1, 4, 4), stride=1, padding=0) (fc): Linear(in_features=512, out_features=400, bias=True) ) ) Total number of trainable parameters: 33371472 Namespace(annotation_path='/home1/haritan.amit@ef.technion.ac.il/YuvalAmit/PycharmProjects/HandGesture/kinetics.json', arch='resnet', batch_size=128, begin_epoch=1, checkpoint=10, clf_queue_size=1, clf_strategy='raw', clf_threshold_final=1, clf_threshold_pre=1, crop_position_in_test='c', dampening=0.9, dataset='egogesture', det_counter=1, det_queue_size=1, det_strategy='raw', downsample=1, ft_begin_index=0, ft_portion='complete', groups=3, initial_scale=1.0, learning_rate=0.1, lr_patience=10, lr_steps=[10, 20, 30, 40, 100], manual_seed=1, mean=[114.7748, 107.7354, 99.475], mean_dataset='activitynet', modality='RGB', modality_clf='RGB', modality_det='RGB', model='resnet', model_clf='resnet', model_depth=18, model_depth_clf=18, model_depth_det=18, model_det='resnet', momentum=0.9, n_classes=400, n_classes_clf=400, n_classes_det=400, n_epochs=200, n_finetune_classes=400, n_finetune_classes_clf=400, n_finetune_classes_det=400, n_scales=5, n_threads=4, n_val_samples=3, nesterov=False, no_cuda=False, no_hflip=False, no_mean_norm=False, no_softmax_in_test=False, no_train=False, no_val=False, norm_value=1, optimizer='sgd', pretrain_path='', pretrain_path_clf='', pretrain_path_det='', resnet_shortcut='B', resnet_shortcut_clf='B', resnet_shortcut_det='B', resnext_cardinality=32, resnext_cardinality_clf=32, resnext_cardinality_det=32, result_path='/home1/haritan.amit@ef.technion.ac.il/YuvalAmit/PycharmProjects/HandGesture/results', resume_path='', resume_path_clf='', resume_path_det='', root_path='/home1/haritan.amit@ef.technion.ac.il/YuvalAmit/PycharmProjects/HandGesture', sample_duration=16, sample_duration_clf=16, sample_duration_det=16, sample_size=112, scale_in_test=1.0, scale_step=0.84089641525, scales=[1.0, 0.84089641525, 0.7071067811803005, 0.5946035574934808, 0.4999999999911653], std=[38.7568578, 37.88248729, 40.02898126], std_norm=False, store_name='model', stride_len=1, test=True, test_subset='val', train_crop='corner', video='data2/EgoGesture/videos/Subject02/Scene1/Color/rgb1.avi', video_path='/home1/haritan.amit@ef.technion.ac.il/YuvalAmit/PycharmProjects/HandGesture/video_kinetics_jpg', weight_decay=0.001, whole_path='video_kinetics_jpg', wide_resnet_k=2, wide_resnet_k_clf=2, wide_resnet_k_det=2, width_mult=1.0, width_mult_clf=1.0, width_mult_det=1.0) Total number of trainable parameters: 33409104 [INFO]: RGB model is used for init model Model 2 DataParallel( (module): ResNet( (conv1): Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), padding=(1, 3, 3), bias=False) (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): BasicBlock( (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (1): BasicBlock( (conv1): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer2): Sequential( (0): BasicBlock( (conv1): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv3d(64, 128, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False) (1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer3): Sequential( (0): BasicBlock( (conv1): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv3d(128, 256, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False) (1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (layer4): Sequential( (0): BasicBlock( (conv1): Conv3d(256, 512, kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (downsample): Sequential( (0): Conv3d(256, 512, kernel_size=(1, 1, 1), stride=(2, 2, 2), bias=False) (1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): BasicBlock( (conv1): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn1): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (conv2): Conv3d(512, 512, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1), bias=False) (bn2): BatchNorm3d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (avgpool): AvgPool3d(kernel_size=(1, 4, 4), stride=1, padding=0) (fc): Linear(in_features=512, out_features=400, bias=True) ) ) Total number of trainable parameters: 33371472 Start Evaluation Average Levenshtein Accuracy= 0 -----Evaluation is finished------ i am not sure this is the correct output. Can you please specify the steps and scripts i need to run? or what i did wrong? Thanks, Amit