def load_dcn_network(self):
"""
Loads the DCN.
"""
config = pdc_utils.getDictFromYamlFilename(NETWORK_CONFIG_FILENAME)
defaults_config = pdc_utils.get_defaults_config()
pdc_utils.set_cuda_visible_devices([0])
dce = DenseCorrespondenceEvaluation(config)
self.dcn = dce.load_network_from_config(NETWORK_NAME)
self.dcn.eval()
self.dataset = self.dcn.load_training_dataset(
) # why do we need to do this?
print "finished loading dcn"
def load_dcn_network(self):
"""
Loads the DCN.
Currently just edit this function to change which
"""
config = pdc_utils.getDictFromYamlFilename(NETWORK_CONFIG_FILENAME)
defaults_config = pdc_utils.get_defaults_config()
pdc_utils.set_cuda_visible_devices([0])
dce = DenseCorrespondenceEvaluation(config)
network_name = self.pick_point_config["network_name"]
self.dcn = dce.load_network_from_config(network_name)
self.dataset = self.dcn.load_training_dataset() # why do we need to do this?
print "finished loading dcn"
def main():
set_cuda_visible_devices([get_freer_gpu()])
#
# model_file, dataset_name = get_DD_model_file()
dataset_name = "box_push_1000_top_down"
dataset_name = "box_push_1000_angled"
dataset_paths = exp_utils.get_dataset_paths(dataset_name)
dataset_root = dataset_paths['dataset_root']
model_file = dataset_paths['dense_descriptor_model_chkpt']
multi_episode_dict = DCDrakeSimEpisodeReader.load_dataset(
dataset_root, max_num_episodes=None)
camera_list = [dataset_paths['main_camera_name']]
target_camera_names = [dataset_paths['main_camera_name']]
# camera_list = ['camera_1_top_down']
# target_camera_names = ['camera_1_top_down']
model_config_file = os.path.join(os.path.dirname(model_file),
'config.yaml')
model_config = getDictFromYamlFilename(model_config_file)
model_config['dataset']['data_augmentation'] = False
dataset = DynamicDrakeSimDataset(
model_config, multi_episode_dict,
phase="valid") # could also use train data
model = torch.load(model_file)
model = model.cuda()
model = model.eval()
heatmap_vis = HeatmapVisualization(model_config,
dataset,
model,
visualize_3D=False,
camera_list=camera_list,
target_camera_names=target_camera_names,
verbose=True,
sample_same_episode=False)
heatmap_vis.run()
import numpy as np
import copy
import transforms3d
import time
import math
import matplotlib.pyplot as plt
# need to import pydrake before pdc
import pydrake
import meshcat
# pdc for setting available GPU's
from dense_correspondence_manipulation.utils.utils import set_cuda_visible_devices
GPU_LIST = [0]
set_cuda_visible_devices(GPU_LIST)
import torch
# dense correspondence
from dense_correspondence_manipulation.utils import constants
from dense_correspondence_manipulation.utils import utils as pdc_utils
from dense_correspondence.network import predict
import dense_correspondence_manipulation.utils.visualization as vis_utils
# key_dynam
from key_dynam.utils.utils import get_project_root, load_yaml, get_current_YYYY_MM_DD_hh_mm_ss_ms, get_data_root, \
load_pickle
from key_dynam.envs.drake_pusher_slider import DrakePusherSliderEnv
from key_dynam.envs.drake_mugs import DrakeMugsEnv
from key_dynam.envs import utils as env_utils
from key_dynam.utils.utils import get_project_root, load_yaml, get_current_YYYY_MM_DD_hh_mm_ss_ms, get_data_root, load_pickle
from key_dynam.utils.torch_utils import get_freer_gpu
from key_dynam.dynamics.utils import set_seed
from dense_correspondence_manipulation.utils.utils import set_cuda_visible_devices
from key_dynam.dense_correspondence.dc_drake_sim_episode_reader import DCDrakeSimEpisodeReader
from key_dynam.dense_correspondence.precompute_descriptors import precompute_descriptor_keypoints
from key_dynam.dense_correspondence.keypoint_selection import score_and_select_spatially_separated_keypoints
from key_dynam.experiments.exp_18_box_on_side import utils as exp_18_utils
from key_dynam.experiments.exp_20_mugs import utils as exp_20_utils
# CUDA_VISIBLE_DEVICES = [0]
# set_cuda_visible_devices(CUDA_VISIBLE_DEVICES)
set_cuda_visible_devices([get_freer_gpu()])
from dense_correspondence_manipulation.utils.visualization import draw_reticles
def get_DD_model_file(dataset_name):
model_name = None
model_file = None
if dataset_name == "dps_box_on_side_600":
model_name = ""
model_file = "/home/manuelli/data/key_dynam/dev/experiments/drake_pusher_slider_box_on_side/dataset_dps_box_on_side_600/trained_models/perception/dense_descriptor/3D_loss_camera_angled_2020-05-13-23-39-35-818188/net_best_dy_model.pth"
elif dataset_name == "correlle_mug-small_single_color_600":
model_name = ""
model_file = "/home/manuelli/data/key_dynam/dev/experiments/20/dataset_correlle_mug-small_single_color_600/trained_models/perception/dense_descriptors/data_aug_2020-06-03-00-27-50-738970/net_best_model.pth"
elif dataset_name == "correlle_mug-small_many_colors_600":
# camera_list = ['camera_1_top_down']
# target_camera_names = ['camera_1_top_down']
model_config_file = os.path.join(os.path.dirname(model_file),
'config.yaml')
model_config = getDictFromYamlFilename(model_config_file)
model_config['dataset']['data_augmentation'] = False
dataset = DynamicDrakeSimDataset(
model_config, multi_episode_dict,
