diff options
Diffstat (limited to 'ethosu/vela/stats_writer.py')
| -rw-r--r-- | ethosu/vela/stats_writer.py | 78 |
1 files changed, 44 insertions, 34 deletions
diff --git a/ethosu/vela/stats_writer.py b/ethosu/vela/stats_writer.py index 6fd68f8..3cd769f 100644 --- a/ethosu/vela/stats_writer.py +++ b/ethosu/vela/stats_writer.py @@ -154,15 +154,14 @@ def write_pass_metrics_csv(nng, pass_filename): for mem_area in mem_areas_to_report(): for purpose, purpose_candidates in purpose_list: for direction, direction_candidates in direction_list: - label = "bytes_%s_%s_%s" % (mem_area.identifier_name(), purpose, direction) + label = "bytes_{}_{}_{}".format(mem_area.identifier_name(), purpose, direction) bandwidth_names.append(label) bandwidth_indices.append((mem_area, purpose_candidates, direction_candidates)) all_macs = MacCount.all() all_cycles = ( PassCycles.Total, - PassCycles.Dpu, - PassCycles.ElementWise, + PassCycles.Npu, PassCycles.Cpu, PassCycles.SramAccess, PassCycles.DramAccess, @@ -253,16 +252,16 @@ def print_performance_metrics_for_strat( if name: print("", file=f) print("Network summary for", name, file=f) - print("Accelerator configuration %20s" % (arch.accelerator_config,), file=f) - print("System configuration %20s" % (arch.system_config,), file=f) - print("Accelerator clock %12d MHz" % (arch.npu_clock / 1e6,), file=f) + print("Accelerator configuration {:20}".format(arch.accelerator_config), file=f) + print("System configuration {:20}".format(arch.system_config), file=f) + print("Accelerator clock {:12d} MHz".format(int(arch.npu_clock / 1e6)), file=f) for mem_area, label in mem_area_labels: print( - "Design peak %-25s %12.2f GB/s" - % (label + " bandwidth", arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000,), + "Design peak {:25} {:12.2f} GB/s".format( + label + " bandwidth", arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000 + ), file=f, ) - print(file=f) for mem_area, label in mem_area_labels: if mem_area not in memory_used: @@ -272,18 +271,19 @@ def print_performance_metrics_for_strat( extra = "" if (mem_area == MemArea.OnChipFlash or mem_area == MemArea.OffChipFlash) and bits_per_element is not None: - extra = " (%.2f bits per element)" % (bits_per_element[mem_area],) + extra = " ({:.2f} bits per element)".format(bits_per_element[mem_area]) - print("Total %-25s %12.2f KiB%s" % (aug_label, memory_used[mem_area] / 1024.0, extra), file=f) + print("Total {:25} {:12.2f} KiB{}".format(aug_label, memory_used[mem_area] / 1024.0, extra), file=f) print(file=f) - print("%d passes fused into %d" % (num_passes, num_cascaded_passes), file=f) + print("{:d} passes fused into {:d}".format(num_passes, num_cascaded_passes), file=f) n_cpu_operations = len(cpu_operations) if n_operations > 0: print( - "%d/%d (%4.1f %%) operations falling back to the CPU" - % (n_cpu_operations, n_operations, n_cpu_operations / n_operations * 100), + "{:d}/{:d} ({:4.1%}) operations falling back to the CPU".format( + n_cpu_operations, n_operations, n_cpu_operations / n_operations * 100 + ), file=f, ) @@ -294,8 +294,9 @@ def print_performance_metrics_for_strat( return " ".join(str(list(tens.shape)) for tens in lst) print( - "CPU operation: %s, inputs %s, outputs %s" - % (op.type, format_tens_list(op.inputs), format_tens_list(op.outputs)), + "CPU operation: {} inputs {}, outputs {}".format( + op.type, format_tens_list(op.inputs), format_tens_list(op.outputs) + ), file=f, ) @@ -308,38 +309,46 @@ def print_performance_metrics_for_strat( fm_bws = bws[TensorPurpose.FeatureMap] aug_label = label + " bandwidth" print( - "Average %-25s %12.2f GB/s" % (aug_label, total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0,), + "Average {:25} {:12.2f} GB/s".format(aug_label, total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0), file=f, ) print( - "Input %-25s %12.2f MB/batch" - % (aug_label, np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0,), + "Input {:25} {:12.2f} MB/batch".format( + aug_label, np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0 + ), file=f, ) - print("Weight %-25s %12.2f MB/batch" % (aug_label, np.sum(weight_bws) / 1000.0 / 1000.0,), file=f) + print("Weight {:25} {:12.2f} MB/batch".format(aug_label, np.sum(weight_bws) / 1000.0 / 1000.0), file=f) print( - "Output %-25s %12.2f MB/batch" - % (aug_label, np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0,), + "Output {:25} {:12.2f} MB/batch".format( + aug_label, np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0 + ), file=f, ) - print("Total %-25s %12.2f MB/batch" % (aug_label, total_bw / 1000.0 / 1000.0,), file=f) + print("Total {:25} {:12.2f} MB/batch".format(aug_label, total_bw / 1000.0 / 1000.0), file=f) print( - "Total %-25s per input %9.2f MB/inference (batch size %d)" - % (aug_label, total_bw / 1000.0 / 1000.0 / batch_size, batch_size), + "Total {:25} per input {:9.2f} MB/inference (batch size {:d})".format( + aug_label, total_bw / 1000.0 / 1000.0 / batch_size, batch_size + ), file=f, ) print(file=f) - print("Neural network macs %12d MACs/batch" % (macs[MacCount.NeuralNetworkMacs],), file=f) - print("Hardware macs %12d MACs/batch" % (macs[MacCount.HardwareMacs],), file=f) print( - "Network Tops/s %12.2f Tops/s" - % (macs[MacCount.NeuralNetworkMacs] * 2 * midpoint_fps / 1e12), + "Neural network macs {:12d} MACs/batch".format(int(macs[MacCount.NeuralNetworkMacs])), + file=f, + ) + print("Hardware macs {:12d} MACs/batch".format(int(macs[MacCount.HardwareMacs])), file=f) + print( + "Network Tops/s {:12.2f} Tops/s".format( + macs[MacCount.NeuralNetworkMacs] * 2 * midpoint_fps / 1e12 + ), file=f, ) print( - "Hardware Tops/s %12.2f Tops/s" - % (macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12), + "Hardware Tops/s {:12.2f} Tops/s".format( + macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12 + ), file=f, ) print(file=f) @@ -347,12 +356,13 @@ def print_performance_metrics_for_strat( for kind in PassCycles.all(): aug_label = kind.display_name() + " cycles" cyc = cycles[kind] - print("%-30s %12d cycles/batch" % (aug_label, cyc,), file=f) + print("{:30} {:12d} cycles/batch".format(aug_label, int(cyc)), file=f) print(file=f) print( - "Batch Inference time %7.2f ms, %7.2f inferences/s (batch size %d)" - % (midpoint_inference_time * 1000, midpoint_fps, batch_size), + "Batch Inference time {:7.2f} ms, {:7.2f} inferences/s (batch size {:d})".format( + midpoint_inference_time * 1000, midpoint_fps, batch_size + ), file=f, ) print(file=f) |