add autodl

AutoDL-Projects/xautodl/procedures/advanced_main.py

@@ -0,0 +1,99 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.04 #
+#####################################################
+# To be finished.
+#
+import os, sys, time, torch
+from typing import Optional, Text, Callable
+
+# modules in AutoDL
+from xautodl.log_utils import AverageMeter, time_string
+from .eval_funcs import obtain_accuracy
+
+
+def get_device(tensors):
+    if isinstance(tensors, (list, tuple)):
+        return get_device(tensors[0])
+    elif isinstance(tensors, dict):
+        for key, value in tensors.items():
+            return get_device(value)
+    else:
+        return tensors.device
+
+
+def basic_train_fn(
+    xloader,
+    network,
+    criterion,
+    optimizer,
+    metric,
+    logger,
+):
+    results = procedure(
+        xloader,
+        network,
+        criterion,
+        optimizer,
+        metric,
+        "train",
+        logger,
+    )
+    return results
+
+
+def basic_eval_fn(xloader, network, metric, logger):
+    with torch.no_grad():
+        results = procedure(
+            xloader,
+            network,
+            None,
+            None,
+            metric,
+            "valid",
+            logger,
+        )
+    return results
+
+
+def procedure(
+    xloader,
+    network,
+    criterion,
+    optimizer,
+    metric,
+    mode: Text,
+    logger_fn: Callable = None,
+):
+    data_time, batch_time = AverageMeter(), AverageMeter()
+    if mode.lower() == "train":
+        network.train()
+    elif mode.lower() == "valid":
+        network.eval()
+    else:
+        raise ValueError("The mode is not right : {:}".format(mode))
+
+    end = time.time()
+    for i, (inputs, targets) in enumerate(xloader):
+        # measure data loading time
+        data_time.update(time.time() - end)
+        # calculate prediction and loss
+
+        if mode == "train":
+            optimizer.zero_grad()
+
+        outputs = network(inputs)
+        targets = targets.to(get_device(outputs))
+
+        if mode == "train":
+            loss = criterion(outputs, targets)
+            loss.backward()
+            optimizer.step()
+
+        # record
+        with torch.no_grad():
+            results = metric(outputs, targets)
+
+        # measure elapsed time
+        batch_time.update(time.time() - end)
+        end = time.time()
+    return metric.get_info()