Python tensorflow.make_ndarray() Examples
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Example #1
Source File: serving_utils.py From BERT with Apache License 2.0 | 6 votes |
def make_grpc_request_fn(servable_name, server, timeout_secs): """Wraps function to make grpc requests with runtime args.""" stub = _create_stub(server) def _make_grpc_request(examples): """Builds and sends request to TensorFlow model server.""" request = predict_pb2.PredictRequest() request.model_spec.name = servable_name request.inputs["input"].CopyFrom( tf.make_tensor_proto( [ex.SerializeToString() for ex in examples], shape=[len(examples)])) response = stub.Predict(request, timeout_secs) outputs = tf.make_ndarray(response.outputs["outputs"]) scores = tf.make_ndarray(response.outputs["scores"]) assert len(outputs) == len(scores) return [{ # pylint: disable=g-complex-comprehension "outputs": output, "scores": score } for output, score in zip(outputs, scores)] return _make_grpc_request
Example #2
Source File: _tf_utils.py From keras-onnx with MIT License | 6 votes |
def cal_tensor_value(tensor): # type: (tensorflow.Tensor)->Union[np.ndarray, None] if _count_input_nodes(tensor) < 0: return None node = tensor.op if node.type in ["Const", "ConstV2"]: make_ndarray = tensorflow.make_ndarray np_arr = make_ndarray(node.get_attr("value")) return np_arr else: try: cls_sess = tensorflow.Session if hasattr(tensorflow, 'Session') else tensorflow.compat.v1.Session with cls_sess(graph=node.graph) as sess: np_arr = sess.run(tensor) return np_arr except (ValueError, tensorflow.errors.InvalidArgumentError, tensorflow.errors.OpError): return None
Example #3
Source File: histograms_plugin.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
def histograms_impl(self, tag, run, downsample_to=50): """Result of the form `(body, mime_type)`, or `ValueError`. At most `downsample_to` events will be returned. If this value is `None`, then no downsampling will be performed. """ try: tensor_events = self._multiplexer.Tensors(run, tag) except KeyError: raise ValueError('No histogram tag %r for run %r' % (tag, run)) events = [[ev.wall_time, ev.step, tf.make_ndarray(ev.tensor_proto).tolist()] for ev in tensor_events] if downsample_to is not None and len(events) > downsample_to: indices = sorted(random.Random(0).sample(list(range(len(events))), downsample_to)) events = [events[i] for i in indices] return (events, 'application/json')
Example #4
Source File: test_predict_utils.py From model_server with Apache License 2.0 | 6 votes |
def test_prepare_output_as_list(serialization_function, outputs_names, shapes, types): outputs = {} x = 0 for key, value in outputs_names.items(): outputs[value] = np.ones(shape=shapes[x], dtype=types[x]) x += 1 output = SERIALIZATION_FUNCTIONS[serialization_function]( inference_output=outputs, model_available_outputs=outputs_names) x = 0 for key, value in outputs_names.items(): temp_output = make_ndarray(output.outputs[key]) assert temp_output.shape == shapes[x] assert temp_output.dtype == types[x] x += 1
Example #5
Source File: test_predict_grpc.py From model_server with Apache License 2.0 | 6 votes |
def test_predict_successful_version(mocker, get_grpc_service_for_predict): results_mock = mocker.patch( 'ie_serving.server.request.Request.wait_for_result') expected_response = np.ones(shape=(2, 2)) results_mock.return_value = ({'output': expected_response}, None) requested_version = 1 request = get_fake_request(model_name='test', data_shape=(1, 1, 1), input_blob='input', version=requested_version) grpc_server = get_grpc_service_for_predict rpc = grpc_server.invoke_unary_unary( PREDICT_SERVICE.methods_by_name['Predict'], (), request, None) rpc.initial_metadata() response, trailing_metadata, code, details = rpc.termination() encoded_response = make_ndarray(response.outputs['output']) assert requested_version == response.model_spec.version.value assert grpc.StatusCode.OK == code assert expected_response.shape == encoded_response.shape
Example #6
Source File: test_predict_grpc.py From model_server with Apache License 2.0 | 6 votes |
