feat(template): add dcgan template

.github/workflows/ci.yml

@@ -70,7 +70,7 @@ jobs:
           restore-keys: |
             pnpm-and-pip-cache-
 
-      - run: pip install "black==20.8b1" "isort==5.7.0"
+      - run: pip install -Uq pip wheel && pip install -Uq "black==20.8b1" "isort==5.7.0"
       - run: npm i -g pnpm
       - run: pnpm i --frozen-lockfile --color
       - run: pnpm lint

CONTRIBUTING.md

@@ -25,7 +25,7 @@ To contribute to Code-Generator App, you will need Nodejs LTS v14.16.x, VSCode,
 
 - Install pnpm from https://pnpm.io/installation. Use standalone script if there is a root issue with installing with npm.
 
-- Install the dependencies with `pnpm install` in the project root directory. This might take a while to install.
+- Install the dependencies with `pnpm install` and `bash .github/run_code_style.sh install` in the project root directory. This might take a while to install.
 
 - Run `pnpm run dev` to start local development server and starts editing the codes in the `src` directory. Changes will be updated on the app.
 
@@ -68,6 +68,8 @@ To add a new template,
 
 8. For the `utils.py`, copy the starter code from the `src/template-common/utils.py`.
 
+9. You can check if the copied codes needed are up-to-date with the base codes with: `python scripts/check_copies.py`
+
 ## Pull Request Guidelines
 
 - Checkout a topic branch from a base branch, e.g. `v1` (currently).

scripts/check_copies.py

@@ -3,44 +3,40 @@
 from pathlib import Path
 
 
-def check_utils():
+def check(fname):
     red = "\033[31m"
     green = "\033[32m"
     reset = "\033[0m"
 
-    with open("./src/templates/template-common/utils.py", "r") as f:
-        common_utils = f.read()
+    with open(f"./src/templates/template-common/{fname}", "r") as f:
+        common = f.readlines()
 
     path = Path("./src/templates/")
 
-    for file in path.rglob("**/utils.py"):
-        utils = file.read_text("utf-8")
-        if utils.find(common_utils) > -1:
-            print(green, "Matched", file, reset)
+    for file in path.rglob(f"**/{fname}"):
+        if str(file).find("common") > -1:
+            continue
         else:
-            print(red, "Unmatched", file, reset)
+            template = file.read_text("utf-8")
 
+            match = []
+            for c in common:
+                match.append(template.find(c) > -1)
 
-def check_readme():
-    red = "\033[31m"
-    green = "\033[32m"
-    reset = "\033[0m"
-
-    with open("./src/templates/template-common/README.md", "r") as f:
-        common_utils = f.read()
-
-    path = Path("./src/templates/")
-
-    for file in path.rglob("**/README.md"):
-        utils = file.read_text("utf-8")
-        if utils.find(common_utils) > -1:
-            print(green, "Matched", file, reset)
-        else:
-            print(red, "Unmatched", file, reset)
+            if all(match):
+                print(green, "Matched", file, reset)
+            else:
+                print(red, "Unmatched", file, reset)
+                exit(1)
 
 
 if __name__ == "__main__":
-    check_utils()
+    check("config.yaml")
+    print()
+    check("main.py")
+    print()
+    check("README.md")
     print()
-    check_readme()
+    check("requirements.txt")
     print()
+    check("utils.py")

