diff --git a/internnav/habitat_extensions/evaluator.py b/internnav/habitat_extensions/evaluator.py index abc7aa86..3bf4c54b 100644 --- a/internnav/habitat_extensions/evaluator.py +++ b/internnav/habitat_extensions/evaluator.py @@ -1,39 +1,47 @@ import argparse -import os import copy -import json -from typing import Any -from collections import OrderedDict -from PIL import Image, ImageDraw, ImageFont -import numpy as np -import tqdm -import quaternion import itertools +import json +import os import random import re +from collections import OrderedDict +from typing import Any +import habitat +import numpy as np +import quaternion import torch -from torch import Tensor -from transformers.image_utils import to_numpy_array +import tqdm from depth_camera_filtering import filter_depth - -from omegaconf import OmegaConf -import habitat -from habitat import logger, Env +from habitat import Env from habitat.config.default import get_agent_config -from habitat_baselines.config.default import get_config as get_habitat_config -from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower from habitat.config.default_structured_configs import ( CollisionsMeasurementConfig, FogOfWarConfig, TopDownMapMeasurementConfig, ) +from habitat.tasks.nav.shortest_path_follower import ShortestPathFollower from habitat.utils.visualizations.utils import images_to_video, observations_to_image -from internnav.model.utils.vln_utils import rho_theta, image_resize, traj_to_actions, chunk_token, split_and_clean, open_image -from internnav.utils.dist import * +from habitat_baselines.config.default import get_config as get_habitat_config +from omegaconf import OmegaConf +from PIL import Image, ImageDraw, ImageFont +from torch import Tensor +from transformers.image_utils import to_numpy_array + +from internnav.model.utils.vln_utils import ( + chunk_token, + image_resize, + open_image, + rho_theta, + split_and_clean, + traj_to_actions, +) +from internnav.utils.dist import * # noqa: F403 DEFAULT_IMAGE_TOKEN = "" + class VLNEvaluator: def __init__( self, @@ -92,51 +100,55 @@ def __init__( camera_fov_rad = np.deg2rad(self.sim_sensors_config.depth_sensor.hfov) self._camera_fov = camera_fov_rad self._fx = self._fy = self.sim_sensors_config.depth_sensor.width / (2 * np.tan(camera_fov_rad / 2)) - + self.model = model self.processor = processor - prompt = f"You are an autonomous navigation assistant. Your task is to . Where should you go next to stay on track? Please output the next waypoint\'s coordinates in the image. Please output STOP when you have successfully completed the task." + prompt = "You are an autonomous navigation assistant. Your task is to . Where should you go next to stay on track? Please output the next waypoint\'s coordinates in the image. Please output STOP when you have successfully completed the task." answer = "" self.conversation = [{"from": "human", "value": prompt}, {"from": "gpt", "value": answer}] - + self.conjunctions = [ - 'you can see ', - 'in front of you is ', - 'there is ', - 'you can spot ', - 'you are toward the ', - 'ahead of you is ', - 'in your sight is ' - ] - - self.actions2idx = OrderedDict({ - 'STOP': [0], - "↑": [1], - "←": [2], - "→": [3], - "↓": [5], - }) - - self.objectnav_instructions =["Search for the {target_object}."] - + 'you can see ', + 'in front of you is ', + 'there is ', + 'you can spot ', + 'you are toward the ', + 'ahead of you is ', + 'in your sight is ', + ] + + self.actions2idx = OrderedDict( + { + 'STOP': [0], + "↑": [1], + "←": [2], + "→": [3], + "↓": [5], + } + ) + + self.objectnav_instructions = ["Search for the {target_object}."] + self.num_frames = args.num_frames self.num_future_steps = args.num_future_steps self.num_history = args.num_history - - def preprocess_depth_image_v2(self, depth_image, do_depth_scale=True, depth_scale=1000, target_height=None, target_width=None): + + def preprocess_depth_image_v2( + self, depth_image, do_depth_scale=True, depth_scale=1000, target_height=None, target_width=None + ): if target_height is None: target_height = self.image_processor.crop_size['height'] # 384 - target_width = self.image_processor.crop_size['width'] # 384 - + target_width = self.image_processor.crop_size['width'] # 384 + resized_depth_image = depth_image.resize((target_width, target_height), Image.NEAREST) - + img = to_numpy_array(resized_depth_image) if do_depth_scale: img = img / depth_scale - + return img, (target_width, target_height) - + def get_intrinsic_matrix(self, sensor_cfg) -> np.ndarray: width = sensor_cfg.width height = sensor_cfg.height @@ -146,19 +158,16 @@ def get_intrinsic_matrix(self, sensor_cfg) -> np.ndarray: cx = (width - 1.0) / 2.0 cy = (height - 1.0) / 2.0 - intrinsic_matrix = np.array([ - [fx, 0.0, cx, 0.0], - [ 0.0, fy, cy, 0.0], - [ 0.0, 0.0, 1.0, 0.0], - [ 0.0, 0.0, 0.0, 1.0] - ]) + intrinsic_matrix = np.array( + [[fx, 0.0, cx, 0.0], [0.0, fy, cy, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]] + ) return intrinsic_matrix - + def preprocess_instrinsic(self, intrinsic, ori_size, target_size): # (V, 4, 4) (resize_shape) (h, w) intrinsic = copy.deepcopy(intrinsic) if len(intrinsic.shape) == 2: intrinsic = intrinsic[None, :, :] # (1, 4, 4) or (B, 