add pretrained models

pretrained_models/panda_push/diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100/args.json

@@ -0,0 +1,103 @@
+{
+    "action_only": false,
+    "action_weight": 10,
+    "add_extras": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "batch_size": 32,
+    "bucket": null,
+    "clip_denoised": false,
+    "commit": "344ddeb162a3b4f1052e62124960a5692227b8d5 main",
+    "config": "config.pandapush_pint",
+    "dataset": "panda_push",
+    "dataset_path": "model_chkpts/3C_ECRL_State_RandColor_20240524-110922/panda_push_replay_buffer_dlp_randcolor.pkl",
+    "device": "cuda:1",
+    "diffusion": "models.GaussianDiffusion",
+    "dropout": 0.0,
+    "droupout": 0.0,
+    "ema_decay": 0.995,
+    "env_config_dir": "config/generalization_num_cubes",
+    "eval_freq": 40,
+    "eval_fstrings": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "exp_name": "diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100",
+    "features_dim": 10,
+    "generate_exp_name": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "get_commit": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "gradient_accumulate_every": 2,
+    "hidden_dim": 512,
+    "horizon": 5,
+    "input_type": "dlp",
+    "learning_rate": 8e-05,
+    "loader": "datasets.GoalDataset",
+    "logbase": "data",
+    "loss_discount": 1,
+    "loss_type": "l1",
+    "loss_weights": null,
+    "max_particles": null,
+    "max_path_length": 100,
+    "mkdir": {
+        "_type": "python_object (type = method)",
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+    },
+    "model": "models.AdaLNPINTDenoiser",
+    "multiview": true,
+    "n_diffusion_steps": 100,
+    "n_heads": 8,
+    "n_layers": 12,
+    "n_reference": 4,
+    "n_saves": 10,
+    "n_steps_per_epoch": 1000,
+    "n_train_steps": 1000000.0,
+    "normalizer": "SafeLimitsNormalizer",
+    "num_entity": 3,
+    "obs_only": false,
+    "overfit": false,
+    "pad_state": false,
+    "particle_normalizer": "ParticleLimitsNormalizer",
+    "positional_bias": false,
+    "predict_epsilon": false,
+    "prefix": "diffusion/3C_adalnpintlarge_dlp_randcolor",
+    "preprocess_fns": [],
+    "projection_dim": 512,
+    "read_config": {
+        "_type": "python_object (type = method)",
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+    },
+    "renderer": "utils.ParticleRenderer",
+    "reproducibility": {
+        "command_line": "python diffuser/scripts/acc_train.py --config config.pandapush_pint",
+        "git_has_uncommitted_changes": true,
+        "git_root": "/scratch/cluster/carlq/research/ECRL",
+        "git_url": "https://github.com/carl-qi/ECDiffuse/tree/344ddeb162a3b4f1052e62124960a5692227b8d5",
+        "time": "Mon Aug  5 23:44:58 2024"
+    },
+    "sample_freq": 0,
+    "save_diff": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "save_freq": 1000,
+    "save_parallel": false,
+    "savepath": "data/panda_push/diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100",
+    "seed": 719594,
+    "set_loadbase": {
+        "_type": "python_object (type = method)",
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+    },
+    "set_seed": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "shuffle_particle_ordering": false,
+    "use_padding": true
+}

pretrained_models/panda_push/diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100/dataset_config.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3737a9b1d3e4f4290732e623a56cdf026489488957d85ec8d17b23782fda1de6
+size 482

pretrained_models/panda_push/diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100/diff.txt

