From 0d1bc8ab9bb413bfb03975083d1e83d46710542f Mon Sep 17 00:00:00 2001 From: superjom Date: Mon, 14 Aug 2017 09:35:41 +0800 Subject: [PATCH] fix res --- .../paddle/v2/framework/tests/test_fc_op.py | 44 ------------------- .../v2/framework/tests/test_recurrent_op.py | 44 +++++++++++-------- 2 files changed, 25 insertions(+), 63 deletions(-) delete mode 100644 python/paddle/v2/framework/tests/test_fc_op.py diff --git a/python/paddle/v2/framework/tests/test_fc_op.py b/python/paddle/v2/framework/tests/test_fc_op.py deleted file mode 100644 index d504bc8b43..0000000000 --- a/python/paddle/v2/framework/tests/test_fc_op.py +++ /dev/null @@ -1,44 +0,0 @@ -import unittest -import numpy as np -import paddle.v2.framework.core as core -from paddle.v2.framework.op import Operator - - -class TestFc(unittest.TestCase): - def setUp(self): - self.x_np_data = np.random.random((1000, 784)) - self.W_np_data = np.random.random((784, 100)) - - def test_fc(self): - scope = core.Scope() - place = core.CPUPlace() - x_tensor = scope.new_var("X").get_tensor() - x_tensor.set_dims(self.x_np_data.shape) - x_tensor.set(self.x_np_data, place) - - W_tensor = scope.new_var("W").get_tensor() - W_tensor.set_dims(self.W_np_data.shape) - W_tensor.set(self.W_np_data, place) - - op = Operator("fc", X="X", Y="Y", W="W") - - for out in op.outputs(): - if scope.find_var(out) is None: - scope.new_var(out).get_tensor() - - Y_tensor = scope.find_var("Y").get_tensor() - op.infer_shape(scope) - self.assertEqual([1000, 100], Y_tensor.shape()) - - ctx = core.DeviceContext.create(place) - - op.run(scope, ctx) - - py_data = np.matmul(self.x_np_data, self.W_np_data) - op_data = np.array(Y_tensor) - print py_data - op_data - self.assertTrue(np.allclose(py_data, op_data)) - - -if __name__ == '__main__': - unittest.main() diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py index 2ac9f86edb..0db66cc4e1 100644 --- a/python/paddle/v2/framework/tests/test_recurrent_op.py +++ b/python/paddle/v2/framework/tests/test_recurrent_op.py @@ -8,22 +8,22 @@ from paddle.v2.framework.op import Operator def py_sigmoid(x): return 1. / (1. + np.exp(-x)) + class PySimpleRNN(object): ''' A simple implementation of RNN based on numpy, to futhur test RecurrentOp's alogorithm ''' - def __init__(self, - input_dim = 30, - batch_size = 50, - weight_dim = 15, - sent_len = 11): + + def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11): self.x = np.random.normal(size=(sent_len, batch_size, input_dim)) self.W = np.random.normal(size=(input_dim, input_dim)) self.U = np.random.normal(size=(input_dim, input_dim)) self.h_boot = np.random.normal(size=(batch_size, input_dim)) # memories - self.mems = [np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len)] + self.mems = [ + np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len) + ] def forward(self): xs = self.segment_inputs() @@ -43,7 +43,7 @@ class PySimpleRNN(object): ''' mem = self.mems[step_id] if step_id > 0: - pre_mem = self.mems[step_id-1] + pre_mem = self.mems[step_id - 1] else: pre_mem = self.h_boot xW = np.matmul(x, self.W) @@ -52,6 +52,7 @@ class PySimpleRNN(object): sum = xW + hU self.mems[step_id] = py_sigmoid(sum) + class PySimpleRNNTest(unittest.TestCase): def setUp(self): self.rnn = PySimpleRNN() @@ -91,11 +92,8 @@ class TestRecurrentOp(unittest.TestCase): sent_len = 11 def setUp(self): - self.py_rnn = PySimpleRNN(self.input_dim, - self.batch_size, - self.weight_dim, - self.sent_len) - + self.py_rnn = PySimpleRNN(self.input_dim, self.batch_size, + self.weight_dim, self.sent_len) def forward(self): self.scope = core.Scope() @@ -111,22 +109,27 @@ class TestRecurrentOp(unittest.TestCase): # create inlink x_np_data = self.py_rnn.x create_tensor(self.scope, "x", - [self.sent_len, self.batch_size, self.input_dim], x_np_data) + [self.sent_len, self.batch_size, self.input_dim], + x_np_data) W_np_data = self.py_rnn.W - create_tensor(self.scope, "W", [self.input_dim, self.input_dim], W_np_data) + create_tensor(self.scope, "W", [self.input_dim, self.input_dim], + W_np_data) U_np_data = self.py_rnn.U - create_tensor(self.scope, "U", [self.input_dim, self.input_dim], U_np_data) + create_tensor(self.scope, "U", [self.input_dim, self.input_dim], + U_np_data) h_boot_np_data = self.py_rnn.h_boot - create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim], h_boot_np_data) + create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim], + h_boot_np_data) self.scope.new_var("step_scopes") self.scope.new_var("h@alias") self.scope.new_var("h") def create_rnn_op(self): # create RNNOp - rnnop = Operator("recurrent_op", + rnnop = Operator( + "recurrent_op", # inputs inlinks=["x"], boot_memories=["h_boot"], @@ -145,8 +148,10 @@ class TestRecurrentOp(unittest.TestCase): var = self.scope.new_var("stepnet") stepnet = var.get_net() - x_fc_op = Operator("fc", X="x@alias", W="W", Y="Wx") - h_fc_op = Operator("fc", X="h@pre", W="U", Y="Uh") + # x_fc_op = Operator("fc", X="x@alias", W="W", Y="Wx") + # h_fc_op = Operator("fc", X="h@pre", W="U", Y="Uh") + x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx") + h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh") sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum") sig_op = Operator("sigmoid", X="sum", Y="h@alias") @@ -163,5 +168,6 @@ class TestRecurrentOp(unittest.TestCase): print 'py_output', py_output self.assertEqual(pd_output.shape, py_output.shape) + if __name__ == '__main__': unittest.main() -- GitLab