4, 4) - + intrinsic[:, 0] /= ori_size[0] / target_size[0] # width intrinsic[:, 1] /= ori_size[1] / target_size[1] # height @@ -169,11 +178,11 @@ def preprocess_instrinsic(self, intrinsic, ori_size, target_size): # (V, 4, 4) intrinsic = intrinsic.squeeze(0) return intrinsic - + def get_axis_align_matrix(self): ma = np.array([[0, 0, 1, 0], [-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 0, 1]]) return ma - + def xyz_yaw_to_tf_matrix(self, xyz: np.ndarray, yaw: float) -> np.ndarray: x, y, z = xyz transformation_matrix = np.array( @@ -206,7 +215,7 @@ def xyz_pitch_to_tf_matrix(self, xyz: np.ndarray, pitch: float) -> np.ndarray: ] ) return transformation_matrix - + def xyz_yaw_pitch_to_tf_matrix(self, xyz: np.ndarray, yaw: float, pitch: float) -> np.ndarray: """Converts a given position and yaw, pitch angles to a 4x4 transformation matrix. @@ -224,7 +233,7 @@ def xyz_yaw_pitch_to_tf_matrix(self, xyz: np.ndarray, yaw: float, pitch: float) transformation_matrix[:3, :3] = rot1 @ rot2 transformation_matrix[:3, 3] = xyz return transformation_matrix - + def pixel_to_gps(self, pixel, depth, intrinsic, tf_camera_to_episodic): ''' Args: @@ -250,14 +259,23 @@ def pixel_to_gps(self, pixel, depth, intrinsic, tf_camera_to_episodic): x = point_episodic[0] y = point_episodic[1] - return (x, y) # same as habitat gps - + return (x, y) # same as habitat gps + def config_env(self) -> Env: env = Env(config=self.config) # env.episodes = env.episodes[0:1] return env - - def dot_matrix_two_dimensional(self, image_or_image_path, save_path = None, dots_size_w = 8, dots_size_h = 8, save_img = False, font_path = 'fonts/arial.ttf', pixel_goal = None): + + def dot_matrix_two_dimensional( + self, + image_or_image_path, + save_path=None, + dots_size_w=8, + dots_size_h=8, + save_img=False, + font_path='fonts/arial.ttf', + pixel_goal=None, + ): """ takes an original image as input, save the processed image to save_path. Each dot is labeled with two-dimensional Cartesian coordinates (x,y). Suitable for single-image tasks. control args: @@ -283,11 +301,11 @@ def dot_matrix_two_dimensional(self, image_or_image_path, save_path = None, dots # Validate pixel coordinates if not (0 <= x_pixel < width and 0 <= y_pixel < height): raise ValueError(f"pixel_goal {pixel_goal} exceeds image dimensions ({width}x{height})") - + # Convert to grid coordinates target_i = round(x_pixel / cell_width) target_j = round(y_pixel / cell_height) - + # Validate grid bounds if not (1 <= target_i <= dots_size_w and 1 <= target_j <= dots_size_h): raise ValueError( @@ -295,7 +313,6 @@ def dot_matrix_two_dimensional(self, image_or_image_path, save_path = None, dots f"valid range is (1,1)-({dots_size_h},{dots_size_w})" ) - count = 0 for j in range(1, grid_size_h): @@ -306,15 +323,18 @@ def dot_matrix_two_dimensional(self, image_or_image_path, save_path = None, dots pixel_color = img.getpixel((x, y)) # choose a more contrasting color from black and white if pixel_color[0] + pixel_color[1] + pixel_color[2] >= 255 * 3 / 2: - opposite_color = (0,0,0) + opposite_color = (0, 0, 0) else: - opposite_color = (255,255,255) - + opposite_color = (255, 255, 255) + if pixel_goal is not None and i == target_i and j == target_j: opposite_color = (255, 0, 0) # Red for target circle_radius = width // 240 # Adjust dot size if needed; default == width // 240 - draw.ellipse([(x - circle_radius, y - circle_radius), (x + circle_radius, y + circle_radius)], fill=opposite_color) + draw.ellipse( + [(x - circle_radius, y - circle_radius), (x + circle_radius, y + circle_radius)], + fill=opposite_color, + ) text_x, text_y = x + 3, y count_w = count // dots_size_w @@ -326,8 +346,16 @@ def dot_matrix_two_dimensional(self, image_or_image_path, save_path = None, dots print(">>> dots overlaid image processed, stored in", save_path) img.save(save_path) return img - - def _pointnav(self, goal: np.ndarray, depth: np.ndarray, step_id: int, robot_xy: np.ndarray, robot_heading: float, stop: bool = False) -> Tensor: + + def _pointnav( + self, + goal: np.ndarray, + depth: np.ndarray, + step_id: int, + robot_xy: np.ndarray, + robot_heading: float, + stop: bool = False, + ) -> Tensor: ''' Args: goal (np.ndarray): goal position @@ -359,8 +387,8 @@ def _pointnav(self, goal: np.ndarray, depth: np.ndarray, step_id: int, robot_xy: return 0 action = self._pointnav_policy.act(obs_pointnav, masks, deterministic=True) return action - - def eval_action(self, idx) -> None: + + def eval_action(self, idx) -> None: # noqa: C901 self.model.eval() env = self.config_env() scene_episode_dict = {} @@ -369,36 +397,42 @@ def eval_action(self, idx) -> None: scene_episode_dict[episode.scene_id] = [] scene_episode_dict[episode.scene_id].append(episode) - intrinsic_matrix = self.get_intrinsic_matrix(self.config.habitat.simulator.agents.main_agent.sim_sensors.rgb_sensor) + intrinsic_matrix = self.get_intrinsic_matrix( + self.config.habitat.simulator.agents.main_agent.sim_sensors.rgb_sensor + ) sucs, spls, oss, nes = [], [], [], [] done_res = [] - - if os.path.exists(os.path.join(self.output_path, f'result.json')): - with open(os.path.join(self.output_path, f'result.json'),'r') as f: + + if os.path.exists(os.path.join(self.output_path, 