Release 20.06 (#824)

* state to runner (#823) * state to runner * import & codestyle fix * unfreeze * remaning & docs * config and freeze * last renaming * froze update * merged with master
catalyst-team · Jun 4, 2020 · 0c08738 · 0c08738
1 parent d1d8f4d
commit 0c08738
Show file tree

Hide file tree

Showing 129 changed files with 1,733 additions and 1,727 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -14,7 +14,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Changed
 
 - docs structure were updated during ([#822](https://github.com/catalyst-team/catalyst/pull/822))
-- `utils.process_components` moved from `utils.distributed` to `utils.components` ([#822](https://github.com/catalyst-team/catalyst/pull/822)) 
+- `utils.process_components` moved from `utils.distributed` to `utils.components` ([#822](https://github.com/catalyst-team/catalyst/pull/822))
+- `catalyst.core.state.State` merged to `catalyst.core.runner._Runner` ([#823](https://github.com/catalyst-team/catalyst/pull/823)) (backward compatibility included)
+    - `catalyst.core.callback.Callback` now works directly with `catalyst.core.runner._Runner`
+    - `state_kwargs` renamed to `stage_kwargs`
 
 ### Removed
 

diff --git a/README.md b/README.md
@@ -74,14 +74,14 @@ class CustomRunner(dl.Runner):
 
         loss = F.cross_entropy(y_hat, y)
         accuracy01, accuracy03 = metrics.accuracy(y_hat, y, topk=(1, 3))
-        self.state.batch_metrics.update(
+        self.batch_metrics.update(
             {"loss": loss, "accuracy01": accuracy01, "accuracy03": accuracy03}
         )
 
-        if self.state.is_train_loader:
+        if self.is_train_loader:
             loss.backward()
-            self.state.optimizer.step()
-            self.state.optimizer.zero_grad()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
 runner = CustomRunner()
 # model training
@@ -233,17 +233,17 @@ class CustomRunner(dl.Runner):
 
         loss = F.cross_entropy(y_hat, y)
         accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
-        self.state.batch_metrics = {
+        self.batch_metrics = {
             "loss": loss,
             "accuracy01": accuracy01,
             "accuracy03": accuracy03,
             "accuracy05": accuracy05,
         }
 
-        if self.state.is_train_loader:
+        if self.is_train_loader:
             loss.backward()
-            self.state.optimizer.step()
-            self.state.optimizer.zero_grad()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
 runner = CustomRunner()
 runner.train(
@@ -304,7 +304,7 @@ class CustomRunner(dl.Runner):
         loss_ae = F.mse_loss(x_, x)
         loss = loss_clf + loss_ae
         accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
-        self.state.batch_metrics = {
+        self.batch_metrics = {
             "loss_clf": loss_clf,
             "loss_ae": loss_ae,
             "loss": loss,
@@ -313,10 +313,10 @@ class CustomRunner(dl.Runner):
             "accuracy05": accuracy05,
         }
 
-        if self.state.is_train_loader:
+        if self.is_train_loader:
             loss.backward()
-            self.state.optimizer.step()
-            self.state.optimizer.zero_grad()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
 runner = CustomRunner()
 runner.train(
@@ -402,7 +402,7 @@ class CustomRunner(dl.Runner):
         loss_logprob = torch.mean(z_logprob) * 0.01
         loss = loss_clf + loss_ae + loss_kld + loss_logprob
         accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
-        self.state.batch_metrics = {
+        self.batch_metrics = {
             "loss_clf": loss_clf,
             "loss_ae": loss_ae,
             "loss_kld": loss_kld,
@@ -413,10 +413,10 @@ class CustomRunner(dl.Runner):
             "accuracy05": accuracy05,
         }
 
-        if self.state.is_train_loader:
+        if self.is_train_loader:
             loss.backward()
-            self.state.optimizer.step()
-            self.state.optimizer.zero_grad()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
 runner = CustomRunner()
 runner.train(
@@ -479,7 +479,7 @@ class CustomRunner(dl.Runner):
         loss_iou = 1 - iou
         loss = loss_clf + loss_iou
         accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
-        self.state.batch_metrics = {
+        self.batch_metrics = {
             "loss_clf": loss_clf,
             "loss_iou": loss_iou,
             "loss": loss,
@@ -489,10 +489,10 @@ class CustomRunner(dl.Runner):
             "accuracy05": accuracy05,
         }
 
