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author | Benjamin Klimczak <benjamin.klimczak@arm.com> | 2022-07-11 12:33:42 +0100 |
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committer | Benjamin Klimczak <benjamin.klimczak@arm.com> | 2022-07-26 14:08:21 +0100 |
commit | 5d81f37de09efe10f90512e50252be9c36925fcf (patch) | |
tree | b4d7cdfd051da0a6e882bdfcf280fd7ca7b39e57 /src/mlia/nn/tensorflow/tflite_metrics.py | |
parent | 7899b908c1fe6d86b92a80f3827ddd0ac05b674b (diff) | |
download | mlia-5d81f37de09efe10f90512e50252be9c36925fcf.tar.gz |
MLIA-551 Rework remains of AIET architecture
Re-factoring the code base to further merge the old AIET code into MLIA.
- Remove last traces of the backend type 'tool'
- Controlled systems removed, including SSH protocol, controller,
RunningCommand, locks etc.
- Build command / build dir and deploy functionality removed from
Applications and Systems
- Moving working_dir()
- Replace module 'output_parser' with new module 'output_consumer' and
merge Base64 parsing into it
- Change the output consumption to optionally remove (i.e. actually
consume) lines
- Use Base64 parsing in GenericInferenceOutputParser, replacing the
regex-based parsing and remove the now unused regex parsing
- Remove AIET reporting
- Pre-install applications by moving them to src/mlia/resources/backends
- Rename aiet-config.json to backend-config.json
- Move tests from tests/mlia/ to tests/
- Adapt unit tests to code changes
- Dependencies removed: paramiko, filelock, psutil
- Fix bug in corstone.py: The wrong resource directory was used which
broke the functionality to download backends.
- Use f-string formatting.
- Use logging instead of print.
Change-Id: I768bc3bb6b2eda57d219ad01be4a8e0a74167d76
Diffstat (limited to 'src/mlia/nn/tensorflow/tflite_metrics.py')
-rw-r--r-- | src/mlia/nn/tensorflow/tflite_metrics.py | 33 |
1 files changed, 13 insertions, 20 deletions
diff --git a/src/mlia/nn/tensorflow/tflite_metrics.py b/src/mlia/nn/tensorflow/tflite_metrics.py index 0fe36e0..2252c6b 100644 --- a/src/mlia/nn/tensorflow/tflite_metrics.py +++ b/src/mlia/nn/tensorflow/tflite_metrics.py @@ -194,9 +194,7 @@ class TFLiteMetrics: aggregation_func = cluster_hist else: - raise NotImplementedError( - "ReportClusterMode '{}' not implemented.".format(mode) - ) + raise NotImplementedError(f"ReportClusterMode '{mode}' not implemented.") uniques = { name: aggregation_func(details) for name, details in self.filtered_details.items() @@ -217,10 +215,10 @@ class TFLiteMetrics: verbose: bool = False, ) -> None: """Print a summary of all the model information.""" - print("Model file: {}".format(self.tflite_file)) + print(f"Model file: {self.tflite_file}") print("#" * 80) print(" " * 28 + "### TFLITE SUMMARY ###") - print("File: {}".format(os.path.abspath(self.tflite_file))) + print(f"File: {os.path.abspath(self.tflite_file)}") print("Input(s):") self._print_in_outs(self.interpreter.get_input_details(), verbose) print("Output(s):") @@ -242,11 +240,11 @@ class TFLiteMetrics: ] if report_sparsity: sparsity = calculate_sparsity(weights, sparsity_accumulator) - row.append("{:.2f}".format(sparsity)) + row.append(f"{sparsity:.2f}") rows.append(row) if verbose: # Print cluster centroids - print("{} cluster centroids:".format(name)) + print(f"{name} cluster centroids:") # Types need to be ignored for this function call because # np.unique does not have type annotation while the # current context does. @@ -259,9 +257,9 @@ class TFLiteMetrics: sparsity_accumulator.total_weights ) if report_sparsity: - summary_row[header.index("Sparsity")] = "{:.2f}".format( - sparsity_accumulator.sparsity() - ) + summary_row[ + header.index("Sparsity") + ] = f"{sparsity_accumulator.sparsity():.2f}" rows.append(summary_row) # Report detailed cluster info if report_cluster_mode is not None: @@ -272,7 +270,7 @@ class TFLiteMetrics: def _print_cluster_details( self, report_cluster_mode: ReportClusterMode, max_num_clusters: int ) -> None: - print("{}:\n{}".format(report_cluster_mode.name, report_cluster_mode.value)) + print(f"{report_cluster_mode.name}:\n{report_cluster_mode.value}") num_clusters = self.num_unique_weights(report_cluster_mode) if ( report_cluster_mode == ReportClusterMode.NUM_CLUSTERS_HISTOGRAM @@ -283,11 +281,9 @@ class TFLiteMetrics: # histogram for unclustered layers. for name, value in num_clusters.items(): if len(value) > max_num_clusters: - num_clusters[name] = "More than {} unique values.".format( - max_num_clusters - ) + num_clusters[name] = f"More than {max_num_clusters} unique values." for name, nums in num_clusters.items(): - print("- {}: {}".format(self._prettify_name(name), nums)) + print(f"- {self._prettify_name(name)}: {nums}") @staticmethod def _print_in_outs(ios: List[dict], verbose: bool = False) -> None: @@ -296,9 +292,6 @@ class TFLiteMetrics: pprint(item) else: print( - "- {} ({}): {}".format( - item["name"], - np.dtype(item["dtype"]).name, - item["shape"], - ) + f"- {item['name']} ({np.dtype(item['dtype']).name}): " + f"{item['shape']}" ) |