'result.json')): + with open(os.path.join(self.output_path, 'result.json'), 'r') as f: for line in f.readlines(): res = json.loads(line) done_res.append([res["scene_id"], res["episode_id"], res["episode_instruction"]]) - if get_rank() == 0: + if get_rank() == 0: # noqa: F405 sucs.append(res['success']) spls.append(res['spl']) oss.append(res['os']) nes.append(res['ne']) - + for scene in sorted(scene_episode_dict.keys()): episodes = scene_episode_dict[scene] scene_id = scene.split('/')[-2] print(f"scene_id = {scene_id}") - process_bar = tqdm.tqdm(range(len(episodes[idx::self.env_num])), desc=f"scene {scene_id}") - for episode in episodes[idx::self.env_num]: - episode_instruction = episode.instruction.instruction_text if 'objectnav' not in self.config_path else episode.object_category + process_bar = tqdm.tqdm(range(len(episodes[idx :: self.env_num])), desc=f"scene {scene_id}") + for episode in episodes[idx :: self.env_num]: + episode_instruction = ( + episode.instruction.instruction_text + if 'objectnav' not in self.config_path + else episode.object_category + ) print("episode start", episode_instruction) episode_id = int(episode.episode_id) if [scene_id, episode_id, episode_instruction] in done_res: continue - + env.current_episode = episode observations = env.reset() - + agent_state = env.sim.get_agent_state() rotation = agent_state.rotation translation = agent_state.position @@ -406,36 +440,31 @@ def eval_action(self, idx) -> None: transformation_matrix = np.eye(4) transformation_matrix[:3, :3] = rotation_matrix transformation_matrix[:3, 3] = translation - - agent = ShortestPathFollower( - env.sim, 0.25, False - ) - + + agent = ShortestPathFollower(env.sim, 0.25, False) + os.makedirs(os.path.join(self.output_path, f'check_sim_{self.epoch}'), exist_ok=True) - Image.fromarray(observations['rgb']).save(os.path.join(self.output_path, f'check_sim_{self.epoch}', f'rgb_{idx}.jpg')) - + Image.fromarray(observations['rgb']).save( + os.path.join(self.output_path, f'check_sim_{self.epoch}', f'rgb_{idx}.jpg') + ) + vis_frames = [] step_id = 0 - + if self.save_video: os.makedirs(os.path.join(self.output_path, f'vis_{self.epoch}', f'{scene_id}'), exist_ok=True) initial_height = env.sim.get_agent_state().position[1] rgb_list = [] - depth_list = [] action_seq = [] - past_key_values = None output_ids = None - + goal = None action = None - look_down_observations = None - look_down_id_list = [] messages = [] - last_action=None local_actions = [] - - while not env.episode_over and step_id <=500: + + while not env.episode_over and step_id <= 500: rgb = observations["rgb"] depth = observations["depth"] x, y = observations["gps"] @@ -447,25 +476,32 @@ def eval_action(self, idx) -> None: agent_state = env.sim.get_agent_state() height = agent_state.position[1] - initial_height camera_position = np.array([x, -y, self._camera_height + height]) - robot_xy = camera_position[:2] - tf_camera_to_episodic = self.xyz_yaw_pitch_to_tf_matrix(camera_position, camera_yaw, np.deg2rad(30)) @ self.get_axis_align_matrix() - + tf_camera_to_episodic = ( + self.xyz_yaw_pitch_to_tf_matrix(camera_position, camera_yaw, np.deg2rad(30)) + @ self.get_axis_align_matrix() + ) + image = Image.fromarray(rgb).convert('RGB') - image_size = image.size save_raw_image = image.copy() - + save_dot = False if action == 5: look_down_image = image save_raw_image = look_down_image.copy() - look_down_depth, resize_shape = self.preprocess_depth_image_v2(Image.fromarray(depth.astype(np.uint16), mode='I;16'), do_depth_scale=True, depth_scale=1000, target_height=224, target_width=224) + look_down_depth, resize_shape = self.preprocess_depth_image_v2( + Image.fromarray(depth.astype(np.uint16), mode='I;16'), + do_depth_scale=True, + depth_scale=1000, + target_height=224, + target_width=224, + ) look_down_depth = torch.as_tensor(np.ascontiguousarray(look_down_depth)).float() look_down_depth[look_down_depth > 5.0] = 5.0 else: image = image.resize((self.args.resize_w, self.args.resize_h)) rgb_list.append(image) - if self.args.mode == 'dual-system': + if self.args.mode == 'dual_system': down_observations = env.step(5) down_observations = env.step(5) @@ -474,7 +510,13 @@ def eval_action(self, idx) -> None: depth = filter_depth(depth.reshape(depth.shape[:2]), blur_type=None) depth = depth * (self._max_depth - self._min_depth) + self._min_depth depth = depth * 1000 - look_down_depth, resize_shape = self.preprocess_depth_image_v2(Image.fromarray(depth.astype(np.uint16), mode='I;16'), do_depth_scale=True, depth_scale=1000, target_height=224, target_width=224) + look_down_depth, resize_shape = self.preprocess_depth_image_v2( + Image.fromarray(depth.astype(np.uint16), mode='I;16'), + do_depth_scale=True, + depth_scale=1000, + target_height=224, + target_width=224, + ) look_down_depth = torch.as_tensor(np.ascontiguousarray(look_down_depth)).float() look_down_depth[look_down_depth > 5.0] = 5.0 @@ -482,120 +524,127 @@ def eval_action(self, idx) -> None: env.step(4) info = env.get_metrics() - + if len(action_seq) == 0 and goal is None: - if action != 5: + if action != 5: sources = copy.deepcopy(self.conversation) - sources[0]["value"] = sources[0]["value"].replace('.', episode.instruction.instruction_text[:-1]) + sources[0]["value"] = sources[0]["value"].replace( + '.', episode.instruction.instruction_text[:-1] + ) cur_images = rgb_list[-1:] if step_id == 0: history_id = [] else: - history_id = np.unique(np.linspace(0, step_id - 1, self.num_history, dtype=np.int32)).tolist() + history_id = np.unique( + np.linspace(0, step_id - 1, self.num_history, dtype=np.int32) + ).tolist() placeholder = (DEFAULT_IMAGE_TOKEN + '\n') * len(history_id) sources[0]["value"] += f' These are your historical observations: {placeholder}.' - + history_id = sorted(history_id) print('history_idddddddd', step_id, history_id) input_images = [rgb_list[i] for i in history_id] + cur_images input_img_id = 0 - else: + else: assert action == 5 sources = [{"from": "human", "value": ""}, {"from": "gpt", "value": ""}] input_images += [look_down_image] - messages.append({ - 'role': 'assistant', - 'content': [{'type': 'text','text': llm_outputs}] - }) + messages.append( + {'role': 'assistant', 'content': [{'type': 'text', 'text': llm_outputs}]} # noqa: F405 + ) input_img_id = -1 - + prompt = random.choice(self.conjunctions) + DEFAULT_IMAGE_TOKEN sources[0]["value"] += f" {prompt}." print('sources', step_id, sources) prompt_instruction = copy.deepcopy(sources[0]["value"]) parts = split_and_clean(prompt_instruction) - + content = [] - for i in range (len(parts)): + for i in range(len(parts)): if parts[i] == "": content.append({"type": "image", "image": input_images[input_img_id]}) - input_img_id +=1 + input_img_id += 1 else: - content.append({"type": "text", "text": parts[i]}) - + content.append({"type": "text", "text": parts[i]}) + messages.append({'role': 'user', 'content': content}) - + print('step_id', step_id, 'messages:', messages) - - text = self.processor.apply_chat_template( - messages,tokenize=False, add_generation_prompt=True + + text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + + inputs = self.processor(text=[text], images=input_images, return_tensors="pt").to( + self.model.device ) - inputs = self.processor(text=[text], images=input_images, return_tensors="pt").to(self.model.device) - with torch.no_grad(): output_ids = self.model.generate(**inputs, max_new_tokens=128, do_sample=False) - - llm_outputs = self.processor.tokenizer.decode(output_ids[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) + + llm_outputs = self.processor.tokenizer.decode( + output_ids[0][inputs.input_ids.shape[1] :], skip_special_tokens=True + ) print('step_id:', step_id, 'output text:', llm_outputs) - + if bool(re.search(r'\d', llm_outputs)): forward_action = 0 coord = [int(c) for c in re.findall(r'\d+', llm_outputs)] pixel_goal = [int(coord[1]), int(coord[0])] - + goal = self.pixel_to_gps(pixel_goal, depth / 1000, intrinsic_matrix, tf_camera_to_episodic) print('before', goal, depth.shape) - goal = (transformation_matrix @ np.array([-goal[1], 0, -goal[0], 1]))[:3] - + goal = (transformation_matrix @ np.array([-goal[1], 0, -goal[0], 1]))[:3] + if not env.sim.pathfinder.is_navigable(np.array(goal)): goal = np.array(env.sim.pathfinder.snap_point(np.array(goal))) # look down --> horizontal env.step(4) env.step(4) - + # Forking logic based on mode if self.args.mode == 'system2': action = agent.get_next_action(goal) if action == 0: goal = None output_ids = None - action = 2 # random action + action = 2 # random action print('conduct a random action 2') observations = env.step(action) step_id += 1 messages = [] continue - else: # dual-system logic + else: # dual-system logic local_actions = [] pixel_values = inputs.pixel_values - image_grid_thw = torch.cat( - [thw.unsqueeze(0) for thw in inputs.image_grid_thw], dim=0 - ) - + image_grid_thw = torch.cat([thw.unsqueeze(0) for thw in inputs.image_grid_thw], dim=0) + with torch.no_grad(): traj_latents = self.model.generate_latents(output_ids, pixel_values, image_grid_thw) - + # prepocess align with navdp - image_dp = torch.tensor(np.array(look_down_image.resize((224, 224)))).to(torch.bfloat16) / 255 + image_dp = ( + torch.tensor(np.array(look_down_image.resize((224, 224)))).to(torch.bfloat16) / 255 + ) pix_goal_image = copy.copy(image_dp) images_dp = torch.stack([pix_goal_image, image_dp]).unsqueeze(0).to(self.device) depth_dp = look_down_depth.unsqueeze(-1).to(torch.bfloat16) pix_goal_depth = copy.copy(depth_dp) depths_dp = torch.stack([pix_goal_depth, depth_dp]).unsqueeze(0).to(self.device) - + with torch.no_grad(): - dp_actions = self.model.generate_traj(traj_latents, images_dp, depths_dp) - + dp_actions = self.model.generate_traj( + traj_latents, images_dp, depths_dp, use_async=True + ) + random_choice = np.random.choice(dp_actions.shape[0]) if self.args.continuous_traj: action_list = traj_to_actions(dp_actions) if len(action_list) < 8: - action_list += [0] * (8-len(action_list)) + action_list += [0] * (8 - len(action_list)) else: action_list = chunk_token(dp_actions[random_choice]) - + local_actions = action_list if len(local_actions) >= 4: local_actions = local_actions[:4] @@ -603,7 +652,7 @@ def eval_action(self, idx) -> None: if action == 0: goal = None output_ids = None - action = 2 # random action + action = 2 # random action print('conduct a random action 2') observations = env.step(action) step_id += 1 @@ -611,10 +660,10 @@ def eval_action(self, idx) -> None: continue print('predicted goal', pixel_goal, goal, flush=True) - else: + else: action_seq = self.parse_actions(llm_outputs) print('actions', action_seq, flush=True) - + if len(action_seq) != 0: action = action_seq[0] action_seq.pop(0) @@ -624,45 +673,49 @@ def eval_action(self, idx) -> None: action = agent.get_next_action(goal) action = action.detach().cpu().numpy()[0] if isinstance(action, torch.Tensor) else action action = action[0] if hasattr(action, "__len__") else action - else: # dual-system logic - if len(local_actions)==0: - ############nav_dp########### + else: # dual-system logic + if len(local_actions) == 0: + # navdp local_actions = [] - image_dp = torch.tensor(np.array(look_down_image.resize((224, 224)))).to(torch.bfloat16) / 255 - + image_dp = ( + torch.tensor(np.array(look_down_image.resize((224, 224)))).to(torch.bfloat16) / 255 + ) + images_dp = torch.stack([pix_goal_image, image_dp]).unsqueeze(0).to(self.device) depth_dp = look_down_depth.unsqueeze(-1).to(torch.bfloat16) - + depths_dp = torch.stack([pix_goal_depth, depth_dp]).unsqueeze(0).to(self.device) with torch.no_grad(): - dp_actions = self.model.generate_traj(traj_latents, images_dp, depths_dp) + dp_actions = self.model.generate_traj( + traj_latents, images_dp, depths_dp, use_async=True + ) random_choice = np.random.choice(dp_actions.shape[0]) if self.args.continuous_traj: action_list = traj_to_actions(dp_actions) if len(action_list) < 8: - action_list += [0] * (8-len(action_list)) + action_list += [0] * (8 - len(action_list)) else: action_list = chunk_token(dp_actions[random_choice]) print("first action_list", action_list) - + local_actions = action_list if len(local_actions) >= 4: local_actions = local_actions[:4] # if len(local_actions) >= 2: # local_actions = local_actions[:2] - + print("local_actions", local_actions) - + action = local_actions.pop(0) - ############nav_dp########### + # navdp else: action = local_actions.pop(0) - - forward_action +=1 + + forward_action += 1 print('forward_action', forward_action, flush=True) if forward_action > 8: - goal = None + goal = None output_ids = None messages = [] step_id += 1 @@ -679,15 +732,17 @@ def eval_action(self, idx) -> None: continue else: action = 0 - + if info['top_down_map'] is not None: if save_dot: - save_raw_image = self.dot_matrix_two_dimensional(save_raw_image, save_img=False, save_path=f'test_{step_id}.jpg', pixel_goal = pixel_goal) + save_raw_image = self.dot_matrix_two_dimensional( + save_raw_image, save_img=False, save_path=f'test_{step_id}.jpg', pixel_goal=pixel_goal + ) frame = observations_to_image({'rgb': np.asarray(save_raw_image)}, info) vis_frames.append(frame) - + print("step_id", step_id, "action", action) - + if action == 5: env.step(action) observations = env.step(action) @@ -695,21 +750,27 @@ def eval_action(self, idx) -> None: observations = env.step(action) step_id += 1 messages = [] - + process_bar.update(1) - + metrics = env.get_metrics() if self.save_video: images_to_video( - vis_frames, os.path.join(self.output_path, f'vis_{self.epoch}', f'{scene_id}'), f'{episode_id:04d}', fps=6, quality=9 + vis_frames, + os.path.join(self.output_path, f'vis_{self.epoch}', f'{scene_id}'), + f'{episode_id:04d}', + fps=6, + quality=9, ) vis_frames.clear() sucs.append(metrics['success']) spls.append(metrics['spl']) oss.append(metrics['oracle_success']) nes.append(metrics["distance_to_goal"]) - print(f"scene_episode {scene_id}_{episode_id:04d} success: {metrics['success']}, spl: {metrics['spl']}, os: {metrics['oracle_success']}, ne: {metrics['distance_to_goal']}") - + print( + f"scene_episode {scene_id}_{episode_id:04d} success: {metrics['success']}, spl: {metrics['spl']}, os: {metrics['oracle_success']}, ne: {metrics['distance_to_goal']}" + ) + result = { "scene_id": scene_id, "episode_id": episode_id, @@ -718,13 +779,19 @@ def eval_action(self, idx) -> None: "os": metrics['oracle_success'], "ne": metrics["distance_to_goal"], "steps": step_id, - "episode_instruction": episode_instruction + "episode_instruction": episode_instruction, } - - with open(os.path.join(self.output_path, f'result.json'), 'a') as f: + + with open(os.path.join(self.output_path, 'result.json'), 'a') as f: f.write(json.dumps(result) + "\n") env.close() - return torch.tensor(sucs).to(self.device), torch.tensor(spls).to(self.device), torch.tensor(oss).to(self.device), torch.tensor(nes).to(self.device), torch.tensor(len(sucs)).to(self.device) + return ( + torch.tensor(sucs).to(self.device), + torch.tensor(spls).to(self.device), + torch.tensor(oss).to(self.device), + torch.tensor(nes).to(self.device), + torch.tensor(len(sucs)).to(self.device), + ) def parse_actions(self, output): action_patterns = '|'.join(re.escape(action) for action in self.actions2idx) @@ -739,7 +806,6 @@ def preprocess_qwenvl(self, source): prompt = random.choice(self.conjunctions) + DEFAULT_IMAGE_TOKEN if len(source[0]["value"]) != 0: source[0]["value"] += f" {prompt}." - else: - source[0]["value"] = f"{prompt}." # Please output the next waypoint\'s coordinates in the image." + else: + source[0]["value"] = f"{prompt}." # Please output the next waypoint\'s coordinates in the image." return source - diff --git a/scripts/eval/inference_only_demo.ipynb b/scripts/eval/inference_only_demo.ipynb index 6a5f27d7..8cbe73ea 100644 --- a/scripts/eval/inference_only_demo.ipynb +++ b/scripts/eval/inference_only_demo.ipynb @@ -119,32 +119,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PROJECT_ROOT_PATH:/cpfs/user/yangyuqiang/tmp/tmp/InternNav\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Gym has been unmaintained since 2022 and does not support NumPy 2.0 amongst other critical functionality.