Add loss and logger code.

test/loss.py

@@ -0,0 +1,45 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn import CrossEntropyLoss
+import math
+
+shift_logits = torch.zeros((16, 4096))
+shift_logits[:, 2] = 10.0
+shift_labels = (torch.ones(16) * 2).long()
+loss = CrossEntropyLoss()(shift_logits, shift_labels)
+print(loss)
+
+
+loss = nn.CrossEntropyLoss()
+input = torch.tensor([[1.0, 2.0, 3.0]])
+target = torch.tensor([0]).long()
+output = loss(input, target)
+print(output)
+
+
+# torch.manual_seed(32)
+# criterion = nn.CrossEntropyLoss()
+# output = torch.randn(1, 5)
+# label = torch.ones(1, dtype=torch.long)*3
+# loss = criterion(output, label)
+
+# print("网络输出为5类:")
+# print(output)
+# print("要计算label的类别:")
+# print(label)
+# print("计算loss的结果:")
+# print(loss)
+
+# first = 0
+# for i in range(1):
+#     first = -output[i][label[i]]
+# second = 0
+# for i in range(1):
+#     for j in range(5):
+#         second += math.exp(output[i][j])
+# res = 0
+# res = (first + math.log(second))
+# print("自己的计算结果：")
+# print(res)

wit/logger.py

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+from pytorch_lightning import loggers
+from pytorch_lightning.utilities import rank_zero_only
+
+
+class TBLogger(loggers.TensorBoardLogger):
+    @rank_zero_only
+    def log_metrics(self, metrics, step):
+        metrics.pop("epoch", None)
+        return super().log_metrics(metrics, step)
+
+
+class WBLogger(loggers.WandbLogger):
+
+    @rank_zero_only
+    def log_metrics(self, metrics, step):
+        metrics.pop("epoch", None)
+        return super().log_metrics(metrics, step)
+
+
+class MLFLogger(loggers.MLFlowLogger):
+
+    @rank_zero_only
+    def log_metrics(self, metrics, step):
+        metrics.pop("epoch", None)
+        return super().log_metrics(metrics, step)