src/templates/template-common/config.yaml

@@ -0,0 +1,52 @@
+backend: null
+seed: 666
+data_path: ./
+train_batch_size: 4
+eval_batch_size: 8
+num_workers: 2
+max_epochs: 2
+train_epoch_length: 4
+eval_epoch_length: 4
+lr: 0.0001
+use_amp: false
+verbose: false
+
+#::: if (it.dist === 'spawn') { :::#
+# distributed spawn
+nproc_per_node: #:::= it.nproc_per_node :::#
+#::: if (it.nnodes) { :::#
+# distributed multi node spawn
+nnodes: #:::= it.nnodes :::#
+node_rank: 0
+master_addr: #:::= it.master_addr :::#
+master_port: #:::= it.master_port :::#
+#::: } :::#
+#::: } :::#
+
+#::: if (it.filename_prefix) { :::#
+filename_prefix: #:::= it.filename_prefix :::#
+#::: } :::#
+
+#::: if (it.n_saved) { :::#
+n_saved: #:::= it.n_saved :::#
+#::: } :::#
+
+#::: if (it.save_every_iters) { :::#
+save_every_iters: #:::= it.save_every_iters :::#
+#::: } :::#
+
+#::: if (it.patience) { :::#
+patience: #:::= it.patience :::#
+#::: } :::#
+
+#::: if (it.limit_sec) { :::#
+limit_sec: #:::= it.limit_sec :::#
+#::: } :::#
+
+#::: if (it.output_dir) { :::#
+output_dir: #:::= it.output_dir :::#
+#::: } :::#
+
+#::: if (it.log_every_iters) { :::#
+log_every_iters: #:::= it.log_every_iters :::#
+#::: } :::#

src/templates/template-common/main.py

@@ -0,0 +1,57 @@
+ckpt_handler_train, ckpt_handler_eval, timer = setup_handlers(
+    trainer, evaluator, config, to_save_train, to_save_eval
+)
+
+#::: if (it.save_training || it.save_evaluation || it.patience || it.terminate_on_nan || it.timer || it.limit_sec) { :::#
+if timer is not None:
+    logger.info("Time per batch: %.4f seconds", timer.value())
+    timer.reset()
+#::: } :::#
+
+#::: if (it.logger) { :::#
+if rank == 0:
+    from ignite.contrib.handlers.wandb_logger import WandBLogger
+
+    if isinstance(exp_logger, WandBLogger):
+        # why handle differently for wandb?
+        # See: https://github.com/pytorch/ignite/issues/1894
+        exp_logger.finish()
+    elif exp_logger:
+        exp_logger.close()
+#::: } :::#
+
+#::: if (it.save_training || it.save_evaluation || it.patience || it.terminate_on_nan || it.timer || it.limit_sec) { :::#
+if ckpt_handler_train is not None:
+    logger.info(
+        "Last training checkpoint name - %s",
+        ckpt_handler_train.last_checkpoint,
+    )
+
+if ckpt_handler_eval is not None:
+    logger.info(
+        "Last evaluation checkpoint name - %s",
+        ckpt_handler_eval.last_checkpoint,
+    )
+#::: } :::#
+
+# main entrypoint
+@hydra.main(config_name="config")
+def main(config):
+    #::: if (it.dist === 'spawn') { :::#
+    #::: if (it.nproc_per_node && it.nnodes && it.master_addr && it.master_port) { :::#
+    kwargs = {
+        "nproc_per_node": config.nproc_per_node,
+        "nnodes": config.nnodes,
+        "node_rank": config.node_rank,
+        "master_addr": config.master_addr,
+        "master_port": config.master_port,
+    }
+    #::: } else if (it.nproc_per_node) { :::#
+    kwargs = {"nproc_per_node": config.nproc_per_node}
+    #::: } :::#
+    with idist.Parallel(config.backend, **kwargs) as p:
+        p.run(run, config=config)
+    #::: } else { :::#
+    with idist.Parallel(config.backend) as p:
+        p.run(run, config=config)
+    #::: } :::#

src/templates/template-common/requirements.txt

@@ -0,0 +1,14 @@
+torch>=1.8.0
+torchvision>=0.9.0
+pytorch-ignite>=0.4.4
+hydra-core>=1.0.0
+
+#::: if (['neptune', 'polyaxon'].includes(it.logger)) { :::#
+
+#:::= it.logger + '-client' :::#
+
+#::: } else { :::#
+
+#:::= it.logger :::#
+
+#::: } :::#

