Add more arguments

map2map/common_args.yaml

@@ -1,88 +1,89 @@
 arguments:
-  - 'in-norms':
+  'in-norms':
     type: str_list
     help: 'comma-sep. list of input normalization functions'
-  - 'tgt-norms':
+  'tgt-norms':
     type: str_list
     help: 'comma-sep. list of target normalization functions'
-  - 'crop':
+  'crop':
     type: int_tuple
     help: 'size to crop the input and target data. Default is the field size. Comma-sep. list of 1 or d integers'
-  - 'crop-start':
+  'crop-start':
     type: int_tuple
     help: 'starting point of the first crop. Default is the origin. Comma-sep. list of 1 or d integers'
-  - 'crop-stop':
+  'crop-stop':
     type: int_tuple
     help: 'stopping point of the last crop. Default is the opposite corner to the origin. Comma-sep. list of 1 or d integers'
-  - 'crop-step':
+  'crop-step':
     type: int_tuple
     help: 'spacing between crops. Default is the crop size. Comma-sep. list of 1 or d integers'
-  - 'in-pad':
-      pad: 0
+  'in-pad':
+    default: 0
     type: int_tuple
     help: 'size to pad the input data beyond the crop size, assuming periodic boundary condition. Comma-sep. list of 1, d, or dx2 integers, to pad equally along all axes, symmetrically on each, or by the specified size on every boundary, respectively'
-  - 'tgt-pad':
+  'tgt-pad':
     default: 0
     type: int_tuple
     help: 'size to pad the target data beyond the crop size, assuming periodic boundary condition, useful for super-resolution. Comma-sep. list with the same format as in-pad'
-  - 'scale-factor':
+  'scale-factor':
     default: 1
     type: int
     help: 'upsampling factor for super-resolution, in which case crop and pad are sizes of the input resolution'
-  - 'model':
+  'model':
     type: str
     required: true
     help: '(generator) model'
-  - 'criterion':
+  'criterion':
     default: 'MSELoss'
     type: str
     help: 'loss function'
-  - 'load-state':
+  'load-state':
     default: ckpt_link
     type: str
     help: 'path to load the states of model, optimizer, rng, etc. Default is the checkpoint. Start from scratch in case of empty string or missing checkpoint'
-  - 'load-state-non-strict':
-      action: 'store_false'
+  'load-state-non-strict':
+   # action: 'store_false'
     help: 'allow incompatible keys when loading model states'
-      dest: 'load_state_strict'
-  - 'batch-size':
-      'batches': 0
+   # dest: 'load_state_strict'
+  'batch-size':
+    default: 0
     type: int
     required: true
     help: 'mini-batch size, per GPU in training or in total in testing'
-  - 'loader-workers':
+  'loader-workers':
     default: 8
     type: int
     help: 'number of subprocesses per data loader. 0 to disable multiprocessing'
-  - 'callback-at':
-      type: 'lambda s: os.path.abspath(s)'
+  'callback-at':
+    type: 'abspath'
     help: 'directory of custorm code defining callbacks for models, norms, criteria, and optimizers. Disabled if not set. This is appended to the default locations, thus has the lowest priority'
-  - 'misc-kwargs':
+  'misc-kwargs':
     default: '{}'
-      type: json.loads
+    type: json
     help: 'miscellaneous keyword arguments for custom models and norms. Be careful with name collisions'
-      arguments:
-        - 'optimizer':
-          default: 'Adam'
-          type: str
-          help: 'optimizer for training'
-        - 'learning-rate':
-          default: 0.001
-          type: float
-          help: 'learning rate for training'
-        - 'num-epochs':
-          default: 100
-          type: int
-          help: 'number of training epochs'
-        - 'save-interval':
-          default: 10
-          type: int
-          help: 'interval for saving checkpoints during training'
-        - 'log-interval':
-          default: 10
-          type: int
-          help: 'interval for logging training progress'
-        - 'device':
-          default: 'cuda'
-          type: str
-          help: 'device for training (cuda or cpu)'
+
+#    arguments:
+#      - 'optimizer':
+#        default: 'Adam'
+#        type: str
+#        help: 'optimizer for training'
+#      - 'learning-rate':
+#        default: 0.001
+#        type: float
+#        help: 'learning rate for training'
+#      - 'num-epochs':
+#        default: 100
+#        type: int
+#        help: 'number of training epochs'
+#      - 'save-interval':
+#        default: 10
+#        type: int
+#        help: 'interval for saving checkpoints during training'
+#      - 'log-interval':
+#        default: 10
+#        type: int
+#        help: 'interval for logging training progress'
+#      - 'device':
+#        default: 'cuda'
+#        type: str
+#        help: 'device for training (cuda or cpu)'

