openvino_vision_resampler_model.xml

<?xml version="1.0"?>
<net name="Model6" version="11">
	<layers>
		<layer id="0" name="image_features" type="Parameter" version="opset1">
			<data shape="?,?,1024" element_type="f32" />
			<output>
				<port id="0" precision="FP32" names="image_features">
					<dim>-1</dim>
					<dim>-1</dim>
					<dim>1024</dim>
				</port>
			</output>
		</layer>
		<layer id="1" name="prim::ListConstruct" type="Const" version="opset1">
			<data element_type="i64" shape="4" offset="0" size="32" />
			<output>
				<port id="0" precision="I64">
					<dim>4</dim>
				</port>
			</output>
		</layer>
		<layer id="2" name="aten::view/Reshape" type="Reshape" version="opset1">
			<data special_zero="true" />
			<input>
				<port id="0" precision="FP32">
					<dim>-1</dim>
					<dim>-1</dim>
					<dim>1024</dim>
				</port>
				<port id="1" precision="I64">
					<dim>4</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32" names="14">
					<dim>-1</dim>
					<dim>24</dim>
					<dim>24</dim>
					<dim>1024</dim>
				</port>
			</output>
		</layer>
		<layer id="3" name="Constant_77059" type="Const" version="opset1">
			<data element_type="i64" shape="4" offset="32" size="32" />
			<output>
				<port id="0" precision="I64" names="19">
					<dim>4</dim>
				</port>
			</output>
		</layer>
		<layer id="4" name="aten::permute/Transpose" type="Transpose" version="opset1">
			<input>
				<port id="0" precision="FP32">
					<dim>-1</dim>
					<dim>24</dim>
					<dim>24</dim>
					<dim>1024</dim>
				</port>
				<port id="1" precision="I64">
					<dim>4</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32" names="20,input">
					<dim>-1</dim>
					<dim>1024</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</output>
		</layer>
		<layer id="5" name="__module.pool/aten::avg_pool2d/AvgPool" type="AvgPool" version="opset14">
			<data kernel="2, 2" strides="2, 2" pads_begin="0, 0" pads_end="0, 0" exclude-pad="false" auto_pad="explicit" rounding_type="floor" />
			<input>
				<port id="0" precision="FP32">
					<dim>-1</dim>
					<dim>1024</dim>
					<dim>24</dim>
					<dim>24</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32" names="39,image_features_spatial_pool">
					<dim>-1</dim>
					<dim>1024</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
			</output>
		</layer>
		<layer id="6" name="Constant_79184" type="Const" version="opset1">
			<data element_type="i64" shape="3" offset="64" size="24" />
			<output>
				<port id="0" precision="I64">
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="7" name="aten::flatten/Reshape" type="Reshape" version="opset1">
			<data special_zero="true" />
			<input>
				<port id="0" precision="FP32">
					<dim>-1</dim>
					<dim>1024</dim>
					<dim>12</dim>
					<dim>12</dim>
				</port>
				<port id="1" precision="I64">
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32" names="24">
					<dim>-1</dim>
					<dim>1024</dim>
					<dim>144</dim>
				</port>
			</output>
		</layer>
		<layer id="8" name="aten::transpose/Constant" type="Const" version="opset1">
			<data element_type="i32" shape="3" offset="88" size="12" />
			<output>
				<port id="0" precision="I32">
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="9" name="aten::transpose/Transpose" type="Transpose" version="opset1">
			<input>
				<port id="0" precision="FP32">
					<dim>-1</dim>
					<dim>1024</dim>
					<dim>144</dim>
				</port>
				<port id="1" precision="I32">
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32" names="hidden_states">
					<dim>-1</dim>
					<dim>144</dim>
					<dim>1024</dim>
				</port>
			</output>
		</layer>
		<layer id="10" name="Result_77206" type="Result" version="opset1" output_names="hidden_states">
			<input>
				<port id="0" precision="FP32">
					<dim>-1</dim>
					<dim>144</dim>
					<dim>1024</dim>
				</port>
			</input>
		</layer>
	</layers>
	<edges>
		<edge from-layer="0" from-port="0" to-layer="2" to-port="0" />
		<edge from-layer="1" from-port="0" to-layer="2" to-port="1" />
		<edge from-layer="2" from-port="2" to-layer="4" to-port="0" />
		<edge from-layer="3" from-port="0" to-layer="4" to-port="1" />
		<edge from-layer="4" from-port="2" to-layer="5" to-port="0" />
		<edge from-layer="5" from-port="1" to-layer="7" to-port="0" />
		<edge from-layer="6" from-port="0" to-layer="7" to-port="1" />
		<edge from-layer="7" from-port="2" to-layer="9" to-port="0" />
		<edge from-layer="8" from-port="0" to-layer="9" to-port="1" />
		<edge from-layer="9" from-port="2" to-layer="10" to-port="0" />
	</edges>
	<rt_info>
		<Runtime_version value="2025.2.0-19140-c01cd93e24d-releases/2025/2" />
		<conversion_parameters>
			<framework value="pytorch" />
			<is_python_object value="True" />
		</conversion_parameters>
		<nncf>
			<friendly_names_were_updated value="True" />
			<version value="2.17.0" />
			<weight_compression>
				<advanced_parameters value="{'statistics_path': None, 'awq_params': {'subset_size': 32, 'percent_to_apply': 0.002, 'alpha_min': 0.0, 'alpha_max': 1.0, 'steps': 100, 'prefer_data_aware_scaling': True}, 'scale_estimation_params': {'subset_size': 64, 'initial_steps': 5, 'scale_steps': 5, 'weight_penalty': -1.0}, 'gptq_params': {'damp_percent': 0.1, 'block_size': 128, 'subset_size': 128}, 'lora_correction_params': {'adapter_rank': 8, 'num_iterations': 3, 'apply_regularization': True, 'subset_size': 128, 'use_int8_adapters': True}, 'lora_adapter_rank': 256, 'backend_params': {}}" />
				<all_layers value="False" />
				<awq value="False" />
				<backup_mode value="int8_asym" />
				<compression_format value="dequantize" />
				<gptq value="False" />
				<group_size value="-1" />
				<ignored_scope value="[]" />
				<lora_correction value="False" />
				<mode value="int8_sym" />
				<ratio value="1.0" />
				<scale_estimation value="False" />
				<sensitivity_metric value="weight_quantization_error" />
			</weight_compression>
		</nncf>
		<optimum>
			<nncf_version value="2.17.0" />
			<optimum_intel_version value="1.24.0" />
			<optimum_version value="1.26.1" />
			<pytorch_version value="2.7.1" />
			<transformers_version value="4.52.4" />
		</optimum>
	</rt_info>
</net>