Merge pull request #135 from masa-su/develop/v0.2.1

Develop/v0.2.1

.gitignore

@@ -71,6 +71,7 @@ target/
 
 # Jupyter Notebook
 .ipynb_checkpoints
+data/
 
 # pyenv
 .python-version

README.md

@@ -4,7 +4,7 @@
 
 [![pypi](https://img.shields.io/pypi/v/pixyz.svg)](https://pypi.python.org/pypi/pixyz)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Python Version](https://img.shields.io/pypi/pyversions/Django.svg)](https://github.com/masa-su/pixyz)
+[![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20-blue)](https://github.com/masa-su/pixyz)
 [![Pytorch Version](https://img.shields.io/badge/pytorch-1.0-yellow.svg)](https://github.com/masa-su/pixyz)
 [![Read the Docs](https://readthedocs.org/projects/pixyz/badge/?version=latest)](http://docs.pixyz.io)
 [![TravisCI](https://travis-ci.org/masa-su/pixyz.svg?branch=master)](https://github.com/masa-su/pixyz)

docs/source/conf.py

@@ -165,7 +165,7 @@
 ]
 
 autodoc_member_order = 'bysource'
-autodoc_default_flags = ['show-inheritance']
+autodoc_default_options = {'show-inheritance': True}
 
 napoleon_numpy_docstring = True
 napoleon_include_init_with_doc = True

examples/cvae.ipynb

@@ -680,7 +680,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'cvae'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "plot_number = 1\n",
     "\n",

examples/gan.ipynb

@@ -1788,7 +1788,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'gan'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "z_sample = torch.randn(64, z_dim).to(device)\n",
     "_x, _y = iter(test_loader).next()\n",

examples/glow.ipynb

@@ -813,7 +813,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'glow'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "z_sample = torch.randn(64, 3, 32, 32).to(device)\n",
     "_x, _ = iter(test_loader).next()\n",

examples/hierarchical_variational_inference.ipynb

@@ -683,7 +683,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'hierarchical_variational_inference'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "z_sample = 0.5 * torch.randn(64, z_dim).to(device)\n",
     "_x, _ = iter(test_loader).next()\n",

examples/jmvae.ipynb

@@ -665,7 +665,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'jmvae'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "plot_number = 1\n",
     "\n",

examples/jmvae_poe.ipynb

@@ -700,7 +700,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'jmvae_poe'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "plot_number = 1\n",
     "\n",

examples/m2.ipynb

@@ -816,7 +816,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'm2'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "for epoch in range(1, epochs + 1):\n",
     "    train_loss = train(epoch)\n",

examples/maximum_likelihood.ipynb

@@ -510,7 +510,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'maximum_likelihood'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "for epoch in range(1, epochs + 1):\n",
     "    train_loss = train(epoch)\n",

examples/mmd_vae.ipynb

@@ -630,7 +630,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'mmd_vae'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "z_sample = 0.5 * torch.randn(64, z_dim).to(device)\n",
     "_x, _ = iter(test_loader).next()\n",

examples/mvae.ipynb

@@ -3395,7 +3395,14 @@
    ],
    "source": [
     "# for visualising in TensorBoard\n",
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'mvae'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "plot_number = 1\n",
     "\n",

examples/real_nvp.ipynb

@@ -536,7 +536,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'real_nvp'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "z_sample = torch.randn(64, z_dim).to(device)\n",
     "_x, _ = iter(test_loader).next()\n",

examples/real_nvp_cifar.ipynb

@@ -406,7 +406,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'real_nvp_cifar'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "z_sample = torch.randn(64, 3, 32, 32).to(device)\n",
     "_x, _ = iter(test_loader).next()\n",

examples/real_nvp_cond.ipynb

@@ -566,7 +566,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'real_nvp_cond'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "plot_number = 5\n",
     "\n",

examples/real_nvp_toy.ipynb

@@ -13,6 +13,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "!pip install sklearn\n",
+    "\n",
     "from __future__ import print_function\n",
     "import torch\n",
     "import torch.utils.data\n",