src/templates/template-vision-dcgan/README.md

@@ -0,0 +1,137 @@
+# Template by Code-Generator
+
+## Getting Started
+
+Install the dependencies with `pip`:
+
+```sh
+pip install -r requirements.txt --progress-bar off -U
+```
+
+## Training
+
+#::: if (it.dist === 'launch') { :::#
+#::: if (it.nproc_per_node) { :::#
+#::: if (it.nnodes && it.master_addr && it.master_port) { :::#
+
+### Multi Node, Multi GPU Training (`torch.distributed.launch`) (recommended)
+
+- Execute on master node
+
+```sh
+python -m torch.distributed.launch \
+  --nproc_per_node #:::= nproc_per_node :::# \
+  --nnodes #:::= it.nnodes :::# \
+  --node_rank 0 \
+  --master_addr #:::= it.master_addr :::# \
+  --master_port #:::= it.master_port :::# \
+  --use_env main.py backend=nccl \
+  hydra.run.dir=. \
+  hydra.output_subdir=null \
+  hydra/job_logging=disabled \
+  hydra/hydra_logging=disabled
+```
+
+- Execute on worker nodes
+
+```sh
+python -m torch.distributed.launch \
+  --nproc_per_node #:::= nproc_per_node :::# \
+  --nnodes #:::= it.nnodes :::# \
+  --node_rank <node_rank> \
+  --master_addr #:::= it.master_addr :::# \
+  --master_port #:::= it.master_port :::# \
+  --use_env main.py backend=nccl \
+  hydra.run.dir=. \
+  hydra.output_subdir=null \
+  hydra/job_logging=disabled \
+  hydra/hydra_logging=disabled
+```
+
+#::: } else { :::#
+
+### Multi GPU Training (`torch.distributed.launch`) (recommended)
+
+```sh
+python -m torch.distributed.launch \
+  --nproc_per_node #:::= it.nproc_per_node :::# \
+  --use_env main.py backend=nccl \
+  hydra.run.dir=. \
+  hydra.output_subdir=null \
+  hydra/job_logging=disabled \
+  hydra/hydra_logging=disabled
+```
+
+#::: } :::#
+#::: } :::#
+#::: } :::#
+
+#::: if (it.dist === 'spawn') { :::#
+#::: if (it.nproc_per_node) { :::#
+#::: if (it.nnodes && it.master_addr && it.master_port) { :::#
+
+### Multi Node, Multi GPU Training (`torch.multiprocessing.spawn`)
+
+- Execute on master node
+
+```sh
+python main.py  \
+  nproc_per_node=#:::= nproc_per_node :::# \
+  nnodes=#:::= it.nnodes :::# \
+  node_rank=0 \
+  master_addr=#:::= it.master_addr :::# \
+  master_port=#:::= it.master_port :::# \
+  backend=nccl \
+  hydra.run.dir=. \
+  hydra.output_subdir=null \
+  hydra/job_logging=disabled \
+  hydra/hydra_logging=disabled
+```
+
+- Execute on worker nodes
+
+```sh
+python main.py  \
+  nproc_per_node=#:::= nproc_per_node :::# \
+  nnodes=#:::= it.nnodes :::# \
+  node_rank=<node_rank> \
+  master_addr=#:::= it.master_addr :::# \
+  master_port=#:::= it.master_port :::# \
+  backend=nccl \
+  hydra.run.dir=. \
+  hydra.output_subdir=null \
+  hydra/job_logging=disabled \
+  hydra/hydra_logging=disabled
+```
+
+#::: } else { :::#
+
+### Multi GPU Training (`torch.multiprocessing.spawn`)
+
+```sh
+python main.py  \
+  nproc_per_node=#:::= it.nproc_per_node :::# \
+  backend=nccl \
+  hydra.run.dir=. \
+  hydra.output_subdir=null \
+  hydra/job_logging=disabled \
+  hydra/hydra_logging=disabled
+```
+
+#::: } :::#
+#::: } :::#
+#::: } :::#
+
+#::: if (!it.nproc_per_node) { :::#
+
+### 1 GPU Training
+
+```sh
+python main.py \
+  hydra.run.dir=. \
+  hydra.output_subdir=null \
+  hydra/job_logging=disabled \
+  hydra/hydra_logging=disabled
+```
+
+#::: } :::#