{"action":"created","comment":{"url":"https://api.github.com/repos/ecs-vlc/torchbearer/pulls/comments/211234562","pull_request_review_id":147610261,"id":211234562,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIxMTIzNDU2Mg==","diff_hunk":"@@ -0,0 +1,893 @@\n+import warnings\n+\n+import torch\n+from torch.utils.data import DataLoader, TensorDataset\n+from torch.optim import Optimizer\n+\n+import torchbearer\n+from torchbearer import State\n+from torchbearer.metrics import MetricList\n+from torchbearer.callbacks import Callback, CallbackList, Tqdm, AggregatePredictions\n+\n+\n+class MockOptimizer(Optimizer):\n+    \"\"\"The Mock Optimizer will be used inplace of an optimizer in the event that none is passed to the Trial class.\n+    \"\"\"\n+    def __init__(self):\n+        super(MockOptimizer, self).__init__([torch.zeros(1)], [])\n+\n+    def add_param_group(self, param_group):\n+        pass  # Do Nothing\n+\n+    def load_state_dict(self, state_dict):\n+        pass  # Do Nothing\n+\n+    def state_dict(self):\n+        return {}  # Return Empty\n+\n+    def step(self, closure=None):\n+        pass  # Do Nothing\n+\n+    def zero_grad(self):\n+        pass  # Do Nothing\n+\n+\n+class CallbackListInjection(CallbackList):\n+    \"\"\"This class allows for an callback to be injected into a callback list, without masking the methods available for\n+    mutating the list. In this way, callbacks (such as printers) can be injected seamlessly into the methods of the\n+    trial class.\n+\n+    :param callback: The callback to inject\n+    :param callback_list: The underlying callback list\n+    :type callback_list: CallbackList\n+    \"\"\"\n+    def __init__(self, callback, callback_list):\n+        super(CallbackListInjection, self).__init__([])\n+\n+        self.callback = callback\n+        self.callback_list = callback_list\n+\n+    def state_dict(self):\n+        return self.callback_list.state_dict()\n+\n+    def load_state_dict(self, state_dict):\n+        self.callback_list.load_state_dict(state_dict)\n+        return self\n+\n+    def __iter__(self):\n+        return self.callback_list.__iter__()\n+\n+    def __copy__(self):\n+        return self.callback_list.copy()\n+\n+    def copy(self):\n+        return self.__copy__()\n+\n+    def append(self, callback_list):\n+        self.callback_list.append(callback_list)\n+\n+    def _for_list(self, function):\n+        function(self.callback)  # Call injected callback BEFORE the callback list\n+        function(self.callback_list)\n+\n+\n+def inject_printer(validation_label_letter='v'):\n+    \"\"\"The inject printer decorator is used to inject the appropriate printer callback, according to the verbosity level.\n+\n+    :param validation_label_letter: The validation label letter to use\n+    :return: A decorator\n+    \"\"\"\n+    def decorator(func):\n+        def wrapper(self, *args, **kwargs):\n+            import inspect\n+            verbose = kwargs['verbose'] if 'verbose' in kwargs else inspect.signature(func).parameters['verbose'].default  # Populate default value\n+\n+            printer = get_printer(verbose=verbose, validation_label_letter=validation_label_letter)\n+\n+            callback_list_old = self.state[torchbearer.CALLBACK_LIST]\n+\n+            self.state[torchbearer.CALLBACK_LIST] = CallbackListInjection(printer, callback_list_old)\n+\n+            res = func(self, *args, **kwargs)\n+\n+            self.state[torchbearer.CALLBACK_LIST] = callback_list_old\n+            return res\n+        return wrapper\n+    return decorator\n+\n+\n+def get_printer(verbose, validation_label_letter):\n+    if verbose >= 2:\n+        printer = Tqdm(validation_label_letter=validation_label_letter)\n+    elif verbose >= 1:\n+        printer = Tqdm(validation_label_letter=validation_label_letter, on_epoch=True)\n+    else:\n+        printer = Callback()\n+\n+    return printer\n+\n+\n+def deep_to(batch, device, dtype):\n+    \"\"\" Static method to call :func:`to` on tensors or tuples. All items in tuple will have :func:`deep_to` called\n+    :param batch: The mini-batch which requires a :func:`to` call\n+    :type batch: tuple, list, torch.Tensor\n+    :param device: The desired device of the batch\n+    :type device: torch.device\n+    :param dtype: The desired datatype of the batch\n+    :type dtype: torch.dtype\n+    :return: The moved or casted batch\n+    :rtype: tuple, list, torch.Tensor\n+    \"\"\"\n+    is_tuple = isinstance(batch, tuple)\n+\n+    if isinstance(batch, list) or isinstance(batch, tuple):\n+        batch = list(batch)\n+        for i in range(len(batch)):\n+            batch[i] = deep_to(batch[i], device, dtype)\n+        batch = tuple(batch) if is_tuple else batch\n+    elif isinstance(batch, dict):\n+        for key in batch:\n+            batch[key] = deep_to(batch[key], device, dtype)\n+    else:\n+        if batch.dtype.is_floating_point:\n+            batch = batch.to(device, dtype)\n+        else:\n+            batch = batch.to(device)\n+\n+    return batch\n+\n+\n+def load_batch_standard(state):\n+    \"\"\" Load a standard (input data, target) tuple mini-batch from iterator into state\n+\n+    :param state: The current state dict of the :class:`Trial`.