-        if self.state.is_train_loader:
+        if self.is_train_loader:
             loss.backward()
-            self.state.optimizer.step()
-            self.state.optimizer.zero_grad()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
 runner = CustomRunner()
 runner.train(
@@ -590,7 +590,7 @@ class CustomRunner(dl.Runner):
         batch_metrics["loss_generator"] = \
           F.binary_cross_entropy_with_logits(predictions, misleading_labels)
 
-        self.state.batch_metrics.update(**batch_metrics)
+        self.batch_metrics.update(**batch_metrics)
 
 runner = CustomRunner()
 runner.train(
@@ -703,7 +703,7 @@ class CustomRunner(dl.Runner):
         loss_ae = F.mse_loss(x_, x)
         loss = loss_clf + loss_ae
         accuracy01, accuracy03, accuracy05 = metrics.accuracy(y_hat, y, topk=(1, 3, 5))
-        self.state.batch_metrics = {
+        self.batch_metrics = {
             "loss_clf": loss_clf,
             "loss_ae": loss_ae,
             "loss": loss,
@@ -712,10 +712,10 @@ class CustomRunner(dl.Runner):
             "accuracy05": accuracy05,
         }
 
-        if self.state.is_train_loader:
+        if self.is_train_loader:
             loss.backward()
-            self.state.optimizer.step()
-            self.state.optimizer.zero_grad()
+            self.optimizer.step()
+            self.optimizer.zero_grad()
 
 def datasets_fn():
     dataset = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
@@ -757,7 +757,7 @@ utils.distributed_cmd_run(train)
 
 ### Structure
 - **core** - framework core with main abstractions - 
-    Experiment, Runner, Callback and State.
+    Experiment, Runner and Callback.
 - **data** - useful tools and scripts for data processing.
 - **dl** – runner for training and inference,
    all of the classic ML and CV/NLP/RecSys metrics

diff --git a/bin/tests/check_dl_core.sh → bin/tests/check_dl2_core.sh b/bin/tests/check_dl_core.sh → bin/tests/check_dl2_core.sh
diff --git a/bin/tests/check_dl_core_callbacks.sh b/bin/tests/check_dl_core_callbacks.sh
@@ -596,7 +596,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
   python3 -c "
 import torch
 from torch.utils.data import DataLoader, TensorDataset
-from catalyst.dl import SupervisedRunner, State, Callback, CallbackOrder, CheckpointCallback
+from catalyst.dl import SupervisedRunner, CheckpointCallback
 
 # experiment_setup
 logdir = '${LOGDIR}'
@@ -661,7 +661,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
   python3 -c "
 import torch
 from torch.utils.data import DataLoader, TensorDataset
-from catalyst.dl import SupervisedRunner, State, Callback, CallbackOrder, CheckpointCallback
+from catalyst.dl import SupervisedRunner, CheckpointCallback
 
 # experiment_setup
 logdir = '${LOGDIR}'
@@ -748,7 +748,7 @@ echo ${LOG_MSG}
 PYTHONPATH=./examples:./catalyst:${PYTHONPATH} python3 -c "
 import torch
 from torch.utils.data import DataLoader, TensorDataset
-from catalyst.dl import SupervisedRunner, State, Callback, CallbackOrder, CheckpointCallback
+from catalyst.dl import SupervisedRunner, CheckpointCallback
 
 # experiment_setup
 logdir = '${LOGDIR}'
@@ -810,7 +810,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
   python3 -c "
 import torch
 from torch.utils.data import DataLoader, TensorDataset
-from catalyst.dl import SupervisedRunner, State, Callback, CallbackOrder, CheckpointCallback
+from catalyst.dl import SupervisedRunner, CheckpointCallback
 
