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@ -40,6 +40,29 @@ class CodeTable(object):
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return self.c & (1 << bit)
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class CodeTableWithCustomTree(object):
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def __init__(self, ptable, pcode, index):
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self.ptable_ = ptable
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self.pcode_ = pcode
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self.index_ = index
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def cal_index(self, bit):
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return self.ptable_[self.index_][bit]
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def get_length(self):
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length = 0
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for ele in self.ptable_[self.index_]:
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if ele >= 0:
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length = length + 1
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else:
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return length
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return length
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def cal_bit(self, bit):
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return self.pcode_[self.index_][bit]
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def hsigmoid(x, w, label, bias, num_classes):
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batch_size = x.shape[0]
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code_length = find_latest_set(num_classes - 1)
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@ -48,10 +71,12 @@ def hsigmoid(x, w, label, bias, num_classes):
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pre_sum = np.zeros((batch_size, 1))
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out = np.zeros((batch_size, 1)).astype("float32")
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for i in range(batch_size):
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#print("\n leaf {leaf}: \n".format(leaf = label[i]))
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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#print("index {index} ".format(index = j))
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pre_output[i][j] += bias[0][idx]
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for i in range(batch_size):
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code_table = CodeTable(num_classes, label[i])
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@ -63,10 +88,12 @@ def hsigmoid(x, w, label, bias, num_classes):
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pre_output = np.clip(pre_output, -40.0, 40.0)
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# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
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for i in range(batch_size):
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#print("\n leaf {leaf}: \n".format(leaf = label[i]))
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code_table = CodeTable(num_classes, label[i])
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length = code_table.get_length()
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sum = 0.0
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for j in range(length):
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#print("bit {bit} ".format(bit = code_table.cal_bit(j)))
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if code_table.cal_bit(j):
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sum += pre_output[i][j]
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out[i] = -1.0 * sum
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@ -77,25 +104,101 @@ def hsigmoid(x, w, label, bias, num_classes):
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return pre_output, out
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class TestHSigmoidOp(OpTest):
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def hsigmoidWithCustomTree(x, w, ptable, pcode, label, bias, num_classes):
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batch_size = x.shape[0]
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code_length = len(ptable[0])
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code_table = [0 for _ in range(code_length)]
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pre_output = np.zeros((batch_size, code_length))
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pre_sum = np.zeros((batch_size, 1))
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out = np.zeros((batch_size, 1)).astype("float32")
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for i in range(batch_size):
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code_table = CodeTableWithCustomTree(ptable, pcode, i)
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += bias[0][idx]
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for i in range(batch_size):
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code_table = CodeTableWithCustomTree(ptable, pcode, i)
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length = code_table.get_length()
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for j in range(length):
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idx = code_table.cal_index(j)
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pre_output[i][j] += np.dot(w[idx], x[i])
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# clip[-40.0, 40.0]
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pre_output = np.clip(pre_output, -40.0, 40.0)
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# out(i, 0) = \sum_j bit(i, j) * preout(i, j)
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for i in range(batch_size):
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code_table = CodeTableWithCustomTree(ptable, pcode, i)
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length = code_table.get_length()
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sum = 0.0
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for j in range(length):
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if code_table.cal_bit(j):
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sum += pre_output[i][j]
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out[i] = -1.0 * sum
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# soft relu
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pre_output = np.log(1 + np.exp(pre_output))
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pre_sum = pre_output.sum(1).reshape((batch_size, 1))
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out += pre_sum
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return pre_output, out
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# class TestHSigmoidOp(OpTest):
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# def setUp(self):
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# self.op_type = "hierarchical_sigmoid"
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# num_classes = 6
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# feature_size = 8
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# batch_size = 7
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# x = np.random.random((batch_size, feature_size)).astype("float32")
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# w = np.random.random((num_classes - 1, feature_size)).astype("float32")
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# label = np.random.randint(0, num_classes, (batch_size, 1))
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# bias = np.random.random((1, num_classes - 1)).astype("float32")
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# self.attrs = {'num_classes': num_classes}
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# self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
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# pre_output, out = hsigmoid(x, w, label, bias, num_classes)
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# self.outputs = {'PreOut': pre_output, 'Out': out}
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# def test_check_output(self):
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# self.check_output()
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# def test_check_grad(self):
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# self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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class TestHSigmoidOpWithCostumTree(OpTest):
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def setUp(self):
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self.op_type = "hierarchical_sigmoid"
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num_classes = 6
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num_classes = 6 #using 1,2,3,4,5,6 to build a huffman tree and select 1,2,5,6 as sample
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feature_size = 8
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batch_size = 4
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x = np.random.random((batch_size, feature_size)).astype("float32")
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w = np.random.random((num_classes - 1, feature_size)).astype("float32")
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label = np.random.randint(0, num_classes, (batch_size, 1))
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x = np.random.random((batch_size, feature_size)).astype("float32") * 10
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w = np.random.random(
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(num_classes - 1, feature_size)).astype("float32") * 10
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label = np.array([0, 1, 4, 5])
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ptable = np.array(
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[(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (0, 1, 4, -1, -1),
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(0, 2, -1, -1,
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-1)]) #np.array to store 1,2,5,6s' non-leaf path(root -> leaf)
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pcode = np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
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1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]) #np.array to store
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bias = np.random.random((1, num_classes - 1)).astype("float32")
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self.attrs = {'num_classes': num_classes}
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self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
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pre_output, out = hsigmoid(x, w, label, bias, num_classes)
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self.inputs = {
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'X': x,
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'W': w,
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'PTable': ptable,
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'PCode': pcode,
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'Label': label,
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'Bias': bias
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}
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pre_output, out = hsigmoidWithCustomTree(x, w, ptable, pcode, label,
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bias, num_classes)
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self.outputs = {'PreOut': pre_output, 'Out': out}
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def test_check_output(self):
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print("checking output in CostumTree")
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self.check_output()
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def test_check_grad(self):
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print("checking outputGrad in CostumTree")
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self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
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JiabinYang
6 years ago