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import os
import sys
import numpy as np
import hydra
from cliport.dataset import RavensDataset
from cliport.utils import utils
from cliport import tasks
from cliport.environments.environment import Environment
import torch
import matplotlib
import matplotlib.pyplot as plt
mode = 'train'
augment = True
### Uncomment the task you want to generate ###
# task = 'align-rope'
# task = 'assembling-kits-seq-seen-colors'
# task = 'assembling-kits-seq-unseen-colors'
# task = 'assembling-kits-seq-full'
# task = 'packing-shapes'
# task = 'packing-boxes-pairs-seen-colors'
# task = 'packing-boxes-pairs-unseen-colors'
# task = 'packing-boxes-pairs-full'
# task = 'packing-seen-google-objects-seq'
# task = 'packing-unseen-google-objects-seq'
# task = 'packing-seen-google-objects-group'
# task = 'packing-unseen-google-objects-group'
# task = 'put-block-in-bowl-seen-colors'
# task = 'put-block-in-bowl-unseen-colors'
# task = 'put-block-in-bowl-full'
task = 'align-box-corner'
# task = 'stack-block-pyramid-seq-unseen-colors'
# task = 'stack-block-pyramid-seq-full'
# task = 'separating-piles-seen-colors'
# task = 'separating-piles-unseen-colors'
# task = 'separating-piles-full'
# task = 'towers-of-hanoi-seq-seen-colors'
# task = 'towers-of-hanoi-seq-unseen-colors'
# task = 'towers-of-hanoi-seq-full'
### visualization settings
max_episodes = 1
max_steps = 100
root_dir = os.environ['CLIPORT_ROOT']
config_file = 'train.yaml'
cfg = utils.load_hydra_config(os.path.join(root_dir, f'cliport/cfg/{config_file}'))
# Override defaults
cfg['task'] = task
cfg['mode'] = mode
cfg['train']['data_augmentation'] = True
data_dir = os.path.join(root_dir, 'data')
task = tasks.names[cfg['task']]()
task.mode = mode
ds = RavensDataset(os.path.join(data_dir, f'{cfg["task"]}-{cfg["mode"]}'), cfg, n_demos=10, augment=augment)
color_sums = []
depth_sums = []
total_images = 0
for i in range(0, min(max_episodes, ds.n_episodes)):
print(f'\n\nEpisode: {i + 1}/{ds.n_episodes}')
episode, seed = ds.load(i)
total_images += len(episode)-1
total_reward = 0
for step in range(min(max_steps, len(episode))):
print(f"\nStep: {step+1}/{len(episode)}")
obs, act, reward, info = episode[step]
total_reward += reward
batch = ds[i]
num_images = len(obs['color'])
fig, axs = plt.subplots(2, num_images+1, figsize=(15, 6))
for n in range(num_images):
axs[1, n].imshow(obs['color'][n])
axs[1, n].set_title(f'Raw RGB {n+1}')
axs[0, n].imshow(obs['depth'][n])
axs[0, n].set_title(f'Raw Depth {n+1}')
color_sums.append(np.mean(obs['color'][0], axis=(0,1)) / 255.0)
depth_sums.append(np.mean(obs['depth'][0], axis=(0,1)))
cam_config = None
if b'camera_info' in info:
cam_config = ds.get_cam_config(info[b'camera_info'])
img_depth = ds.get_image(obs, cam_config=cam_config)
img_tensor = torch.from_numpy(img_depth)
img = np.uint8(img_tensor.detach().cpu().numpy())
img = img.transpose(1,0,2)
if step < len(episode)-1 and episode[step]:
batch = ds.process_sample(episode[step], augment=augment)
else:
batch = ds.process_goal(episode[step], perturb_params=None)
img_sample = batch['img']
img_sample = torch.from_numpy(img_sample)
color = np.uint8(img_sample.detach().cpu().numpy())[:,:,:3]
color = color.transpose(1,0,2)
depth = np.array(img_sample.detach().cpu().numpy())[:,:,3]
depth = depth.transpose(1,0)
axs[0, num_images].imshow(depth)
axs[0, num_images].set_title('Depth')
axs[1,num_images].imshow(color)
axs[1,num_images].set_title('RGB + Oracle Pick & Place')
if act and step < len(episode)-1:
p0 = batch['p0']
p1 = batch['p1']
p0_theta = batch['p0_theta']
p1_theta = batch['p1_theta'] + p0_theta
pick = p0
place = p1
line_len = 30
pick0 = (pick[0] + line_len/2.0 * np.sin(p0_theta), pick[1] + line_len/2.0 * np.cos(p0_theta))
pick1 = (pick[0] - line_len/2.0 * np.sin(p0_theta), pick[1] - line_len/2.0 * np.cos(p0_theta))
axs[1,num_images].plot((pick1[0], pick0[0]), (pick1[1], pick0[1]), color='r', linewidth=2)
place0 = (place[0] + line_len/2.0 * np.sin(p1_theta), place[1] + line_len/2.0 * np.cos(p1_theta))
place1 = (place[0] - line_len/2.0 * np.sin(p1_theta), place[1] - line_len/2.0 * np.cos(p1_theta))
axs[1,num_images].plot((place1[0], place0[0]), (place1[1], place0[1]), color='g', linewidth=2)
c_pick = plt.Circle(pick, 3, color='r', fill=False)
c_place = plt.Circle(place, 3, color='g', fill=False)
axs[1,num_images].add_patch(c_pick)
axs[1,num_images].add_patch(c_place)
plt.show()
print(f"Language Goal: {batch['lang_goal']}")
print(f"Step Reward: {reward}")
print(f"Total Reward: {total_reward}")
print(f"Done, Total Reward: {total_reward}")
print("\n\nDataset Statistics: ")
print(f"Color Mean: {np.mean(color_sums, axis=0)}, Std: {np.std(color_sums, axis=0)}")
print(f"Depth Mean: {np.mean(depth_sums, axis=0)}, Std: {np.std(depth_sums, axis=0)}")
print(f"Total Image-Action Pairs: {total_images}") |