map2map/main.py

@@ -10,44 +10,98 @@ except ImportError:
     from yaml import Loader
 
 import importlib.resources
+import json
+from functools import partial
 
 def _load_resource_file(resource_path):
-    package = importlib.import_module('map2map')  # Import the package
-    with importlib.resources.path('map2map', resource_path) as path:
-        return path.read_text()  # Read the file and return its content
+    # Import the package
+    pkg_files = importlib.resources.files()
+    with pkg_files.open(resource_path) as file:
+            return file.read_text()  # Read the file and return its content
 
-def str_list(s):
-    return s.split(',')
+def _str_list(value):
+    return value.split(',')
 
-def m2m_options(f):
-    common_args = _load_resource_file('common_args.yaml')
+def _int_tuple(value):
+    t = value.split(',')
+    t = tuple(int(i) for i in t)
+    return t
 
-    for arg in common_args['arguments']:
+class VariadicType(click.ParamType):
+
+    _mapper = {
+        "str_list": {"type": "string_list", "func": _str_list},
+        "int_tuple": {"type": "int_tuple", "func": _int_tuple},
+        "json": {"type": "json", "func": json.loads},
+        "int": {"type": "int"},
+        "float": {"type": "float"},
+        "str": {"type": "str"},
+        "abspath": {"type": "path", "func": os.path.abspath},
+    }
+
+    def __init__(self, typename):
+        if typename in self._mapper:
+            self._type = self._mapper[typename]
+        elif type(typename) == dict:
+            self._type = self._mapper[typename["type"]]   
+            self.args = typename["opts"]
+        else:
+            raise ValueError(f"Unknown type: {typename}")         
+        self._typename = typename
+        self.name = self._type["type"]
+        if "func" not in self._type:
+            self._type["func"] = eval(self._type['type'])
+
+    def convert(self, value, param, ctx):
+        try:
+            return self.type(value)
+        except Exception as e:
+            self.fail(f"Could not parse {self._typename}: {e}", param, ctx)
+
+
+def _apply_options(options_file, f):
+    common_args = yaml.load(_load_resource_file(options_file), Loader=Loader)
+    common_args = common_args['arguments']
+
+    for arg in common_args:
         argopt = common_args[arg]
         if 'type' in argopt:
-            argopt['type'] = eval(argopt['type'])
+            if type(argopt['type']) == dict and argopt['type']['type'] == 'choice':
+                argopt['type'] = click.Choice(argopt['type']['opts'])
+            else:
+                argopt['type'] = VariadicType(argopt['type'])
             f = click.option(f'--{arg}', **argopt)(f)
         else:
             f = click.option(f'--{arg}', **argopt)(f)
 
     return f
 
+def m2m_options(f):
+    return _apply_options("common_args.yaml", f)
+
+
 @click.group()
-@click.option("--config", type=click.Path())
+@click.option("--config", type=click.Path(), help="Path to config file")
 @click.pass_context
 def main(ctx, config):
-    if os.path.exists(config):
+    if config is not None and os.path.exists(config):
         with open(config, 'r') as f:
             config = yaml.load(f.read(), Loader=Loader)
         ctx.default_map = config
 
-@main.command()
-@m2m_options
-def train(**kwargs):
-    args = get_args()
-    train.node_worker(args)
+# Make a class that provides access to dict with the attribute mechanism
+class DictProxy:
+    def __init__(self, d):
+        self.__dict__ = d
 