 # experiment_setup
 logdir = '${LOGDIR}'

diff --git a/bin/tests/check_dl_core_periodic_loader_callback.sh b/bin/tests/check_dl_core_periodic_loader_callback.sh
@@ -73,8 +73,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from catalyst.dl import (
-    SupervisedRunner, State, Callback, CallbackOrder,
-    PeriodicLoaderCallback,
+    SupervisedRunner, Callback, CallbackOrder, PeriodicLoaderCallback,
 )
 
 # experiment_setup
@@ -139,8 +138,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from catalyst.dl import (
-    SupervisedRunner, State, Callback, CallbackOrder,
-    PeriodicLoaderCallback,
+    SupervisedRunner, Callback, CallbackOrder, PeriodicLoaderCallback,
 )
 
 # experiment_setup
@@ -205,8 +203,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from catalyst.dl import (
-    SupervisedRunner, State, Callback, CallbackOrder,
-    PeriodicLoaderCallback,
+    SupervisedRunner, Callback, CallbackOrder, PeriodicLoaderCallback,
 )
 
 # experiment_setup
@@ -279,8 +276,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from catalyst.dl import (
-    SupervisedRunner, State, Callback, CallbackOrder,
-    PeriodicLoaderCallback,
+    SupervisedRunner, Callback, CallbackOrder, PeriodicLoaderCallback,
 )
 
 # experiment_setup
@@ -485,8 +481,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from catalyst.dl import (
-    SupervisedRunner, State, Callback, CallbackOrder,
-    PeriodicLoaderCallback,
+    SupervisedRunner, Callback, CallbackOrder, PeriodicLoaderCallback,
 )
 
 # experiment_setup
@@ -577,8 +572,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from catalyst.dl import (
-    SupervisedRunner, State, Callback, CallbackOrder,
-    PeriodicLoaderCallback,
+    SupervisedRunner, Callback, CallbackOrder, PeriodicLoaderCallback,
 )
 
 # experiment_setup
@@ -687,8 +681,7 @@ PYTHONPATH=./examples:./catalyst:${PYTHONPATH} \
 import torch
 from torch.utils.data import DataLoader, TensorDataset
 from catalyst.dl import (
-    SupervisedRunner, State, Callback, CallbackOrder,
-    PeriodicLoaderCallback,
+    SupervisedRunner, Callback, CallbackOrder, PeriodicLoaderCallback,
 )
 
 # experiment_setup

diff --git a/catalyst/__version__.py b/catalyst/__version__.py
@@ -1 +1 @@
-__version__ = "20.05.1"
+__version__ = "20.06"
diff --git a/catalyst/contrib/dl/callbacks/__init__.py b/catalyst/contrib/dl/callbacks/__init__.py
@@ -4,18 +4,18 @@
 from catalyst.tools import settings
 
 from .cutmix_callback import CutmixCallback
-from .knn import KNNMetricCallback
-from .optimizer import SaveModelGradsCallback
-from .periodic_loader import PeriodicLoaderCallback
-from .perplexity import PerplexityMetricCallback
+from .gradnorm_logger import GradNormLogger
+from .knn_metric import KNNMetricCallback
+from .periodic_loader_callback import PeriodicLoaderCallback
+from .perplexity_metric import PerplexityMetricCallback
 from .telegram_logger import TelegramLogger
-from .trace import TracerCallback
+from .tracer_callback import TracerCallback
 
 logger = logging.getLogger(__name__)
 
 try:
     import imageio
-    from .inference import InferMaskCallback
+    from .mask_inference import InferMaskCallback
 except ImportError as ex:
     if settings.cv_required:
         logger.warning(
@@ -26,7 +26,7 @@
 
 try:
     import alchemy
-    from .alchemy import AlchemyLogger
+    from .alchemy_logger import AlchemyLogger
 except ImportError as ex:
     if settings.alchemy_logger_required:
         logger.warning(
@@ -48,7 +48,7 @@
 
 try:
     import neptune
-    from .neptune import NeptuneLogger
+    from .neptune_logger import NeptuneLogger
 except ImportError as ex:
     if settings.neptune_logger_required:
         logger.warning(
@@ -59,7 +59,7 @@
 
 try:
     import wandb
-    from .wandb import WandbLogger
+    from .wandb_logger import WandbLogger
 except ImportError as ex:
     if settings.wandb_logger_required:
         logger.warning(