def run_many(self, positions, use_random_symmetry=True):
"""Compute the policy and value output for given positions.
Args:
positions: A list of positions for go board status
use_random_symmetry: Apply random symmetry (defined in symmetries.py) to
the extracted features (defined in features.py) of the given positions
Returns:
probabilities, value: The policy and value outputs (defined in
dualnet_model.py)
"""
def _extract_features(positions):
return features.extract_features(self.hparams.board_size,
positions)
processed = list(map(_extract_features, positions))
# processed = [
# features.extract_features(self.hparams.board_size, p) for p in positions]
if use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
# feed_dict is a dict object to provide the input examples for the step of
# inference. sess.run() returns the inference predictions (indicated by
# self.inference_output) of the given input as outputs
outputs = self.sess.run(self.inference_output,
feed_dict={self.inference_input: processed})
probabilities, value = outputs['policy_output'], outputs[
'value_output']
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
self.hparams.board_size, syms_used, probabilities)
return probabilities, value
def run_many(self, positions, use_random_symmetry=True):
processed = list(map(features.extract_features, positions))
# print(processed[0].shape)
if use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
# processed: list [] of (18, N, N)
processed = [
np.reshape(item, (1, 18, go.N, go.N)) for item in processed
]
processed = np.concatenate(processed, axis=0).astype(np.float32)
if len(processed.shape) == 3:
processed = np.expand_dims(processed, 0)
batch = torch.from_numpy(processed)
if self.cuda:
batch = batch.cuda()
outputs = self.model(batch)
probabilities, value = outputs['pi'], outputs['V']
if self.cuda:
probabilities = probabilities.cpu()
value = value.cpu()
probabilities = probabilities.detach().numpy()
value = value.detach().numpy()
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value.flatten()
def run_many(self, positions, use_random_symmetry=True):
"""Compute the policy and value output for given positions.
Args:
positions: A list of positions for go board status
use_random_symmetry: Apply random symmetry (defined in symmetries.py) to
the extracted features (defined in features.py) of the given positions
Returns:
probabilities, value: The policy and value outputs (defined in
dualnet_model.py)
"""
def _extract_features(positions):
return features.extract_features(self.hparams.board_size, positions)
processed = list(map(_extract_features, positions))
# processed = [
# features.extract_features(self.hparams.board_size, p) for p in positions]
if use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(processed)
# feed_dict is a dict object to provide the input examples for the step of
# inference. sess.run() returns the inference predictions (indicated by
# self.inference_output) of the given input as outputs
outputs = self.sess.run(
self.inference_output, feed_dict={self.inference_input: processed})
probabilities, value = outputs['policy_output'], outputs['value_output']
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
self.hparams.board_size, syms_used, probabilities)
return probabilities, value
def run_batch_test(self, position, batch_size, use_random_symmetry=True):
"""Test batch inference execution to ensure proper IPU setup without C++
"""
positions = [position]
processed = list(map(features_lib.extract_features, positions))
if FLAGS.use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
position = processed[0]
s = position.shape
input_shape = [s[0], s[1], s[2]]
x_data = np.zeros(input_shape, np.float32)
outputs = self.sess.run(
self.inference_output,
feed_dict={self.inference_input: [x_data] * batch_size})
probabilities, value = outputs['policy_output'], outputs[
'value_output']
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value
def run_many(self, positions, use_random_symmetry=True):
processed = list(map(features.extract_features, positions))
if use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
outputs = self.sess.run(self.inference_output,
feed_dict={self.inference_input['pos_tensor']: processed})
probabilities, value = outputs['policy_output'], outputs['value_output']
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value
def run_many(self, positions, use_random_symmetry=True):
processed = list(map(features.extract_features, positions))
if use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
outputs = self.sess.run(self.inference_output,
feed_dict={self.inference_input: processed})
probabilities, value = outputs['policy_output'], outputs['value_output']
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value
def run_many(self, positions):
f = get_features()
processed = [features_lib.extract_features(p, f) for p in positions]
if FLAGS.use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
outputs = self.sess.run(self.inference_output,
feed_dict={self.inference_input: processed})
probabilities, value = outputs['policy_output'], outputs['value_output']
if FLAGS.use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value
def run_many(self, positions, use_random_symmetry=True):
processed = list(map(features.extract_features, positions))
if use_random_symmetry:
syms_used, processed = symmetries.randomize_symmetries_feat(
processed)
processed = np.array(processed)
processed = np.moveaxis(processed, -1, 1)
processed = torch.from_numpy(processed)
probabilities, value, logits = self.model(processed.float())
probabilities = probabilities.detach().cpu().numpy()
value = value.detach().cpu().numpy()
value = np.squeeze(value, axis=1)
if use_random_symmetry:
probabilities = symmetries.invert_symmetries_pi(
syms_used, probabilities)
return probabilities, value