phase="valid") # could also use train data
model = torch.load(model_file)
model = model.cuda()
model = model.eval()
heatmap_vis = HeatmapVisualization(model_config,
dataset,
model,
visualize_3D=False,
camera_list=camera_list,
target_camera_names=target_camera_names,
verbose=True,
sample_same_episode=False)
heatmap_vis.run()
if __name__ == "__main__":
set_cuda_visible_devices([1])
main()
save_dir, SceneStructure.descriptor_image_filename(img_idx))
np.save(descriptor_filename, res)
print("descriptor_filename", descriptor_filename)
print("processing image %d of %d" % (counter, num_images))
counter += 1
if __name__ == "__main__":
dc_source_dir = utils.getDenseCorrespondenceSourceDir()
config_filename = os.path.join(dc_source_dir, 'config',
'dense_correspondence', 'evaluation',
'lucas_evaluation.yaml')
eval_config = utils.getDictFromYamlFilename(config_filename)
default_config = utils.get_defaults_config()
utils.set_cuda_visible_devices(default_config['cuda_visible_devices'])
dce = DenseCorrespondenceEvaluation(eval_config)
network_name = "caterpillar_M_background_0.500_3"
dcn = dce.load_network_from_config(network_name)
dataset_config_file = os.path.join(dc_source_dir, 'config',
'dense_correspondence', 'dataset',
'composite', 'caterpillar_only_9.yaml')
dataset_config = utils.getDictFromYamlFilename(dataset_config_file)
dataset = SpartanDataset(config=dataset_config)
scene_name = SCENE_NAME
save_dir = SAVE_DIR
compute_descriptor_images_for_single_scene(dataset, scene_name, dcn,
save_dir)
"""
import os
import pydrake
import torch
from key_dynam.dense_correspondence.precompute_descriptors import compute_descriptor_confidences
from key_dynam.utils.utils import get_project_root, load_yaml, get_current_YYYY_MM_DD_hh_mm_ss_ms, get_data_root, load_pickle
from dense_correspondence_manipulation.utils.utils import set_cuda_visible_devices
from key_dynam.dense_correspondence.dc_drake_sim_episode_reader import DCDrakeSimEpisodeReader
from key_dynam.dense_correspondence.precompute_descriptors import precompute_descriptor_keypoints
from key_dynam.dense_correspondence.keypoint_selection import score_and_select_spatially_separated_keypoints
CUDA_VISIBLE_DEVICES = [0]
set_cuda_visible_devices(CUDA_VISIBLE_DEVICES)
def load_episodes():
# DATASET_NAME = "2020-03-25-19-57-26-556093_constant_velocity_500"
# DATASET_NAME = "2020-04-15-21-15-56-602712_T_aug_random_velocity_500"
DATASET_NAME = "2020-04-20-14-58-21-418302_T_aug_random_velocity_1000"
dataset_root = os.path.join(get_data_root(), "dev/experiments/09/data",
DATASET_NAME)
max_num_episodes = None
multi_episode_dict = DCDrakeSimEpisodeReader.load_dataset(
dataset_root, max_num_episodes=max_num_episodes)
return DATASET_NAME, multi_episode_dict
import dense_correspondence_manipulation.utils.utils as utils
utils.add_dense_correspondence_to_python_path()
from dense_correspondence.training.training import *
import sys
import logging
import argparse
#utils.set_default_cuda_visible_devices()
utils.set_cuda_visible_devices([0]) # use this to manually set CUDA_VISIBLE_DEVICES
from dense_correspondence.training.training import DenseCorrespondenceTraining
from dense_correspondence.dataset.spartan_dataset_masked import SpartanDataset
logging.basicConfig(level=logging.INFO)
from dense_correspondence.evaluation.evaluation import DenseCorrespondenceEvaluation
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--data_name", type=str, default="caterpillar_upright.yaml")
parser.add_argument("--run_prefix", type=str, default="caterpillar")
parser.add_argument("--training_yaml", type=str, default="training.yaml")
args = parser.parse_args()
config_filename = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', 'dense_correspondence',
'dataset', 'composite', args.data_name)
config = utils.getDictFromYamlFilename(config_filename)
from dense_correspondence.dataset.spartan_dataset_masked import SpartanDataset, ImageType
import dense_correspondence
from dense_correspondence.evaluation.evaluation import *
from dense_correspondence.evaluation.plotting import normalize_descriptor
from dense_correspondence.network.dense_correspondence_network import DenseCorrespondenceNetwork
sys.path.append(
os.path.join(os.path.dirname(__file__),
"../simple-pixel-correspondence-labeler"))
from annotate_correspondences import label_colors, draw_reticle, pil_image_to_cv2, drawing_scale_config, numpy_to_cv2, label_colors
COLOR_RED = np.array([0, 0, 255])
COLOR_GREEN = np.array([0, 255, 0])
utils.set_cuda_visible_devices([0])
eval_config_filename = os.path.join(utils.getDenseCorrespondenceSourceDir(),
'config', 'dense_correspondence',
'evaluation', 'evaluation.yaml')
EVAL_CONFIG = utils.getDictFromYamlFilename(eval_config_filename)
class HeatmapVisualization(object):
"""
Launches a live interactive heatmap visualization.
Edit config/dense_correspondence/heatmap_vis/heatmap.yaml to specify which networks
to visualize. Specifically add the network you want to visualize to the "networks" list.
Make sure that this network appears in the file pointed to by EVAL_CONFIG
Usage: Launch this file with python after sourcing the environment with
