Removed custom events from Image Classification template (#98)

* Removed custom events from Image Classification template

* Added skip for slow tests on dataset (downloading)
Fixed failing tests for image classification

Author: vfdev-5

.github/run_code_style.sh

@@ -7,8 +7,8 @@ if [ $1 == "lint" ]; then
     isort app templates/*/_sidebar.py tests --check --settings pyproject.toml
     black app templates/*/_sidebar.py tests --check --config pyproject.toml
 elif [ $1 == "fmt" ]; then
-    isort app templates/*/_sidebar.py tests --color --settings pyproject.toml
-    black app templates/*/_sidebar.py tests --config pyproject.toml
+    isort app templates/*/_sidebar.py templates/*/test_all.py tests --color --settings pyproject.toml
+    black app templates/*/_sidebar.py templates/*/test_all.py tests --config pyproject.toml
 elif [ $1 == "install" ]; then
     pip install flake8 "black==20.8b1" "isort==5.7.0"
 fi

.github/run_test.sh

@@ -5,6 +5,7 @@ set -xeuo pipefail
 if [ $1 == "generate" ]; then
     python ./tests/generate.py
 elif [ $1 == "unittest" ]; then
+    python ./tests/generate.py
     for dir in $(find ./tests/dist -type d -mindepth 1 -maxdepth 1 -not -path "./tests/dist/launch" -not -path "./tests/dist/spawn")
     do
         cd $dir
@@ -18,6 +19,7 @@ elif [ $1 == "unittest" ]; then
         cd ../../../
     done
 elif [ $1 == "default" ]; then
+    python ./tests/generate.py
     for file in $(find ./tests/dist -iname "main.py" -not -path "./tests/dist/launch/*" -not -path "./tests/dist/spawn/*" -not -path "./tests/dist/single/*")
     do
         python $file \
@@ -28,6 +30,7 @@ elif [ $1 == "default" ]; then
             --eval_epoch_length 10
     done
 elif [ $1 == "launch" ]; then
+    python ./tests/generate.py
     for file in $(find ./tests/dist/launch -iname "main.py" -not -path "./tests/dist/launch/single/*")
     do
         python -m torch.distributed.launch \
@@ -41,6 +44,7 @@ elif [ $1 == "launch" ]; then
             --log_every_iters 2
     done
 elif [ $1 == "spawn" ]; then
+    python ./tests/generate.py
     for file in $(find ./tests/dist/spawn -iname "main.py" -not -path "./tests/dist/spawn/single/*")
     do
         python $file \

.github/workflows/ci.yml

@@ -41,12 +41,12 @@ jobs:
       - run: pip install -r requirements.txt --progress-bar off
       - run: pip install -r requirements-dev.txt -f https://download.pytorch.org/whl/cpu/torch_stable.html --progress-bar off
       - run: python -m torch.utils.collect_env
-      - run: bash .github/run_test.sh generate
       - run: bash .github/run_test.sh unittest
       - run: bash .github/run_test.sh default
       - run: bash .github/run_test.sh spawn
       - run: bash .github/run_test.sh launch
         env:
+          RUN_SLOW_TESTS: 1
           OMP_NUM_THREADS: 1
 
   lint:

templates/gan/test_all.py

@@ -1,8 +1,10 @@
+import os
 from argparse import Namespace
 from numbers import Number
 from typing import Iterable
 
 import ignite.distributed as idist
+import pytest
 import torch
 from datasets import get_datasets
 from ignite.engine import Engine
@@ -23,6 +25,7 @@ def set_up():
     return model, optimizer, device, loss_fn, batch
 
 
+@pytest.mark.skipif(os.getenv("RUN_SLOW_TESTS", 0) == 0, reason="Skip slow tests")
 def test_get_datasets(tmp_path):
     dataset, _ = get_datasets("cifar10", tmp_path)
 

templates/image_classification/_test_internal.py

@@ -24,10 +24,8 @@
 from test_all import set_up
 from torch import nn, optim
 from trainers import (
-    TrainEvents,
     create_trainers,
     evaluate_function,
-    train_events_to_attr,
     train_function,
 )
 from utils import (
@@ -92,88 +90,6 @@ def test_get_logger(tmp_path):
     assert isinstance(logger_handler, types), "Should be Ignite provided loggers or None"
 
