Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.12 +/- 0.05
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-PandaReachDense-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8092334743f3b707bb84ac8d3d51df2528162f1d3f34da030939bf62250cdfdc
+size 106831

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0

a2c-PandaReachDense-v3/data

@@ -0,0 +1,97 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x79135c513eb0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x79135c519040>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691821235714656795,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.5617624  -0.39298043  0.6694504 ]\n [ 0.23638605  0.00524063  0.4324758 ]\n [ 0.23638605  0.00524063  0.4324758 ]\n [-0.3474552  -0.2064385   0.44150198]]",
+        "desired_goal": "[[ 1.5401412  -1.0417049   0.53613067]\n [ 1.1344776   1.4204007   1.0601388 ]\n [-0.9360339   0.9282963   0.7890916 ]\n [-0.5062157  -0.08826521  1.6783646 ]]",
+        "observation": "[[ 0.5617624  -0.39298043  0.6694504   1.6879784  -1.5924929   1.1209141 ]\n [ 0.23638605  0.00524063  0.4324758   0.47973585 -0.00333088  0.38255954]\n [ 0.23638605  0.00524063  0.4324758   0.47973585 -0.00333088  0.38255954]\n [-0.3474552  -0.2064385   0.44150198  0.13224934 -0.22071528  1.0882586 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAQCUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[ 0.03362104  0.14425746  0.04519756]\n [-0.02678247 -0.14645198  0.2471663 ]\n [-0.00496545  0.09431564  0.09980056]\n [-0.10583678 -0.14829227  0.04056188]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 50000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3375e8562e582c3f089e159ef9fb2aac7a7d57343ff4c22ecd446810c753816
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36c01d99b183f951274c1f34c6a02f3b9ed9c00dc5501edd67721131dd4db021
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x79135c513eb0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79135c519040>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691821235714656795, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.5617624  -0.39298043  0.6694504 ]\n [ 0.23638605  0.00524063  0.4324758 ]\n [ 0.23638605  0.00524063  0.4324758 ]\n [-0.3474552  -0.2064385   0.44150198]]", "desired_goal": "[[ 1.5401412  -1.0417049   0.53613067]\n [ 1.1344776   1.4204007   1.0601388 ]\n [-0.9360339   0.9282963   0.7890916 ]\n [-0.5062157  -0.08826521  1.6783646 ]]", "observation": "[[ 0.5617624  -0.39298043  0.6694504   1.6879784  -1.5924929   1.1209141 ]\n [ 0.23638605  0.00524063  0.4324758   0.47973585 -0.00333088  0.38255954]\n [ 0.23638605  0.00524063  0.4324758   0.47973585 -0.00333088  0.38255954]\n [-0.3474552  -0.2064385   0.44150198  0.13224934 -0.22071528  1.0882586 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAQCUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.03362104  0.14425746  0.04519756]\n [-0.02678247 -0.14645198  0.2471663 ]\n [-0.00496545  0.09431564  0.09980056]\n [-0.10583678 -0.14829227  0.04056188]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "gAWVsAMAAAAAAACMFWd5bW5hc2l1bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwUZ3ltbmFzaXVtLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowNYm91bmRlZF9iZWxvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYDAAAAAAAAAAEBAZRoE4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoHCiWAwAAAAAAAAABAQGUaCBLA4WUaCR0lFKUjAZfc2hhcGWUSwOFlIwDbG93lGgcKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFksDhZRoJHSUUpSMBGhpZ2iUaBwolgwAAAAAAAAAAAAgQQAAIEEAACBBlGgWSwOFlGgkdJRSlIwIbG93X3JlcHKUjAUtMTAuMJSMCWhpZ2hfcmVwcpSMBDEwLjCUjApfbnBfcmFuZG9tlE51YowMZGVzaXJlZF9nb2FslGgNKYGUfZQoaBBoFmgZaBwolgMAAAAAAAAAAQEBlGggSwOFlGgkdJRSlGgnaBwolgMAAAAAAAAAAQEBlGggSwOFlGgkdJRSlGgsSwOFlGguaBwolgwAAAAAAAAAAAAgwQAAIMEAACDBlGgWSwOFlGgkdJRSlGgzaBwolgwAAAAAAAAAAAAgQQAAIEEAACBBlGgWSwOFlGgkdJRSlGg4jAUtMTAuMJRoOowEMTAuMJRoPE51YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgWaBloHCiWBgAAAAAAAAABAQEBAQGUaCBLBoWUaCR0lFKUaCdoHCiWBgAAAAAAAAABAQEBAQGUaCBLBoWUaCR0lFKUaCxLBoWUaC5oHCiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBZLBoWUaCR0lFKUaDNoHCiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBZLBoWUaCR0lFKUaDiMBS0xMC4wlGg6jAQxMC4wlGg8TnVidWgsTmgQTmg8TnViLg==", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.11900525111705065, "std_reward": 0.05451362264381769, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-12T07:07:17.104614"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5c6e866cd2b4ccbe220652d8df6ee898caf89cea67ccdf945f10a9b4e2f3731d
+size 2623