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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,117 +1,117 @@ | ||
| import sys | ||
|
|
||
| import torchstudio.tcpcodec as tc | ||
| from torchstudio.modules import safe_exec | ||
| import os | ||
| import io | ||
| from collections.abc import Iterable | ||
| from tqdm.auto import tqdm | ||
| import pickle | ||
|
|
||
| original_path=sys.path | ||
|
|
||
| app_socket = tc.connect() | ||
| print("Analyze script connected\n", file=sys.stderr) | ||
| while True: | ||
| msg_type, msg_data = tc.recv_msg(app_socket) | ||
|
|
||
| if msg_type == 'SetAnalyzerCode': | ||
| print("Setting analyzer code...\n", file=sys.stderr) | ||
| analyzer = None | ||
| analyzer_code = tc.decode_strings(msg_data)[0] | ||
| error_msg, analyzer_env = safe_exec(analyzer_code, description='analyzer definition') | ||
| if error_msg is not None or 'analyzer' not in analyzer_env: | ||
| print("Unknown analyzer definition error" if error_msg is None else error_msg, file=sys.stderr) | ||
|
|
||
| if msg_type == 'StartAnalysisServer' and 'analyzer' in analyzer_env: | ||
| print("Analyzing...\n", file=sys.stderr) | ||
|
|
||
| analysis_server, address = tc.generate_server() | ||
|
|
||
| if analyzer_env['analyzer'].train is None: | ||
| request_msg='AnalysisServerRequestingAllSamples' | ||
| elif analyzer_env['analyzer'].train==True: | ||
| request_msg='AnalysisServerRequestingTrainingSamples' | ||
| elif analyzer_env['analyzer'].train==False: | ||
| request_msg='AnalysisServerRequestingValidationSamples' | ||
| tc.send_msg(app_socket, request_msg, tc.encode_strings(address)) | ||
| dataset_socket=tc.start_server(analysis_server) | ||
|
|
||
| while True: | ||
| dataset_msg_type, dataset_msg_data = tc.recv_msg(dataset_socket) | ||
|
|
||
| if dataset_msg_type == 'NumSamples': | ||
| num_samples=tc.decode_ints(dataset_msg_data)[0] | ||
| pbar=tqdm(total=num_samples, desc='Analyzing...', bar_format='{l_bar}{bar}| {remaining} left\n\n') #see https://github.com/tqdm/tqdm#parameters | ||
|
|
||
| if dataset_msg_type == 'InputTensorsID': | ||
| input_tensors_id=tc.decode_ints(dataset_msg_data) | ||
|
|
||
| if dataset_msg_type == 'OutputTensorsID': | ||
| output_tensors_id=tc.decode_ints(dataset_msg_data) | ||
|
|
||
| if dataset_msg_type == 'Labels': | ||
| labels=tc.decode_strings(dataset_msg_data) | ||
|
|
||
| if dataset_msg_type == 'StartSending': | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].start_analysis, (num_samples, input_tensors_id, output_tensors_id, labels), description='analyzer definition') | ||
| if error_msg is not None: | ||
| pbar.close() | ||
| print(error_msg, file=sys.stderr) | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| break | ||
|
|
||
| if dataset_msg_type == 'TrainingSample': | ||
| pbar.update(1) | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].analyze_sample, (tc.decode_numpy_tensors(dataset_msg_data), True), description='analyzer definition') | ||
| if error_msg is not None: | ||
| pbar.close() | ||
| print(error_msg, file=sys.stderr) | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| break | ||
|
|
||
| if dataset_msg_type == 'ValidationSample': | ||
| pbar.update(1) | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].analyze_sample, (tc.decode_numpy_tensors(dataset_msg_data), False), description='analyzer definition') | ||
| if error_msg is not None: | ||
| pbar.close() | ||
| print(error_msg, file=sys.stderr) | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| break | ||
|
|
||
| if dataset_msg_type == 'DoneSending': | ||
| pbar.close() | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].finish_analysis, description='analyzer definition') | ||
| tc.send_msg(dataset_socket, 'DoneReceiving') | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| if error_msg is not None: | ||
| print(error_msg, file=sys.stderr) | ||
| else: | ||
| buffer=io.BytesIO() | ||
| pickle.dump(analyzer_env['analyzer'].state_dict(), buffer) | ||
| tc.send_msg(app_socket, 'AnalyzerState',buffer.getvalue()) | ||
| tc.send_msg(app_socket, 'AnalysisWeights',tc.encode_floats(analyzer_env['analyzer'].weights)) | ||
| print("Analysis complete") | ||
| break | ||
|
|
||
| if msg_type == 'LoadAnalyzerState': | ||
| if 'analyzer' in analyzer_env: | ||
| buffer=io.BytesIO(msg_data) | ||
| analyzer_env['analyzer'].load_state_dict(pickle.load(buffer)) | ||
| print("Analyzer state loaded") | ||
|
|
||
| if msg_type == 'RequestAnalysisReport': | ||
| resolution = tc.decode_ints(msg_data) | ||
| if 'analyzer' in analyzer_env: | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].generate_report, (resolution[0:2],resolution[2]), description='analyzer definition') | ||
| if error_msg is not None: | ||
| print(error_msg, file=sys.stderr) | ||
| if return_value is not None: | ||
| tc.send_msg(app_socket, 'ReportImage', tc.encode_image(return_value)) | ||
|
|
||
| if msg_type == 'Exit': | ||