\n+    :type state: dict[str,any]\n+    \"\"\"\n+    state[torchbearer.X], state[torchbearer.Y_TRUE] = deep_to(next(state[torchbearer.ITERATOR]),\n+                                                              state[torchbearer.DEVICE],\n+                                                              state[torchbearer.DATA_TYPE])\n+\n+\n+def load_batch_none(state):\n+    \"\"\" Load a none (none, none) tuple mini-batch into state\n+\n+    :param state: The current state dict of the :class:`Trial`.\n+    :type state: dict[str,any]\n+    \"\"\"\n+    state[torchbearer.X], state[torchbearer.Y_TRUE] = None, None\n+\n+\n+def load_batch_predict(state):\n+    \"\"\" Load a prediction (input data, target) or (input data) mini-batch from iterator into state\n+\n+    :param state: The current state dict of the :class:`Trial`.\n+    :type state: dict[str,any]\n+    \"\"\"\n+    data = deep_to(next(state[torchbearer.ITERATOR]), state[torchbearer.DEVICE], state[torchbearer.DATA_TYPE])\n+    if isinstance(data, list) or isinstance(data, tuple):\n+        state[torchbearer.X], state[torchbearer.Y_TRUE] = data\n+    else:\n+        state[torchbearer.X] = data\n+        \n+        \n+class Sampler:\n+    \"\"\"\n+    Sampler wraps a batch loader function and executes it when :meth:`Sampler.sample` is called\n+\n+    :param batch_loader: The batch loader to execute\n+    :type batch_loader: function\n+    \"\"\"\n+    def __init__(self, batch_loader):\n+        super().__init__()\n+        self.batch_loader = batch_loader\n+\n+    def sample(self, state):\n+        self.batch_loader(state)\n+\n+\n+def inject_sampler(generator, predict=False):\n+    \"\"\" Decorator to inject a :class:`Sampler` into state[torchbearer.SAMPLER]\n+\n+    :param generator: The data generator for the sampler to load data from\n+    :type generator: DataLoader\n+    :param predict: If true, the prediction batch loader is used, if false the standard data loader is used\n+    :type predict: bool\n+    :return: the decorator\n+    \"\"\"\n+    def decorator(func):\n+        def wrapper(self, *args, **kwargs):\n+            if self.state[generator] is None:\n+                loader = load_batch_none\n+            elif predict:\n+                loader = load_batch_predict\n+            else:\n+                loader = load_batch_standard\n+\n+            self.state[torchbearer.SAMPLER] = Sampler(loader)\n+\n+            res = func(self, *args, **kwargs)\n+\n+            return res\n+        return wrapper\n+    return decorator\n+\n+\n+def inject_callback(callback):\n+    \"\"\" Decorator to inject a callback into the callback list and remove the callback after the decorated function has executed\n+    \n+    :param callback: Callback to be injected\n+    :type callback: Callback\n+    :return: the decorator\n+    \"\"\"\n+    def decorator(func):\n+        def wrapper(self, *args, **kwargs):\n+            callback_list_old = self.state[torchbearer.CALLBACK_LIST]\n+\n+            self.state[torchbearer.CALLBACK_LIST] = CallbackListInjection(callback, callback_list_old)\n+\n+            res = func(self, *args, **kwargs)\n+\n+            self.state[torchbearer.CALLBACK_LIST] = callback_list_old\n+            return res\n+        return wrapper\n+    return decorator\n+\n+\n+def fluent(func):\n+    \"\"\"Decorator for class methods which forces return of self.\n+    \"\"\"\n+    def wrapper(self, *args, **kwargs):\n+        func(self, *args, **kwargs)\n+        return self\n+    return wrapper\n+\n+\n+def update_device_and_dtype(state, *args, **kwargs):\n+    \"\"\"Function get data type and device values from the args / kwargs and update state.\n+\n+    :param state: The dict to update\n+    :type state: State\n+    :param args: Arguments to the :func:`Trial.to` function\n+    :param kwargs: Keyword arguments to the :func:`Trial.to` function\n+    :return: device, dtype pair\n+    :rtype: tuple\n+    \"\"\"\n+    for key, _ in kwargs.items():\n+        if key == str(torchbearer.DATA_TYPE):\n+            state[torchbearer.DATA_TYPE] = kwargs['dtype']\n+        elif str(torchbearer.DEVICE) in kwargs:\n+            state[torchbearer.DEVICE] = kwargs['device']\n+\n+    for arg in args:\n+        if isinstance(arg, torch.dtype):\n+            state[torchbearer.DATA_TYPE] = arg\n+        else:\n+            state[torchbearer.DEVICE] = arg\n+\n+    return state\n+\n+\n+class Trial(object):\n+    \"\"\" The trial class contains all of the required hyper-parameters for model running in torchbearer and presents an\n+    API for model fitting, evaluating and predicting.