@@ -0,0 +1,908 @@
+diff --git a/.gitignore b/.gitignore
+index c84e0e1..61c977f 100644
+--- a/.gitignore
++++ b/.gitignore
+@@ -6,4 +6,5 @@ logs
+ model_chkpts
+ results
+ debug
+-failure_cases
+
++failure_cases
++data
+
+diff --git a/analylize_trajectory.ipynb b/analylize_trajectory.ipynb
+index ee01840..6ac8c75 100644
+--- a/analylize_trajectory.ipynb
++++ b/analylize_trajectory.ipynb
+@@ -2,7 +2,7 @@
+  "cells": [
+   {
+    "cell_type": "code",
+-   "execution_count": 2,
++   "execution_count": 1,
+    "metadata": {},
+    "outputs": [],
+    "source": [
+@@ -180,19 +180,20 @@
+   },
+   {
+    "cell_type": "code",
+-   "execution_count": 5,
++   "execution_count": 7,
+    "metadata": {},
+    "outputs": [
+     {
+      "name": "stdout",
+      "output_type": "stream",
+      "text": [
+-      "t: 0 target_object: 51 max_obj_idxes: [20, 51, 100, 154, 202]\n",
+-      "t: 5 target_object: 7 max_obj_idxes: [7, 54, 106, 150, 212]\n",
+-      "t: 10 target_object: 1 max_obj_idxes: [1, 63, 111, 157, 215]\n",
+-      "t: 15 target_object: 1 max_obj_idxes: [1, 71, 111, 169, 203]\n",
+-      "t: 20 target_object: 10 max_obj_idxes: [10, 56, 122, 168, 212]\n",
+-      "t: 25 target_object: 4 max_obj_idxes: [4, 71, 115, 170, 220]\n"
++      "t: 0 target_object: 51 max_obj_idxes: [20, 13, 16, 10, 11, 51, 62, 70, 63, 57, 100, 109, 112, 119, 117, 154, 171, 161, 149, 151, 202, 212, 201, 200, 207] hit_percentage: 0.32\n",
++      "t: 5 target_object: 7 max_obj_idxes: [7, 17, 8, 4, 23, 54, 65, 63, 62, 69, 106, 110, 112, 99, 113, 150, 161, 158, 163, 166, 204, 207, 210, 201, 211] hit_percentage: 0.28\n",
++      "t: 10 target_object: 1 max_obj_idxes: [1, 23, 6, 4, 8, 63, 57, 54, 70, 66, 111, 101, 104, 120, 100, 157, 165, 148, 170, 166, 215, 212, 201, 207, 204] hit_percentage: 0.28\n",
++      "t: 15 target_object: 1 max_obj_idxes: [1, 8, 21, 3, 16, 71, 54, 51, 65, 69, 111, 107, 103, 106, 100, 169, 163, 151, 159, 162, 203, 218, 205, 215, 207] hit_percentage: 0.6\n",
++      "t: 20 target_object: 10 max_obj_idxes: [10, 16, 14, 23, 15, 56, 57, 50, 55, 64, 122, 106, 100, 103, 116, 168, 159, 148, 155, 153, 212, 202, 200, 198, 201] hit_percentage: 0.36\n",
++      "t: 25 target_object: 4 max_obj_idxes: [4, 5, 10, 6, 1, 71, 58, 65, 51, 73, 115, 111, 113, 107, 112, 170, 160, 148, 155, 167, 218, 220, 210, 211, 203] hit_percentage: 0.24\n",
++      "overall_hit_percentage: 0.3466666666666667\n"
+      ]
+     }
+    ],
+@@ -213,21 +214,29 @@
+     "    new_idx = i * 24 + j\n",
+     "    return new_idx\n",
+     "\n",
++    "overall_hit_percentage = []\n",
+     "for t in [0, 5, 10, 15, 20, 25]:\n",
+     "    with open(f'logs/panda_push/plans/3C_plan_3C_pintlarge_dlp_analysis_H5_T100/step_latest/particles_{t}.pkl', 'rb') as f:\n",
+     "        particles = pickle.load(f)\n",
+     "    particles = particles[0, :, :240]\n",
+     "    particles = particles.reshape(-1, 10)[:, 2:]\n",
+-    "    particle_similarity = cdist(particles, particles, metric='cosine')\n",
++    "    particle_similarity = cdist(particles, particles, metric='euclidean')\n",
+     "    particle_similarity = 1 - particle_similarity\n",
+     "\n",
+     "\n",
+     "    target_obj = objects_of_interest_dict[t][0]\n",
+     "    target_particle = convert_obj_idx_to_particle_idx(target_obj)\n",
+-    "    max_particle_idxes = np.argmax(particle_similarity[target_particle].reshape(5, -1), axis=1)\n",
+-    "    max_particle_idxes = [i*24 + j for i, j in enumerate(max_particle_idxes)]\n",
++    "    # max_particle_idxes = np.argmax(particle_similarity[target_particle].reshape(5, -1), axis=1)\n",
++    "    top_k_particle_indices = np.argsort(particle_similarity[target_particle].reshape(5, -1), axis=-1)[:, ::-1][:, :5]\n",
++    "    max_particle_idxes = []\n",
++    "    for i in range(5):\n",
++    "        for idx in top_k_particle_indices[i]:\n",
++    "                max_particle_idxes.append(i*24 + idx)\n",
++    "    # max_particle_idxes = [i*24 + j for i, j in enumerate(max_particle_idxes)]\n",
+     "    max_obj_idxes = [convert_particle_idx_to_obj_idx(idx) for idx in max_particle_idxes]\n",
+-    "    print(\"t:\", t, \"target_object:\", target_obj, \"max_obj_idxes:\", max_obj_idxes)\n",
++    "    hit_percentage = np.mean([1 if obj_idx in objects_of_interest_dict[t] else 0 for obj_idx in max_obj_idxes])\n",
++    "    print(\"t:\", t, \"target_object:\", target_obj, \"max_obj_idxes:\", max_obj_idxes, \"hit_percentage:\", hit_percentage)\n",
++    "    overall_hit_percentage.append(hit_percentage)\n",
+     "    n_particles = 120\n",
+     "\n",
+     "    # fig, ax = plt.subplots()\n",
+@@ -238,33 +247,16 @@
+     "    # ax.imshow(particle_similarity)\n",
+     "    # ax.set_title(f\"Particle Similarity\", fontsize=12)\n",
+     "    # ax.set_xticks(range(n_particles), [f\"{i}\" for i in range(n_particles)])\n",
+-    "    # ax.set_yticks(range(n_particles), [f\"{i}\" for i in range(n_particles)])"
++    "    # ax.set_yticks(range(n_particles), [f\"{i}\" for i in range(n_particles)])\n",
++    "print(\"overall_hit_percentage:\", np.mean(overall_hit_percentage))"
+    ]
+   },
+   {
+    "cell_type": "code",
+-   "execution_count": 38,
++   "execution_count": null,
+    "metadata": {},
+-   "outputs": [
+-    {
+-     "data": {
+-      "text/plain": [
+-       "(5, 24)"
+-      ]
+-     },
+-     "execution_count": 38,
+-     "metadata": {},
+-     "output_type": "execute_result"
+-    }
+-   ],
+-   "source": [
+-    "t: 0 target_object: 51 max_obj_idxes: [20, 51, 100, 154, 202]\n",
+-    "t: 5 target_object: 7 max_obj_idxes: [7, 54, 106, 150, 204]\n",
+-    "t: 10 target_object: 1 max_obj_idxes: [1, 63, 111, 157, 215]\n",
+-    "t: 15 target_object: 1 max_obj_idxes: [1, 71, 111, 169, 203]\n",
+-    "t: 20 target_object: 10 max_obj_idxes: [10, 56, 122, 168, 212]\n",
+-    "t: 25 target_object: 4 max_obj_idxes: [4, 71, 115, 170, 218]"
+-   ]
++   "outputs": [],
++   "source": []
+   },
+   {
+    "cell_type": "code",
+diff --git a/diffuser/config/pandapush.py b/diffuser/config/pandapush.py
+index 47f1d38..d876bbf 100644
+--- a/diffuser/config/pandapush.py
++++ b/diffuser/config/pandapush.py
+@@ -18,10 +18,10 @@ args_to_watch = [
+ 
+ logbase = 'logs'
+ input_type = 'state'
+-num_entity = 2
++num_entity = 3
+ mode = str(num_entity)+'C_'+input_type
+ overfit = False
+-exp_name = '2C_UNet_state'
++exp_name = '3C_UNet_state'
+ if overfit:
+     exp_name = 'overfit_' + exp_name
+ mode_to_args = {
+@@ -58,6 +58,14 @@ mode_to_args = {
+                 'particle_normalizer': None,
+                 'device': 'cuda:1',
+                 },
++    '3C_state': {'env_config_dir': 'config/n_cubes_state',
++              'dataset_path': '/scratch/cluster/carlq/research/ECRL/model_chkpts/3C_ECRL_State_20240519-181229/panda_push_replay_buffer_state_onehot.pkl',
++              'n_diffusion_steps': 5,
++              'horizon': 16,
++              'max_path_length': 100,
++              'particle_normalizer': None,
++              'device': 'cuda:1',
++              },
+ }
+ 
+ 
+@@ -97,7 +105,7 @@ base = {
+         ## training
+         'n_steps_per_epoch': 1000,
+         'loss_type': 'l2',
+-        'n_train_steps': 5e5,
++        'n_train_steps': 1e6,
+         'batch_size': 4,
+         'learning_rate': 2e-4,
+         'gradient_accumulate_every': 2,
+@@ -107,7 +115,7 @@ base = {
+         'sample_freq': 2000 if 'state' in mode else 0, # not supported for dlp
+         'n_saves': 1,
+         'save_parallel': False,
+-        'n_reference': 8,
++        'n_reference': 4,
+         'bucket': None,
+         'device': 'cuda:0',
+         'seed': None,
+diff --git a/diffuser/config/pandapush_eit.py b/diffuser/config/pandapush_eit.py
+index 40cf8fe..73451d1 100644
+--- a/diffuser/config/pandapush_eit.py
++++ b/diffuser/config/pandapush_eit.py
+@@ -18,11 +18,11 @@ args_to_watch = [
+ 
+ #------------------------ overrides ------------------------#
+ logbase = 'data'
+-input_type = 'state'
++input_type = 'dlp'
+ rand_color = False
+-num_entity = 1
++num_entity = 3
+ mode = str(num_entity)+'C_'+input_type
+-overfit = True
++overfit = False
+ 
+ exp_name = f'{num_entity}C_eitpolicy_{input_type}'
+ if overfit:
+@@ -88,24 +88,22 @@ mode_to_args = {
+             'multiview': True,
+             'n_diffusion_steps': 5,
+             'particle_normalizer': 'ParticleLimitsNormalizer',
+-            'horizon': 16,
+             'device': 'cuda:1',
+-            'droupout': 0.0,
+             'max_path_length': 50,
+-            'loss_type': 'l1',
+             'renderer': 'utils.ParticleRenderer',
++            'model': 'models.EITActorModel',
++            'horizon': 1,
+             },
+     '3C_dlp': {'env_config_dir': 'config/n_cubes',
+             'features_dim': 10,
+             'multiview': True,
+             'n_diffusion_steps': 5,
+             'particle_normalizer': 'ParticleLimitsNormalizer',
+-            'horizon': 5,
+             'device': 'cuda:1',
+-            'droupout': 0.0,
+             'max_path_length': 100,
+-            'loss_type': 'l1',
+             'renderer': 'utils.ParticleRenderer',
++            'model': 'models.EITActorModel',
++            'horizon': 1,
+             },
+     '3C_dlp_randcolor': {'env_config_dir': 'config/generalization_num_cubes',
+             'features_dim': 10,
+@@ -169,10 +167,10 @@ base = {
+         'n_train_steps': 1e6,
+         'batch_size': 1024,
+         'learning_rate': 8e-5,  # 2e-4
+-        'gradient_accumulate_every': 2,
++        'gradient_accumulate_every': 1,
+         'ema_decay': 0.995,
+         'save_freq': 1000,
+-        'eval_freq': 20,
++        'eval_freq': 10,
+         'sample_freq': 0, # not supported for action
+         'n_saves': 10,
+         'save_parallel': False,
+diff --git a/diffuser/config/pandapush_pint.py b/diffuser/config/pandapush_pint.py
+index 3214a4b..3e457dc 100644
+--- a/diffuser/config/pandapush_pint.py
++++ b/diffuser/config/pandapush_pint.py
+@@ -17,14 +17,15 @@ args_to_watch = [
+ ]
+ 
+ #------------------------ overrides ------------------------#
+-logbase = 'logs'
++logbase = 'data'
+ input_type = 'dlp'
+-rand_color = False
+-num_entity = 2
++rand_color = True
++num_entity = 3
