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Tomasz Dołbniak authored
* Constify the onnx importer conv * Extract and fix the groups attribute validation for Conv * Check if the convolution's data input rank is static * Validate the groups attribute against channels and filters * Validate the conv operation in a separate function * Dynamically broadcast the conv bias if needed * Import a test model with dynamic batch conv op * Run a conv test with dynamic batch * Cleanup of conv bias handling code * Use a proper Broadcast constructor for bias in onnx conv * Handle dynamic ReduceMean with statically defined rank * Use the target shape rank to construct the default output shape for Broadcast * Handle ONNX Squeeze with dynamic input and static rank * Handle ONNX Shape with dynamic input and static rank * Handle the dynamic target shape in ONNX Reshape * Fix for the ONNX Shape input validation * Handle ONNX Softmax with dynamic input and static rank * Fix the failing Broadcast type prop test * Code formatting * Dont broadcast bias before adding it to the conv node * Drop the conv node validation and rely on the core op implementation checks * Code review feedback * Revert the Broadcast op changes * More code review feedback * Dynamic conv test using ng test case * Obsolete headers removal * Code formatting * Variable names refactor * Disable model_conv_with_dynamic_batch test on GPU * Code formatting Co-authored-by:Sang Ik Lee <sang.ik.lee@intel.com>