def test_predict_successful(mocker, get_grpc_service_for_predict, get_fake_model): results_mock = mocker.patch( 'ie_serving.server.request.Request.wait_for_result') expected_response = np.ones(shape=(2, 2)) results_mock.return_value = ({'output': expected_response}, 0) request = get_fake_request(model_name='test', data_shape=(1, 1, 1), input_blob='input') grpc_server = get_grpc_service_for_predict rpc = grpc_server.invoke_unary_unary( PREDICT_SERVICE.methods_by_name['Predict'], (), request, None) rpc.initial_metadata() response, trailing_metadata, code, details = rpc.termination() encoded_response = make_ndarray(response.outputs['output']) assert get_fake_model.default_version == response.model_spec.version.value assert grpc.StatusCode.OK == code assert expected_response.shape == encoded_response.shape
Example #7
Source File: serving_utils.py From training_results_v0.5 with Apache License 2.0 | 6 votes |
def make_grpc_request_fn(servable_name, server, timeout_secs): """Wraps function to make grpc requests with runtime args.""" stub = _create_stub(server) def _make_grpc_request(examples): """Builds and sends request to TensorFlow model server.""" request = predict_pb2.PredictRequest() request.model_spec.name = servable_name request.inputs["input"].CopyFrom( tf.contrib.util.make_tensor_proto( [ex.SerializeToString() for ex in examples], shape=[len(examples)])) response = stub.Predict(request, timeout_secs) outputs = tf.make_ndarray(response.outputs["outputs"]) scores = tf.make_ndarray(response.outputs["scores"]) assert len(outputs) == len(scores) return [{ "outputs": outputs[i], "scores": scores[i] } for i in range(len(outputs))] return _make_grpc_request
Example #8
Source File: serving_utils.py From fine-lm with MIT License | 6 votes |
def make_grpc_request_fn(servable_name, server, timeout_secs): """Wraps function to make grpc requests with runtime args.""" stub = _create_stub(server) def _make_grpc_request(examples): """Builds and sends request to TensorFlow model server.""" request = predict_pb2.PredictRequest() request.model_spec.name = servable_name request.inputs["input"].CopyFrom( tf.contrib.util.make_tensor_proto( [ex.SerializeToString() for ex in examples], shape=[len(examples)])) response = stub.Predict(request, timeout_secs) outputs = tf.make_ndarray(response.outputs["outputs"]) scores = tf.make_ndarray(response.outputs["scores"]) assert len(outputs) == len(scores) return [{ "outputs": outputs[i], "scores": scores[i] } for i in range(len(outputs))] return _make_grpc_request
Example #9
Source File: ende_client.py From OpenNMT-tf with MIT License | 6 votes |
def extract_prediction(result): """Parses a translation result. Args: result: A `PredictResponse` proto. Returns: A generator over the hypotheses. """ batch_lengths = tf.make_ndarray(result.outputs["length"]) batch_predictions = tf.make_ndarray(result.outputs["tokens"]) for hypotheses, lengths in zip(batch_predictions, batch_lengths): # Only consider the first hypothesis (the best one). best_hypothesis = hypotheses[0].tolist() best_length = lengths[0] if best_hypothesis[best_length - 1] == b"</s>": best_length -= 1 yield best_hypothesis[:best_length]
Example #10
Source File: audio_plugin.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def _audio_response_for_run(self, tensor_events, run, tag, sample): """Builds a JSON-serializable object with information about audio. Args: tensor_events: A list of image event_accumulator.TensorEvent objects. run: The name of the run. tag: The name of the tag the audio entries all belong to. sample: The zero-indexed sample of the audio sample for which to retrieve information. For instance, setting `sample` to `2` will fetch information about only the third audio clip of each batch, and steps with fewer than three audio clips will be omitted from the results. Returns: A list of dictionaries containing the wall time, step, URL, width, and height for each audio entry. """ response = [] index = 0 filtered_events = self._filter_by_sample(tensor_events, sample) content_type = self._get_mime_type(run, tag) for (index, tensor_event) in enumerate(filtered_events): data = tf.make_ndarray(tensor_event.tensor_proto) label = data[sample, 1] response.append({ 'wall_time': tensor_event.wall_time, 'step': tensor_event.step, 'label': plugin_util.markdown_to_safe_html(label), 'contentType': content_type, 'query': self._query_for_individual_audio(run, tag, sample, index) }) return response
Example #11