+ mode = str(num_entity)+'C_'+input_type
+ overfit = False
++upweight_color = False
+ 
+-exp_name = f'{num_entity}C_pint_{input_type}_actiononly'
++exp_name = f'{num_entity}C_adalnpintlarge_{input_type}'
+ if overfit:
+     exp_name = 'overfit_' + exp_name
+ if rand_color:
+@@ -87,7 +88,7 @@ mode_to_args = {
+             'multiview': True,
+             'n_diffusion_steps': 5,
+             'particle_normalizer': 'ParticleLimitsNormalizer',
+-            'horizon': 16,
++            'horizon': 5,
+             'max_path_length': 30,
+             'device': 'cuda:1',
+             'droupout': 0.0,
+@@ -111,29 +112,31 @@ mode_to_args = {
+             'multiview': True,
+             'n_diffusion_steps': 5,
+             'particle_normalizer': 'ParticleLimitsNormalizer',
+-            'horizon': 16,
++            'horizon': 5,
+             'device': 'cuda:1',
+             'droupout': 0.0,
+             'max_path_length': 100,
++            'action_only': True,
+             'loss_type': 'l1',
+             'renderer': 'utils.ParticleRenderer',
+             },
+     '3C_dlp_randcolor': {'env_config_dir': 'config/generalization_num_cubes',
++            'model': 'models.AdaLNPINTDenoiser',
+             'features_dim': 10,
+             'multiview': True,
+-            'n_diffusion_steps': 5,
++            'n_diffusion_steps': 100,
+             'particle_normalizer': 'ParticleLimitsNormalizer',
+             'horizon': 5,
+             'device': 'cuda:1',
+             'droupout': 0.0,
+             'max_path_length': 100,
+             'loss_type': 'l1',
+-            'action_only': True,
++            'action_only': False,
+             'renderer': 'utils.ParticleRenderer',
+             'hidden_dim': 512,   # 512
+             'projection_dim': 512,  # 512
+             'n_heads': 8,  # 4,  8
+-            'n_layers': 6, # 4,  6,  # 12
++            'n_layers': 12, # 4,  6,  # 12
+             },
+ }
+ 
+@@ -233,5 +236,8 @@ base = {
+ 
+ #------------------------ overrides ------------------------#
+ from dataset_paths import get_diffuser_dataset_path
++from diffuser.models.helpers import get_weighted_colors_dict
+ mode_to_args[mode]['dataset_path'] = get_diffuser_dataset_path(num_entity, input_type, rand_color=rand_color)
+-base['diffusion'].update(mode_to_args[mode])
+
++base['diffusion'].update(mode_to_args[mode])
++if upweight_color:
++    base['diffusion']['loss_weights'] = get_weighted_colors_dict()
+
+diff --git a/diffuser/config/pandapush_pint_action.py b/diffuser/config/pandapush_pint_action.py
+index 355ed8d..b5e6645 100644
+--- a/diffuser/config/pandapush_pint_action.py
++++ b/diffuser/config/pandapush_pint_action.py
+@@ -18,11 +18,11 @@ args_to_watch = [
+ 
+ #------------------------ overrides ------------------------#
+ logbase = 'logs'
+-input_type = 'dlp'
++input_type = 'state'
+ rand_color = False
+-num_entity = 2
++num_entity = 1
+ mode = str(num_entity)+'C_'+input_type
+-overfit = False
++overfit = True
+ 
+ exp_name = f'{num_entity}C_pintaction_{input_type}'
+ if overfit:
+@@ -37,7 +37,7 @@ mode_to_args = {
+             'features_dim': 4,
+             'multiview': False,
+             'n_diffusion_steps': 5,
+-            'horizon': 16,
++            'horizon': 1,
+             'max_path_length': 30,
+             'particle_normalizer': 'ParticleLimitsNormalizer',
+             'device': 'cuda:1',
+@@ -141,6 +141,8 @@ base = {
+         'dropout': 0.0,  # turn it on when things are working
+         'n_diffusion_steps': 5,
+         'action_weight': 10,
++        'action_only': False,
++        'pad_state': False,
+         'max_particles': None,
+         "positional_bias": False,
+         "multiview": False,
+@@ -150,7 +152,7 @@ base = {
+         'renderer': 'utils.MatplotlibRenderer',
+ 
+         ## dataset
+-        'loader': 'datasets.GoalConditionedActionDataset',
++        'loader': 'datasets.GoalConditionedOneStepActionDataset',
+         'normalizer': 'SafeLimitsNormalizer',
+         'particle_normalizer': 'ParticleGaussianNormalizer',
+         'preprocess_fns': [],
+diff --git a/diffuser/config/plan_config/plan_pandapush_pint.py b/diffuser/config/plan_config/plan_pandapush_pint.py
+index 0cc6fbb..a9da777 100644
+--- a/diffuser/config/plan_config/plan_pandapush_pint.py
++++ b/diffuser/config/plan_config/plan_pandapush_pint.py
+@@ -23,7 +23,7 @@ input_type = 'dlp'
+ # ask for number of entities
+ num_entity = int(input('Enter number of entities: '))
+ 
+-exp_name = f'{num_entity}C_plan_3C_pintlarge_{input_type}_testing'
++exp_name = f'{num_entity}C_plan_3C_pintlarge_{input_type}_analysis'
+ mode_to_args = {
+     'state': {'env_config_dir': 'config/state_generalization_num_cubes',
+             'n_diffusion_steps': 5,
+@@ -34,15 +34,16 @@ mode_to_args = {
+             # 'diffusion_loadpath': 'diffusion/3C_pintlarge_state_padded2_randcolor_H5_T5',
+     },
+     'dlp': {'env_config_dir': 'config/generalization_num_cubes',
+-            'n_diffusion_steps': 5,
++            'n_diffusion_steps': 100,
+             'horizon': 5,
+             'device': 'cuda:1',
+             'max_path_length': entity_to_steps[num_entity],
+             # 'diffusion_loadpath': 'diffusion/2C_pintnew_dlp_H5_T5_lr8e-05',
+             # 'diffusion_loadpath': 'diffusion/3C_pintnew_dlp_randcolor_H5_T5_lr8e-05',
+             # 'diffusion_loadpath': 'diffusion/3C_pintnew_dlplonger_randcolor_H5_T100_lr8e-05',
+-            # 'diffusion_loadpath': 'diffusion/3C_pintlarge_dlp_acc_randcolor_H5_T100_lr8e-05',
+-            'diffusion_loadpath': 'diffusion/3C_pintlarge_dlp_acc2_randcolor_H5_T5_lr8e-05',
++            'diffusion_loadpath': 'diffusion/3C_pintlarge_dlp_acc_randcolor_H5_T100_lr8e-05',
++            # 'diffusion_loadpath': 'diffusion/3C_pintlarge_dlp_acc2_randcolor_H5_T5_lr8e-05',
++            'vis_freq': 5,
+             },
+     'dlp_action': {'env_config_dir': 'config/generalization_num_cubes',
+             'n_diffusion_steps': 5,
+diff --git a/diffuser/diffuser/datasets/sequence.py b/diffuser/diffuser/datasets/sequence.py
+index 492bbc3..cf96af8 100644
+--- a/diffuser/diffuser/datasets/sequence.py
++++ b/diffuser/diffuser/datasets/sequence.py
+@@ -247,7 +247,9 @@ class GoalConditionedOneStepActionDataset(GoalDataset):
+         observations = np.concatenate(
+                         [self.fields.normed_observations[path_ind, start:start+1],
+                          self.fields.normed_goals[path_ind, -1:]])
+-        actions = self.fields.normed_actions[path_ind, start:start+1]
++        actions = np.concatenate([
++            self.fields.normed_actions[path_ind, start:start+1],
++            np.zeros((1, self.action_dim), dtype=self.fields.normed_actions.dtype)])
+         if self.shuffle_particle_ordering:
+             # Shuffle the ordering of the particles
+             T, f = observations.shape
+diff --git a/diffuser/diffuser/models/__init__.py b/diffuser/diffuser/models/__init__.py
+index 33178af..1554183 100644
+--- a/diffuser/diffuser/models/__init__.py
++++ b/diffuser/diffuser/models/__init__.py
+@@ -2,6 +2,6 @@ from .temporal import TemporalUnet, ValueFunction
+ from .diffusion import GaussianDiffusion, ValueDiffusion, ActionDiffusion, default_sample_fn, sample_fn_return_attn
+ from .edm_diffusion import EDMDiffusion
+ from .eit_action import EITActorModel
+-from .pint import PINTDenoiser
++from .pint import PINTDenoiser, AdaLNPINTDenoiser
+ from .pint_action import PINTActionDenoiser
+ from .pint_value import PINTValueDenoiser
+
+diff --git a/diffuser/diffuser/models/diffusion.py b/diffuser/diffuser/models/diffusion.py
+index 68d65e1..fae0c02 100644
+--- a/diffuser/diffuser/models/diffusion.py
++++ b/diffuser/diffuser/models/diffusion.py
+@@ -303,7 +303,10 @@ class ActionDiffusion(GaussianDiffusion):
+ 
+         x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ 
+-        cond_input = torch.cat([cond[0].unsqueeze(1), cond[self.horizon-1].unsqueeze(1)], dim=1)
++        if self.horizon == 1:
++            cond_input = torch.cat([cond[0].unsqueeze(1), cond[self.horizon].unsqueeze(1)], dim=1)
++        else:
++            cond_input = torch.cat([cond[0].unsqueeze(1), cond[self.horizon-1].unsqueeze(1)], dim=1)
+ 
+         x_recon = self.model(x_noisy, cond_input, t)
+ 
+@@ -311,7 +314,7 @@ class ActionDiffusion(GaussianDiffusion):
+         return loss, info
+ 
+     @torch.no_grad()
+-    def p_sample_loop(self, shape, cond, verbose=True, return_chain=False, sample_fn=default_sample_fn, sort_by_value=True, **sample_kwargs):
++    def p_sample_loop(self, shape, cond, verbose=True, return_chain=False, sample_fn=default_sample_fn, return_attention=False, sort_by_value=True, **sample_kwargs):
+         device = self.betas.device
+ 
+         batch_size = shape[0]
+@@ -335,7 +338,7 @@ class ActionDiffusion(GaussianDiffusion):
+         return Sample(x, values, chain)
+ 
+     @torch.no_grad()
+-    def conditional_sample(self, cond, horizon=None, sort_by_value=True, **sample_kwargs):
++    def conditional_sample(self, cond, horizon=None, sort_by_value=True, return_attention=False, **sample_kwargs):
+         '''
+             conditions : [ (time, state), ... ]
+         '''
+diff --git a/diffuser/diffuser/models/eit_action.py b/diffuser/diffuser/models/eit_action.py
+index c008cbe..79d2ab3 100644
+--- a/diffuser/diffuser/models/eit_action.py
++++ b/diffuser/diffuser/models/eit_action.py
+@@ -140,7 +140,7 @@ class EITActorModel(nn.Module):
+ 
+     def loss(self, action, cond):
+         particles, goal_particles = cond[0], cond[1]
+-        action = action[0]
++        action = action[:, 0, :]
+         action_pred = self(particles, goal_particles)
+         loss = self.loss_fn(action_pred, action)
+         return loss, {}
+
+diff --git a/diffuser/diffuser/models/helpers.py b/diffuser/diffuser/models/helpers.py
+index d4039ac..426dd2e 100644
+--- a/diffuser/diffuser/models/helpers.py
++++ b/diffuser/diffuser/models/helpers.py
+@@ -399,6 +399,15 @@ Losses = {
+     'value_l2': ValueL2,
+ }
+ 
++def get_weighted_colors_dict(upweight=5):
++    weight_vector = torch.ones(10)
++    weight_vector[5:9] *= upweight
++    weight_vector = torch.tile(weight_vector, (48, 1)).flatten()
