Merge branch 'release/1.2-dev' into ppo

CHANGELOG.md

@@ -32,6 +32,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Changed `automatic casting` for LoggerConnector `metrics` ([#5218](https://github.com/PyTorchLightning/pytorch-lightning/pull/5218))
+
+
 - `stat_scores` metric now calculates stat scores over all classes and gains new parameters, in line with the new `StatScores` metric ([#4839](https://github.com/PyTorchLightning/pytorch-lightning/pull/4839))
 
 

pytorch_lightning/accelerators/ddp_accelerator.py

@@ -223,7 +223,6 @@ def ddp_train(self, process_idx, model):
 
         Args:
             process_idx:
-            mp_queue: multiprocessing queue
             model:
 
         Returns:

pytorch_lightning/accelerators/ddp_hpc_accelerator.py

@@ -112,7 +112,6 @@ def ddp_train(self, process_idx, model):
 
         Args:
             process_idx:
-            mp_queue: multiprocessing queue
             model:
 
         Returns:

pytorch_lightning/accelerators/ddp_spawn_accelerator.py

@@ -86,7 +86,7 @@ def train(self):
         self.__recover_child_process_weights(model, best_path, last_path)
         return results
 
-    def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0):
+    def ddp_train(self, process_idx, mp_queue, model, is_master: bool = False, proc_offset: int = 0):
         """
         Entry point for ddp
 

pytorch_lightning/accelerators/tpu_accelerator.py

@@ -171,7 +171,6 @@ def to_device(self, batch):
 
         Args:
             batch: A tensor or collection of tensors.
-            tpu_id: The id of the TPU core. If omitted, the first available core is chosen.
 
         Return:
             the tensor on the TPU device.

pytorch_lightning/callbacks/early_stopping.py

@@ -19,14 +19,12 @@
 Monitor a metric and stop training when it stops improving.
 
 """
-import numbers
 
 import numpy as np
 import torch
 
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.metrics.metric import Metric
-from pytorch_lightning.utilities import _TPU_AVAILABLE, rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn
 
 
 class EarlyStopping(Callback):
@@ -196,15 +194,6 @@ def _run_early_stopping_check(self, trainer, pl_module):
         # when in dev debugging
         trainer.dev_debugger.track_early_stopping_history(self, current)
 
-        if current is not None:
-            if isinstance(current, Metric):
-                current = current.compute()
-            elif isinstance(current, numbers.Number):
-                current = torch.tensor(current, device=pl_module.device, dtype=torch.float)
-
-        if trainer.use_tpu and _TPU_AVAILABLE:
-            current = current.cpu()
-
         if self.monitor_op(current - self.min_delta, self.best_score):
             self.best_score = current
             self.wait_count = 0

pytorch_lightning/callbacks/model_checkpoint.py

@@ -20,7 +20,6 @@
 
 """
 
-import numbers
 import os
 import re
 from copy import deepcopy
@@ -33,7 +32,6 @@
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.metrics.metric import Metric
 from pytorch_lightning.utilities import rank_zero_info, rank_zero_only, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -554,12 +552,6 @@ def _save_top_k_checkpoints(self, trainer, pl_module, metrics):
         epoch = metrics.get("epoch")
         step = metrics.get("step")
 
-        if current is not None:
-            if isinstance(current, Metric):
-                current = current.compute()
-            elif isinstance(current, numbers.Number):
-                current = torch.tensor(current, device=pl_module.device, dtype=torch.float)
-
         if self.check_monitor_top_k(current):
             self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics)
         elif self.verbose:
@@ -587,7 +579,7 @@ def _update_best_and_save(
             self.best_k_models.pop(del_filepath)
 
         # do not save nan, replace with +/- inf
-        if torch.isnan(current):
+        if isinstance(current, torch.Tensor) and torch.isnan(current):
             current = torch.tensor(float('inf' if self.mode == "min" else '-inf'))
 
         filepath = self._get_metric_interpolated_filepath_name(ckpt_name_metrics, epoch, step, del_filepath)

pytorch_lightning/metrics/functional/average_precision.py

@@ -67,7 +67,7 @@ def average_precision(
             which for binary problem is translate to 1. For multiclass problems
             this argument should not be set as we iteratively change it in the
             range [0,num_classes-1]
-        sample_weight: sample weights for each data point
+        sample_weights: sample weights for each data point
 
     Returns:
         tensor with average precision. If multiclass will return list

pytorch_lightning/metrics/functional/explained_variance.py

@@ -62,7 +62,7 @@ def explained_variance(
     Computes explained variance.
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
         multioutput: Defines aggregation in the case of multiple output scores. Can be one
             of the following strings (default is `'uniform_average'`.):

pytorch_lightning/metrics/functional/f_beta.py

@@ -75,7 +75,7 @@ def fbeta(
     If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
 
     Args:
-        pred: estimated probabilities
+        preds: estimated probabilities
         target: ground-truth labels
         num_classes: Number of classes in the dataset.
         beta: Beta coefficient in the F measure.
@@ -128,7 +128,7 @@ def f1(
     If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
 
     Args:
-        pred: estimated probabilities
+        preds: estimated probabilities
         target: ground-truth labels
         num_classes: Number of classes in the dataset.
         threshold:

pytorch_lightning/metrics/functional/mean_squared_error.py

@@ -34,7 +34,7 @@ def mean_squared_error(preds: torch.Tensor, target: torch.Tensor) -> torch.Tenso
     Computes mean squared error
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
 
     Return:

pytorch_lightning/metrics/functional/mean_squared_log_error.py

@@ -34,7 +34,7 @@ def mean_squared_log_error(preds: torch.Tensor, target: torch.Tensor) -> torch.T
     Computes mean squared log error
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
 