\n", - "Please upgrade to Gymnasium, the maintained drop-in replacement of Gym, or contact the authors of your software and request that they upgrade.\n", - "See the migration guide at https://gymnasium.farama.org/introduction/migration_guide/ for additional information.\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/cpfs/user/yangyuqiang/tmp/tmp/InternNav/scripts/eval/../../internnav/model/basemodel/LongCLIP/model/longclip.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " from pkg_resources import packaging\n", - "xFormers not available\n", - "xFormers not available\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import os\n", @@ -173,20 +150,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model path: checkpoints/InternVLA-N1\n", - "Device: cuda:0\n", - "Image size: 384x384\n", - "History frames: 8\n" - ] - } - ], + "outputs": [], "source": [ "class Args:\n", " def __init__(self):\n", @@ -215,560 +181,14 @@ "metadata": {}, "source": [ "## 3. Initialize Agent\n", - "Load the InternVLA-N1 model and initialize the agent. If you meet error about transformers, please check that the `flash_attn` is correctly installed." + "Load the InternVLA-N1 model and initialize the agent. If you meet error about transformers, please check that the `flash_attn` and `accelerate` is correctly installed." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading model...\n", - "args.model_pathcheckpoints/InternVLA-N1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.cls_token: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.pos_embed: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.patch_embed.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.patch_embed.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.0.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.1.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.2.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.3.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.4.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.5.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.6.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.7.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.8.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.9.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.10.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.attn.qkv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.attn.qkv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.attn.proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.attn.proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.ls1.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.blocks.11.ls2.gamma: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.norm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for pretrained.norm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.0.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.0.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.projects.3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.resize_layers.0.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.resize_layers.0.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.resize_layers.1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.resize_layers.1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.resize_layers.3.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.resize_layers.3.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.layer1_rn.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.layer2_rn.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.layer3_rn.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.layer4_rn.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.out_conv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.out_conv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit1.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit1.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit2.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet1.resConfUnit2.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.out_conv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.out_conv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit1.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit1.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit2.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet2.resConfUnit2.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.out_conv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.out_conv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit1.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit1.