 @main.command()
 @m2m_options
-def test():
-    test.test(args)    
+@partial(_apply_options, "train_args.yaml")
+def train(**kwargs):
+    train.node_worker(DictProxy(kwargs))
+
+@main.command()
+@m2m_options
+@partial(_apply_options, "test_args.yaml")
+def test(**kwargs):
+    test.test(DictProxy(kwargs))    

map2map/test_args.yaml

@@ -0,0 +1,16 @@
+arguments:
+  'test-style-pattern':
+    type: str
+    required: true
+    help: glob pattern for test data styles
+  'test-in-patterns':
+    type: str_list
+    required: true
+    help: comma-sep. list of glob patterns for test input data
+  'test-tgt-patterns':
+    type: str_list
+    required: true
+    help: comma-sep. list of glob patterns for test target data
+  'num-threads':
+    type: int
+    help: number of CPU threads when cuda is unavailable. Default is the number of CPUs on the node by slurm

map2map/train_args.yaml

@@ -0,0 +1,86 @@
+arguments:
+  'train-style-pattern':
+    type: str
+    required: true
+    help: 'glob pattern for training data styles'
+  'train-in-patterns':
+    type: str_list
+    required: true
+    help: 'comma-sep. list of glob patterns for training input data'
+  'train-tgt-patterns':
+    type: str_list
+    required: true
+    help: 'comma-sep. list of glob patterns for training target data'
+  'val-style-pattern':
+    type: str
+    help: 'glob pattern for validation data styles'
+  'val-in-patterns':
+    type: str_list
+    help: 'comma-sep. list of glob patterns for validation input data'
+  'val-tgt-patterns':
+    type: str_list
+    help: 'comma-sep. list of glob patterns for validation target data'
+  'augment':
+    is_flag: true
+    help: 'enable data augmentation of axis flipping and permutation'
+  'aug-shift':
+    type: int_tuple
+    help: 'data augmentation by shifting cropping by [0, aug_shift) pixels, useful for models that treat neighboring pixels differently, e.g. with strided convolutions. Comma-sep. list of 1 or d integers'
+  'aug-add':
+    type: float
+    help: 'additive data augmentation, (normal) std, same factor for all fields'
+  'aug-mul':
+    type: float
+    help: 'multiplicative data augmentation, (log-normal) std, same factor for all fields'
+  'optimizer':
+    default: 'Adam'
+    type: str
+    help: 'optimization algorithm'
+  'lr':
+    type: float
+    required: true
+    help: 'initial learning rate'
+  'optimizer-args':
+    default: '{}'
+    type: json
+    help: "optimizer arguments in addition to the learning rate, e.g. --optimizer-args '{\"betas\": [0.5, 0.9]}'"
+  'reduce-lr-on-plateau':
+    is_flag: true
+    help: 'Enable ReduceLROnPlateau learning rate scheduler'
+  'scheduler-args':
+    default: '{"verbose": true}'
+    type: json
+    help: 'arguments for the ReduceLROnPlateau scheduler'
+  'init-weight-std':
+    type: float
+    help: 'weight initialization std'
+  'epochs':
+    default: 128
+    type: int
+    help: 'total number of epochs to run'
+  'seed':
+    default: 42
+    type: int
+    help: 'seed for initializing training'
+  'div-data':
+    is_flag: true
+    help: 'enable data division among GPUs for better page caching. Data division is shuffled every epoch. Only relevant if there are multiple crops in each field'
+  'div-shuffle-dist':
+    default: 1
+    type: float
+    help: 'distance to further shuffle cropped samples relative to their fields, to be used with --div-data. Only relevant if there are multiple crops in each file. The order of each sample is randomly displaced by this value. Setting it to 0 turn off this randomization, and setting it to N limits the shuffling within a distance of N files. Change this to balance cache locality and stochasticity'
+  'dist-backend':
+    default: 'nccl'
+    type: 
+      type: "choice"
+      opts:
+        - 'gloo'
+        - 'nccl'
+    help: 'distributed backend'
+  'log-interval':
+    default: 100
+    type: int
+    help: 'interval (batches) between logging training loss'
+  'detect-anomaly':
+    is_flag: true
+    help: 'enable anomaly detection for the autograd engine'

pyproject.toml

@@ -18,7 +18,7 @@ dependencies = [
         'scipy',
         'matplotlib',
         'tensorboard',
-        'click']
+        'click','pyyaml']
 
 authors = [
   {name = "Yin Li", email = "eelregit@gmail.com"},