++    weight_dict = {}
++    for i in range(weight_vector.shape[0]):
++        weight_dict[i] = weight_vector[i].item()
++    
++    return weight_dict
+ 
+ 
+ if __name__ == '__main__':
+diff --git a/diffuser/diffuser/models/pint.py b/diffuser/diffuser/models/pint.py
+index 93dfe7c..3b5dbbe 100644
+--- a/diffuser/diffuser/models/pint.py
++++ b/diffuser/diffuser/models/pint.py
+@@ -1,8 +1,6 @@
+-import math
+-
+ import torch
+ from torch import nn
+-from diffuser.models.transformer_modules import ParticleTransformer, SinusoidalPosEmb, RandomOrLearnedSinusoidalPosEmb
++from diffuser.models.transformer_modules import ParticleTransformer, AdaLNParticleTransformer, SinusoidalPosEmb, RandomOrLearnedSinusoidalPosEmb
+ 
+ class PINTDenoiser(nn.Module):
+     def __init__(self, features_dim=2, action_dim=3, hidden_dim=256, projection_dim=256,
+@@ -78,9 +76,6 @@ class PINTDenoiser(nn.Module):
+         bs, h, f = x.size(0), x.size(1), x.size(2)
+         x, actions = x[:, :, self.action_dim:].view(bs, h, -1, self.features_dim), x[:, :, :self.action_dim]
+ 
+-        # randomly shuffle particles   (this is wrong)
+-        # x = x[:, :, torch.randperm(x.size(2)), :]
+-
+         # Bs, H, N, F
+         # Nth dimension is ordered: (0, 1, 2)
+ 
+@@ -89,11 +84,6 @@ class PINTDenoiser(nn.Module):
+         action_particle = self.action_projection(actions)
+         # add action encoding 
+         action_particle = action_particle + self.action_encoding.repeat(bs, h, 1)
+-
+-        ### alternative way: add action particles to the state particles
+-        # actions = actions.unsqueeze(2).repeat(1, 1, x.size(2), 1)
+-        # x = torch.cat([x, actions], dim=-1)
+-
+         # x: [bs, T, n_particles, features_dim + action_dim]
+         # time: [bs, ]
+ 
+@@ -111,8 +101,8 @@ class PINTDenoiser(nn.Module):
+             new_state_particles = state_particles + self.particle_encoding.repeat(bs, h, state_particles.size(2), 1)
+ 
+         #### U-Net style input
+-        # x = state_particles.unsqueeze(2)  # [bs, T, 1, embed_dim]
+-        x = torch.cat([action_particle.unsqueeze(2), new_state_particles], dim=2)  # [bs, T, n_particles + 1, embed_dim]
++        # x = state_particles.unsqueeze(2)  # [bs, T, 1, projection_dim]
++        x = torch.cat([action_particle.unsqueeze(2), new_state_particles], dim=2)  # [bs, T, n_particles + 1, projection_dim]
+         x_proj = x + t[:, None, None, :]   # (Bs, 1, 1, projection_dim)
+         # [bs, T, n_particles + 1, projection_dim]
+ 
+@@ -139,6 +129,129 @@ class PINTDenoiser(nn.Module):
+         else:
+             return x_out
+ 
++class AdaLNPINTDenoiser(nn.Module):
++    def __init__(self, features_dim=2, action_dim=3, hidden_dim=256, projection_dim=256,
++                 n_head=8, n_layer=6, block_size=50, dropout=0.1,
++                 predict_delta=False, positional_bias=True, max_particles=4,
++                 learned_sinusoidal_cond=False, random_fourier_features=False, learned_sinusoidal_dim=16, multiview=False, action_only=False):
++        super(AdaLNPINTDenoiser, self).__init__()
++        """
++        DLP Dynamics Module
++        """
++        self.features_dim = features_dim
++        self.action_dim = action_dim
++        self.predict_delta = predict_delta
++        self.projection_dim = projection_dim
++        self.max_particles = max_particles  # for positional bias
++        self.multiview = multiview
++        self.action_only = action_only
++        # time embeddings
++        ## block_size is the time horizon
++
++        time_dim = projection_dim * 4
++
++        self.random_or_learned_sinusoidal_cond = learned_sinusoidal_cond or random_fourier_features
++
++        if self.random_or_learned_sinusoidal_cond:
++            sinu_pos_emb = RandomOrLearnedSinusoidalPosEmb(learned_sinusoidal_dim, random_fourier_features)
++            fourier_dim = learned_sinusoidal_dim + 1
++        else:
++            sinu_pos_emb = SinusoidalPosEmb(projection_dim)
++            fourier_dim = projection_dim
++
++        self.time_mlp = nn.Sequential(
++            sinu_pos_emb,
++            nn.Linear(fourier_dim, time_dim),
++            nn.GELU(),
++            nn.Linear(time_dim, projection_dim)
++        )
++
++        self.particle_projection = nn.Sequential(nn.Linear(self.features_dim, hidden_dim),
++                                                 # nn.ReLU(True),
++                                                 nn.GELU(),
++                                                 nn.Linear(hidden_dim, self.projection_dim))
++        self.action_projection = nn.Sequential(nn.Linear(action_dim, hidden_dim),
++                                               nn.GELU(),
++                                               nn.Linear(hidden_dim, self.projection_dim))
++        self.particle_transformer = AdaLNParticleTransformer(self.projection_dim, n_head, n_layer,
++                                                        block_size, self.projection_dim,
++                                                        attn_pdrop=dropout, resid_pdrop=dropout,
++                                                        hidden_dim_multiplier=4,
++                                                        positional_bias=positional_bias,
++                                                        activation='gelu', max_particles=max_particles, action_only=self.action_only)
++        self.particle_decoder = nn.Sequential(nn.Linear(self.projection_dim, hidden_dim),
++                                              # nn.ReLU(True),
++                                              nn.GELU(),
++                                              nn.Linear(hidden_dim, self.features_dim))
++        self.action_decoder = nn.Sequential(nn.Linear(self.projection_dim, hidden_dim),
++                                            # nn.ReLU(True),
++                                            nn.GELU(),
++                                            nn.Linear(hidden_dim, self.action_dim))
++        # particle encoding
++        if self.multiview:
++            self.view1_encoding = nn.Parameter(0.02 * torch.randn(1, 1, 1, projection_dim))
++            self.view2_encoding = nn.Parameter(0.02 * torch.randn(1, 1, 1, projection_dim))
++        else:
++            self.particle_encoding = nn.Parameter(0.02 * torch.randn(1, 1, 1, projection_dim))
++        
++        self.action_encoding = nn.Parameter(0.02 * torch.randn(1, 1, projection_dim))
++
++    def forward(self, x, cond, time, return_attention=False):
++
++        # first format x into the shape we want
++        # x is the state: bs, h, (action_dim + features_dim )
++        bs, h, f = x.size(0), x.size(1), x.size(2)
++        x, actions = x[:, :, self.action_dim:].view(bs, h, -1, self.features_dim), x[:, :, :self.action_dim]
++
++        # project actions to add action particles
++        action_particle = self.action_projection(actions)
++        # add action encoding 
++        action_particle = action_particle + self.action_encoding.repeat(bs, h, 1)
++        # x: [bs, T, n_particles, features_dim + action_dim]
++        # time: [bs, ]
++
++        # time
++        t_embed = self.time_mlp(time)
++        # project particles
++        state_particles = self.particle_projection(x)
++        if self.multiview:
++            n_particles = state_particles.size(2) // 2
++            # add view identifying encoding
++            particles_view1 = state_particles[:, :, :n_particles, :] + self.view1_encoding.repeat(bs, h, n_particles, 1)
++            particles_view2 = state_particles[:, :, n_particles:, :] + self.view2_encoding.repeat(bs, h, n_particles, 1)
++            new_state_particles = torch.cat([particles_view1, particles_view2], dim=2)
++        else:
++            new_state_particles = state_particles + self.particle_encoding.repeat(bs, h, state_particles.size(2), 1)
++
++        #### U-Net style input
++        # x = state_particles.unsqueeze(2)  # [bs, T, 1, projection_dim]
++        x = torch.cat([action_particle.unsqueeze(2), new_state_particles], dim=2)  # [bs, T, n_particles + 1, projection_dim]
++        x_proj = x + t_embed[:, None, None, :]   # (Bs, 1, 1, projection_dim)
++        # [bs, T, n_particles + 1, projection_dim]
++
++        x_proj = x_proj.permute(0, 2, 1, 3)
++        # [bs, n_particles + 1, T, projection_dim]
++        if return_attention:
++            particles_trans, attention_dict = self.particle_transformer(x_proj, action_particle, t_embed, return_attention=return_attention)
++        else:
++            particles_trans = self.particle_transformer(x_proj, action_particle, t_embed)
++        # [bs, n_particles + 1, T, projection_dim]
++        particles_trans = particles_trans.permute(0, 2, 1, 3)
++        # [bs, T, n_particles + 1, projection_dim]
++
++        # decode transformer output
++        particle_decoder_out = self.particle_decoder(particles_trans[:, :, 1:, :])
++        action_decoder_out = self.action_decoder(particles_trans[:, :, 0, :])
++        # [bs, T, n_particles + 1, features_dim]
++
++        x_out = torch.cat([action_decoder_out, particle_decoder_out.view(bs, h, -1)], dim=-1)
++
++        # x_out = self.particle_decoder(particles_trans).squeeze(2)
++        if return_attention:
++            return x_out, attention_dict
++        else:
++            return x_out
++
+ 
+ 
+ """
+@@ -148,7 +261,7 @@ END Particle Interaction Transformer (PINT)
+ if '__main__' == __name__:
+     batch_size = 32
+     timessteps = 5
+-    model = PINTDenoiser(features_dim=10, action_dim=3, hidden_dim=256, projection_dim=256,
++    model = AdaLNPINTDenoiser(features_dim=10, action_dim=3, hidden_dim=256, projection_dim=256,
+                         n_head=8, n_layer=6, block_size=timessteps, dropout=0.1,
+                         predict_delta=False, positional_bias=False, max_particles=None,
+                         learned_sinusoidal_cond=False, random_fourier_features=False, learned_sinusoidal_dim=16)
+@@ -158,4 +271,4 @@ if '__main__' == __name__:
+ 
+     x = torch.cat([actions, in_particles], dim=-1)
+     # model_out = model(x, None, t)
+-    model_out, attention_dict = model(x, None, t, return_attention=True)
+
++    model_out = model(x, None, t, return_attention=False)