Source File: audio_plugin.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def _serve_individual_audio(self, request): """Serve encoded audio data.""" tag = request.args.get('tag') run = request.args.get('run') index = int(request.args.get('index')) sample = int(request.args.get('sample', 0)) events = self._filter_by_sample(self._multiplexer.Tensors(run, tag), sample) data = tf.make_ndarray(events[index].tensor_proto)[sample, 0] mime_type = self._get_mime_type(run, tag) return http_util.Respond(request, data, mime_type)
Example #12
Source File: pr_curves_plugin.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def _process_tensor_event(self, event, thresholds): """Converts a TensorEvent into a dict that encapsulates information on it. Args: event: The TensorEvent to convert. thresholds: An array of floats that ranges from 0 to 1 (in that direction and inclusive of 0 and 1). Returns: A JSON-able dictionary of PR curve data for 1 step. """ data_array = tf.make_ndarray(event.tensor_proto) # Trim entries for which TP + FP = 0 (precision is undefined) at the tail of # the data. true_positives = [int(v) for v in data_array[metadata.TRUE_POSITIVES_INDEX]] false_positives = [ int(v) for v in data_array[metadata.FALSE_POSITIVES_INDEX]] tp_index = metadata.TRUE_POSITIVES_INDEX fp_index = metadata.FALSE_POSITIVES_INDEX positives = data_array[[tp_index, fp_index], :].astype(int).sum(axis=0) end_index_inclusive = len(positives) - 1 while end_index_inclusive > 0 and positives[end_index_inclusive] == 0: end_index_inclusive -= 1 end_index = end_index_inclusive + 1 return { 'wall_time': event.wall_time, 'step': event.step, 'precision': data_array[metadata.PRECISION_INDEX, :end_index].tolist(), 'recall': data_array[metadata.RECALL_INDEX, :end_index].tolist(), 'true_positives': true_positives[:end_index], 'false_positives': false_positives[:end_index], 'true_negatives': [int(v) for v in data_array[metadata.TRUE_NEGATIVES_INDEX][:end_index]], 'false_negatives': [int(v) for v in data_array[metadata.FALSE_NEGATIVES_INDEX][:end_index]], 'thresholds': thresholds[:end_index], }
Example #13
Source File: text_plugin.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def process_string_tensor_event(event): """Convert a TensorEvent into a JSON-compatible response.""" string_arr = tf.make_ndarray(event.tensor_proto) html = text_array_to_html(string_arr) return { 'wall_time': event.wall_time, 'step': event.step, 'text': html, }
Example #14
Source File: debugger_plugin.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def _process_health_pill_value(self, wall_time, step, device_name, output_slot, node_name, tensor_proto, node_name_set=None): """Creates a HealthPillEvent containing various properties of a health pill. Args: wall_time: The wall time in seconds. step: The session run step of the event. device_name: The name of the node's device. output_slot: The numeric output slot. node_name: The name of the node (without the output slot). tensor_proto: A tensor proto of data. node_name_set: An optional set of node names that are relevant. If not provided, no filtering by relevance occurs. Returns: An event_accumulator.HealthPillEvent. Or None if one could not be created. """ if node_name_set and node_name not in node_name_set: # This event is not relevant. return None # Since we seek health pills for a specific step, this function # returns 1 health pill per node per step. The wall time is the # seconds since the epoch. elements = list(tf.make_ndarray(tensor_proto)) return HealthPillEvent( wall_time=wall_time, step=step, device_name=device_name, output_slot=output_slot, node_name=node_name, dtype=repr(tf.as_dtype(elements[12])), shape=elements[14:], value=elements)
Example #15
Source File: parse_layer_parameters.py From receptive_field with Apache License 2.0 | 5 votes |
def _padding_size_pad_layer(node, name_to_node): """Computes padding size given a TF padding node. Args: node: Tensorflow node (NodeDef proto). name_to_node: Dict keyed by node name, each entry containing the node's NodeDef. Returns: total_padding_x: Total padding size for horizontal direction (integer). padding_x: Padding size for horizontal direction, left side (integer). total_padding_y: Total padding size for vertical direction (integer). padding_y: Padding size for vertical direction, top side (integer). Raises: ValueError: If padding layer is invalid. """ paddings_layer_name = node.input[1] if not paddings_layer_name.endswith("/paddings"): raise ValueError("Padding layer name does not end with '/paddings'") paddings_node = name_to_node[paddings_layer_name] if paddings_node.op != "Const": raise ValueError("Padding op is not Const") value = paddings_node.attr["value"] t = tf.make_ndarray(value.tensor) padding_y = t[1][0] padding_x = t[2][0] total_padding_y = padding_y + t[1][1] total_padding_x = padding_x + t[2][1] if (t[0][0] != 0) or (t[0][1] != 0): raise ValueError("padding is not zero for first tensor dim") if (t[3][0] != 0) or (t[3][1] != 0): raise ValueError("padding is not zero for last tensor dim") return total_padding_x, padding_x, total_padding_y, padding_y