 
-def test_train_fn():
-    model, optimizer, device, loss_fn, batch = set_up()
-    engine = Engine(lambda e, b: 1)
-    engine.register_events(*TrainEvents, event_to_attr=train_events_to_attr)
-    backward = MagicMock()
-    optim = MagicMock()
-    engine.add_event_handler(TrainEvents.BACKWARD_COMPLETED, backward)
-    engine.add_event_handler(TrainEvents.OPTIM_STEP_COMPLETED, optim)
-    config = Namespace(use_amp=False)
-    output = train_function(config, engine, batch, model, loss_fn, optimizer, device)
-    assert isinstance(output, dict)
-    assert hasattr(engine.state, "backward_completed")
-    assert hasattr(engine.state, "optim_step_completed")
-    assert engine.state.backward_completed == 1
-    assert engine.state.optim_step_completed == 1
-    assert backward.call_count == 1
-    assert optim.call_count == 1
-    assert backward.called
-    assert optim.called
-
-
-def test_train_fn_event_filter():
-    model, optimizer, device, loss_fn, batch = set_up()
-    config = Namespace(use_amp=False)
-    engine = Engine(lambda e, b: train_function(config, e, b, model, loss_fn, optimizer, device))
-    engine.register_events(*TrainEvents, event_to_attr=train_events_to_attr)
-    backward = MagicMock()
-    optim = MagicMock()
-    engine.add_event_handler(TrainEvents.BACKWARD_COMPLETED(event_filter=lambda _, x: (x % 2 == 0) or x == 3), backward)
-    engine.add_event_handler(TrainEvents.OPTIM_STEP_COMPLETED(event_filter=lambda _, x: (x % 2 == 0) or x == 3), optim)
-    engine.run([batch] * 5)
-    assert hasattr(engine.state, "backward_completed")
-    assert hasattr(engine.state, "optim_step_completed")
-    assert engine.state.backward_completed == 5
-    assert engine.state.optim_step_completed == 5
-    assert backward.call_count == 3
-    assert optim.call_count == 3
-    assert backward.called
-    assert optim.called
-
-
-def test_train_fn_every():
-    model, optimizer, device, loss_fn, batch = set_up()
-
-    config = Namespace(use_amp=False)
-    engine = Engine(lambda e, b: train_function(config, e, b, model, loss_fn, optimizer, device))
-    engine.register_events(*TrainEvents, event_to_attr=train_events_to_attr)
-    backward = MagicMock()
-    optim = MagicMock()
-    engine.add_event_handler(TrainEvents.BACKWARD_COMPLETED(every=2), backward)
-    engine.add_event_handler(TrainEvents.OPTIM_STEP_COMPLETED(every=2), optim)
-    engine.run([batch] * 5)
-    assert hasattr(engine.state, "backward_completed")
-    assert hasattr(engine.state, "optim_step_completed")
-    assert engine.state.backward_completed == 5
-    assert engine.state.optim_step_completed == 5
-    assert backward.call_count == 2
-    assert optim.call_count == 2
-    assert backward.called
-    assert optim.called
-
-
-def test_train_fn_once():
-    model, optimizer, device, loss_fn, batch = set_up()
-    config = Namespace(use_amp=False)
-    engine = Engine(lambda e, b: train_function(config, e, b, model, loss_fn, optimizer, device))
-    engine.register_events(*TrainEvents, event_to_attr=train_events_to_attr)
-    backward = MagicMock()
-    optim = MagicMock()
-    engine.add_event_handler(TrainEvents.BACKWARD_COMPLETED(once=3), backward)
-    engine.add_event_handler(TrainEvents.OPTIM_STEP_COMPLETED(once=3), optim)
-    engine.run([batch] * 5)
-    assert hasattr(engine.state, "backward_completed")
-    assert hasattr(engine.state, "optim_step_completed")
-    assert engine.state.backward_completed == 5
-    assert engine.state.optim_step_completed == 5
-    assert backward.call_count == 1
-    assert optim.call_count == 1
-    assert backward.called
-    assert optim.called
-
-
 def test_evaluate_fn():
     model, optimizer, device, loss_fn, batch = set_up()
     engine = Engine(lambda e, b: 1)
@@ -193,8 +109,6 @@ def test_create_trainers():
     )
     assert isinstance(trainer, Engine)
     assert isinstance(evaluator, Engine)
-    assert hasattr(trainer.state, "backward_completed")
-    assert hasattr(trainer.state, "optim_step_completed")
 
 
 def test_get_default_parser():

templates/image_classification/test_all.py

@@ -1,8 +1,10 @@
+import os
 from argparse import Namespace
 from numbers import Number
 from typing import Iterable
 
 import ignite.distributed as idist
+import pytest
 import torch
 from datasets import get_datasets
 from ignite.contrib.handlers.param_scheduler import ParamScheduler
@@ -25,6 +27,7 @@ def set_up():
     return model, optimizer, device, loss_fn, batch
 
 
+@pytest.mark.skipif(os.getenv("RUN_SLOW_TESTS", 0) == 0, reason="Skip slow tests")
 def test_get_datasets(tmp_path):
     train_ds, eval_ds = get_datasets(tmp_path)
 

templates/image_classification/trainers.py

@@ -8,8 +8,6 @@
 from torch.cuda.amp import autocast
 from torch.optim.optimizer import Optimizer
 
-{% include "_events.py" %}
-
 
 # Edit below functions the way how the model will be training
 
@@ -62,13 +60,7 @@ def train_function(
         loss = loss_fn(outputs, targets)
 
     loss.backward()
-    engine.state.backward_completed += 1
-    engine.fire_event(TrainEvents.BACKWARD_COMPLETED)
-
     optimizer.step()
-    engine.state.optim_step_completed += 1
-    engine.fire_event(TrainEvents.OPTIM_STEP_COMPLETED)
-
     optimizer.zero_grad()
 
     loss_value = loss.item()
@@ -163,5 +155,4 @@ def create_trainers(config, model, optimizer, loss_fn, device) -> Tuple[Engine,
             device=device
         )
     )
-    trainer.register_events(*TrainEvents, event_to_attr=train_events_to_attr)
     return trainer, evaluator

templates/text_classification/test_all.py

@@ -1,8 +1,10 @@
+import os
 from argparse import Namespace
 from numbers import Number
 from typing import Iterable
 
 import ignite.distributed as idist
+import pytest
 import torch
 from dataset import get_dataflow, get_dataset
 from ignite.contrib.handlers.param_scheduler import ParamScheduler
@@ -41,6 +43,7 @@ def test_initialize():
     assert isinstance(lr_scheduler, (_LRScheduler, ParamScheduler))
 
 
+@pytest.mark.skipif(os.getenv("RUN_SLOW_TESTS", 0) == 0, reason="Skip slow tests")
 def test_get_dataflow():
     config = Namespace(
         data_dir="/tmp/data",
@@ -55,6 +58,7 @@ def test_get_dataflow():
     assert isinstance(test_loader, DataLoader)
 
 
+@pytest.mark.skipif(os.getenv("RUN_SLOW_TESTS", 0) == 0, reason="Skip slow tests")
 def test_get_dataset():
     cache_dir = "/tmp"
     tokenizer_name = "bert-base-uncased"