Here are the examples of the python api torch.from_numpy.float taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
145 Examples
3
View Complete Implementation : incremental_model_text_kernel_chaplot_resnet.py
Copyright GNU General Public License v3.0
Author : lil-lab
Copyright GNU General Public License v3.0
Author : lil-lab
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
astert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
instructions_batch = cuda_var(torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
3
View Complete Implementation : incremental_model_chaplot_resnet.py
Copyright GNU General Public License v3.0
Author : lil-lab
Copyright GNU General Public License v3.0
Author : lil-lab
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
astert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
instructions_batch = cuda_var(torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
3
View Complete Implementation : incremental_model_chaplot.py
Copyright GNU General Public License v3.0
Author : lil-lab
Copyright GNU General Public License v3.0
Author : lil-lab
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
astert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
instructions_batch = cuda_var(torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
3
View Complete Implementation : tmp_blocks_incremental_model_chaplot.py
Copyright GNU General Public License v3.0
Author : lil-lab
Copyright GNU General Public License v3.0
Author : lil-lab
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
astert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
instructions_batch = cuda_var(torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
3
View Complete Implementation : tmp_house_incremental_model_chaplot.py
Copyright GNU General Public License v3.0
Author : lil-lab
Copyright GNU General Public License v3.0
Author : lil-lab
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
astert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
instructions_batch = cuda_var(torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
3
View Complete Implementation : tmp_streetview_incremental_model_recurrent_policy_network.py
Copyright GNU General Public License v3.0
Author : lil-lab
Copyright GNU General Public License v3.0
Author : lil-lab
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
astert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
instructions_batch = cuda_var(torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
3
View Complete Implementation : bilinear_test.py
Copyright Apache License 2.0
Author : UKPLab
Copyright Apache License 2.0
Author : UKPLab
def test_forward_works_with_higher_order_tensors(self):
# pylint: disable=protected-access
bilinear = BilinearSimilarity(4, 7)
weights = numpy.random.rand(4, 7)
bilinear._weight_matrix = Parameter(torch.from_numpy(weights).float())
bilinear._bias = Parameter(torch.from_numpy(numpy.asarray([0])).float())
a_vectors = numpy.random.rand(5, 4, 3, 6, 4)
b_vectors = numpy.random.rand(5, 4, 3, 6, 7)
a_variables = torch.from_numpy(a_vectors).float()
b_variables = torch.from_numpy(b_vectors).float()
result = bilinear(a_variables, b_variables).data.numpy()
astert result.shape == (5, 4, 3, 6)
expected_result = numpy.dot(numpy.dot(numpy.transpose(a_vectors[3, 2, 1, 3]), weights),
b_vectors[3, 2, 1, 3])
astert_almost_equal(result[3, 2, 1, 3], expected_result, decimal=5)
3
View Complete Implementation : intra_sentence_attention_test.py
Copyright Apache License 2.0
Author : UKPLab
Copyright Apache License 2.0
Author : UKPLab
def test_forward_works_with_multi_headed_attention(self):
# We're not going to check the output values here, as that's complicated; we'll just make
# sure the code runs and the shapes are correct.
similarity = MultiHeadedSimilarity(3, 24)
encoder = IntraSentenceAttentionEncoder(input_dim=24,
projection_dim=24,
similarity_function=similarity,
num_attention_heads=3,
combination="1+2")
input_tensor = torch.from_numpy(numpy.random.rand(4, 6, 24)).float()
encoder_output = encoder(input_tensor, None)
astert list(encoder_output.size()) == [4, 6, 24]
3
View Complete Implementation : util_test.py
Copyright Apache License 2.0
Author : UKPLab
Copyright Apache License 2.0
Author : UKPLab
def test_weighted_sum_works_on_simple_input(self):
batch_size = 1
sentence_length = 5
embedding_dim = 4
sentence_array = numpy.random.rand(batch_size, sentence_length, embedding_dim)
sentence_tensor = torch.from_numpy(sentence_array).float()
attention_tensor = torch.FloatTensor([[.3, .4, .1, 0, 1.2]])
aggregated_array = util.weighted_sum(sentence_tensor, attention_tensor).data.numpy()
astert aggregated_array.shape == (batch_size, embedding_dim)
expected_array = (0.3 * sentence_array[0, 0] +
0.4 * sentence_array[0, 1] +
0.1 * sentence_array[0, 2] +
0.0 * sentence_array[0, 3] +
1.2 * sentence_array[0, 4])
numpy.testing.astert_almost_equal(aggregated_array, [expected_array], decimal=5)
3
View Complete Implementation : util_test.py
Copyright Apache License 2.0
Author : UKPLab
Copyright Apache License 2.0
Author : UKPLab
def test_weighted_sum_works_on_simple_input(self):
batch_size = 1
sentence_length = 5
embedding_dim = 4
sentence_array = numpy.random.rand(batch_size, sentence_length, embedding_dim)
sentence_tensor = torch.from_numpy(sentence_array).float()
attention_tensor = torch.FloatTensor([[.3, .4, .1, 0, 1.2]])
aggregated_array = util.weighted_sum(sentence_tensor, attention_tensor).data.numpy()
astert aggregated_array.shape == (batch_size, embedding_dim)
expected_array = (0.3 * sentence_array[0, 0] +
0.4 * sentence_array[0, 1] +
0.1 * sentence_array[0, 2] +
0.0 * sentence_array[0, 3] +
1.2 * sentence_array[0, 4])
numpy.testing.astert_almost_equal(aggregated_array, [expected_array], decimal=5)