Example #16
Source File: scalars_plugin.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def scalars_impl(self, tag, run, output_format): """Result of the form `(body, mime_type)`.""" tensor_events = self._multiplexer.Tensors(run, tag) values = [[tensor_event.wall_time, tensor_event.step, tf.make_ndarray(tensor_event.tensor_proto).item()] for tensor_event in tensor_events] if output_format == OutputFormat.CSV: string_io = StringIO() writer = csv.writer(string_io) writer.writerow(['Wall time', 'Step', 'Value']) writer.writerows(values) return (string_io.getvalue(), 'text/csv') else: return (values, 'application/json')
Example #17
Source File: parse_layer_parameters.py From receptive_field with Apache License 2.0 | 5 votes |
def _pool_kernel_size(node, name_to_node): """Computes kernel size given a TF pooling node. Args: node: Tensorflow node (NodeDef proto). name_to_node: For MaxPoolV2, mapping from node name to NodeDef. Returns: kernel_size_x: Kernel size for horizontal direction (integer). kernel_size_y: Kernel size for vertical direction (integer). Raises: ValueError: If pooling is invalid. """ if node.op == "MaxPoolV2": ksize_input_name = node.input[1] if not ksize_input_name.endswith("/ksize"): raise ValueError("Kernel size name does not end with '/ksize'") ksize_node = name_to_node[ksize_input_name] value = ksize_node.attr["value"] t = tf.make_ndarray(value.tensor) kernel_size_y = t[1] kernel_size_x = t[2] if t[0] != 1: raise ValueError("pool ksize for first dim is not 1") if t[3] != 1: raise ValueError("pool ksize for last dim is not 1") else: ksize = node.attr["ksize"] kernel_size_y = ksize.list.i[1] kernel_size_x = ksize.list.i[2] if ksize.list.i[0] != 1: raise ValueError("pool ksize for first dim is not 1") if ksize.list.i[3] != 1: raise ValueError("pool ksize for last dim is not 1") return kernel_size_x, kernel_size_y
Example #18
Source File: parse_layer_parameters.py From receptive_field with Apache License 2.0 | 5 votes |
def _stride_size(node, name_to_node): """Computes stride size given a TF node. Args: node: Tensorflow node (NodeDef proto). name_to_node: For MaxPoolV2, mapping from variable name Tensorflow node. Returns: stride_x: Stride size for horizontal direction (integer). stride_y: Stride size for vertical direction (integer). Raises: ValueError: If stride input cannot be found in `name_to_node`. """ if node.op == "MaxPoolV2": strides_input_name = node.input[2] if not strides_input_name.endswith("/strides"): raise ValueError("Strides name does not end with '/strides'") strides_node = name_to_node[strides_input_name] value = strides_node.attr["value"] t = tf.make_ndarray(value.tensor) stride_y = t[1] stride_x = t[2] else: strides_attr = node.attr["strides"] logging.vlog(4, "strides_attr = %s", strides_attr) stride_y = strides_attr.list.i[1] stride_x = strides_attr.list.i[2] return stride_x, stride_y
Example #19
Source File: data_loading.py From rl-reliability-metrics with Apache License 2.0 | 5 votes |
def get_summary_value(summary, load_tensors): if load_tensors: proto = summary.tensor_proto value = tf.make_ndarray(proto) return value.item() else: return summary.value
Example #20
Source File: pipeline_invoke_tfserving.py From models with Apache License 2.0 | 5 votes |
def _transform_response(response): # Convert from tf.tensor, np.array, etc. to bytes # TODO: Optimize this to avoid tf.make_ndarray similar to _transform_request() above class_list = tf.make_ndarray(response['classes']).tolist()[0] class_list_str = [clazz.decode('utf-8') for clazz in class_list] score_list = tf.make_ndarray(response['scores']).tolist()[0] return {"classes": class_list_str, "scores": score_list}
Example #21
Source File: converter.py From utensor_cgen with Apache License 2.0 | 5 votes |
