def eval_at_state(self, model_options, frame):
model_input = ModelInput()
# if self.episode.current_frame is None:
# model_input.state = self.state()
# else:
# model_input.state = self.episode.current_frame
#process_frame to shape [1,3,224,224], for input to resnet18
processed_frame = self.preprocess_frame(
resnet_input_transform(frame, 224).unsqueeze(0))
resnet18_features = self.resnet18(processed_frame)
model_input.state = resnet18_features
model_input.hidden = self.hidden
model_input.target_class_embedding = gpuify(torch.Tensor(
self.target_glove_embedding),
gpu_id=self.gpu_id)
model_input.action_probs = self.last_action_probs
return model_input, self.model.forward(model_input, model_options)
def preprocess_frame(self, frame):
""" Preprocess the current frame for input into the model. """
frame = resnet_input_transform(frame, 84)
state = torch.Tensor(frame)
return gpuify(state.unsqueeze(0), self.gpu_id)