     Return:

pytorch_lightning/metrics/functional/precision_recall_curve.py

@@ -173,7 +173,7 @@ def precision_recall_curve(
             which for binary problem is translate to 1. For multiclass problems
             this argument should not be set as we iteratively change it in the
             range [0,num_classes-1]
-        sample_weight: sample weights for each data point
+        sample_weights: sample weights for each data point
 
     Returns: 3-element tuple containing
 

pytorch_lightning/metrics/functional/psnr.py

@@ -46,8 +46,6 @@ def psnr(
             - ``'elementwise_mean'``: takes the mean (default)
             - ``'sum'``: takes the sum
             - ``'none'``: no reduction will be applied
-        return_state: returns a internal state that can be ddp reduced
-            before doing the final calculation
 
     Return:
         Tensor with PSNR score

pytorch_lightning/metrics/functional/r2score.py

@@ -98,7 +98,7 @@ def r2score(
     be provided as the ``adjusted`` argument.
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
         adjusted: number of independent regressors for calculating adjusted r2 score.
             Default 0 (standard r2 score).

pytorch_lightning/metrics/functional/roc.py

@@ -98,7 +98,7 @@ def roc(
             which for binary problem is translate to 1. For multiclass problems
             this argument should not be set as we iteratively change it in the
             range [0,num_classes-1]
-        sample_weight: sample weights for each data point
+        sample_weights: sample weights for each data point
 
     Returns: 3-element tuple containing
 

pytorch_lightning/metrics/functional/ssim.py

@@ -125,7 +125,7 @@ def ssim(
     Computes Structual Similarity Index Measure
 
     Args:
-        pred: estimated image
+        preds: estimated image
         target: ground truth image
         kernel_size: size of the gaussian kernel (default: (11, 11))
         sigma: Standard deviation of the gaussian kernel (default: (1.5, 1.5))

pytorch_lightning/metrics/utils.py

@@ -232,7 +232,7 @@ def class_reduce(
 
     Args:
         num: numerator tensor
-        decom: denominator tensor
+        denom: denominator tensor
         weights: weights for each class
         class_reduction: reduction method for multiclass problems
 

pytorch_lightning/overrides/data_parallel.py

@@ -16,10 +16,12 @@
 import threading
 from collections.abc import Iterable, Mapping
 from itertools import chain
+from typing import Optional
 
 import torch
+from torch import Tensor
 from torch.cuda._utils import _get_device_index
-from torch.nn import DataParallel
+from torch.nn import DataParallel, Module
 from torch.nn.parallel import DistributedDataParallel
 from torch.nn.parallel._functions import Gather
 
@@ -222,15 +224,20 @@ def warn_missing_output(fx_called):
         warning_cache.warn("Your training_step returned None. Make sure that was your intention!")
 
 
-def parallel_apply(modules, inputs, kwargs_tup=None, devices=None):  # pragma: no-cover
+def parallel_apply(
+        modules: Module,
+        inputs: Tensor,
+        kwargs_tup: Optional[tuple] = None,
+        devices: Optional[list] = None,
+):  # pragma: no-cover
     r"""Applies each `module` in :attr:`modules` in parallel on arguments
     contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
     on each of :attr:`devices`.
 
     Args:
-        modules (Module): modules to be parallelized
-        inputs (tensor): inputs to the modules
-        devices (list of int or torch.device): CUDA devices
+        modules: modules to be parallelized
+        inputs: inputs to the modules
+        devices: CUDA devices
 
     :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and
     :attr:`devices` (if given) should all have same length. Moreover, each

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -345,10 +345,11 @@ def hpc_load(self, checkpoint_path: str, on_gpu: bool):
         model.on_hpc_load(checkpoint)
 
     def max_ckpt_in_folder(self, dir_path: Union[str, Path], name_key: str = 'ckpt_') -> Optional[int]:
-        """List up files in `dir_path` with name_key, then yield maximum suffix number.
+        """List up files in `dir_path` with `name_key`, then yield maximum suffix number.
 
         Args:
             dir_path: path of directory which may contain files whose name include `name_key`
+            name_key: file name prefix
 
         Returns:
             None if no-corresponding-file else maximum suffix number

pytorch_lightning/trainer/connectors/logger_connector/epoch_result_store.py

@@ -379,7 +379,7 @@ def update_logger_connector(self) -> None:
 
             if is_train:
                 # Only log and add to callback epoch step during evaluation, test.
-                logger_connector.logged_metrics.update(batch_log_metrics)
+                logger_connector._logged_metrics.update(batch_log_metrics)
                 callback_metrics.update(batch_pbar_metrics)
                 callback_metrics.update(batch_log_metrics)
         else:
@@ -389,8 +389,8 @@ def update_logger_connector(self) -> None:
 
             # get logged_metrics
             epoch_log_metrics = self.get_epoch_log_metrics()
-            logger_connector.logged_metrics.update(epoch_log_metrics)
-            logger_connector.logged_metrics.update(epoch=self.trainer.current_epoch)
+            logger_connector._logged_metrics.update(epoch_log_metrics)
+            logger_connector._logged_metrics.update({"epoch": self.trainer.current_epoch})
 
             # get forked_metrics
             forked_metrics = self.get_forked_metrics()
@@ -403,8 +403,8 @@ def update_logger_connector(self) -> None:
             logger_connector.evaluation_callback_metrics.update(callback_metrics)
 
         # update callback_metrics
-        logger_connector.callback_metrics.update(callback_metrics)
-        logger_connector.callback_metrics.pop("epoch", None)
+        logger_connector._callback_metrics.update(callback_metrics)
+        logger_connector._callback_metrics.pop("epoch", None)
 
         batch_pbar_metrics.pop("debug_epoch", None)
         return batch_pbar_metrics, batch_log_metrics