\n+\n+    :param model: The base pytorch model\n+    :type model: torch.nn.Module\n+    :param criterion: The final loss criterion that provides a loss value to the optimizer\n+    :type criterion: function or None\n+    :param optimizer: The optimizer used for pytorch model weight updates\n+    :type optimizer: torch.optim.Optimizer\n+    :param metrics: The list of :class:`torchbearer.Metric <.Metric>` instances to process during fitting\n+    :type metrics: list\n+    :param callbacks: The list of :class:`torchbearer.Callback <.Callback>` instances to call during fitting\n+    :type callbacks: list\n+    :param pass_state: If True, the torchbearer state will be passed to the model during fitting\n+    :type pass_state: bool\n+    \"\"\"\n+    def __init__(self, model, optimizer=None, criterion=None, metrics=[], callbacks=[], pass_state=False):\n+        if criterion is None:\n+            def criterion(_, y_true):","path":"torchbearer/trial.py","position":289,"original_position":289,"commit_id":"fa236126906d1753d79e18268546185695c8ade8","original_commit_id":"fa236126906d1753d79e18268546185695c8ade8","user":{"login":"codacy-bot","id":19940114,"node_id":"MDQ6VXNlcjE5OTQwMTE0","avatar_url":"https://avatars1.githubusercontent.com/u/19940114?v=4","gravatar_id":"","url":"https://api.github.com/users/codacy-bot","html_url":"https://github.com/codacy-bot","followers_url":"https://api.github.com/users/codacy-bot/followers","following_url":"https://api.github.com/users/codacy-bot/following{/other_user}","gists_url":"https://api.github.com/users/codacy-bot/gists{/gist_id}","starred_url":"https://api.github.com/users/codacy-bot/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/codacy-bot/subscriptions","organizations_url":"https://api.github.com/users/codacy-bot/orgs","repos_url":"https://api.github.com/users/codacy-bot/repos","events_url":"https://api.github.com/users/codacy-bot/events{/privacy}","received_events_url":"https://api.github.com/users/codacy-bot/received_events","type":"User","site_admin":false},"body":"![Codacy](https://app.codacy.com/assets/images/favicon.png) Issue found: [function already defined line 230](https://app.codacy.com/app/ewah1g13/torchbearer/pullRequest?prid=2066626)","created_at":"2018-08-20T11:53:41Z","updated_at":"2018-08-20T11:53:41Z","html_url":"https://github.com/ecs-vlc/torchbearer/pull/351#discussion_r211234562","pull_request_url":"https://api.github.com/repos/ecs-vlc/torchbearer/pulls/351","author_association":"NONE","_links":{"self":{"href":"https://api.github.com/repos/ecs-vlc/torchbearer/pulls/comments/211234562"},"html":{"href":"https://github.com/ecs-vlc/torchbearer/pull/351#discussion_r211234562"},"pull_request":{"href":"https://api.github.com/repos/ecs-vlc/torchbearer/pulls/351"}}},"pull_request":{"url":"https://api.github.com/repos/ecs-vlc/torchbearer/pulls/351","id":209490740,"node_id":"MDExOlB1bGxSZXF1ZXN0MjA5NDkwNzQw","html_url":"https://github.com/ecs-vlc/torchbearer/pull/351","diff_url":"https://github.com/ecs-vlc/torchbearer/pull/351.diff","patch_url":"https://github.com/ecs-vlc/torchbearer/pull/351.patch","issue_url":"https://api.github.com/repos/ecs-vlc/torchbearer/issues/351","number":351,"state":"open","locked":false,"title":"Feature/trial api","user":{"login":"MattPainter01","id":23357783,"node_id":"MDQ6VXNlcjIzMzU3Nzgz","avatar_url":"https://avatars0.githubusercontent.com/u/23357783?v=4","gravatar_id":"","url":"https://api.github.com/users/MattPainter01","html_url":"https://github.com/MattPainter01","followers_url":"https://api.github.com/users/MattPainter01/followers","following_url":"https://api.github.com/users/MattPainter01/following{/other_user}","gists_url":"https://api.github.com/users/MattPainter01/gists{/gist_id}","starred_url":"https://api.github.com/users/MattPainter01/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/MattPainter01/subscriptions","organizations_url":"https://api.github.com/users/MattPainter01/orgs","repos_url":"https://api.github.com/users/MattPainter01/repos","events_url":"https://api.github.com/users/MattPainter01/events{/privacy}","received_events_url":"https://api.github.com/users/MattPainter01/received_events","type":"User","site_admin":false},"body":"Closes #307, closes #312, closes #314, closes #324, closes #329, closes 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