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit2.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet3.resConfUnit2.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.out_conv.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.out_conv.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit1.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit1.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit1.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit1.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit2.conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit2.conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit2.conv2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.refinenet4.resConfUnit2.conv2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.output_conv1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.output_conv1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.output_conv2.0.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.output_conv2.0.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.output_conv2.2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/torch/nn/modules/module.py:2397: UserWarning: for depth_head.scratch.output_conv2.2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading navdp model: NavDP_Policy_DPT_CriticSum_DAT\n", - "Pretrained: None\n", - "No pretrained weights provided, initializing randomly.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading checkpoint shards: 100%|██████████| 4/4 [00:04<00:00, 1.15s/it]\n", - "Some weights of the model checkpoint at checkpoints/InternVLA-N1 were not used when initializing InternVLAN1ForCausalLM: ['model.navdp.rgbd_encoder.depth_model.mask_token', 'model.navdp.rgbd_encoder.rgb_model.mask_token']\n", - "- This IS expected if you are initializing InternVLAN1ForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", - "- This IS NOT expected if you are initializing InternVLAN1ForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", - "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warming up model...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:631: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.1` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:636: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:653: UserWarning: `do_sample` is set to `False`. However, `top_k` is set to `1` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_k`.\n", - " warnings.warn(\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output 1 →→→→ cost: 0.9194700717926025s\n", - "Model loaded successfully!\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Loading model...\")\n", "agent = InternVLAN1AsyncAgent(args)\n", @@ -793,27 +213,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Scene directory: ../../assets/realworld_sample_data1\n", - "Instruction: Turn around and walk out of this office. Turn towards your slight right at the chair. Move forward to the walkway and go near the red bin. You can see an open door on your right side, go inside the open door. Stop at the computer monitor.\n", - "\n", - "Found 152 images\n", - "\n", - "First 5 images:\n", - " 1. debug_raw_0000.jpg\n", - " 2. debug_raw_0001.jpg\n", - " 3. debug_raw_0002.jpg\n", - " 4. debug_raw_0003.jpg\n", - " 5. debug_raw_0004.jpg\n" - ] - } - ], + "outputs": [], "source": [ "# Configure data directory (single scene per folder)\n", "scene_dir = '../../assets/realworld_sample_data1'\n", @@ -980,266 +382,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\n", - "Processing scene: realworld_sample_data1\n", - "Instruction: 'Turn around and walk out of this office. Turn towards your slight right at the chair. Move forward to the walkway and go near the red bin. You can see an open door on your right side, go inside the open door. Stop at the computer monitor.'\n", - "Total images: 152\n", - "================================================================================\n", - "\n", - "output 1 →→→→ cost: 0.16082239151000977s\n", - " Output action: [3, 3, 3, 3]\n", - "output 2 →→→→ cost: 0.17944908142089844s\n", - " Output action: [3, 3, 3, 3]\n", - "output 3 →→→→ cost: 0.21164512634277344s\n", - " Output action: [3, 3, 3, 3]\n", - "output 4 →→→→ cost: 0.23621869087219238s\n", - " Output action: [3, 3, 3, 3]\n", - "output 5 →→→→ cost: 0.2659335136413574s\n", - " Output action: [3, 3, 3, 3]\n", - "output 6 →→→→ cost: 0.2969348430633545s\n", - " Output action: [3, 3, 3, 3]\n", - "output 7 →→→→ cost: 0.32748961448669434s\n", - " Output action: [3, 3, 3, 3]\n", - "output 8 →→→→ cost: 0.3569812774658203s\n", - " Output action: [3, 3, 3, 3]\n", - "output 9 →→→→ cost: 0.39242029190063477s\n", - " Output action: [3, 3, 3, 3]\n", - "output 10 ↓ cost: 0.3273155689239502s\n", - " Output action: [5]\n", - "output 11 ↓ cost: 0.32593870162963867s\n", - " Output action: [5]\n", - "output 11 460 210 cost: 0.4580264091491699s\n", - "output_trajectory: [[0.0, 