+
+diff --git a/diffuser/diffuser/models/pint_action.py b/diffuser/diffuser/models/pint_action.py
+index 9782388..494c3fa 100644
+--- a/diffuser/diffuser/models/pint_action.py
++++ b/diffuser/diffuser/models/pint_action.py
+@@ -7,7 +7,7 @@ class PINTActionDenoiser(nn.Module):
+     def __init__(self, features_dim=2, action_dim=3, hidden_dim=256, projection_dim=256,
+                  n_head=8, n_layer=6, block_size=2, dropout=0.1,
+                  predict_delta=False, positional_bias=False, max_particles=None,
+-                 learned_sinusoidal_cond=False, random_fourier_features=False, learned_sinusoidal_dim=16, multiview=False):
++                 learned_sinusoidal_cond=False, random_fourier_features=False, learned_sinusoidal_dim=16, multiview=False, **kwargs):
+         super(PINTActionDenoiser, self).__init__()
+         """
+         PINT-based network that only diffuses over the actions
+diff --git a/diffuser/diffuser/models/transformer_modules.py b/diffuser/diffuser/models/transformer_modules.py
+index bc77f24..f349881 100644
+--- a/diffuser/diffuser/models/transformer_modules.py
++++ b/diffuser/diffuser/models/transformer_modules.py
+@@ -447,4 +447,153 @@ class ParticleTransformerDecoder(nn.Module):
+         x = self.ln_f(x)
+         logits = self.head(x)
+ 
+-        return logits
+
++        return logits
++
++def modulate(x, shift, scale):
++    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
++
++class FinalLayer(nn.Module):
++    def __init__(self, dim):
++        super().__init__()
++        self.ln_final = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
++        self.gamma = nn.Linear(dim, dim)
++        self.beta = nn.Linear(dim, dim)
++        # Zero-out output layers:
++        nn.init.zeros_(self.gamma.weight)
++        nn.init.zeros_(self.beta.weight)
++        nn.init.zeros_(self.gamma.bias)
++        nn.init.zeros_(self.beta.bias) 
++
++    def forward(self, x, c):
++        scale = self.gamma(c)
++        shift = self.beta(c)
++        x = modulate(self.ln_final(x), shift, scale)
++        return x
++
++class AdaLNPINTBlock(nn.Module):
++    def __init__(self, n_embed, n_head, block_size, attn_pdrop=0.1, resid_pdrop=0.1, hidden_dim_multiplier=4,
++                 positional_bias=False, activation='gelu', max_particles=None, att_type='self', causal_att=False):
++        super().__init__()
++        self.ln_1 = nn.LayerNorm(n_embed, elementwise_affine=False, eps=1e-6)
++        self.attn = CausalParticleAttention(n_embed, n_head, block_size, attn_pdrop, resid_pdrop,
++                                                positional_bias=positional_bias,
++                                                max_particles=max_particles, att_type=att_type, causal_att=causal_att)
++        self.ln_2 = nn.LayerNorm(n_embed, elementwise_affine=False, eps=1e-6)
++        self.mlp = MLP(n_embed, resid_pdrop, hidden_dim_multiplier, activation=activation)
++        self.gamma_1 = nn.Linear(n_embed, n_embed)
++        self.beta_1 = nn.Linear(n_embed, n_embed)
++        self.gamma_2 = nn.Linear(n_embed, n_embed)
++        self.beta_2 = nn.Linear(n_embed, n_embed)
++        self.scale_1 = nn.Linear(n_embed, n_embed)
++        self.scale_2 = nn.Linear(n_embed, n_embed)
++
++        nn.init.zeros_(self.gamma_1.weight)
++        nn.init.zeros_(self.beta_1.weight)
++        nn.init.zeros_(self.gamma_1.bias)
++        nn.init.zeros_(self.beta_1.bias)  
++
++        nn.init.zeros_(self.gamma_2.weight)
++        nn.init.zeros_(self.beta_2.weight)
++        nn.init.zeros_(self.gamma_2.bias)
++        nn.init.zeros_(self.beta_2.bias)  
++
++        nn.init.zeros_(self.scale_1.weight)
++        nn.init.zeros_(self.scale_2.weight)
++        nn.init.zeros_(self.scale_1.bias)
++        nn.init.zeros_(self.scale_2.bias)  
++
++    def forward(self, x, c):
++        #c = self.ln_act(c)
++        scale_msa = self.gamma_1(c)
++        shift_msa = self.beta_1(c)
++        scale_mlp = self.gamma_2(c)
++        shift_mlp = self.beta_2(c)
++        gate_msa = self.scale_1(c).unsqueeze(1)
++        gate_mlp = self.scale_2(c).unsqueeze(1)
++        x = self.attn(modulate(self.ln_1(x), shift_msa, scale_msa)) * gate_msa + x
++        return self.mlp(modulate(self.ln_2(x), shift_mlp, scale_mlp)) * gate_mlp + x
++    
++
++class AdaLNParticleTransformer(nn.Module):
++    def __init__(self, n_embed, n_head, n_layer, block_size, output_dim, attn_pdrop=0.1, resid_pdrop=0.1,
++                 hidden_dim_multiplier=4, positional_bias=False,
++                 activation='gelu', max_particles=None, action_only=False):
++        super().__init__()
++        assert action_only == False, "Action-only not supported for AdaLN"
++        self.positional_bias = positional_bias
++        self.max_particles = max_particles  # for positional bias
++        self.action_only = action_only
++        # input embedding stem
++        if self.positional_bias:
++            self.pos_emb = nn.Identity()
++            # print(f'particle transformer: using relative positional bias')
++        else:
++            # print(f'particle transformer: using positional embeddings')
++            self.pos_emb = nn.Parameter(torch.zeros(1, block_size, n_embed))
++            if self.action_only:
++                self.pos_emb_sg = nn.Parameter(torch.zeros(1, 2, n_embed))
++        # transformer
++        self.blocks = nn.Sequential(*[AdaLNPINTBlock(n_embed, n_head, block_size, attn_pdrop,
++                                                resid_pdrop, hidden_dim_multiplier,
++                                                positional_bias, activation=activation,
++                                                max_particles=max_particles)
++                                      for _ in range(n_layer)])
++        # decoder head
++        self.ln_f = FinalLayer(n_embed)
++        self.head = nn.Linear(n_embed, output_dim, bias=False)
++
++        self.block_size = block_size
++        self.n_embed = n_embed
++        self.n_layer = n_layer
++
++        # initialize layers
++        self.apply(self._init_weights)
++        if self.positional_bias:
++            for m in self.blocks:
++                m.attn.rel_pos_bias.reset_parameters()
++        print(f"particle transformer # parameters: {sum(p.numel() for p in self.parameters())}")
++
++    def get_block_size(self):
++        return self.block_size
++
++    def _init_weights(self, module):
++        std = 0.02
++        # std = 0.05
++        if isinstance(module, nn.Embedding):
++            # pass
++            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
++            if isinstance(module, nn.Linear) and module.bias is not None:
++                torch.nn.init.zeros_(module.bias)
++        if isinstance(module, nn.Linear):
++            # pass
++            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
++            if isinstance(module, nn.Linear) and module.bias is not None:
++                torch.nn.init.zeros_(module.bias)
++        elif isinstance(module, ParticleTransformer):
++            if not self.positional_bias:
++                torch.nn.init.normal_(module.pos_emb, mean=0.0, std=std)
++
++    def forward(self, x, action_embed, t_embed, return_attention=False):
++        b, n, t, f = x.size()
++        # n is the number of particles
++        assert t <= self.block_size, "Cannot forward, model block size is exhausted."
++        assert f == self.n_embed, "invalid particle feature dim"
++        c = action_embed + t_embed.unsqueeze(1) # [bs, T, embed_dim]
++        if return_attention:
++            attention_dict = {}
++        if not self.positional_bias:
++            position_embeddings = self.pos_emb[:, None, :t, :]  # (bs, 1, block_size, f)
++            x = x + position_embeddings
++        for i, block in enumerate(self.blocks):
++            if return_attention:
++                x, attention_matrix = block(x, return_attention=True)
++                attention_dict[f'layer_{i}'] = attention_matrix
++            else:
++                x = block(x, c)
++        x = self.ln_f(x, c)
++        logits = self.head(x)
++
++        if return_attention:
++            return logits, attention_dict
++        else:
++            return logits
+
+diff --git a/diffuser/scripts/plan_gc_pandapush.py b/diffuser/scripts/plan_gc_pandapush.py
+index 61fe6eb..e1e575b 100644
+--- a/diffuser/scripts/plan_gc_pandapush.py
++++ b/diffuser/scripts/plan_gc_pandapush.py
+@@ -1,3 +1,5 @@
++import warnings
++warnings.filterwarnings('ignore')
+ import sys
+ import os
+ os.environ['MESA_VK_DEVICE_SELECT'] = '10de:2233'
+@@ -93,6 +95,9 @@ if __name__ == '__main__':
+     ### modify env_config ###
+     isaac_env_cfg['env']['numObjects'] = args.num_entity
+     isaac_env_cfg["env"]["episodeLength"] = args.max_path_length
++    isaac_env_cfg['env']['numEnvs'] = 1
++    isaac_env_cfg['env']['numColors'] = 3
++    isaac_env_cfg['env']['RandColor'] = False
+     ### modify env_config ###
+ 
+     check_config(config, isaac_env_cfg, isaac_policy_config)
+@@ -130,9 +135,8 @@ if __name__ == '__main__':
+     wandb_run.name = savepath_folder_name
+ 
+     # eval_stat_dict = evaluate_policy(policy, env, args, logger, num_eval_episodes=100, exe_steps=args.exe_steps, stat_save_path=None)
+-    # wandb_log_eval_stats(env, eval_stat_dict, args)
+-    eval_stat_dict = analyze_policy(policy, env, args, logger, exe_steps=args.exe_steps, stat_save_path=None)
+-
++    eval_stat_dict = analyze_policy(policy, env, dataset, args, logger, exe_steps=args.exe_steps, stat_save_path=None)
++    wandb_log_eval_stats(env, eval_stat_dict, args)
+ 
+     # Old
+     # evaluate_policy_old(policy, env)
+
+diff --git a/diffuser/scripts/train.py b/diffuser/scripts/train.py
+index 6ef959c..f11fee5 100644
+--- a/diffuser/scripts/train.py
++++ b/diffuser/scripts/train.py
+@@ -87,7 +87,7 @@ if 'pint' in args.config:
+         n_head=args.n_heads,
+         n_layer=args.n_layers,
+         dropout=args.dropout,
+-        block_size=args.horizon,
++        block_size=max(2, args.horizon),
+         positional_bias=args.positional_bias,
+         max_particles=args.max_particles,
+         multiview=args.multiview,

