Merge pull request #171 from Jittor/randn

add more pool & random method

python/jittor/__init__.py

@@ -7,7 +7,7 @@
 # This file is subject to the terms and conditions defined in
 # file 'LICENSE.txt', which is part of this source code package.
 # ***************************************************************
-__version__ = '1.2.2.10'
+__version__ = '1.2.2.11'
 from . import lock
 with lock.lock_scope():
     ori_int = int
@@ -960,3 +960,26 @@ from . import numpy2cupy
 from .contrib import concat
 from .misc import *
 from . import sparse
+
+
+def randn(*size, dtype="float32", requires_grad=False):
+    if isinstance(size, tuple) and isinstance(size[0], tuple): size = size[0]
+    arr = jt.random(size, dtype, "normal")
+    if not requires_grad: return arr.stop_grad()
+    return arr
+
+def rand(*size, dtype="float32", requires_grad=False):
+    if isinstance(size, tuple) and isinstance(size[0], tuple): size = size[0]
+    arr = jt.random(size, dtype)
+    if not requires_grad: return arr.stop_grad()
+    return arr
+
+def normal(mean, std, size=None, dtype="float32"):
+    if size is None:
+        if isinstance(mean, Var) and isinstance(std, Var):
+            assert mean.shape == std.shape
+            size = mean.shape
+        else:
+            if isinstance(mean, Var): size = mean.shape
+            if isinstance(std, Var): size = std.shape
+    return jt.init.gauss(size, dtype, mean, std)

python/jittor/nn.py

@@ -16,7 +16,7 @@ import numpy as np
 import collections
 import math
 from collections import OrderedDict
-from jittor.pool import Pool, pool, AdaptiveAvgPool2d
+from jittor.pool import *
 from jittor.optim import *
 from jittor.misc import _pair
 

python/jittor/pool.py

@@ -191,4 +191,24 @@ class AdaptiveAvgPool2d(Module):
         return xx.reduce("mean", [4,5])
 
 def pool(x, kernel_size, op, padding=0, stride=None):
-    return Pool(kernel_size, stride, padding, op=op)(x)
+    return Pool(kernel_size, stride, padding, op=op)(x)
+
+class AvgPool2d(Module):
+    def __init__(self, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True):
+        self.layer = Pool(kernel_size=kernel_size, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, op="mean")
+    
+    def execute(self, x):
+        return self.layer(x)
+
+def avg_pool2d(x, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True):
+    return AvgPool2d(kernel_size, stride, padding, ceil_mode, count_include_pad)(x)
+
+class MaxPool2d(Module):
+    def __init__(self, kernel_size, stride=None, padding=0, dilation=None, return_indices=None, ceil_mode=False):
+        self.layer = Pool(kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, return_indices=return_indices, ceil_mode=ceil_mode, op="maximum")
+    
+    def execute(self, x):
+        return self.layer(x)
+
+def max_pool2d(x, kernel_size, stride=None, padding=0, dilation=None, return_indices=None, ceil_mode=False):
+    return MaxPool2d(kernel_size, stride, padding, dilation, return_indices, ceil_mode)(x)

python/jittor/test/test_arg_pool_op.py

@@ -5,7 +5,9 @@
 # ***************************************************************
 import unittest
 import jittor as jt
-from jittor.nn import Pool, pool
+from jittor.nn import Pool, pool, AvgPool2d, avg_pool2d
+from jittor.nn import MaxPool2d as j_MaxPool2d
+from jittor.nn import max_pool2d as j_max_pool2d
 import numpy as np
 from .test_core import expect_error
 from .test_grad import ngrad
@@ -101,7 +103,7 @@ class TestArgPoolOp(unittest.TestCase):
         check(jt_model, torch_model, shape, False)
         for i in range(10):
             check(jt_model, torch_model, [1,1,300,300], True)
-            
+
     def test_cpu_(self):
         # x = jt.random([32, 128, 157, 300])
         x = jt.random([4, 128, 157, 300])
@@ -138,5 +140,50 @@ class TestArgPoolOp(unittest.TestCase):
         shape = (2, 16, 33, 33)
         check(jt_model, torch_model, shape, False)
 
