import unittest import numpy as np import random from op_test import OpTest class TestSeqProject(OpTest): def setUp(self): self.init_test_case() self.op_type = 'sequence_project' if self.context_length == 1 and self.context_start == 0 and self.padding_trainable: print "If context_start is 0 and context_length is 1, padding_trainable should be false." return # one level, batch size x = np.random.uniform( 0.1, 1, [self.input_size[0], self.input_size[1]]).astype('float32') self.begin_pad = np.max([0, -self.context_start]) self.end_pad = np.max([0, self.context_start + self.context_length - 1]) self.total_pad = self.begin_pad + self.end_pad if self.total_pad == 0: self.total_pad = 1 # PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0) padding_data = np.random.uniform( 0.1, 1, [self.total_pad, self.input_size[1]]).astype('float32') self.inputs = { 'X': (x, self.lod), 'PaddingData': (padding_data, [[0, self.total_pad]]) } self.attrs = { 'context_start': self.context_start, 'context_length': self.context_length, 'padding_trainable': self.padding_trainable, 'context_stride': self.context_stride } out = np.zeros((self.input_size[0], self.input_size[1] * self.context_length)).astype('float32') self.outputs = {'Out': out} self.compute() def compute(self): x, lod = self.inputs['X'] pading_data, _ = self.inputs['PaddingData'] out = self.outputs['Out'] lod = lod[0] begin_pad = np.max([0, -self.context_start]) for i in range(len(lod) - 1): for j in range(self.context_length): in_begin = lod[i] + self.context_start + j in_end = lod[i + 1] + self.context_start + j out_begin = lod[i] out_end = lod[i + 1] if in_begin < lod[i]: pad_size = np.min([lod[i] - in_begin, lod[i + 1] - lod[i]]) if self.padding_trainable: sub_w = pading_data[j:j + pad_size, :] out[lod[i]:lod[i] + pad_size, j * self.input_size[1]:( j + 1) * self.input_size[1]] = sub_w out_begin = lod[i] + pad_size in_begin = lod[i] if in_end > lod[i + 1]: pad_size = np.min( [in_end - lod[i + 1], lod[i + 1] - lod[i]]) if self.padding_trainable: sub_w = pading_data[begin_pad + self.context_start + j - pad_size:begin_pad + self.context_start + j, :] out[lod[i + 1] - pad_size:lod[i + 1], j * self. input_size[1]:(j + 1) * self.input_size[1]] = sub_w in_end = lod[i + 1] out_end = lod[i + 1] - pad_size if in_end <= in_begin: continue in_sub = x[in_begin:in_end, :] out[out_begin:out_end, j * self.input_size[1]:(j + 1) * self.input_size[1]] += in_sub def test_check_output(self): self.check_output() def test_check_grad(self): if self.padding_trainable: self.check_grad( set(['X', 'PaddingData']), 'Out', max_relative_error=0.05) def test_check_grad_no_filter(self): self.check_grad( ['X'], 'Out', max_relative_error=0.05, no_grad_set=set(['PaddingData'])) def test_check_grad_no_input(self): if self.padding_trainable: self.check_grad( ['PaddingData'], 'Out', max_relative_error=0.05, no_grad_set=set(['X'])) def init_test_case(self): self.op_type = "sequence_project" self.input_row = 11 self.context_start = 0 self.context_length = 1 self.padding_trainable = False self.context_stride = 1 self.input_size = [self.input_row, 23] self.lod = [[0, 4, 5, 8, self.input_row]] class TestSeqProjectCase1(TestSeqProject): def init_test_case(self): self.op_type = "sequence_project" self.input_row = 11 self.context_start = -1 self.context_length = 3 self.padding_trainable = True self.context_stride = 1 self.input_size = [self.input_row, 23] self.lod = [[0, 4, 5, 8, self.input_row]] class TestSeqProjectCase2(TestSeqProject): def init_test_case(self): self.op_type = "sequence_project" self.input_row = 25 self.context_start = 2 self.context_length = 3 self.padding_trainable = True self.context_stride = 1 self.input_size = [self.input_row, 23] idx = range(self.input_size[0]) del idx[0] self.lod = [[0] + np.sort(random.sample(idx, 8)).tolist() + [self.input_size[0]]] ''' class TestSeqProjectCases(TestSeqProject): def setUp(self): self.init_test_case() self.op_type = 'sequence_project' num = 0 for context_start in [-5, -3, -1, 0, 3]: for context_length in [1, 2, 5, 7]: for batch_size in [1, 2, 5, 7]: for padding_trainable in [False, True]: if context_length == 1 and context_start == 0 and padding_trainable: continue self.context_start = context_start self.context_length = context_length self.padding_trainable = padding_trainable self.input_size = [batch_size, 23] x = np.random.uniform(0.1, 1, self.input_size).astype('float32') self.lod = [[0, self.input_size[0]]] if self.input_size[0] > 2: idx = range(self.input_size[0]) del idx[0] self.lod = [ [0] + np.sort(random.sample(idx, 2)).tolist() + [self.input_size[0]] ] self.begin_pad = np.max([0, -self.context_start]) self.end_pad = np.max([0, self.context_start + self.context_length - 1]) self.total_pad = self.begin_pad + self.end_pad if self.total_pad == 0: self.total_pad = 1 # PaddingData mast be not empty. Otherwise(EnforceNotMet: enforce numel() > 0 failed, 0 <= 0) padding_data = np.random.uniform( 0.1, 1, [self.total_pad, self.input_size[1]]).astype('float32') self.inputs = { 'X': (x, self.lod), 'PaddingData': (padding_data, [[0, self.total_pad]]) } self.attrs = { 'context_start': self.context_start, 'context_length': self.context_length, 'padding_trainable': self.padding_trainable, 'context_stride': self.context_stride } out = np.zeros((self.input_size[0], self.input_size[1] * self.context_length)).astype('float32') self.outputs = {'Out': out} print num print self.attrs print batch_size print padding_trainable print "$$$$$$$$$$$$$" self.compute() self.test_check_output() num += 1 ''' if __name__ == '__main__': unittest.main()