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+size 364

pretrained_models/panda_push/diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100/model_config.pkl

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+oid sha256:af7682a5f27fcbd2282edfb0e31a13b66d839f1a33c3427fc358e50f928e5643
+size 322

pretrained_models/panda_push/diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100/render_config.pkl

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+oid sha256:4cbd3bc7d28785a7ead9e83152556cf0bfce898ea6382bb444b860a2cba6018b
+size 157

pretrained_models/panda_push/diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100/state_1200000.pt

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+size 485446653

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df42af849ed899727ee7192a0350dca4f94548a09ffc60d367954f28d56737d8
+size 1810

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/args.json

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+{
+    "action_only": false,
+    "action_weight": 10,
+    "add_extras": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "batch_size": 32,
+    "bucket": null,
+    "clip_denoised": false,
+    "commit": "1144ec8dc37afd4321aa464468a22c1bcc3ed26f main",
+    "config": "config.pandapush_pint",
+    "dataset": "panda_push",
+    "dataset_path": "model_chkpts/3C_ECRL_State_PushT_20240917-124156/panda_push_replay_buffer_dlp.pkl",
+    "device": "cuda:1",
+    "diffusion": "models.GaussianDiffusion",
+    "dropout": 0.0,
+    "droupout": 0.0,
+    "ema_decay": 0.995,
+    "env_config_dir": "config/push_t",
+    "eval_freq": 40,
+    "eval_fstrings": {
+        "_type": "python_object (type = method)",
+        "_value": "gASVXAcAAAAAAACMCGJ1aWx0aW5zlIwHZ2V0YXR0cpSTlIwTZGlmZnVzZXIudXRpbHMuYXJnc5SMCkFyZ3NQYXJzZXKUk5QpgZR9lCiMB25fc2F2ZXOUSwqMEW5fc3RlcHNfcGVyX2Vwb2NolE3oA4wEbW9kZZSMDDNDX2RscF9wdXNodJSMDWxlYXJuaW5nX3JhdGWURz8U+LWI42jxjA9wb3NpdGlvbmFsX2JpYXOUiYwEc2VlZJRLvIwGYnVja2V0lE6MBmxvYWRlcpSMFGRhdGFzZXRzLkdvYWxEYXRhc2V0lIwNbG9zc19kaXNjb3VudJRLAYwNYWN0aW9uX3dlaWdodJRLCowTcGFydGljbGVfbm9ybWFsaXplcpSMGFBhcnRpY2xlTGltaXRzTm9ybWFsaXplcpSMCmhpZGRlbl9kaW2UTQACjA5lbnZfY29uZmlnX2RpcpSMDWNvbmZpZy9wdXNoX3SUjA9wcmVkaWN0X2Vwc2lsb26UiYwZZ3JhZGllbnRfYWNjdW11bGF0ZV9ldmVyeZRLAowGY29uZmlnlIwVY29uZmlnLnBhbmRhcHVzaF9waW50lIwJbXVsdGl2aWV3lIiMB2Ryb3BvdXSURwAAAAAAAAAAjAthY3Rpb25fb25seZSJjAZkZXZpY2WUjAZjdWRhOjGUjA9tYXhfcGF0aF9sZW5ndGiUS5aMCXNhdmVfZGlmZpRoAmgGaCOGlFKUjApub3JtYWxpemVylIwUU2FmZUxpbWl0c05vcm1hbGl6ZXKUjAlwYWRfc3RhdGWUiYwIZXhwX25vdGWUjAlwaW50bGFyZ2WUjA1uX3RyYWluX3N0ZXBzlEdBLoSAAAAAAIwGcHJlZml4lIwgZGlmZnVzaW9uL1B1c2hUXzNDX2RscF9waW50bGFyZ2WUjAxkYXRhc2V0X3BhdGiUjFFtb2RlbF9jaGtwdHMvM0NfRUNSTF9TdGF0ZV9QdXNoVF8yMDI0MDkxNy0xMjQxNTYvcGFuZGFfcHVzaF9yZXBsYXlfYnVmZmVyX2RscC5wa2yUjAxsb3NzX3dlaWdodHOUTowKYWRkX2V4dHJhc5RoAmgGaDGGlFKUjAZwdXNoX3SUiIwJZXZhbF9mcmVxlEsojApyYW5kX2NvbG9ylImMCXNhdmVfZnJlcZRN6AOMB2RhdGFzZXSUjApwYW5kYV9wdXNolIwJbG9zc190eXBllIwCbDGUjAtyZWFkX2NvbmZpZ5RoAmgGaDyGlFKUjApnZXRfY29tbWl0lGgCaAZoP4aUUpSMDXBsYW5uaW5nX29ubHmUiYwJZW1hX2RlY2F5lEc/79cKPXCj14wQd2FuZGJfZ3JvdXBfbmFtZZSMFlB1c2hUXzNDX2RscF9waW50bGFyZ2WUjAdvdmVyZml0lImMCGRyb3Vwb3V0lEcAAAAAAAAAAIwNbWF4X3BhcnRpY2xlc5ROjAhzZXRfc2VlZJRoAmgGaEmGlFKUjA1jbGlwX2Rlbm9pc2VklImMDW1peGVkX2RhdGFzZXSUiYwIbl9sYXllcnOUSwyMDnByZXByb2Nlc3NfZm5zlF2UjBFnZW5lcmF0ZV9leHBfbmFtZZRoAmgGaFGGlFKUjA5wcm9qZWN0aW9uX2RpbZRNAAKMEW5fZGlmZnVzaW9uX3N0ZXBzlEsFjAxzZXRfbG9hZGJhc2WUaAJoBmhWhpRSlIwKYmF0Y2hfc2l6ZZRLIIwLbl9yZWZlcmVuY2WUSwSMCHNhdmVwYXRolIw+ZGF0YS9wYW5kYV9wdXNoL2RpZmZ1c2lvbi9QdXNoVF8zQ19kbHBfcGludGxhcmdlX0g1X1Q1X3NlZWQxODiUjA1ldmFsX2ZzdHJpbmdzlGgCaAZoXYaUUpSMB2hvcml6b26USwWMB2xvZ2Jhc2WUjARkYXRhlIwIZXhwX25hbWWUjC5kaWZmdXNpb24vUHVzaFRfM0NfZGxwX3BpbnRsYXJnZV9INV9UNV9zZWVkMTg4lIwHbl9oZWFkc5RLCIwGY29tbWl0lIwtMTE0NGVjOGRjMzdhZmQ0MzIxYWE0NjQ0NjhhMjJjMWJjYzNlZDI2ZiBtYWlulIwIcmVuZGVyZXKUjBZ1dGlscy5QYXJ0aWNsZVJlbmRlcmVylIwZc2h1ZmZsZV9wYXJ0aWNsZV9vcmRlcmluZ5SJjAh2aXNfZnJlcZRLFIwFbW9kZWyUjBhtb2RlbHMuQWRhTE5QSU5URGVub2lzZXKUjAtzYW1wbGVfZnJlcZRLAIwOdmlzX3RyYWpfd2FuZGKUiYwFbWtkaXKUaAJoBmhwhpRSlIwNc2F2ZV9wYXJhbGxlbJSJjAppbnB1dF90eXBllIwDZGxwlIwHa2l0Y2hlbpSJjAhvYnNfb25seZSJjApudW1fZW50aXR5lEsDjAxmZWF0dXJlc19kaW2USwyMCWRpZmZ1c2lvbpSMGG1vZGVscy5HYXVzc2lhbkRpZmZ1c2lvbpSMC3VzZV9wYWRkaW5nlIh1YmhdhpRSlC4="
+    },
+    "exp_name": "diffusion/PushT_3C_dlp_pintlarge_H5_T5_seed188",
+    "exp_note": "pintlarge",
+    "features_dim": 12,
+    "generate_exp_name": {
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+    },
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+    "horizon": 5,
+    "input_type": "dlp",
+    "kitchen": false,
+    "learning_rate": 8e-05,
+    "loader": "datasets.GoalDataset",
+    "logbase": "data",
+    "loss_discount": 1,
+    "loss_type": "l1",
+    "loss_weights": null,
+    "max_particles": null,
+    "max_path_length": 150,
+    "mixed_dataset": false,
+    "mkdir": {
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+    "mode": "3C_dlp_pusht",
+    "model": "models.AdaLNPINTDenoiser",
+    "multiview": true,
+    "n_diffusion_steps": 5,
+    "n_heads": 8,
+    "n_layers": 12,
+    "n_reference": 4,
+    "n_saves": 10,
+    "n_steps_per_epoch": 1000,
+    "n_train_steps": 1000000.0,
+    "normalizer": "SafeLimitsNormalizer",
+    "num_entity": 3,
+    "obs_only": false,
+    "overfit": false,
+    "pad_state": false,
+    "particle_normalizer": "ParticleLimitsNormalizer",
+    "planning_only": false,
+    "positional_bias": false,
+    "predict_epsilon": false,
+    "prefix": "diffusion/PushT_3C_dlp_pintlarge",
+    "preprocess_fns": [],
+    "projection_dim": 512,
+    "push_t": true,
+    "rand_color": false,
+    "read_config": {
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+    },
+    "renderer": "utils.ParticleRenderer",
+    "reproducibility": {
+        "command_line": "python diffuser/scripts/acc_train.py --config config.pandapush_pint --push_t --num_entity 3 --exp_note pintlarge --seed 188",
+        "git_has_uncommitted_changes": true,
+        "git_root": "/scratch/cluster/carlq/research/ECRL",
+        "git_url": "https://github.com/carl-qi/ECDiffuse/tree/1144ec8dc37afd4321aa464468a22c1bcc3ed26f",
+        "time": "Mon Sep 23 15:42:13 2024"
+    },
+    "sample_freq": 0,
+    "save_diff": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "save_freq": 1000,
+    "save_parallel": false,
+    "savepath": "data/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5_seed188",
+    "seed": 188,
+    "set_loadbase": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "set_seed": {
+        "_type": "python_object (type = method)",
+        "_value": "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"
+    },
+    "shuffle_particle_ordering": false,
+    "use_padding": true,
+    "vis_freq": 20,
+    "vis_traj_wandb": false,
+    "wandb_group_name": "PushT_3C_dlp_pintlarge"
+}