+    def test_AvgPool2d(self):
+        from torch.nn import AvgPool2d as t_AvgPool2d
+        jt_model = AvgPool2d(3, 1, 1, ceil_mode=True)
+        torch_model = t_AvgPool2d(3, 1, 1, ceil_mode=True)
+        shape = (2, 16, 33, 33)
+        check(jt_model, torch_model, shape, False)
+
+        jt_model = AvgPool2d(3, 1, 1, ceil_mode=True, count_include_pad=False)
+        torch_model = t_AvgPool2d(3, 1, 1, ceil_mode=True, count_include_pad=False)
+        shape = (2, 16, 100, 100)
+        check(jt_model, torch_model, shape, False)
+        print('finish')
+
+    def test_avg_pool2d(self):
+        from torch.nn.functional import avg_pool2d as t_avg_pool2d
+        arr = np.random.random((2, 16, 33, 33))
+        jt_model = avg_pool2d(jt.array(arr), 3, 1, 1, ceil_mode=True)
+        torch_model = t_avg_pool2d(torch.Tensor(arr), 3, 1, 1, ceil_mode=True)
+        assert np.allclose(jt_model.numpy(), torch_model.numpy())
+
+        jt_model = avg_pool2d(jt.array(arr), 3, 1, 1, ceil_mode=True, count_include_pad=False)
+        torch_model = t_avg_pool2d(torch.Tensor(arr), 3, 1, 1, ceil_mode=True, count_include_pad=False)
+        assert np.allclose(jt_model.numpy(), torch_model.numpy())
+        print('finish')
+
+    def test_MaxPool2d(self):
+        from torch.nn import MaxPool2d
+        jt_model = j_MaxPool2d(3, 1, 1, ceil_mode=True)
+        torch_model = MaxPool2d(3, 1, 1, ceil_mode=True)
+        shape = (2, 16, 33, 33)
+        check(jt_model, torch_model, shape, False)
+        print('finish')
+
+    def test_max_pool2d(self):
+        from torch.nn.functional import max_pool2d
+        arr = np.random.random((2, 16, 33, 33))
+        jt_model = j_max_pool2d(jt.array(arr), 3, 1, 1, ceil_mode=True)
+        torch_model = max_pool2d(torch.Tensor(arr), 3, 1, 1, ceil_mode=True)
+        assert np.allclose(jt_model.numpy(), torch_model.numpy())
+
+        jt_model = j_max_pool2d(jt.array(arr), 3, 1, 1)
+        torch_model = max_pool2d(torch.Tensor(arr), 3, 1, 1)
+        assert np.allclose(jt_model.numpy(), torch_model.numpy())
+        print('finish')
+
 if __name__ == "__main__":
     unittest.main()

python/jittor/test/test_random_op.py

@@ -18,6 +18,12 @@ import unittest
 from .test_reorder_tuner import simple_parser
 from .test_log import find_log_with_re
 
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+except:
+    skip_this_test = True
+
 class TestRandomOp(unittest.TestCase):
     @unittest.skipIf(not jt.has_cuda, "Cuda not found")
     @jt.flag_scope(use_cuda=1)
@@ -56,6 +62,26 @@ class TestRandomOp(unittest.TestCase):
         assert (np.abs((data<(1)).mean() - 0.5) < 0.1)
         assert (np.abs((data<(1+3)).mean() - (1-r)) < 0.1)
 
+        np_res = np.random.normal(1, 0.1, (100, 100))
+        jt_res = jt.normal(1., 0.1, (100, 100))
+        assert (np.abs(np_res.mean() - jt_res.data.mean()) < 0.1)
+        assert (np.abs(np_res.std() - jt_res.data.std()) < 0.1)
+
+        np_res = torch.normal(torch.arange(1., 10000.), 1)
+        jt_res = jt.normal(jt.arange(1, 10000), 1)
+        assert (np.abs(np_res.mean() - jt_res.data.mean()) < 0.1)
+        assert (np.abs(np_res.std() - jt_res.data.std()) < 1)
+
+        np_res = np.random.randn(100, 100)
+        jt_res = jt.randn(100, 100)
+        assert (np.abs(np_res.mean() - jt_res.data.mean()) < 0.1)
+        assert (np.abs(np_res.std() - jt_res.data.std()) < 0.1)
+
+        np_res = np.random.rand(100, 100)
+        jt_res = jt.rand(100, 100)
+        assert (np.abs(np_res.mean() - jt_res.data.mean()) < 0.1)
+        assert (np.abs(np_res.std() - jt_res.data.std()) < 0.1)
+
     @unittest.skipIf(not jt.has_cuda, "Cuda not found")
     @jt.flag_scope(use_cuda=1)
     def test_normal_cuda(self):