def get_generic_value(cls, value): """quantized numpy array is mapped to np.uint8 typed FIXME: I'm not sure if it's a good idea """ np_array = make_ndarray(value) dtype = np_array.dtype if dtype.fields is None: pass elif dtype[0] in [np.uint8, np.int8]: np_array = np_array.astype(dtype[0]) else: raise ValueError('Unsupported numpy dtype: %s' % dtype) return cls.__utensor_generic_type__(np_array=np_array, dtype=dtype)
Example #22
Source File: greeter_plugin.py From tensorboard-plugin-example with Apache License 2.0 | 5 votes |
def _process_string_tensor_event(self, event): """Convert a TensorEvent into a JSON-compatible response.""" string_arr = tf.make_ndarray(event.tensor_proto) text = string_arr.astype(np.dtype(str)).tostring() return { 'wall_time': event.wall_time, 'step': event.step, 'text': text, }
Example #23
Source File: tf_utils.py From video_prediction with MIT License | 5 votes |
def convert_tensor_to_gif_summary(summ): if isinstance(summ, bytes): summary_proto = tf.Summary() summary_proto.ParseFromString(summ) summ = summary_proto summary = tf.Summary() for value in summ.value: tag = value.tag try: images_arr = tf.make_ndarray(value.tensor) except TypeError: summary.value.add(tag=tag, image=value.image) continue if len(images_arr.shape) == 5: images_arr = np.concatenate(list(images_arr), axis=-2) if len(images_arr.shape) != 4: raise ValueError('Tensors must be 4-D or 5-D for gif summary.') channels = images_arr.shape[-1] if channels < 1 or channels > 4: raise ValueError('Tensors must have 1, 2, 3, or 4 color channels for gif summary.') encoded_image_string = ffmpeg_gif.encode_gif(images_arr, fps=4) image = tf.Summary.Image() image.height = images_arr.shape[-3] image.width = images_arr.shape[-2] image.colorspace = channels # 1: grayscale, 2: grayscale + alpha, 3: RGB, 4: RGBA image.encoded_image_string = encoded_image_string summary.value.add(tag=tag, image=image) return summary
Example #24
Source File: tf2_summary.py From RLs with Apache License 2.0 | 5 votes |
def tf2summary2dict(path, tags=[]): serialized_examples = tf.data.TFRecordDataset(path) data = {} for serialized_example in serialized_examples: event = event_pb2.Event.FromString(serialized_example.numpy()) for value in event.summary.value: if value.tag in tags or tags == []: t = tf.make_ndarray(value.tensor) t = float(t) try: data[f'{value.tag}'].append([t, event.step]) except: data[f'{value.tag}'] = [[t, event.step]] pass return data
Example #25
Source File: extract_tensorboard.py From deeprl_signal_control with MIT License | 5 votes |
def extract_scalar(multiplexer, run_name, tag): tensor_events = multiplexer.Tensors(run_name, tag) data = {'wall_time': [], 'step': [], 'value': []} for event in tensor_events: data['wall_time'].append(event.wall_time) data['step'].append(event.step) data['value'].append(tf.make_ndarray(event.tensor_proto).item()) return pd.DataFrame(data)
Example #26
Source File: fetch_events.py From planet with Apache License 2.0 | 5 votes |
def extract_values(reader, tag): events = reader.Tensors('run', tag) steps = [event.step for event in events] times = [event.wall_time for event in events] values = [tf.make_ndarray(event.tensor_proto) for event in events] return steps, times, values
Example #27
Source File: grpc.py From model_server with Apache License 2.0 | 5 votes |
def infer(img, input_tensor, grpc_stub, model_spec_name, model_spec_version, output_tensors): request = predict_pb2.PredictRequest() request.model_spec.name = model_spec_name if model_spec_version is not None: request.model_spec.version.value = model_spec_version print("input shape ", img.shape) request.inputs[input_tensor].CopyFrom( make_tensor_proto(img, shape=list(img.shape))) result = grpc_stub.Predict(request, 10.0) data = {} for output_tensor in output_tensors: data[output_tensor] = make_ndarray(result.outputs[output_tensor]) return data
Example #28
Source File: executor_service_utils.py From federated with Apache License 2.0 | 5 votes |