examples/vae.ipynb

@@ -731,7 +731,14 @@
    ],
    "source": [
     "# for visualising in TensorBoard\n",
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'vae'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "# fix latent variable z for watching generative model improvement \n",
     "z_sample = 0.5 * torch.randn(64, z_dim).to(device)\n",

examples/vae_with_vae_class.ipynb

@@ -723,7 +723,14 @@
    ],
    "source": [
     "# for visualising in TensorBoard\n",
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'vae_with_vae_class'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "# fix latent variable z for watching generative model improvement \n",
     "z_sample = 0.5 * torch.randn(64, z_dim).to(device)\n",

examples/vi.ipynb

@@ -599,7 +599,14 @@
     }
    ],
    "source": [
-    "writer = SummaryWriter()\n",
+    "import pixyz\n",
+    "import datetime\n",
+    "\n",
+    "dt_now = datetime.datetime.now()\n",
+    "exp_time = dt_now.strftime('%Y%m%d_%H:%M:%S')\n",
+    "v = pixyz.__version__\n",
+    "nb_name = 'vi'\n",
+    "writer = SummaryWriter(\"runs/\" + v + \".\" + nb_name + exp_time)\n",
     "\n",
     "z_sample = 0.5 * torch.randn(64, z_dim).to(device)\n",
     "_x, _ = iter(test_loader).next()\n",

pixyz/__init__.py

@@ -1,2 +1,2 @@
 name = "pixyz"
-__version__ = "0.2.0"
+__version__ = "0.2.1"

pixyz/distributions/distributions.py

@@ -4,7 +4,7 @@
 from torch import nn
 from copy import deepcopy
 
-from ..utils import get_dict_values, replace_dict_keys, replace_dict_keys_split, delete_dict_values,\
+from ..utils import get_dict_values, replace_dict_keys, delete_dict_values,\
     tolist, sum_samples, convert_latex_name, lru_cache_for_sample_dict
 from ..losses import LogProb, Prob
 
@@ -653,7 +653,9 @@ def _set_buffers(self, **params_dict):
         for key in params_dict.keys():
             if type(params_dict[key]) is str:
                 if params_dict[key] in self._cond_var:
-                    self.replace_params_dict[params_dict[key]] = key
+                    if params_dict[key] not in self.replace_params_dict:
+                        self.replace_params_dict[params_dict[key]] = []
+                    self.replace_params_dict[params_dict[key]].append(key)
                 else:
                     raise ValueError("parameter setting {}:{} is not valid because cond_var does not contains {}."
                                      .format(key, params_dict[key], params_dict[key]))
@@ -772,10 +774,16 @@ def get_log_prob(self, x_dict, sum_features=True, feature_dims=None):
 
     @lru_cache_for_sample_dict()
     def get_params(self, params_dict={}, **kwargs):
-        params_dict, vars_dict = replace_dict_keys_split(params_dict, self.replace_params_dict)
-        output_dict = self.forward(**vars_dict)
+        replaced_params_dict = {}
+        for key, value in params_dict.items():
+            if key in self.replace_params_dict:
+                for replaced_key in self.replace_params_dict[key]:
+                    replaced_params_dict[replaced_key] = value
 
-        output_dict.update(params_dict)
+        vars_dict = {key: value for key, value in params_dict.items() if key not in self.replace_params_dict}
+        output_dict = self(**vars_dict)
+
+        output_dict.update(replaced_params_dict)
 
         # append constant parameters to output_dict
         constant_params_dict = get_dict_values(dict(self.named_buffers()), self.params_keys,
@@ -1067,7 +1075,7 @@ def __init__(self, p, replace_dict):
         self._input_var = _input_var
 
     def forward(self, *args, **kwargs):
-        return self.p.forward(*args, **kwargs)
+        return self.p(*args, **kwargs)
 
     def get_params(self, params_dict={}):
         params_dict = replace_dict_keys(params_dict, self._replace_inv_cond_var_dict)
@@ -1204,7 +1212,7 @@ def __init__(self, p, marginalize_list):
         self._marginalize_list = marginalize_list
 
     def forward(self, *args, **kwargs):
-        return self.p.forward(*args, **kwargs)
+        return self.p(*args, **kwargs)
 
     def get_params(self, params_dict={}):
         return self.p.get_params(params_dict)