| break | ||
| import sys | ||
|
|
||
| import torchstudio.tcpcodec as tc | ||
| from torchstudio.modules import safe_exec | ||
| import os | ||
| import io | ||
| from collections.abc import Iterable | ||
| from tqdm.auto import tqdm | ||
| import pickle | ||
|
|
||
| original_path=sys.path | ||
|
|
||
| app_socket = tc.connect() | ||
| print("Analyze script connected\n", file=sys.stderr) | ||
| while True: | ||
| msg_type, msg_data = tc.recv_msg(app_socket) | ||
|
|
||
| if msg_type == 'SetAnalyzerCode': | ||
| print("Setting analyzer code...\n", file=sys.stderr) | ||
| analyzer = None | ||
| analyzer_code = tc.decode_strings(msg_data)[0] | ||
| error_msg, analyzer_env = safe_exec(analyzer_code, description='analyzer definition') | ||
| if error_msg is not None or 'analyzer' not in analyzer_env: | ||
| print("Unknown analyzer definition error" if error_msg is None else error_msg, file=sys.stderr) | ||
|
|
||
| if msg_type == 'StartAnalysisServer' and 'analyzer' in analyzer_env: | ||
| print("Analyzing...\n", file=sys.stderr) | ||
|
|
||
| analysis_server, address = tc.generate_server() | ||
|
|
||
| if analyzer_env['analyzer'].train is None: | ||
| request_msg='AnalysisServerRequestingAllSamples' | ||
| elif analyzer_env['analyzer'].train==True: | ||
| request_msg='AnalysisServerRequestingTrainingSamples' | ||
| elif analyzer_env['analyzer'].train==False: | ||
| request_msg='AnalysisServerRequestingValidationSamples' | ||
| tc.send_msg(app_socket, request_msg, tc.encode_strings(address)) | ||
| dataset_socket=tc.start_server(analysis_server) | ||
|
|
||
| while True: | ||
| dataset_msg_type, dataset_msg_data = tc.recv_msg(dataset_socket) | ||
|
|
||
| if dataset_msg_type == 'NumSamples': | ||
| num_samples=tc.decode_ints(dataset_msg_data)[0] | ||
| pbar=tqdm(total=num_samples, desc='Analyzing...', bar_format='{l_bar}{bar}| {remaining} left\n\n') #see https://github.com/tqdm/tqdm#parameters | ||
|
|
||
| if dataset_msg_type == 'InputTensorsID': | ||
| input_tensors_id=tc.decode_ints(dataset_msg_data) | ||
|
|
||
| if dataset_msg_type == 'OutputTensorsID': | ||
| output_tensors_id=tc.decode_ints(dataset_msg_data) | ||
|
|
||
| if dataset_msg_type == 'Labels': | ||
| labels=tc.decode_strings(dataset_msg_data) | ||
|
|
||
| if dataset_msg_type == 'StartSending': | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].start_analysis, (num_samples, input_tensors_id, output_tensors_id, labels), description='analyzer definition') | ||
| if error_msg is not None: | ||
| pbar.close() | ||
| print(error_msg, file=sys.stderr) | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| break | ||
|
|
||
| if dataset_msg_type == 'TrainingSample': | ||
| pbar.update(1) | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].analyze_sample, (tc.decode_numpy_tensors(dataset_msg_data), True), description='analyzer definition') | ||
| if error_msg is not None: | ||
| pbar.close() | ||
| print(error_msg, file=sys.stderr) | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| break | ||
|
|
||
| if dataset_msg_type == 'ValidationSample': | ||
| pbar.update(1) | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].analyze_sample, (tc.decode_numpy_tensors(dataset_msg_data), False), description='analyzer definition') | ||
| if error_msg is not None: | ||
| pbar.close() | ||
| print(error_msg, file=sys.stderr) | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| break | ||
|
|
||
| if dataset_msg_type == 'DoneSending': | ||
| pbar.close() | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].finish_analysis, description='analyzer definition') | ||
| tc.send_msg(dataset_socket, 'DoneReceiving') | ||
| dataset_socket.close() | ||
| analysis_server.close() | ||
| if error_msg is not None: | ||
| print(error_msg, file=sys.stderr) | ||
| else: | ||
| buffer=io.BytesIO() | ||
| pickle.dump(analyzer_env['analyzer'].state_dict(), buffer) | ||
| tc.send_msg(app_socket, 'AnalyzerState',buffer.getvalue()) | ||
| tc.send_msg(app_socket, 'AnalysisWeights',tc.encode_floats(analyzer_env['analyzer'].weights)) | ||
| print("Analysis complete") | ||
| break | ||
|
|
||
| if msg_type == 'LoadAnalyzerState': | ||
| if 'analyzer' in analyzer_env: | ||
| buffer=io.BytesIO(msg_data) | ||
| analyzer_env['analyzer'].load_state_dict(pickle.load(buffer)) | ||
| print("Analyzer state loaded") | ||
|
|
||
| if msg_type == 'RequestAnalysisReport': | ||
| resolution = tc.decode_ints(msg_data) | ||
| if 'analyzer' in analyzer_env: | ||
| error_msg, return_value = safe_exec(analyzer_env['analyzer'].generate_report, (resolution[0:2],resolution[2]), description='analyzer definition') | ||
| if error_msg is not None: | ||
| print(error_msg, file=sys.stderr) | ||
| if return_value is not None: | ||
| tc.send_msg(app_socket, 'ReportImage', tc.encode_image(return_value)) | ||
|
|
||
| if msg_type == 'Exit': | ||
| break |
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