def deserialize_tensor_value(value_proto): """Deserializes a tensor value from `executor_pb2.Value`. Args: value_proto: An instance of `executor_pb2.Value`. Returns: A tuple `(value, type_spec)`, where `value` is a Numpy array that represents the deserialized value, and `type_spec` is an instance of `tff.TensorType` that represents its type. Raises: TypeError: If the arguments are of the wrong types. ValueError: If the value is malformed. """ py_typecheck.check_type(value_proto, executor_pb2.Value) which_value = value_proto.WhichOneof('value') if which_value != 'tensor': raise ValueError('Not a tensor value: {}'.format(which_value)) # TODO(b/134543154): Find some way of creating the `TensorProto` using a # proper public interface rather than creating a dummy value that we will # overwrite right away. tensor_proto = tf.make_tensor_proto(values=0) if not value_proto.tensor.Unpack(tensor_proto): raise ValueError('Unable to unpack the received tensor value.') tensor_value = tf.make_ndarray(tensor_proto) value_type = computation_types.TensorType( dtype=tf.dtypes.as_dtype(tensor_proto.dtype), shape=tf.TensorShape(tensor_proto.tensor_shape)) return tensor_value, value_type
Example #29
Source File: client.py From hncynic with MIT License | 4 votes |
def __call__(self, title, n=8, timeout=50.0): if self.preprocessor: # FIXME: Tried to reuse the process, but something seems to be buffering preprocessor = subprocess.Popen([self.preprocessor], stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.DEVNULL) title_pp = preprocessor.communicate((title.strip() + '\n').encode())[0].decode('utf-8') else: title_pp = title title_bpe = self.bpe.segment_tokens(title_pp.strip().lower().split(' ')) #print(title_bpe) request = predict_pb2.PredictRequest() request.model_spec.name = self.model_name request.inputs['tokens'].CopyFrom( tf.make_tensor_proto([title_bpe] * n, shape=(n, len(title_bpe)))) request.inputs['length'].CopyFrom( tf.make_tensor_proto([len(title_bpe)] * n, shape=(n,))) future = self.stub.Predict.future(request, timeout) result = future.result() batch_predictions = tf.make_ndarray(result.outputs["tokens"]) batch_lengths = tf.make_ndarray(result.outputs["length"]) batch_scores = tf.make_ndarray(result.outputs["log_probs"]) hyps = [] for (predictions, lengths, scores) in zip(batch_predictions, batch_lengths, batch_scores): # ignore </s> prediction = predictions[0][:lengths[0]-1] comment = ' '.join([token.decode('utf-8') for token in prediction]) comment = comment.replace('@@ ', '') #comment = comment.replace('<NL>', '\n') # FIXME: Tried to reuse the process, but something seems to be buffering if self.postprocessor: postprocessor = subprocess.Popen([self.postprocessor], stdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.DEVNULL) prediction_ready = postprocessor.communicate((comment + '\n').encode())[0].decode('utf-8') else: prediction_ready = comment hyps.append((prediction_ready.strip(), float(scores[0]))) #hyps.sort(key=lambda hyp: hyp[1] / len(hyp), reverse=True) return hyps
Example #30
Source File: tensorboard_output.py From gym-sawyer with MIT License | 4 votes |
def _dump_tensors(self): if not self._has_recorded_tensor: return layout_categories = [] for scope in self._scope_tensor: chart = [] for name in self._scope_tensor[scope]: chart.append( layout_pb2.Chart( title=name, multiline=layout_pb2.MultilineChartContent( tag=[r'name(?!.*margin.*)'.replace('name', name) ]))) category = layout_pb2.Category(title=scope, chart=chart) layout_categories.append(category) if layout_categories: layout_proto_to_write = layout_pb2.Layout( category=layout_categories) try: # Load former layout_proto from self._layout_writer_dir. multiplexer = event_multiplexer.EventMultiplexer() multiplexer.AddRunsFromDirectory(self._layout_writer_dir) multiplexer.Reload() tensor_events = multiplexer.Tensors( '.', metadata.CONFIG_SUMMARY_TAG) shutil.rmtree(self._layout_writer_dir) # Parse layout proto from disk. string_array = tf.make_ndarray(tensor_events[0].tensor_proto) content = np.asscalar(string_array) layout_proto_from_disk = layout_pb2.Layout() layout_proto_from_disk.ParseFromString( tf.compat.as_bytes(content)) # Merge two layout proto. merged_layout_json = merge( json_format.MessageToJson(layout_proto_from_disk), json_format.MessageToJson(layout_proto_to_write)) merged_layout_proto = layout_pb2.Layout() json_format.Parse(str(merged_layout_json), merged_layout_proto) self._layout_writer = tf.summary.FileWriter( self._layout_writer_dir) layout_summary = summary_lib.custom_scalar_pb( merged_layout_proto) self._layout_writer.add_summary(layout_summary) self._layout_writer.close() except KeyError: # Write the current layout proto into disk # when there is no layout. self._layout_writer = tf.summary.FileWriter( self._layout_writer_dir) layout_summary = summary_lib.custom_scalar_pb( layout_proto_to_write) self._layout_writer.add_summary(layout_summary) self._layout_writer.close()