Skip to content

use TF's built-in method to get numpy dtype #1035

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
amcadmus merged 2 commits into from
Aug 26, 2021
+4 −12
Merged

use TF's built-in method to get numpy dtype #1035

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
9a3cd65
use TF's built-in method to get numpy dtype
njzjz Aug 25, 2021
17e0866
sadly only `tf.as_dtype` is supported in TF 1.8
njzjz Aug 25, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 0 additions & 6 deletions deepmd/common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -42,12 +42,6 @@
"float64": tf.float64,
}

PRECISION_MAPPING: Dict[int, type] = {
1: np.float32,
2: np.float64,
19: np.float16,
}


def gelu(x: tf.Tensor) -> tf.Tensor:
"""Gaussian Error Linear Unit.
Expand Down
5 changes: 2 additions & 3 deletions deepmd/entrypoints/transfer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@

from typing import Dict, Optional, Sequence, Tuple
from deepmd.env import tf
from deepmd.common import PRECISION_MAPPING
import re
import numpy as np
import logging
Expand Down Expand Up @@ -121,8 +120,8 @@ def transform_graph(raw_graph: tf.Graph, old_graph: tf.Graph) -> tf.Graph:

check_dim(raw_graph_node, old_graph_node, node.name)
tensor_shape = [dim.size for dim in raw_node.tensor_shape.dim]
old_graph_dtype = PRECISION_MAPPING[old_node.dtype]
raw_graph_dtype = PRECISION_MAPPING[raw_node.dtype]
old_graph_dtype = tf.as_dtype(old_node.dtype).as_numpy_dtype
raw_graph_dtype = tf.as_dtype(raw_node.dtype).as_numpy_dtype
log.info(
f"{node.name} is passed from old graph({old_graph_dtype}) "
f"to raw graph({raw_graph_dtype})"
Expand Down
5 changes: 2 additions & 3 deletions deepmd/utils/graph.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@
import numpy as np
from typing import Tuple, Dict
from deepmd.env import tf
from deepmd.common import PRECISION_MAPPING
from deepmd.utils.sess import run_sess
from deepmd.utils.errors import GraphWithoutTensorError

Expand Down Expand Up @@ -174,7 +173,7 @@ def get_embedding_net_variables_from_graph_def(graph_def : tf.GraphDef) -> Dict:
embedding_net_nodes = get_embedding_net_nodes_from_graph_def(graph_def)
for item in embedding_net_nodes:
node = embedding_net_nodes[item]
dtype = PRECISION_MAPPING[node.dtype]
dtype = tf.as_dtype(node.dtype).as_numpy_dtype
tensor_shape = tf.TensorShape(node.tensor_shape).as_list()
if (len(tensor_shape) != 1) or (tensor_shape[0] != 1):
tensor_value = np.frombuffer(node.tensor_content)
Expand Down Expand Up @@ -262,7 +261,7 @@ def get_fitting_net_variables_from_graph_def(graph_def : tf.GraphDef) -> Dict:
fitting_net_nodes = get_fitting_net_nodes_from_graph_def(graph_def)
for item in fitting_net_nodes:
node = fitting_net_nodes[item]
dtype= PRECISION_MAPPING[node.dtype]
dtype= tf.as_dtype(node.dtype).as_numpy_dtype
tensor_shape = tf.TensorShape(node.tensor_shape).as_list()
if (len(tensor_shape) != 1) or (tensor_shape[0] != 1):
tensor_value = np.frombuffer(node.tensor_content)
Expand Down