Video inference sample code

I used my own video classification trained with the configuration of ucf101. It seems that there is a little overfitting. I want to use my code for inference, but I can’t find a related tutorial. I can only find it, and I don’t know what to do next. plaese help me @mannatsingh

from classy_vision.generic.util import load_checkpoint
from classy_vision.models import ClassyModel

# This is important: importing models here will register your custom models with Classy Vision
# so that it can instantiate them appropriately from the checkpoint file
# See more information at https://classyvision.ai/api/models.html#classy_vision.models.register_model
from classy_vision import models


# Update this with your actual directory:
checkpoint_dir = '/home/sucom/hdd_1T/project/video_rec_0831/ClassyVision/checkpoint_hand/model_phase-2198_end.torch'
checkpoint_data = load_checkpoint(checkpoint_dir)
model = ClassyModel.from_checkpoint(checkpoint_data)

[mannatsingh]

Thanks for the code sample, @shinianzhihou !

[shinianzhihou]

state_path = "YOUR/PATH/TO/checkpoints/checkpoint.torch"
state = torch.load(state_path)
model = ClassyModel.from_checkpoint(state)
dataset = build_dataset(state["input_args"]["config"]["dataset"]["test"])

dataset.batchsize_per_replica = 10 # to test in the single GPU with small memory

targets,outputs = [],[]
model = model.cuda().eval()
for idx,data in enumerate(dataset.iterator()):
    output = model(data['input'].cuda()).argmax(dim=1)
    outputs += list(output.detach().cpu().numpy())
    targets += list(data["target"].numpy())

from sklearn.metrics import classification_report
print(classification_report(targets, outputs, target_names=CLASSES))

This is how I make my inference. May be useful