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/dataset_config.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c690bcca67f045b77a9a1012fe39d5a8061b71f55e34fb0b927f5801a3350219
+size 468

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/diff.txt

@@ -0,0 +1,488 @@
+diff --git a/analylize_trajectory.ipynb b/analylize_trajectory.ipynb
+index ee01840..6ac8c75 100644
+--- a/analylize_trajectory.ipynb
++++ b/analylize_trajectory.ipynb
+@@ -2,7 +2,7 @@
+  "cells": [
+   {
+    "cell_type": "code",
+-   "execution_count": 2,
++   "execution_count": 1,
+    "metadata": {},
+    "outputs": [],
+    "source": [
+@@ -180,19 +180,20 @@
+   },
+   {
+    "cell_type": "code",
+-   "execution_count": 5,
++   "execution_count": 7,
+    "metadata": {},
+    "outputs": [
+     {
+      "name": "stdout",
+      "output_type": "stream",
+      "text": [
+-      "t: 0 target_object: 51 max_obj_idxes: [20, 51, 100, 154, 202]\n",
+-      "t: 5 target_object: 7 max_obj_idxes: [7, 54, 106, 150, 212]\n",
+-      "t: 10 target_object: 1 max_obj_idxes: [1, 63, 111, 157, 215]\n",
+-      "t: 15 target_object: 1 max_obj_idxes: [1, 71, 111, 169, 203]\n",
+-      "t: 20 target_object: 10 max_obj_idxes: [10, 56, 122, 168, 212]\n",
+-      "t: 25 target_object: 4 max_obj_idxes: [4, 71, 115, 170, 220]\n"
++      "t: 0 target_object: 51 max_obj_idxes: [20, 13, 16, 10, 11, 51, 62, 70, 63, 57, 100, 109, 112, 119, 117, 154, 171, 161, 149, 151, 202, 212, 201, 200, 207] hit_percentage: 0.32\n",
++      "t: 5 target_object: 7 max_obj_idxes: [7, 17, 8, 4, 23, 54, 65, 63, 62, 69, 106, 110, 112, 99, 113, 150, 161, 158, 163, 166, 204, 207, 210, 201, 211] hit_percentage: 0.28\n",
++      "t: 10 target_object: 1 max_obj_idxes: [1, 23, 6, 4, 8, 63, 57, 54, 70, 66, 111, 101, 104, 120, 100, 157, 165, 148, 170, 166, 215, 212, 201, 207, 204] hit_percentage: 0.28\n",
++      "t: 15 target_object: 1 max_obj_idxes: [1, 8, 21, 3, 16, 71, 54, 51, 65, 69, 111, 107, 103, 106, 100, 169, 163, 151, 159, 162, 203, 218, 205, 215, 207] hit_percentage: 0.6\n",
++      "t: 20 target_object: 10 max_obj_idxes: [10, 16, 14, 23, 15, 56, 57, 50, 55, 64, 122, 106, 100, 103, 116, 168, 159, 148, 155, 153, 212, 202, 200, 198, 201] hit_percentage: 0.36\n",
++      "t: 25 target_object: 4 max_obj_idxes: [4, 5, 10, 6, 1, 71, 58, 65, 51, 73, 115, 111, 113, 107, 112, 170, 160, 148, 155, 167, 218, 220, 210, 211, 203] hit_percentage: 0.24\n",
++      "overall_hit_percentage: 0.3466666666666667\n"
+      ]
+     }
+    ],
+@@ -213,21 +214,29 @@
+     "    new_idx = i * 24 + j\n",
+     "    return new_idx\n",
+     "\n",
++    "overall_hit_percentage = []\n",
+     "for t in [0, 5, 10, 15, 20, 25]:\n",
+     "    with open(f'logs/panda_push/plans/3C_plan_3C_pintlarge_dlp_analysis_H5_T100/step_latest/particles_{t}.pkl', 'rb') as f:\n",
+     "        particles = pickle.load(f)\n",
+     "    particles = particles[0, :, :240]\n",
+     "    particles = particles.reshape(-1, 10)[:, 2:]\n",
+-    "    particle_similarity = cdist(particles, particles, metric='cosine')\n",
++    "    particle_similarity = cdist(particles, particles, metric='euclidean')\n",
+     "    particle_similarity = 1 - particle_similarity\n",
+     "\n",
+     "\n",
+     "    target_obj = objects_of_interest_dict[t][0]\n",
+     "    target_particle = convert_obj_idx_to_particle_idx(target_obj)\n",
+-    "    max_particle_idxes = np.argmax(particle_similarity[target_particle].reshape(5, -1), axis=1)\n",
+-    "    max_particle_idxes = [i*24 + j for i, j in enumerate(max_particle_idxes)]\n",
++    "    # max_particle_idxes = np.argmax(particle_similarity[target_particle].reshape(5, -1), axis=1)\n",
++    "    top_k_particle_indices = np.argsort(particle_similarity[target_particle].reshape(5, -1), axis=-1)[:, ::-1][:, :5]\n",
++    "    max_particle_idxes = []\n",
++    "    for i in range(5):\n",
++    "        for idx in top_k_particle_indices[i]:\n",
++    "                max_particle_idxes.append(i*24 + idx)\n",
++    "    # max_particle_idxes = [i*24 + j for i, j in enumerate(max_particle_idxes)]\n",
+     "    max_obj_idxes = [convert_particle_idx_to_obj_idx(idx) for idx in max_particle_idxes]\n",
+-    "    print(\"t:\", t, \"target_object:\", target_obj, \"max_obj_idxes:\", max_obj_idxes)\n",
++    "    hit_percentage = np.mean([1 if obj_idx in objects_of_interest_dict[t] else 0 for obj_idx in max_obj_idxes])\n",
++    "    print(\"t:\", t, \"target_object:\", target_obj, \"max_obj_idxes:\", max_obj_idxes, \"hit_percentage:\", hit_percentage)\n",
++    "    overall_hit_percentage.append(hit_percentage)\n",
+     "    n_particles = 120\n",
+     "\n",
+     "    # fig, ax = plt.subplots()\n",
+@@ -238,33 +247,16 @@
+     "    # ax.imshow(particle_similarity)\n",
+     "    # ax.set_title(f\"Particle Similarity\", fontsize=12)\n",
+     "    # ax.set_xticks(range(n_particles), [f\"{i}\" for i in range(n_particles)])\n",
+-    "    # ax.set_yticks(range(n_particles), [f\"{i}\" for i in range(n_particles)])"
++    "    # ax.set_yticks(range(n_particles), [f\"{i}\" for i in range(n_particles)])\n",
++    "print(\"overall_hit_percentage:\", np.mean(overall_hit_percentage))"
+    ]
+   },
+   {
+    "cell_type": "code",
+-   "execution_count": 38,
++   "execution_count": null,
+    "metadata": {},
+-   "outputs": [
+-    {
+-     "data": {
+-      "text/plain": [
+-       "(5, 24)"
+-      ]
+-     },
+-     "execution_count": 38,
+-     "metadata": {},
+-     "output_type": "execute_result"
+-    }
+-   ],
+-   "source": [
+-    "t: 0 target_object: 51 max_obj_idxes: [20, 51, 100, 154, 202]\n",
+-    "t: 5 target_object: 7 max_obj_idxes: [7, 54, 106, 150, 204]\n",
+-    "t: 10 target_object: 1 max_obj_idxes: [1, 63, 111, 157, 215]\n",
+-    "t: 15 target_object: 1 max_obj_idxes: [1, 71, 111, 169, 203]\n",
+-    "t: 20 target_object: 10 max_obj_idxes: [10, 56, 122, 168, 212]\n",
+-    "t: 25 target_object: 4 max_obj_idxes: [4, 71, 115, 170, 218]"
+-   ]
++   "outputs": [],
++   "source": []
+   },
+   {
+    "cell_type": "code",
+diff --git a/config/TrainDLPConfig.yaml b/config/TrainDLPConfig.yaml
+index 6cfcd34..528c308 100644
+--- a/config/TrainDLPConfig.yaml
++++ b/config/TrainDLPConfig.yaml
+@@ -23,14 +23,14 @@ beta_kl: 0.1  # original
+ beta_rec: 1.0
+ scale_std: 0.3  # default
+ offset_std: 0.2  # default
+-n_kp_enc: 20  # total kp to output from the encoder / filter from prior
+-n_kp_prior: 32
+-patch_size: 16 # prior patch size need to be lower than posterior patch size: posterior is (image size * anchor_s)
++n_kp_enc: 50  # total kp to output from the encoder / filter from prior
++n_kp_prior: 128
++patch_size: 8 # prior patch size need to be lower than posterior patch size: posterior is (image size * anchor_s)
+ learned_feature_dim: 4  # latent visual features for each kp (excluding bg)
+ bg_learned_feature_dim: 1
+ topk: 10  # display top-10 kp with the smallest variance
+ recon_loss_type: "mse"
+-anchor_s: 0.25   # reduce this to 0.125 for small glimpses
++anchor_s: 0.125   # reduce this to 0.125 for small glimpses
+ kl_balance: 0.001
+   
+   
+diff --git a/config/push_t/IsaacPandaPushConfig.yaml b/config/push_t/IsaacPandaPushConfig.yaml
+index 311aa77..36d195d 100644
+--- a/config/push_t/IsaacPandaPushConfig.yaml
++++ b/config/push_t/IsaacPandaPushConfig.yaml
+@@ -28,9 +28,9 @@ env:
+   tableDims: [0.5, 0.6, 0.05]
+   tablePos: [0.0, 0.0, 1.0]
+   cubeSize: 0.035
+-  numObjects: 6
+-  numColors: 3
+-  numGoalObjects: 3
++  numObjects: 3
++  numColors: 1
++  numGoalObjects: 1
+ 
+   PushT: True
+ 
+diff --git a/config/push_t_old/IsaacPandaPushConfig.yaml b/config/push_t_old/IsaacPandaPushConfig.yaml
+index 7cf1f07..2b20a55 100644
+--- a/config/push_t_old/IsaacPandaPushConfig.yaml
++++ b/config/push_t_old/IsaacPandaPushConfig.yaml
+@@ -30,6 +30,7 @@ env:
+   cubeSize: 0.035
+   numObjects: 1
+   numColors: 1
++  numGoalObjects: 1
+ 
+   PushT: True
+ 
+diff --git a/dataset_paths.py b/dataset_paths.py
+index 7f82c30..d42c7a7 100644
+--- a/dataset_paths.py
++++ b/dataset_paths.py
+@@ -57,6 +57,8 @@ def get_diffuser_dataset_path(num_entity, input_type, rand_color=False, mixed_da
+                 return f'model_chkpts/{num_entity}C_ECRL_State_RandColor_20240613-115954/panda_push_replay_buffer_vqvae_randcolor.pkl'
+         else:
+             if num_entity == 1:
++                if push_t:
++                    return 'data_model_chkpts/1C_ECRL_State_PushT_20240821-004734/panda_push_replay_buffer_vqvae.pkl'
+                 return f'model_chkpts/{num_entity}C_ECRL_State_20240428-161357/panda_push_replay_buffer_vqvae.pkl'
+             elif num_entity == 2:
+                 return f'model_chkpts/{num_entity}C_ECRL_State_20240513-185534/panda_push_replay_buffer_vqvae.pkl'
+diff --git a/diffuser/config/pandapush.py b/diffuser/config/pandapush.py
+index 14f18fd..c199d21 100644
+--- a/diffuser/config/pandapush.py
++++ b/diffuser/config/pandapush.py
+@@ -84,6 +84,15 @@ mode_to_args = {
+             'max_path_length': 100,
+             'vis_freq': 999,
+             },
++    '1C_vqvae_pusht': {'env_config_dir': 'config/vqvae_push_t',
++            'n_diffusion_steps': 5,
++            'horizon': 5,
++            'max_path_length': 50,
++            'particle_normalizer': None,
++            'device': 'cuda:1',
++            'vis_freq': 999,
++            'n_train_steps': 1e6,
++            },
+ }
+ 
+ 
+diff --git a/diffuser/config/pandapush_pint.py b/diffuser/config/pandapush_pint.py
+index e18ff84..9cec27a 100644
+--- a/diffuser/config/pandapush_pint.py
++++ b/diffuser/config/pandapush_pint.py
+@@ -70,7 +70,11 @@ mode_to_args = {
+             'device': 'cuda:1',
+             'droupout': 0.0,
+             'renderer': 'utils.ParticleRenderer',
+-            'vis_freq': 999,
++            'vis_freq': 20,
++            'hidden_dim': 512,   # 512
++            'projection_dim': 512,  # 512
++            'n_heads': 8,  # 4,  8
++            'n_layers': 12, # 4,  6,  # 12
+     },
+     '1C_state': {'env_config_dir': 'config/n_cubes_state',
+             'features_dim': 4,
+diff --git a/diffuser/config/plan_config/plan_pandapush_pint.py b/diffuser/config/plan_config/plan_pandapush_pint.py
+index 0e75a08..d742936 100644
+--- a/diffuser/config/plan_config/plan_pandapush_pint.py
++++ b/diffuser/config/plan_config/plan_pandapush_pint.py
+@@ -18,7 +18,7 @@ args_to_watch = [
+ logbase = 'data'
+ #------------------------ overrides ------------------------#
+ entity_to_steps = {1:30, 2: 50, 3: 100, 4: 150, 5: 200, 6:200}
+-PushT_entity_to_steps = {1:50, 2: 100}
++PushT_entity_to_steps = {1:50, 2: 100, 3: 150, 4: 200, 5: 250, 6:300}
+ kitchen_entity_to_steps = {1:500}
+ 
+ mode_to_args = {
+@@ -47,15 +47,15 @@ mode_to_args = {
+             'diffusion_loadpath': 'diffusion/3C_adalnpintlarge_dlp_randcolor_H5_T100',
+             'vis_freq': 10,
+             },
+-    'dlp_pusht': {'env_config_dir': 'config/push_t_old',
++    'dlp_pusht': {'env_config_dir': 'config/push_t',
+                   'n_diffusion_steps': 5,
+-                  'horizon': 16,
++                  'horizon': 5,
+                   'device': 'cuda:1',
+-                #   'diffusion_loadpath': 'diffusion/PushT_1C_dlp_adalnpint_H5_T5',
++                  'diffusion_loadpath': 'diffusion/PushT_3C_dlp_adalnpint_new_H5_T5',
+                 #   'diffusion_loadpath': 'diffusion/PushT_1C_dlp_eit_H1_T5',
+-                  'vis_freq': 999,
++                  # 'vis_freq': 999,
+                 #   'policy': 'sampling.GoalConditionedBCPolicy',
+-                  'diffusion_loadpath': 'diffusion/PushT_1C_dlp_pint_action_H16_T5',
++                  # 'diffusion_loadpath': 'diffusion/PushT_1C_dlp_pint_action_H16_T5',
+                   },
+     'dlp_action': {'env_config_dir': 'config/generalization_num_cubes',