0.0], [0.08659052848815918, -0.008930981159210205], [0.1714315414428711, -0.02344149351119995], [0.23385441303253174, -0.04275400936603546], [0.28055113554000854, -0.06705068424344063], [0.32100480794906616, -0.09261317923665047], [0.34672266244888306, -0.11875501647591591], [0.367924265563488, -0.1411449797451496], [0.38493258133530617, -0.15768635645508766], [0.39753447845578194, -0.17283473536372185], [0.4113854803144932, -0.18578103557229042], [0.42664463445544243, -0.19660458713769913], [0.441229235380888, -0.20944863557815552], [0.4529699571430683, -0.22032871842384338], [0.46688373014330864, -0.2319333702325821], [0.4804397989064455, -0.24219205230474472], [0.4915638715028763, -0.2533899135887623], [0.503329761326313, -0.26210515573620796], [0.5180559456348419, -0.27345648780465126], [0.5341812670230865, -0.2843875624239445], [0.5451795402914286, -0.2913499213755131], [0.5524066481739283, -0.2968406043946743], [0.5593739319592714, -0.3006090931594372], [0.5685115922242403, -0.3042212836444378], [0.5771693158894777, -0.3074386902153492], [0.5800204630941153, -0.3084157966077328], [0.5803284551948309, -0.30843130126595497], [0.5803011115640402, -0.30843479558825493], [0.5803246479481459, -0.30864667519927025], [0.580126253888011, -0.3087734244763851], [0.5802086498588324, -0.3088274262845516], [0.5802167411893606, -0.3088805638253689], [0.5803017746657133, -0.30908624827861786]]\n", - "output_pixel: [210, 460]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_17640/3632390943.py:99: MatplotlibDeprecationWarning: The tostring_rgb function was deprecated in Matplotlib 3.8 and will be removed in 3.10. Use buffer_rgba instead.\n", - " plot_img = np.frombuffer(canvas.tostring_rgb(), dtype=np.uint8)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output_trajectory: [[0.0, 0.0], [0.08256661891937256, 0.027923524379730225], [0.16881370544433594, 0.0661931037902832], [0.24290108680725098, 0.11898757517337799], [0.31348085403442383, 0.18880082666873932], [0.3749065399169922, 0.2609211355447769], [0.4261155128479004, 0.329192653298378], [0.4781756103038788, 0.3995744436979294], [0.5225162208080292, 0.46002958714962006], [0.5633377134799957, 0.5219624191522598], [0.5990551710128784, 0.5775522589683533], [0.6283277720212936, 0.6236331611871719], [0.6623674780130386, 0.6693335436284542], [0.686418280005455, 0.7085095010697842], [0.7130109220743179, 0.7466136999428272], [0.736764445900917, 0.7791865430772305], [0.7592355757951736, 0.8097589276731014], [0.7815831750631332, 0.8390821553766727], [0.7925338894128799, 0.8529066257178783], [0.8052936941385269, 0.8717189095914364], [0.8137369751930237, 0.8864557184278965], [0.8184954822063446, 0.8949254676699638], [0.8224688619375229, 0.9025629833340645], [0.8241771161556244, 0.9057843312621117], [0.825649231672287, 0.9083721339702606], [0.8261764571070671, 0.9106888622045517], [0.8259661421179771, 0.9104586094617844], [0.8256968632340431, 0.9105620309710503], [0.8253541439771652, 0.9103842303156853], [0.8248700946569443, 0.9103136137127876], [0.8246832452714443, 0.9104026481509209], [0.8243147544562817, 0.9102655798196793], [0.8238722495734692, 0.9100026339292526]]\n", - 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] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:631: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.1` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:636: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n", - " warnings.warn(\n", - "/root/miniconda3/envs/internvla/lib/python3.9/site-packages/transformers/generation/configuration_utils.py:653: UserWarning: `do_sample` is set to `False`. However, `top_k` is set to `1` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_k`.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output 18 208 227 cost: 0.3908498287200928s\n", - "output_trajectory: [[0.0, 0.0], [0.0745704174041748, -0.005706816911697388], [0.149530827999115, -0.011543631553649902], [0.20113742351531982, -0.010433673858642578], [0.22944402694702148, -0.0034567415714263916], [0.24878980219364166, 0.0017610713839530945], [0.25412873923778534, 0.0017748735845088959], [0.25503866374492645, 0.0025987885892391205], [0.2602487578988075, 0.0012995414435863495], [0.2691432908177376, 0.0045538656413555145], [0.27799204736948013, 0.011330261826515198], [0.2832188680768013, 0.021190591156482697], [0.2887192741036415, 0.03052666038274765], [0.2945338115096092, 0.0366721972823143], [0.30164342373609543, 0.04868323355913162], [0.310389406979084, 0.06067415326833725], [0.3139064088463783, 0.07023052871227264], [0.31673451513051987, 0.07914803922176361], [0.3169716186821461, 0.0830104649066925], [0.31820984557271004, 0.08500789105892181], [0.31866631656885147, 0.08353064954280853], [0.3180432431399822, 0.08212738484144211], [0.3181079886853695, 0.08218146115541458], [0.3178662247955799, 0.0799063965678215], [0.3179646022617817, 0.07727530412375927], [0.31964336708188057, 0.07469849847257137], [0.3200826831161976, 0.073790667578578], [0.32002067938447, 0.07320972718298435], [0.3198618181049824, 0.0727736447006464], [0.3195936270058155, 0.07202706672251225], [0.3196745403110981, 0.07194929756224155], [0.3195176012814045, 0.07168189622461796], [0.3191588409245014, 0.07157356478273869]]\n", - 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"\n", - "Scene realworld_sample_data1 completed!\n" - ] - } - ], + "outputs": [], "source": [ "# Reset agent\n", "agent.reset()\n",