Upload transfer.py

transfer.py

@@ -0,0 +1,85 @@
+import os
+import io
+import av
+import json
+from pickle import dumps, loads
+import numpy as np
+import torch
+from torchvision.transforms.functional import resize
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from einops import rearrange
+
+def decode_inst(insts):
+    # Utility to decode encoded language instructions
+    decoded_insts = []
+    for inst in insts:
+        decoded_insts.append(bytes(inst[np.where(inst != 0)].tolist()).decode("utf-8"))
+    return decoded_insts
+
+def save_video(file, video):
+    container = av.open(file, 'w', 'mp4')
+    stream = container.add_stream('libx264', rate=30)
+    stream.height = video[0].shape[0]
+    stream.width = video[0].shape[1]
+    stream.bit_rate = 2000000 # 2Mbps
+    stream.pix_fmt = 'yuv420p'
+    for i in range(len(video)):
+        frame = av.VideoFrame.from_ndarray(video[i], format='rgb24')
+        frame = frame.reformat(format=stream.pix_fmt)
+        for packet in stream.encode(frame):
+            container.mux(packet)
+    # Flush stream
+    for packet in stream.encode():
+        container.mux(packet)
+    container.close()
+
+if __name__ == '__main__':
+    tf_builder = tfds.builder_from_directory('./droid/1.0.0/')
+    tf_dataset = tf_builder.as_dataset(split="train")
+    skip_episode = 78663
+    js_path = 'index.json'
+    if os.path.exists(js_path):
+        js_data = json.load(open(js_path, 'r'))
+    else:
+        js_data = []
+    for episode_id, episode in enumerate(tf_dataset):
+        file_path = episode['episode_metadata']['file_path'].numpy().decode('utf-8')
+        recording_folderpath = episode['episode_metadata']['recording_folderpath'].numpy().decode('utf-8')
+        if episode_id <= skip_episode or 'success' not in file_path:
+            print(f'skipping {episode_id}/{len(tf_dataset)}')
+            continue
+        left_camera = []
+        arm_camera = []
+        right_camera = []
+        inst = []
+        skip_episode = False
+        for step_id, single_step in enumerate(episode['steps']):
+            if single_step['language_instruction'].numpy().decode('utf-8') not in inst:
+                inst.append(single_step['language_instruction'].numpy().decode('utf-8'))
+            if single_step['language_instruction_2'].numpy().decode('utf-8') not in inst:
+                inst.append(single_step['language_instruction_2'].numpy().decode('utf-8'))
+            if single_step['language_instruction_3'].numpy().decode('utf-8') not in inst:
+                inst.append(single_step['language_instruction_3'].numpy().decode('utf-8'))
+            if len(inst) == 1 and inst[0] == '':
+                skip_episode = True
+                break
+            left_camera.append(single_step['observation']['exterior_image_1_left'].numpy())
+            right_camera.append(single_step['observation']['exterior_image_2_left'].numpy())
+            arm_camera.append(single_step['observation']['wrist_image_left'].numpy())
+        if skip_episode:
+            print(f'skipping {episode_id}/{len(tf_dataset)}')
+            continue
+        print(f'saving {episode_id}/{len(tf_dataset)}')
+        save_video(f'droid_videos/episode_{episode_id}_left_camera.mp4', left_camera)
+        save_video(f'droid_videos/episode_{episode_id}_right_camera.mp4', right_camera)
+        save_video(f'droid_videos/episode_{episode_id}_arm_camera.mp4', arm_camera)
+        for i in range(len(inst)):
+            if inst[i] == '':
+                continue
+            js_data.append({"path": f'droid_videos/episode_{episode_id}_left_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]})
+            js_data.append({"path": f'droid_videos/episode_{episode_id}_right_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]})
+            js_data.append({"path": f'droid_videos/episode_{episode_id}_arm_camera.mp4', "recording_folder": recording_folderpath, "cap": [inst[i]]})
+        if episode_id % 1000 < 10:
+            json.dump(js_data, open(js_path, 'w'), indent=4)
+    json.dump(js_data, open(js_path, 'w'), indent=4)