[POT] Added typecast while nodes updating & creation (#8621)

* Update type casting while update

* Fix Pylint

* Update precision setting for FQ values

* Update FQ values precision setting

* Fix scales dump

* Update reference

* Another reference update

tools/pot/openvino/tools/pot/graph/node_utils.py

@@ -84,7 +84,10 @@ def set_node_value(node: Node, value: np.ndarray):
       """
     if node.type != 'Const':
         raise Exception('Can\'t set value for non-constant node {}'.format(node.name))
-    node.out_port(0).data.set_value(value)
+    data_type = np.float32
+    if node.out_port(0).is_data_type_defined():
+        data_type = node.out_port(0).get_data_type()
+    node.out_port(0).data.set_value(np.array(value).astype(data_type))
 
 
 def get_node_value(node: Node):

tools/pot/openvino/tools/pot/graph/passes.py

@@ -27,7 +27,7 @@ from . import node_utils as nu
 from .pattern_utils import get_fq_result_pattern
 from .special_operations import OPERATIONS_WITH_WEIGHTS, DETECTION_OUTPUT_FINAL_TYPES, SPLIT_OPERATIONS
 from .utils import find_operation_matches, is_ignored, get_hw_aware_ignored_patterns
-from ..graph.node_utils import get_all_node_outputs, get_node_inputs, get_node_input
+from ..graph.node_utils import get_all_node_outputs, get_node_inputs, get_node_input, get_weights_for_node
 from ..graph.special_patterns import get_ignored_patterns
 from ..utils.logger import get_logger
 
@@ -683,8 +683,12 @@ def create_bias_node(graph: Graph, src_node):
     bias_shape = src_node.out_port(0).data.get_shape()
     add_bias_shape = [1] * len(bias_shape)
     add_bias_shape[1] = bias_shape[1]
+    weights = get_weights_for_node(src_node)
+    bias_dtype = np.float32
+    if weights and weights.out_port(0).is_data_type_defined():
+        bias_dtype = weights.out_port(0).get_data_type()
     add_bias = Const(graph,
-                     {'value': np.zeros(add_bias_shape, dtype=np.float32),
+                     {'value': np.zeros(add_bias_shape, dtype=bias_dtype),
                       'shape': add_bias_shape,
                       'need_shape_inference': True
                       }).create_node()
@@ -706,10 +710,10 @@ def create_fake_quantize_node(graph: Graph, name):
     fq = FakeQuantize(graph, {'name': name, 'levels': 0,
                               'stop_value_propagation': True}).create_node()
 
-    input_low = Const(graph, {'value': 0.0}).create_node()
-    input_height = Const(graph, {'value': 0.0}).create_node()
-    output_low = Const(graph, {'value': 0.0}).create_node()
-    output_height = Const(graph, {'value': 0.0}).create_node()
+    input_low = Const(graph, {'value': np.array(0.0).astype(np.float32)}).create_node()
+    input_height = Const(graph, {'value': np.array(0.0).astype(np.float32)}).create_node()
+    output_low = Const(graph, {'value': np.array(0.0).astype(np.float32)}).create_node()
+    output_height = Const(graph, {'value': np.array(0.0).astype(np.float32)}).create_node()
 
     input_low.out_port(0).connect(fq.in_port(1))
     input_height.out_port(0).connect(fq.in_port(2))

tools/pot/tests/test_scales.py

@@ -4,6 +4,7 @@
 import json
 import os
 
+from copy import deepcopy
 import numpy as np
 import pytest
 from addict import Dict
@@ -124,8 +125,7 @@ def test_activation_scales(tmp_path, models, preset, bits, stats_path, clipping_
     nodes = normalize(get_fq_nodes_stats_algo(model, preset, bits, False,
                                               clipping_value=clipping_value))
     local_path = os.path.join(tmp_path, '{}.json'.format(stats_path.split("_")[-2]))
-    local_file = open(local_path, 'w')
-    json.dump(nodes, local_file)
+    dump_intermediate_scales(local_path, nodes)
 
     assert len(ref_nodes) == len(nodes)
     processed_nodes = []
@@ -152,9 +152,7 @@ def test_weights_scales(tmp_path, models):
     ref_weights = get_ref_stats(path_to_weights)
     weights = get_fq_nodes_stats_algo(model, False, 8, True)
     local_path = os.path.join(tmp_path, '{}.json'.format('mv2_weights'))
-    dumped = json.dumps(weights, cls=NumpyEncoder)
-    local_file = open(local_path, 'w')
-    json.dump(dumped, local_file)
+    dump_intermediate_scales(local_path, weights)
 
     for fq_name in weights:
         item_min, item_max = weights[fq_name]['low_level'], weights[fq_name]['high_level']
@@ -359,3 +357,9 @@ def _get_tf_accuracy_checker_config(path_to_dataset):
                             }]
                 }
             ]}]})
+
+
+def dump_intermediate_scales(local_path, data):
+    data = json.dumps(deepcopy(data), cls=NumpyEncoder)
+    local_file = open(local_path, 'w')
+    json.dump(data, local_file)