+             'n_diffusion_steps': 5,
+diff --git a/diffuser/diffuser/eval_utils.py b/diffuser/diffuser/eval_utils.py
+index cd32447..7f514a7 100644
+--- a/diffuser/diffuser/eval_utils.py
++++ b/diffuser/diffuser/eval_utils.py
+@@ -13,7 +13,7 @@ from typing import Callable, Dict, Optional, Tuple, Union
+ import gym
+ import einops
+ from PIL import Image
+-from utils import extract_dlp_features_with_bg, get_recon_from_dlps
++from utils import extract_dlp_features_with_bg, get_recon_from_dlps, extract_dlp_image
+ 
+ def get_kitchen_goal_fn(
+     dataset,
+@@ -183,37 +183,7 @@ def evaluate_dataset_goals(policy, env, dataset, args, logger, num_eval_episodes
+             init_goals_dict = {'cube1': init_goals}
+ 
+             obs = env.reset(init_states_dict, init_goals_dict)
+-            # # perform zero actions
+-            # actions = np.zeros((env.num_envs, env.action_space.shape[0]))
+-            # _, _, _, infos = env.step(actions)
+-
+-            # #### debug
+-            # from dlp2.utils.util_func import plot_keypoints_on_image
+-            # from PIL import Image
+-            # images = torch.tensor(infos[0]["goal_image"])
+-            # dlps = env._image_to_latent_rep(images, obs_mode='dlp')
+-            # dlps2 = env._image_to_latent_rep(images, obs_mode='dlp')
+-
+-            # pixel_xy = torch.tensor(dataset.fields['goals'][0, -1, :, :2].reshape(2, 24, 2))
+-            # cur_pixel_xy = dlps[:, :, :2].view(2, 24, 2)
+-
+-            # # print('goal dlp diff', cur_pixel_xy - pixel_xy)
+-            # print('dlps diff', abs(dlps - dlps2).sum())
+-            # print('saved dlps diff', torch.tensor(dataset.fields['goals'][0, 0]) - dlps.view(-1, 10))
+-            # normalized_images = images.cpu().to(torch.float32) / 255
+-            # dlp_images2 = []
+-            # cur_dlp_images = []
+-            # for cur_kp_xy, kp_xy, image in zip(cur_pixel_xy, pixel_xy, normalized_images):
+-            #     dlp_images2.append(
+-            #         plot_keypoints_on_image(kp_xy, image, radius=2, thickness=1, kp_range=(-1, 1), plot_numbers=False))
+-            #     cur_dlp_images.append(
+-            #         plot_keypoints_on_image(cur_kp_xy, image, radius=2, thickness=1, kp_range=(-1, 1), plot_numbers=False))
+-
+-            # dlp_images2 = np.concatenate(dlp_images2, axis=0)
+-            # cur_dlp_images = np.concatenate(cur_dlp_images, axis=0)
+-            # Image.fromarray(dlp_images2).save('saved_dlp_exe_goals.png')
+-            # Image.fromarray(cur_dlp_images).save('cur_dlp_exe_goals.png')
+-            # import ipdb; ipdb.set_trace()
++
+             t = 0
+             all_conditions = {}
+             while t < env.horizon:
+@@ -449,6 +419,9 @@ def evaluate_policy(policy, env, args, logger, num_eval_episodes=100, exe_steps=
+                                 _, side_bg = extract_dlp_features_with_bg(side_img, env.latent_rep_model, env.device)
+                             
+                         img_list.append(np.moveaxis(infos[0]["image"][0], 0, -1))
++                        # img = infos[0]["image"][0]
++                        # img = extract_dlp_image(img, env.latent_rep_model, env.device)
++                        # img_list.append(img)
+                         if t == env.horizon - 1:
+                             if goal_success_frac[0] == 1:
+                                 eval_vid_success = True
+diff --git a/diffuser/scripts/acc_train.py b/diffuser/scripts/acc_train.py
+index fa3c550..b4133e9 100644
+--- a/diffuser/scripts/acc_train.py
++++ b/diffuser/scripts/acc_train.py
+@@ -13,18 +13,17 @@ import diffuser.utils as utils
+ import wandb
+ import yaml
+ from pathlib import Path
+-
++import time
+ from utils import check_config, load_pretrained_rep_model, set_ipdb_debugger
+ 
++from diffuser.utils.args import ArgsParser
+ #-----------------------------------------------------------------------------#
+ #----------------------------------- setup -----------------------------------#
+ #-----------------------------------------------------------------------------#
+ 
+-class Parser(utils.Parser):
+-    dataset: str = 'panda_push'
+-    config: str = 'config.pandapush_pint'
+-
+-args = Parser().parse_args('diffusion')
++args = ArgsParser().parse_args('diffusion')
++## set default device for data and models to be args.device
++set_global_device(args.device)
+ 
+ from accelerate import Accelerator, InitProcessGroupKwargs
+ from datetime import timedelta
+@@ -66,7 +65,8 @@ render_config = utils.Config(
+     args.renderer,
+     savepath=(args.savepath, 'render_config.pkl') if accelerator.is_local_main_process else None,
+     env=None,
+-    num_entity=args.num_entity,
++    num_entity=6 if args.pad_state else args.num_entity,
++    particle_dim=args.features_dim if 'pint' in args.config else 10,
+ )
+ 
+ dataset = dataset_config()
+@@ -169,8 +169,9 @@ diffusion = diffusion_config(model)
+ ### Trainer
+ trainer = trainer_config(diffusion, dataset, renderer)
+ ### Trainer
+-
+-plan_args = Parser().parse_args('plan')
++if not accelerator.is_local_main_process:
++    time.sleep(5)
++plan_args = ArgsParser().parse_args('plan')
+ plan_args.inv_action = False
+ plan_args.update_condition = False
+ plan_args.exe_steps = 1
+@@ -213,6 +214,9 @@ if accelerator.is_local_main_process:
+ 
+     ### modify env_config ###
+     isaac_env_cfg['env']['numObjects'] = args.num_entity
++    if args.push_t:
++        isaac_env_cfg['env']['numColors'] = args.num_entity
++        isaac_env_cfg['env']['numGoalObjects'] = args.num_entity
+     if args.overfit:
+         isaac_env_cfg['env']['numEnvs'] = 2
+     ### modify env_config ###
+@@ -261,9 +265,10 @@ print('✓')
+ #-----------------------------------------------------------------------------#
+ if accelerator.is_local_main_process:
+     wandb_run = wandb.init(
++                dir='/data/carlq/project_data/ECRL/wandb',
+                 entity="carltheq",
+                 project="diffuser",
+-                group=str(args.num_entity)+'C_'+args.input_type,
++                group=args.wandb_group_name,
+                 config=args,
+                 sync_tensorboard=False,
+                 settings=wandb.Settings(start_method="fork"),
+diff --git a/diffuser/scripts/train.py b/diffuser/scripts/train.py
+index 5109646..199dc46 100644
+--- a/diffuser/scripts/train.py
++++ b/diffuser/scripts/train.py
+@@ -219,6 +219,7 @@ isaac_policy_config = yaml.safe_load(Path('config/PolicyConfig.yaml').read_text(
+ isaac_env_cfg['env']['numObjects'] = args.num_entity
+ if args.push_t:
+     isaac_env_cfg['env']['numColors'] = args.num_entity
++    isaac_env_cfg['env']['numGoalObjects'] = args.num_entity
+ if args.overfit:
+     isaac_env_cfg['env']['numEnvs'] = 2
+ # else:
+diff --git a/get_vae_features_from_buffer.py b/get_vae_features_from_buffer.py
+index d83fb36..0b9fc87 100644
+--- a/get_vae_features_from_buffer.py
++++ b/get_vae_features_from_buffer.py
+@@ -36,9 +36,10 @@ if __name__ == '__main__':
+     #            Config             #
+     #################################
+ 
+-    num_entity = 3
++    num_entity = 2
+     rand_color = False
+-    model_path, buffer_path = get_ecrl_buffer_and_model_path(num_entity, rand_color=rand_color)
++    push_t = True
++    model_path, buffer_path = get_ecrl_buffer_and_model_path(num_entity, rand_color=rand_color, push_t=push_t)
+ 
+     config_dir = args.config_dir
+     # load config files
+diff --git a/run_batch_plan.sh b/run_batch_plan.sh
+index 47315f9..41036f2 100644
+--- a/run_batch_plan.sh
++++ b/run_batch_plan.sh
+@@ -1,10 +1,10 @@
+ #!/bin/bash
+ 
+ seeds=(42 188 288 388 488)
+-gpu_numbers=(1 2 3 1 2)
++gpu_numbers=(3 2 3 1 2)
+ 
+ for i in "${!seeds[@]}"; do
+     seed=${seeds[$i]}
+     gpu_number=${gpu_numbers[$i]}
+-    CUDA_VISIBLE_DEVICES=0,$gpu_number python diffuser/scripts/plan_gc_pandapush.py --planning_only --config config.plan_config.plan_pandapush_pint --seed $seed --num_entity 1 --push_t --exp_note pint_action &
++    CUDA_VISIBLE_DEVICES=0,$gpu_number python diffuser/scripts/plan_gc_pandapush.py --vis_traj_wandb --planning_only --config config.plan_config.plan_pandapush_pint --seed $seed --num_entity 6 --push_t --exp_note adalnpint &
+ done
+
+diff --git a/run_batch_train_diffuser.sh b/run_batch_train_diffuser.sh
+index b78cf2c..6fbdae9 100644
+--- a/run_batch_train_diffuser.sh
++++ b/run_batch_train_diffuser.sh
+@@ -1,10 +1,10 @@
+ #!/bin/bash
+ 
+ seeds=(42 188 288 388 488)
+-gpu_numbers=(4 5 6 7 5)
++gpu_numbers=(1 2 3 4 5)
+ 
+ for i in "${!seeds[@]}"; do
+     seed=${seeds[$i]}
+     gpu_number=${gpu_numbers[$i]}
+-    CUDA_VISIBLE_DEVICES=0,$gpu_number python diffuser/scripts/train.py --config config.pandapush --input_type vqvae --seed $seed --num_entity 1 --exp_note unet_new &
++    CUDA_VISIBLE_DEVICES=0,$gpu_number python diffuser/scripts/train.py --config config.pandapush --input_type vqvae --push_t --seed $seed --num_entity 1 --exp_note unet &
+ done
+
+diff --git a/vq_bet_official/examples/kitchen_env.py b/vq_bet_official/examples/kitchen_env.py
+index a980c65..55e8424 100644
+--- a/vq_bet_official/examples/kitchen_env.py
++++ b/vq_bet_official/examples/kitchen_env.py
+@@ -11,7 +11,7 @@ from d4rl.kitchen.adept_envs.franka.kitchen_multitask_v0 import KitchenTaskRelax
+ from gym.envs.registration import register
+ import einops
+ import torch.nn.functional as F
+-from vq_bet_official.examples.dataset import RelayKitchenTrajectoryDataset
++from examples.dataset import RelayKitchenTrajectoryDataset
+ 
+ OBS_ELEMENT_INDICES = {
+     "bottom burner": np.array([11, 12]),
+diff --git a/vq_bet_official/examples/train.py b/vq_bet_official/examples/train.py
+index 0b18cd0..7caebc7 100644
+--- a/vq_bet_official/examples/train.py
++++ b/vq_bet_official/examples/train.py
+@@ -49,6 +49,21 @@ def main(cfg):
+         learning_rate=cfg.optim.lr,
+         betas=cfg.optim.betas,
+     )
++    def calc_model_size(model):
++        num_trainable_params = sum([p.numel() for p in model.parameters() if p.requires_grad])
++        # estimate model size on disk: https://discuss.pytorch.org/t/finding-model-size/130275/2
++        param_size = 0
++        for param in model.parameters():
++            param_size += param.nelement() * param.element_size()
++        buffer_size = 0
++        for buffer in model.buffers():
++            buffer_size += buffer.nelement() * buffer.element_size()
++        size_all_mb = (param_size + buffer_size) / 1024 ** 2
++        return {'n_params': num_trainable_params, 'size_mb': size_all_mb}
++
++    model_size = calc_model_size(cbet_model)
++    print(model_size)
++    exit()
+     env = hydra.utils.instantiate(cfg.env.gym)
+     goal_fn = hydra.utils.instantiate(cfg.goal_fn)
+     run = wandb.init(

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/diffusion_config.pkl

@@ -0,0 +1,3 @@
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+size 364

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/model_config.pkl

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+size 322

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/render_config.pkl

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+oid sha256:f2467ff3ad0dc346a0d28cd4bd0ce325689a4559760aec5e978b74d66a1cd308
+size 174

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/state_1100000.pt

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+oid sha256:bbbc76e9c44657ec9047e0b1e24f165aa91c4657a7b57bb8be6a2f0e98ab72a2
+size 485453821

pretrained_models/panda_push/diffusion/PushT_3C_dlp_pintlarge_H5_T5/trainer_config.pkl

@@ -0,0 +1,3 @@
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+oid sha256:dec1c217b3f699d55b185b888398346b4c5d56a20a40fdcea946e0a0f76799b4
+size 1807