Merge branch 'master' of https://github.com/Jittor/jittor

python/jittor/__init__.py

@@ -341,6 +341,24 @@ def detach(x):
     return x.clone().stop_grad().clone()
 Var.detach = detach
 
+def std(x):
+    matsize=1
+    for i in x.shape:
+        matsize *= i
+    out=(x-x.mean()).sqr().sum()
+    out=out/(matsize-1)
+    out=out.sqrt()
+    return out
+Var.std = std
+
+def norm(x, k, dim):
+    assert k==1 or k==2
+    if k==1:
+        return x.abs().sum(dim)
+    if k==2:
+        return (x**2).sum(dim).sqrt()
+Var.norm = norm
+
 origin_reshape = reshape
 def reshape(x, *shape):
     if len(shape) == 1 and isinstance(shape[0], Sequence):

python/jittor/nn.py

@@ -112,6 +112,48 @@ def cross_entropy_loss(output, target, ignore_index=None):
     else:
         return loss.sum() / jt.maximum(mask.int().sum(), 1)
 
+def mse_loss(output, target):
+    return (output-target).sqr().mean()
+
+def bce_loss(output, target):
+    return - (target * jt.log(jt.maximum(output, 1e-20)) + (1 - target) * jt.log(jt.maximum(1 - output, 1e-20))).mean()
+
+def l1_loss(output, target):
+    return (output-target).abs().mean()
+
+class CrossEntropyLoss(Module):
+    def __init__(self):
+        pass
+    def execute(self, output, target):
+        return cross_entropy_loss(output, target)
+
+class MSELoss(Module):
+    def __init__(self):
+        pass
+    def execute(self, output, target):
+        return mse_loss(output, target)
+
+class BCELoss(Module):
+    def __init__(self):
+        pass
+    def execute(self, output, target):
+        return bce_loss(output, target)
+
+class L1Loss(Module):
+    def __init__(self):
+        pass
+    def execute(self, output, target):
+        return l1_loss(output, target)
+
+class BCEWithLogitsLoss(Module):
+    def __init__(self):
+        self.sigmoid = Sigmoid()
+        self.bce = BCELoss()
+    def execute(self, output, target):
+        output = self.sigmoid(output)
+        output = self.bce(output, target)
+        return output
+
 class SGD(object):
     """ Usage:
     optimizer = nn.SGD(model.parameters(), lr)

python/jittor/test/test_loss.py

@@ -0,0 +1,74 @@
+# ***************************************************************
+# Copyright (c) 2020 Jittor. Authors: 
+#     Dun Liang <randonlang@gmail.com>. 
+# All Rights Reserved.
+# This file is subject to the terms and conditions defined in
+# file 'LICENSE.txt', which is part of this source code package.
+# ***************************************************************
+import unittest
+import jittor as jt
+import os
+import numpy as np
+import jittor.nn as jnn
+
+from jittor.test.test_log import find_log_with_re
+skip_this_test = False
+
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+    import torch.nn as tnn
+except:
+    skip_this_test = True
+
+@unittest.skipIf(skip_this_test, "No Torch found")
+class TestLoss(unittest.TestCase):
+    def test_l1_loss(self):
+        jt_loss=jnn.L1Loss()
+        tc_loss=tnn.L1Loss()
+        output=np.random.randn(10,100).astype(np.float32)
+        target=np.random.randn(10,100).astype(np.float32)
+        jt_y=jt_loss(jt.array(output), jt.array(target))
+        tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
+        assert np.allclose(jt_y.numpy(), tc_y.numpy())
+        
+    def test_mse_loss(self):
+        jt_loss=jnn.MSELoss()
+        tc_loss=tnn.MSELoss()
+        output=np.random.randn(10,100).astype(np.float32)
+        target=np.random.randn(10,100).astype(np.float32)
+        jt_y=jt_loss(jt.array(output), jt.array(target))
+        tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
+        assert np.allclose(jt_y.numpy(), tc_y.numpy())
+
+    def test_cross_entropy_loss(self):
+        jt_loss=jnn.CrossEntropyLoss()
+        tc_loss=tnn.CrossEntropyLoss()
+        output=np.random.randn(10,10).astype(np.float32)
+        target=np.random.randint(10, size=(10))
+        jt_y=jt_loss(jt.array(output), jt.array(target))
+        tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
+        assert np.allclose(jt_y.numpy(), tc_y.numpy())
+        
+    def test_bce_loss(self):
+        jt_loss=jnn.BCELoss()
+        tc_loss=tnn.BCELoss()
+        jt_sig = jnn.Sigmoid()
+        tc_sig = tnn.Sigmoid()
+        output=np.random.randn(100).astype(np.float32)
+        target=np.random.randint(2, size=(100)).astype(np.float32)
+        jt_y=jt_loss(jt_sig(jt.array(output)), jt.array(target))
+        tc_y=tc_loss(tc_sig(torch.from_numpy(output)), torch.from_numpy(target))
+        assert np.allclose(jt_y.numpy(), tc_y.numpy())
+        
+    def test_bce_with_logits_loss(self):
+        jt_loss=jnn.BCEWithLogitsLoss()
+        tc_loss=tnn.BCEWithLogitsLoss()
+        output=np.random.randn(100).astype(np.float32)
+        target=np.random.randint(2, size=(100)).astype(np.float32)
+        jt_y=jt_loss(jt.array(output), jt.array(target))
+        tc_y=tc_loss(torch.from_numpy(output), torch.from_numpy(target))
+        assert np.allclose(jt_y.numpy(), tc_y.numpy())
+        
+if __name__ == "__main__":
+    unittest.main()

python/jittor/test/test_std.py

@@ -0,0 +1,42 @@
+# ***************************************************************
+# Copyright (c) 2020 Jittor. Authors: 
+#     Dun Liang <randonlang@gmail.com>. 
+# All Rights Reserved.
+# This file is subject to the terms and conditions defined in
+# file 'LICENSE.txt', which is part of this source code package.
+# ***************************************************************
+import unittest
+import jittor as jt
+import os
+import numpy as np
+import jittor.nn as jnn
+
+from jittor.test.test_log import find_log_with_re
+skip_this_test = False
+
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+    import torch.nn as tnn
+except:
+    skip_this_test = True
+
+@unittest.skipIf(skip_this_test, "No Torch found")
+class TestStd(unittest.TestCase):
+    def test_std(self):
+        x=np.random.randn(100,1000).astype(np.float32)
+        jt_x=jt.array(x)
+        tc_x=torch.from_numpy(x)
+        assert np.allclose(jt_x.std().numpy(), tc_x.std().numpy(), 1e-4) ,(x, jt_x.std().numpy(), tc_x.std().numpy())
+
+    def test_norm(self):
+        x=np.random.randn(100,1000).astype(np.float32)
+        jt_x=jt.array(x)
+        tc_x=torch.from_numpy(x)
+        assert np.allclose(jt_x.norm(1,1).numpy(), tc_x.norm(1,1).numpy())
+        assert np.allclose(jt_x.norm(1,0).numpy(), tc_x.norm(1,0).numpy())
+        assert np.allclose(jt_x.norm(2,1).numpy(), tc_x.norm(2,1).numpy())
+        assert np.allclose(jt_x.norm(2,0).numpy(), tc_x.norm(2,0).numpy())
+        
+if __name__ == "__main__":
+    unittest.main()